thesis resource orchestration

Advances, Systems and Applications

• Open access
• Published: 10 September 2020

Cloud resource orchestration in the multi-cloud landscape: a systematic review of existing frameworks

• Orazio Tomarchio 1   na1 ,
• Domenico Calcaterra 1   na1 &
• Giuseppe Di Modica 1   na1  

Journal of Cloud Computing volume  9 , Article number:  49 ( 2020 ) Cite this article

14k Accesses

37 Citations

22 Altmetric

Metrics details

The number of both service providers operating in the cloud market and customers consuming cloud-based services is constantly increasing, proving that the cloud computing paradigm has successfully delivered its potential. Nevertheless, the unceasing growth of the cloud market is posing hard challenges on its participants. On the provider side, the capability of orchestrating resources in order to maximise profits without failing customers’ expectations is a matter of concern. On the customer side, the efficient resource selection from a plethora of similar services advertised by a multitude of providers is an open question. In such a multi-cloud landscape, several research initiatives advocate the employment of software frameworks (namely, cloud resource orchestration frameworks - CROFs) capable of orchestrating the heterogeneous resources offered by a multitude of cloud providers in a way that best suits the customer’s need. The objective of this paper is to provide the reader with a systematic review and comparison of the most relevant CROFs found in the literature, as well as to highlight the multi-cloud computing open issues that need to be addressed by the research community in the near future.

Introduction

Over the last few years, cloud computing has established itself as a new model of distributed computing by offering complex hardware and software services in very different fields. As reported in the RightScale 2019 State of the Cloud Report [ 1 ], many companies and organisations have successfully adopted the cloud computing paradigm worldwide, while more and more are approaching it as they see a real opportunity to grow their business. According to that report, 94 percent of IT professionals surveyed said their companies are using cloud computing services, and 91 percent are using the public cloud. Organisations leverage almost 5 clouds on average, and companies are running about 40 percent of their workloads in the cloud. The enterprise cloud spend is growing quickly as companies plan to spend 24 percent more on public cloud in 2019 vs. 2018.

The competition between cloud providers is getting stronger in order to acquire increasing market shares: a key point to optimise resource usage and fully exploit the potential of cloud computing is the issue of resource orchestration [ 2 ]. Cloud resource orchestration regards complex operations such as selection, deployment, monitoring, and run-time control of resources. The overall goal of orchestration is to guarantee full and seamless delivery of applications by meeting Quality of Service (QoS) goals of both cloud application owners and cloud resource providers. Resource orchestration is considered to be a challenging activity because of the scale dimension that resources have reached, and the proliferation of heterogeneous cloud providers offering resources at different levels of the cloud stack.

Cloud Resource Orchestration Frameworks (CROFs) have emerged as systems to manage the resource life-cycle, from the selection phase to the monitoring one [ 2 – 4 ]. Today most of commercial cloud providers offer a cloud orchestration platform to end-users [ 5 ]: however, these products are proprietary and, for obvious business reasons, are not portable. In addition, although modern configuration management solutions exist (e.g., Amazon OpsWorks, Ansible, Puppet, Chef) that provide support for handling resource configuration over cloud services, all potential users (ranging from professional programmers and system administrators to non-expert end-users) are often required to understand various low-level cloud service APIs and procedural programming constructs in order to create and maintain complex resource configurations.

The advent of the multi-cloud computing further exacerbates the already challenging orchestration issues. The multi-cloud paradigm is a very recent technological trend within the cloud computing landscape, which revolves around the opportunity of taking advantage of services and resources provided by multiple clouds [ 6 , 7 ]. Multi-cloud presumes there is no a priori agreement between cloud providers, and a third party is responsible for the services. That is the case for Cloud brokerage scenarios, where a broker intermediates between cloud providers and cloud consumers [ 8 ]. In order to enable an effective multi-cloud paradigm, it is essential to guarantee an easy portability of applications among cloud providers [ 9 , 10 ]. This new requirement calls for more powerful resource orchestration mechanisms cross-cutting multiple cloud administrative domains, i.e., capable of dealing with the heterogeneity of the underlying cloud resources and services.

This work explores the many issues of resource orchestration in the cloud landscape. A review of existing works in the addressed field is conducted in order to identify the challenges that have mostly attracted researchers in recent years, and highlight the aspects that have not been fully covered yet. The main contribution of our work is twofold. Firstly, by deeply analysing recently appeared literature, we build a comprehensive taxonomy of desirable features and dimensions useful to characterise CROFs. Then, in accordance with the identified features, we compare several CROFs from both industry and academia. This will help the reader not only to understand the strengths of each framework, but also to identify the unsolved challenges that have to be addressed in the near future.

The remainder of the paper is organised as follows. In “ Research methodology ” section the methodology followed in our study is described. “ Related surveys ” section presents a survey of existing works related to our study. In “ Analysis framework ” section we identify the CROF capabilities which have been used to carry out the review presented in “ Review of cROFs ” section. In “ Critical discussion ” section we summarise the results of the review, emphasising current limitations and open challenges. Finally, “ Conclusion ” section concludes our work.

Research methodology

The primary motivation of this study is to shed light on the recent advances that both industry and academia have made in facing the cloud resource orchestration’s issues in the multi-cloud landscape.

With this aim in mind, we identified the fields relevant to our study in order to clearly frame the research scope. Beyond the quite expected cloud resource orchestration topic, the following macro topics were also investigated: cloud interoperability , cloud brokerage , interconnected clouds . As outlined in “ Introduction ” section, cloud resource orchestration deals with the discovery, selection, allocation, and management of cloud resources. When multiple clouds are in place, cloud brokering and interoperability issues due to the simultaneous access to heterogeneous services of interconnected providers cannot be neglected in the analysis of cloud resource orchestration.

We surveyed the literature recently produced in the mentioned fields. Specifically, we sought for proposals, frameworks, prototypes, commercial products somehow addressing the above discussed issues. The databases taken into consideration in this survey are the following: Scopus Footnote 1 , ACM Digital Library Footnote 2 , IEEE Xplore Digital Library Footnote 3 , Elsevier ScienceDirect Footnote 4 , and SpringerLink Footnote 5 . We also took care of filtering out research items that are dated earlier than the last decade.

We found out that many researchers have already published surveys that are relevant to our object of study. Each of these surveys lists and classifies, under different perspectives, numerous initiatives taken under the big umbrella of the cloud resource orchestration field, be them fully-fledged CROFs or minor proposals focusing just on a restricted set of orchestration features. The primary objective of the study proposed in this work is to provide a new, unified analysis of the existing initiatives, which embraces all the analysis perspectives proposed by the past surveys and eventually identifies the missing ones.

Therefore, as shown in Fig.  1 , the first step of our study consisted in reviewing the literature surveys with the aim of a) consolidating the list of CROFs and, in general, proposals on which to run a qualitative comparative analysis, b) extracting the analysis dimensions addressed in each survey. Then, the second step was to elaborate an analysis framework in order to provide a more comprehensive set of features on which a new comparison step would be run. Next, following the references found in the surveys, each CROF on the list was further revised according to the above-mentioned comparative guidelines, and the output of the analysis was eventually gathered in a synoptic table, helping the reader to compare the different features. Finally, the comparison results were the basis for a critical discussion on the state of the art, open challenges and future expectations on CROFs.

Research methodology steps

Related surveys

This section presents the results of a literature survey we conducted in order to identify published studies that relate to our work to varying degrees. Specifically, we investigated the vast area of cloud computing searching for proposals and initiatives falling under the theme cloud resource orchestration in the multi-cloud landscape.

Of particular importance in the context of the discussion were the following works: Inter-cloud Challenges, Expectations and Issues Cluster position paper [ 11 ], and the Manifesto for Future Generation Cloud Computing [ 12 ]. Both works acknowledged resource provisioning and orchestration as an open challenge. In [ 11 ], Ferrer et al. recognised it as a research area with a high business impact in the medium term. Besides, in light of more and more heterogeneous cloud resources distributed across diverse cloud typologies and models, both studies stressed the importance of investigating related research areas, such as cloud interoperability and portability , service discovery and composition (i.e., cloud brokerage ), and interconnected clouds . The relationship of these related research areas with the main topic of this survey are schematically depicted in Fig.  2 . We depicted the multi-cloud resource orchestration research scope as a big umbrella fully covering the cloud resource orchestration research area, and partially sharing themes covered by the cloud brokerage, inter-clouds and cloud interoperability/portability research fields.

Related research areas

We remark that the study conducted in this first investigative step did not intend to seek for actual proposals and initiatives in the focused fields. Instead, it targeted the literature works proposing themselves surveys of the most relevant proposals (step 1 in Fig.  1 ). Here, the aim is to highlight the limits of existing literature surveys and, thus, to provide a motivation to our work. Also, by “surveying existing literature surveys” we were able to collect the pointers to the actual research proposals, which were the object of investigation in the next steps of our study.

Below, we discuss some of the most representative literature surveys broken down into the four above-mentioned cloud sub-topics. In each of the following sections the sub-topic is briefly introduced, and the aspects relevant to the multi-cloud orchestration topic are pointed out.

• Cloud interoperability

The cloud computing community typically uses the term interoperability to refer to the ability of easily moving workloads and data from one cloud provider to another or between private and public clouds [ 13 ]. Ten years ago, the standardisation bodies NIST [ 14 ], OMG [ 15 ] and DMTF [ 16 ] developed, among others, several use cases related to cloud interoperability. All the bodies, independently of each other, defined a common umbrella of interoperability use cases covering topics such as user authentication, workload migration, data migration and workload management.

In [ 17 ], the authors performed a comprehensive survey on cloud interoperability, with a focus on interoperability among different IaaS cloud platforms. They investigated the existing efforts on taxonomies and standardisation of cloud interoperability, and identified some open issues to advance the research topic as well. Nevertheless, the presented solutions and concepts are mainly focused on IaaS interoperability.

In [ 18 ], the authors did their survey on service interoperability and portability on cloud systems with respect to cloud computing service discovery. Still, other interoperability approaches such as the Model Driven Engineering (MDE) and open solutions were not extensively explored.

In [ 19 ], the authors described the main challenges regarding cloud federation and interoperability, as well as showcased and reviewed the potential standards to tackle these issues. Similar to [ 17 ], their work is restricted to IaaS interoperability, with no other service or deployment models being covered.

• Cloud brokerage

According to the Gartner definition [ 20 ], “Cloud services brokerage is an IT role and business model in which a company or other entity adds value to one or more (public or private) cloud services on behalf of one or more consumers of that service via three primary roles including aggregation, integration and customization brokerage”. As defined by NIST [ 21 ], a cloud service broker “... is an entity that manages the use, performance and delivery of cloud services and negotiates relationships between cloud providers and cloud consumers.” From these definitions, it is clear that any business player which intends to act as a broker between the cloud consumers and the cloud providers must cope with the diversity of providers and the heterogeneity of the multitude of services the latter offer.

In [ 6 ], the authors proposed taxonomies for inter-cloud architectures and application brokering. They presented a detailed survey of both academic and industry developments for inter-cloud, cataloguing many projects and fitting them onto the introduced taxonomies. They also analysed the existing works and identified open challenges in the area of inter-cloud application brokering. Their efforts are nonetheless limited to broker-based strategies.

In [ 22 ], a systematic literature survey was conducted to compile studies related to cloud brokerage. The authors presented an understanding of the state of the art and a novel taxonomy to characterise cloud brokers, identifying the main limitations of current solutions and highlighting areas for future research. However, just like [ 6 ], their whole analysis only covers broker-based approaches.

• Interconnected clouds

Interconnected clouds, also called Inter-cloud , can be viewed as a natural evolution of cloud computing. Inter-cloud has been introduced by Cisco [ 23 ] as an interconnected global “cloud of clouds” that mimics the term Inter-net, “network of networks”. Basically, the Inter-cloud refers to a mesh of clouds that are unified based on open standard protocols to provide a cloud interoperability.

A more sophisticated definition of Inter-cloud is given by the Global Inter-cloud Technology Forum (GICTF) [ 24 ]: “Inter-cloud is a cloud model that, for the purpose of guaranteeing service quality, such as the performance and availability of each service, allows on-demand reassignment of resources and transfer of workload through an interworking of cloud systems of different cloud providers based on coordination of each consumer’s requirements for service quality with each provider’s SLA and use of standard interfaces”.

In [ 8 , 9 , 25 ], the author investigated the consumption of resources and services from multiple clouds, as well as proposed a list of requirements for interoperability solutions, highlighting the technological barriers and some well-known solutions for multi-cloud environments. The author did not present the origin of these requirements, nor did she identify the degree of fulfillment of the requirements by theoretical approaches and technical solutions.

In [ 26 ], the authors discussed all the relevant aspects motivating cloud interoperability, categorising and identifying cloud interoperability scenarios and architectures. They provided a taxonomy of the main challenges for the Inter-cloud realisation. A comprehensive review of the state of the art, including standardisation initiatives, ongoing projects and studies in the area, was also conducted.

In [ 27 ], the authors analysed the existing literature to identify how interoperability in cloud computing has been addressed. They investigated requirements and usage scenarios for interoperable applications as well as cloud interoperability solutions, presenting a limited list of open issues and directions for future research.

In [ 28 ], the authors surveyed the literature to analyse and categorise various solutions for solving the interoperability and portability issues of Interconnected clouds, referring to both user-side (Multi-clouds or Aggregated service by Broker) and provider-side (Federated clouds or Hybrid clouds) scenarios, as specified in [ 8 , 25 ]. They also performed a comparative analysis of the literature works falling into the same category, and discussed the challenges of Interconnected clouds along the same lines as [ 17 ] and [ 26 ].

Despite delving into Interconnected clouds, starting with motivation, scenarios, possible solutions for interoperability, and ending with open issues and future directions, all these works ([ 26 – 28 ]) gave limited attention to cloud resource orchestration. In addition, none of them covered aspects pertaining to the application development, deployment, and lifecycle management.

• Cloud resource orchestration

In a panorama where organisations get to use many types of cloud computing systems simultaneously, the complexity of the workloads devoted to the management of the life-cycle of resources (data and applications) across the systems dramatically increases. Cloud orchestration is the process of managing these multiple workloads, in an automated fashion, across several cloud solutions. Typical activities underlying such a complex process are the resource description, selection, configuration, deployment, monitoring and control. Let us not forget that the orchestration problem is exacerbated by the diversity of the cloud systems, for what concerns both technical and administrative features.

In [ 2 ], the authors characterised the cloud resource orchestration in a multi-layered stack, and highlighted the main research challenges involved in programming orchestration operations for different cloud resource types across all layers of a cloud resource stack. The scope of their analysis is nevertheless restricted to the area of cloud resource orchestration.

In [ 3 ], the authors proposed a multidimensional taxonomy for classifying and comparing cloud resource orchestration techniques from both industry and academia, identifying open research issues and offering directions for future study. Similar to [ 2 ], their work only covers the topic of cloud resource orchestration.

In [ 29 ], the authors performed a systematic literature survey to build up a taxonomy of the main research interests regarding TOSCA. Different topics were addressed, such as devising cloud orchestration methods using TOSCA, extending the language of TOSCA, and presenting tools for manipulating TOSCA models. Despite being envisioned as a topic which is expected to play an increasingly important role, interoperability received very limited attention.

Analysis framework

In this section we introduce the desired capabilities for CROFs, focusing on deployment and management aspects. From the consumers’ standpoint, CROFs implement a service-oriented model which ensures successful hosting and delivery of applications by using cloud resources in order to meet their QoS requirements. Our reference architecture for CROFs is depicted in Fig.  3 . Processes and services involved in cloud resource orchestration are categorised depending on their functionalities in relation to this reference model.

Reference architecture for CROFs

The Access Layer regulates interaction with the framework. Users can access services from the lower layers by means of CLIs, Web APIs, and Dashboards. The Application Management Layer concerns the handling of applications throughout their entire lifecycle, from the Development to the Execution passing through the Deployment. The Development refers to languages and models to typically represent applications, workflows, QoS requirements, and policies. Application descriptions define application components as well as their relationships. Workflow descriptions specify the behavioural aspects of applications by means of declarative or imperative approaches. Policy descriptions provide applications with dynamic control behaviours (e.g. defining load-based policies to scale up and down applications) in order to meet QoS requirements. The Deployment refers to the actual application deployment on cloud resources, which might go through a preliminary resource discovery process. The Execution entails effective automation of complex management tasks, such as scaling and failure handling, which typically require a monitoring engine collecting system and application metrics. Based on the captured metrics, a recovery engine and a policy enforcement engine can determine the decisions to make in order to recover from failures and enforce policies, respectively.

The Resource Management Layer includes services (e.g. discovery services, provisioning services, monitoring services) handling resources throughout their whole lifecycle. These services coordinate the required actions from the upper layer by leveraging operations at the Resource Provisioning Layer. The Resource Provisioning Layer encompasses services offering the most basic operations regarding cloud resources. A range of provisioning services (e.g. create, start, scale, stop, and delete) are usually furnished for every supported resource.

Significant research has been done in academic and industry landscapes toward characterisation of cloud orchestration tools. In [ 4 , 30 ], Baur et al. investigated the required features for such tools, and gave a definition of them. In [ 31 ], Ranjan et al. introduced technical dimensions for CROF analysis, thus providing insights into existing frameworks. In [ 3 ], Weerasiri et al. identified the main dimensions and common building blocks which characterise cloud resource orchestration solutions.

In [ 32 – 34 ], the authors presented their vision for cloud computing, including views on future research areas, one of them being resource provisioning and orchestration. A thorough analysis of these research areas and related challenges from different perspectives was carried out.

In [ 35 ], GigaSpaces Research investigated prevalent approaches for managing applications in cloud environments, namely, orchestration, PaaS (Platform as a Service) and CMP (Cloud Management Platform). A number of categories serving as a common ground for comparison between the different approaches were proposed.

Based on the study of Baur et al.[ 4 ], we enriched the list of desirable capabilities pertaining to CROFs by reviewing the literature and integrating the aforementioned works. Such capabilities, summarised in Fig.  4 , can be classified into two main categories as either Cloud Features or Application Features . Details about each set of features are provided in the following subsections.

Taxonomy of the CROF characterisation framework

Cloud features

Cloud features address cloud infrastructure aspects with special focus on supported deployment across multiple cloud providers. Whilst some works [ 4 , 35 ] investigated features such as multi/cross-cloud support and integration of external services and systems, others [ 31 ] focused on capabilities such as interoperability and access modes to CROFs. We propose a comprehensive approach which takes into account all the said aspects that we discuss next.

Multi-cloud support

Supporting multiple cloud providers is one of the most crucial features for CROFs, as it allows to select the best matching offer for an application from a diverse cloud landscape. Cloud providers often differ from each other regarding their APIs. For that reason CROFs should offer a cloud abstraction layer (see “ Interoperability approach ” section), which hides differences and avoids the need for provider-specific customisation causing the vendor lock-in issue.

Cross-cloud support

Cross-cloud support enhances the multi-cloud feature by allowing to distribute component instances of a single application over multiple cloud providers. The advantages of cross-cloud deployment are threefold: a) it allows a sophisticated selection of the best-fitting cloud providers on a per component instance basis, optimising costs or improving quality of services; b) it leverages the application availability as it introduces resilience against the failure of individual cloud providers; and c) it helps coping with privacy issues.

Interoperability approach

In the context of cloud computing, interoperability can be defined as the ability to develop applications that combine resources that can interoperate, or work together from multiple cloud providers, hence taking advantage of specific features provided by each provider [ 27 ]. A few research papers [ 9 , 27 , 28 ] comprehensively reviewed the literature in order to dissect the state of the art in cloud interoperability, resulting in a diverse range of approaches falling into the following categories: open standards , semantics , model-driven engineering (MDE) , and open libraries & services .

Formulating standards for cloud computing is the most obvious solution for interoperability. Even though a plethora of standards have been proposed so far (e.g., OCCI Footnote 6 , CIMI Footnote 7 , OVF Footnote 8 , CDMI Footnote 9 , TOSCA [ 36 ]), lack of widespread accepted standards necessitates investigating other solutions for interoperability. When cloud providers use different APIs and data models in order to exhibit the same features, semantic interoperability becomes involved. Semantic technologies (e.g, OWL Footnote 10 , SPARQL Footnote 11 , SWRL Footnote 12 ) can prove useful to provide semantic interoperability among different cloud providers. Broker-based approaches can also alleviate semantic interoperability by means of ontology-based interfaces concealing the differences among cloud vendors. Cloud interoperability can also be addressed by exploiting MDE techniques [ 10 ]. Another viable solution for cloud interoperability includes open libraries (e.g., Apache jclouds Footnote 13 , Apache Libcloud Footnote 14 ) and services, which rely on abstraction layers in order to decouple application development from proprietary technologies of cloud providers.

Integration

Support for advanced IaaS/PaaS services (e.g., DBaaS, LBaaS, FWaaS) is desirable. It reduces complexity and management efforts for the end user. On a negative note, it comes at the expense of flexibility.

BYON (Bring Your Own Node) captures the ability to use already running servers for application deployment. In particular, it enables the use of servers not managed by a cloud platform or virtual machines on unsupported cloud providers.

This feature captures what interfaces CROFs use to interact with cloud resources. Three types of interfaces are usually supported: command-line, web-based dashboard, and web-based API.

Command-line interfaces wrap cloud-specific API actions as commands or scripts executable through shell environments. Despite command-line interfaces being easier to implement, their usage requires a deep understanding about cloud resources and related orchestration operations.

Web-based dashboards present cloud resources as user-friendly artifacts and resource catalogues. Visual artifacts and catalogues aim at simplifying resource selection, assembly, and deployment. These features make Web-based dashboards simpler and more flexible than command-line interfaces.

Web-based APIs allow other tools and systems (e.g. monitoring tools) to integrate cloud resource management operations into their functionalities. They provide the highest abstraction out of the three interface types.

Application features

Application features address development, deployment, and execution aspects of applications. To this end, unlike all previous works, we collect features according to the application phase they pertain to. For instance, with reference to the development phase, we have identified Portability and Containerisation as relevant features. Furthermore, we also propose a classification of application domains of interest for CROFs.

Application domain

Application domain refers to the types of applications that CROFs have been targeted and customised for. Academic research has been done toward the characterisation of application domains over the past few years [ 31 ][ 37 ][ 38 ]. Grounding on the study of Buyya et al. [ 37 ], we classified application domains into two categories: Scientific applications , and Business applications (see Fig.  5 ).

Application domain classification

Cloud computing systems meet the needs of different types of applications in the scientific domain: high-performance computing (HPC) applications, high-throughput computing (HTC) applications, and Large-scale data analytics/Internet of Things (IoT) , which is a matter of common interest for both scientific and business sectors. In regard to the business domain, cloud computing is the preferred technology for a wide range of applications, from multi-tier web applications (e.g., web, mobile, online gaming applications) to media and content delivery network (CDN) applications (e.g, video encoding & transcoding, video rendering, video streaming, web/mobile content acceleration).

Portability

Portability has been defined as the capability of a program to be executed on various types of data processing systems without converting the program to a different language and with little or no modification [ 39 ]. In the context of cloud computing, portability can be classified into three categories: data portability, function or application portability, and service or platform portability [ 40 ]. In particular, application portability refers to the ability to define application functionalities in a vendor-agnostic way.

Supporting open standards such as CAMP [ 41 ] and TOSCA [ 36 ] for modelling the application topology and the component lifecycles facilitates the usage of CROFs and further increases the reusability of the topology definition, as it restricts the vendor lock-in issue to cloud provider level. Reusability can also be improved via a modularised approach regarding the application description. Methods to achieve modularity include templating, parameterisation, and inheritance. Furthermore, since the initial effort for describing applications and application components is high, model sharing by means of existing libraries or marketplaces would be beneficial.

Containerisation

Container-based virtualisation [ 42 ] is a key approach for sharing the host operating system kernel across multiple guest instances (i.e., containers), while keeping them isolated. Environment-level containers provide a resource isolation mechanism with little overhead compared to OS-level hypervisors [ 43 ]. Moreover, the increased isolation offered by containers allows resource consumption to be configured, controlled, and limited at the instance level. Docker is the leading Linux-based platform for developing, shipping, and running applications through container based virtualisation.

Since managing a large amount of containers inside a Docker cluster can be difficult, container-centric orchestrators such as Docker Swarm Footnote 15 , Google Kubernetes Footnote 16 , and Apache Mesos Footnote 17 have appeared. They perform orchestration at container level by automating the provisioning and management of complex containerised deployments across multiple hosts and locations.

Resource selection

Resource selection refers to the level of automation supported by CROFs with respect to the selection of hardware and software resources. It usually involves identifying and analysing alternative cloud resources based on selection criteria. Resource selection approaches can be classified into four categories.

In a manual binding users provide the concrete unique identifiers of the cloud entities. In an automatic binding they specify abstract requirements (e.g. number of cores), which CROFs are responsible for binding to a concrete offer at runtime. Automatic binding can be enhanced by offering an optimised binding , which leverages optimisation criteria based on attributes of the cloud provider (e.g., price, location) to select the best fitting offer. A dynamic binding offers a solving system that enables changes to the binding based on runtime information (e.g., metric data from the monitoring system).

Lifecycle control

Lifecycle control defines the actions that need to be executed in order to fully manage cloud applications. Existing CROFs provide varying levels of automation, typically categorised as script-based , and DevOps approaches.

A script-based approach consists of a set of shell scripts, which are executed in a specific order. It has limited ability to express dependencies, react to changes, and verify configurations. Script-based approaches can be extended to support DevOps tools (e.g., Chef Footnote 18 , Puppet Footnote 19 , Ansible Footnote 20 ) that offer a more sophisticated approach to deployment management and ready-to-use deployment descriptions.

Wiring & workflow

Most cloud applications are distributed with components residing on different virtual machines. When application deployment takes place, an application instance consisting of one or more component instances gets created. Since dependency relationships may exist between components, the deployment functionality also has the task of wiring component instances together.

A straight-forward approach to resolve those dependencies is attribute and event passing , in which case lifecycle scripts lock/wait for attributes to become available or register listeners on topology change events. An improvement is a manual workflow defined by users in order to take care of the deployment order. Nevertheless, the easiest way for users to deploy applications is an automatic workflow deduction from the lifecycle actions defined on components and their relationships. Additionally, CROFs may offer extensions for external services like IaaS/PaaS services (see “ Integration ” section) to ensure that the deployment engine is aware of this dependency.

Tracking the behaviour of applications is the key to assessing the quality of the deployment and an important building block for adaptation. As a first step this comprises the collection of metrics. CROFs should offer a way to measure system metrics (e.g., CPU usage) and application metrics (e.g., number of requests). If predefined metrics are not sufficient, a well defined way to add custom metrics should be provided. Aggregation mechanisms enable to compute higher-level metrics and combine multiple metrics as well. Access to historical data is also desirable in order to support a higher-level evaluation of monitoring data.

Runtime adaptation

CROFs should automatically adapt applications in order to deal with dynamic deviations (e.g., increased load). Operations to face such changes are mainly scaling , and migration . However, the adaptation support of many CROFs is limited to horizontal scaling with threshold-based triggers. Rule engines leveraging complex metrics and QoS goals would be an improvement.

Since cross-cloud deployments may experience failures, CROFs should also support recovery from undesired, erroneous states. Another feature related to adaptation is continuous integration/continuous delivery (CI/CD) , which allows to modify the topology model of deployed applications reducing changes to as few as possible.

Review of cROFs

This section presents a selection of CROFs from different landscapes. Notwithstanding that the current state of the art embraces a large number of frameworks, this work contemplates a subset of them which we deem to be representative of the characteristics of the majority of existing solutions. We classify the frameworks in two categories: production/commercial CROFs, and experimental/academic ones.

Production/commercial CROFs are used in a production environment by private and public cloud providers. Whereas some of them are closed-source, others are open-source and supported by a thriving community of developers and users. Experimental/academic CROFs usually originate from the research scenery and advance the state of the art, even though their implementation is mostly prototypal.

We discuss next each class of CROFs, and analyse their main capabilities from both cloud and application perspectives, as extensively covered in “ Analysis framework ” section. Table  1 provides a bird’s-eye view of the frameworks taken under consideration. Specifically, each row represents a CROF ( Name ) and specifies the original authors ( Organisation ), basic dates for the initial and latest releases ( Active ), a brief introduction (Description), and the sources consulted ( References ).

Production/commercial cROFs

Nowadays, there is a great variety of production/commercial CROFs around [ 44 ], such as infrastructure-centric services (e.g., Heat, CloudFormation) provided by cloud providers which are also IaaS providers, platform-centric (e.g., Cloud Foundry, OpenShift) and platform-agnostic (e.g., Cloudify, Terraform) tools provisioning resources from IaaS providers. In this section we first debate some of the most relevant solutions introduced in Table  1 , and subsequently summarise their cloud and application features in Tables  2 and 3 respectively.

OpenStack Heat [ 45 ] is a service for managing the entire life-cycle of infrastructure and applications within OpenStack clouds. It implements an orchestration engine to launch multiple composite cloud applications based on either a CloudFormation compatible template format (CFN) or the native OpenStack Heat Orchestration Template format (HOT). HOT templates are defined in YAML.

A Heat template describes the infrastructure of a cloud application in a declarative fashion, enabling creation of most OpenStack resource types as well as more advanced functions (such as instance high availability, instance auto-scaling, and nested stacks) through OpenStack-native REST API calls. The resources, once created, are referred to as stacks . Heat templates are consumed by the OpenStackClient , which provides a command-line interface (CLI) to OpenStack APIs for launching stacks, viewing details of running stacks, and updating and deleting stacks.

Heat only allows a single-cloud deployment on an OpenStack environment. With reference to interoperability, Heat provides neither semantics nor MDE solutions, but it provides support for TOSCA via the independent Heat-Translator project Footnote 21 which translates TOSCA templates to HOT.

Regarding portability, Heat partially supports model standards (TOSCA) and reusability via input parameters, and template composition. It also supports containerisation by means of OpenStack Zun service Footnote 22 .

Cloud resources can only be selected through manual binding, whereas both manual and automatic workflows can leverage script-based or DevOps tools (such as Chef and Puppet) in order to handle the whole application life-cycle. Heat provides horizontal scaling with threshold triggers based on infrastructure metrics. It partially supports continuous delivery by updating existing stacks, resulting in some resources being updated in-place and others being replaced with brand new resources. Failure recovery capabilities are also supported by means of manual workflows and stacks update.

Cloudify [ 46 ] is an open-source orchestration framework based on TOSCA. It provides services in order to model applications and automate their entire life-cycle through a set of built-in workflows. Application templates are referred to as blueprints , which are YAML documents written in Cloudify’s DSL (Domain Specific Language) . Blueprints are normally consumed by the Cloudify CLI, which includes all of the commands necessary to run any actions on Cloudify Manager .

Typical blueprints contain declarations for various resource types, including cloud resources. Cloudify allows multi-cloud and cross-cloud deployments by means of built-in plugins. It also supports BYON, and leverages TOSCA for interoperability and portability. However, despite being aligned with the modelling standard, Cloudify’s DSL does not directly reference the standard types.

Cloudify supports containerisation using Docker. Container orchestration is also available through Kubernetes. Cloud resources can only be selected through manual binding, whereas both manual and automatic workflows can leverage script-based or DevOps tools (such as Ansible, Chef, and Puppet) in order to handle the application life-cycle. Cloudify provides infrastructure, application, and custom metrics. It also enables the definition of custom aggregations and policies using Clojure Footnote 23 and Riemann Footnote 24 .

Cloudify offers built-in workflows for application healing (by applying the uninstall and install workflows’ logic, respectively) and horizontal scaling. Complex scenarios (e.g., vertical scaling, cloud bursting) are not supported out of the box. Live migration is partially-fulfilled in the context of containerised applications, though. Multiple pods with containerised applications can be moved between nodes in the same Kubernetes cluster, without service disruption. Continuous delivery is supported through deployment updates, which allow to modify a running topology by adding/removing/modifying nodes. Modifying existing nodes will cause their automatic reinstallation, though.

Apache Brooklyn [ 47 ] is an open-source framework for modelling, deploying, and managing distributed applications defined using declarative YAML blueprints written in Brooklyn’s DSL . Brooklyn’s YAML format follows the CAMP specification [ 41 ], but uses some custom extensions. Support for TOSCA is planned for the near future. Blueprints are usually consumed by the Brooklyn client CLI in order to access a running Brooklyn Server. A web console and powerful REST-APIs are available as well.

Brooklyn allows multi-cloud and cross-cloud deployments on many public and private clouds. It also supports private infrastructures (BYON), and leverages Apache jclouds as cloud abstraction layer for interoperability. Portability is achieved via model reusability mechanisms (e.g, type inheritance) and model sharing (e.g, types shared either locally or in a Git repository). Brooklyn does not support containers out of the box. However, containerisation can be integrated by means of separate projects (e.g, Cloudsoft Clocker Footnote 25 ).

Brooklyn supports manual as well as basic automatic binding for resource selection, whereas it does not support workflow scenarios. Life-cycle actions (i.e. effectors ) for entities can be configured through either shell scripts or Chef recipes. Brooklyn pulls metrics by either executing remote actions or accessing an external monitoring tool. Nevertheless, it is the user’s responsibility to implement those actions, or to provide an interface to an external monitoring tool.

Metrics/QoS can be fed into policies, which automatically take actions such as restarting failed nodes, or scaling out. By default, a threshold-based policy is available. Continuous delivery is exclusively possible on component level, namely by redeploying single components with updated software.

Apache Stratos [ 48 ] is an open-source PaaS framework which allows developers to build distributed applications and services. Applications are typically composed of sets of cartridges representing descriptions of abstract VMs hosting both business and infrastructure services, combined with deployment and scaling policies. Stratos defines configurations and applications in a specific JSON format, therefore they can be shared. Reusability is limited, since cartridges contain references to IDs of IaaS snapshots and hardware configuration. Applications can be managed by means of Stratos CLI. A web console and powerful REST-APIs are available as well.

Stratos support multiple providers and utilises Apache jclouds as cloud abstraction layer for interoperability. Despite using jclouds, BYON is not supported. No external services are supported either. Stratos leverages Kubernetes as a cluster orchestration framework in order to provide containerisation. Cloud resources are manually selected when configuring cartridges. In addition, while the life cycle description for managing VMs is done by Stratos itself, the software setup is delegated to Puppet. Only manual workflows are supported.

Stratos uses a cartridge agent residing within each VM in order to access system and application metrics. It is not possible to define custom metrics. Using in-flight requests , load average , and free memory metrics combined with a complex event processor and the Drools rule engine Footnote 26 , Stratos enacts a multi-factored horizontal auto-scaling. It also includes cloud bursting, allowing to seamlessly migrate applications between clouds. Recovery actions are supported in case some tasks within VMs of an application topology fail, by automatically destroying and recreating the affected cartridge instance. Continuous delivery is not supported, since users need to undeploy applications before changing their definitions.

Alien4Cloud

Alien4Cloud (Application LIfecycle ENabler for cloud) [ 49 ] is an open-source platform that makes application management on the cloud easy for enterprises. It leverages other existing open-source projects that help orchestrating cloud applications and focus on run-time aspects (e.g., Cloudify). In Alien4Cloud, applications templates ( blueprints ) are modelled in TOSCA in order to allow interoperability and portability. Blueprints can also be shared across platform users via a maintained TOSCA catalog. However, Alien4Cloud supports a slightly modified version of TOSCA Simple Profile.

Application deployment is done through an orchestrator on a location configured for and managed by an orchestrator. Alien4Cloud supports a number of orchestrators (Cloudify, Puccini Footnote 27 , and Marathon Footnote 28 ) via plugins. Locations describe a logical deployment target ranging from private/public clouds to a set of physical machines (BYON), or even Docker containers (Kubernetes and Mesos). Multi-cloud and cross-cloud deployments are supported.

Cloud resources can only be selected through manual binding ( node substitution ), whereas both manual and automatic workflows can leverage script-based or DevOps tools (such as Ansible, Chef, and Puppet) in order to handle the application life-cycle. Regarding monitoring and run-time adaptation, since Cloudify can be used as Alien4Cloud’s backend orchestration solution, the same considerations apply. In particular, Alien4Cloud supports horizontal scaling as well as continuous delivery.

Terraform [ 50 ] is an open-source infrastructure as code tool for building, changing, and versioning infrastructures in a platform-agnostic way. It uses its own high-level configuration language known as Hashicorp Configuration Language (HCL) , or optionally JSON, in order to detail the infrastructure setup. Despite being non-compliant with any model standards, HCL supports reusability via modules and module composition . Reusable modules can also be shared by means of the Terraform Registry as well as other sources (e.g., GitHub, Bitbucket). Configurations are usually consumed by the Terraform CLI , but Terraform Enterprise also provides both a web-based dashboard and REST APIs.

Terraform can manage multiple cloud providers and even cross-cloud dependencies by means of special plugins called providers . Providers are available for Docker containers and container orchestration as well as external cloud services (e.g. Amazon RDS Footnote 29 ). However, no support is provided for BYON. Cloud resources are manually selected during configuration, while life-cycle actions can be configured through provisioners executing scripts or running configuration management (Chef, Puppet, Salt). Only automatic workflows are supported.

Terraform leverages providers in order to provide auto-scaling capabilities with threshold triggers on system metrics gathered by monitoring services (e.g., Azure Monitor Footnote 30 , Amazon CloudWatch Footnote 31 ). Continuous delivery is supported by applying configuration updates, which allow to add/remove/modify resources. When resource arguments cannot be updated in-place, the existing resource will be replaced by a new one instead. No recovery actions are supported out of the box, since any errors need to be addressed manually.

CloudFormation

AWS CloudFormation [ 51 ] is a template-based infrastructure-as-code tool for managing AWS infrastructure deployments. All resources and dependencies are declared in a JSON or YAML template, which CloudFormation uses as a blueprint for building AWS resources. A collection of managed resources is called stack . Although CloudFormation templates do not comply with any model standards, reusability is partially supported via input parameters, and nested stacks. Templates are usually consumed by the CloudFormation console, or REST APIs, or CLI.

CloudFormation can only model and manage AWS resources. No support is provided for multiple cloud providers or BYON. Containerisation is natively supported via Elastic Container Service (ECS) Footnote 32 resources. Container orchestration is also supported by means of Elastic Kubernetes Service (EKS) Footnote 33 resources as well. Cloud resources are selected through manual binding, whereas lifecycle actions can be configured through user-data scripts or DevOps tools (Chef, Puppet). Only automatic workflows are supported.

CloudFormation provides automatic scaling capabilities by means of AWS Auto Scaling Footnote 34 , which uses dynamic scaling and predictive scaling to automatically scale resources based on Amazon CloudWatch metrics. Customised metrics for Application Auto Scaling can also be defined. Live migration is partially-fulfilled in the context of containerised applications. For instance, it’s possible to gracefully migrate existing applications from a worker node group to another. Continuous delivery is supported by stack updates. Depending on the resource and properties being updated, an update might interrupt or even replace an existing resource. Recovery actions are supported by automatically rolling back the existing stack on failure.

Experimental/academic cROFs

In this section, we initially review an ensemble of significant experimental/academic CROFs outlined in Table  1 , and then summarise them according to their cloud and application features in Tables  4 and 5 respectively. Additionally, we briefly run through other research initiatives focusing only on specific aspects of CROFs.

Cloudiator [ 52 , 53 , 62 ] is an open-source cross-cloud orchestration framework, which relies on Apache jclouds in order to support many public and private cloud platforms. The main orchestration component, namely Colosseum , can be accessed via a Java client, or a web-based user interface, or a REST-API.

The application description consists of individual components , which are assembled to form a full application. Each component provides interface operations (e.g., bash scripts) for managing the component life-cycle. Dependencies between application components are described through communication entities linking provided ports and required ports . Despite being non-compliant to any modelling standards, application components are reusable across different applications.

The resource broker is responsible for automatically selecting the correct cloud offer (previously discovered by the discovery engine ), depending on the desired requirements/constraints on virtual machine configuration. The deployment engine acquires the virtual machine and forwards the component installation request to the remote life-cycle agent, namely Lance . Lance runs component instances within Docker containers by default. In addition, only automatic workflows are supported.

Automatic scaling capabilities are provided by means of AXE , a monitoring and adaptation engine embedded in Cloudiator, which implements scalability rules consisting of threshold-based conditions linked to raw or composed metrics. Migration features are partially fulfilled by supporting access to OpenStack’s live migration functionality. Recovery actions are supported by the recovery engine , which detects abnormal states of system entities marking them as failed, and applies solutions based on failure categories. The same mechanism is used in order to represent changes in the models (continuous delivery).

Roboconf [ 54 , 63 ] is an open-source scalable orchestration framework for multi-cloud platforms. Many IaaS providers (e.g., OpenStack, AWS, Azure, vSphere), as well as Docker containers and local deployments for on-premise hosts, are supported by using special plugins. Roboconf partially supports interoperability by means of OCCI extensions and a generic target implementation based on Apache jclouds. In addition, it can be accessed by means of a shell-based console, or a web-based user interface, or a REST API.

Roboconf provides a CSS-inspired DSL, which allows to describe applications and their execution environments in a hierarchical way. A distributed application is seen as a set of components , building an acyclic graph describing both containment and run-time relationships between components, and a group of instances of these components. Component definitions can be reused via abstract types ( facets ), imports, and inheritance.

Roboconf consists of several modules. The Deployment Manager (DM) is in charge of instantiating and managing VMs and remote agents. Agents use plugins (such as Bash or Puppet) in order to handle the life-cycle of software instances. The DM and the agents communicate with each other through an asynchronous messaging server. The SoftwareInstanceManager is responsible for automatically generating software life-cycle management and monitor software instances themselves.

Automatic scaling capabilities are provided by means of autonomic management implemented by the DM and the remote agents. Agents send notifications to the DM whenever certain threshold-based conditions linked to system metrics are met. The DM’s decision engine responds to those notifications using corresponding imperative rules. Monitoring application metrics still needs to be addressed. Both application migrations and global/per-component rollbacks (continuous deployment) are part of Roboconf’s roadmap, but they are not supported out of the box yet.

INDIGO-DataCloud

INDIGO-DataCloud (INtegrating Distributed data Infrastructures for Global ExplOitation) [ 55 , 56 , 64 ] is an open-source data and computing platform targeted at scientific communities, and provisioned over Cloud and Grid-based infrastructures as well as over HTC and HPC clusters. The INDIGO-DataCloud framework has been developed within the homonymous project funded under the EU’s Horizon 2020 Framework Programme [ 65 ].

The INDIGO-DataCloud project extended existing PaaS solutions in order to provide automatic distribution of applications and/or services over a hybrid and heterogeneous set of IaaS infrastructures. Some of the key INDIGO PaaS components include: Orchestrator , Infrastructure Manager (IM) , CloudProviderRanker , Monitoring , SLA Manager (SLAM) , Managed Services/Application (MSA) Deployment , and Data Management Services . The Orchestrator coordinates the process of deploying services and applications on both on-premise and public IaaS platforms. It can be accessed via a command-line interface ( Orchent ), or a GUI-based portlet, or a REST API.

The Orchestrator delegates the deployment to the IM, to OpenStack Heat or to the Mesos frameworks, based on TOSCA templates and a list of providers ranked by the CloudProviderRanker. The Monitoring component collects monitoring data from both PaaS core services and client infrastructure/services by means of specific probes. The SLAM establishes an agreement between customer and provider about capacity and quality targets. The Data Management Services provide an abstraction layer for accessing the data storage in a unified and federated way.

INDIGO-DataCloud supports multi-cloud and cross-cloud deployments, as well as interoperability by leveraging open standards (OCCI, CDMI). It also promotes portability by adopting an extension of TOSCA for describing applications and services. Cloud resources are automatically selected and optimised by the CloudProviderRanker, depending on SLAs and monitoring data. A configuration management solution based on Ansible roles is adopted to carry out both the deployment of the application and the creation of the pre-configured Docker images. Only automatic workflows are supported.

Runtime actions, such as horizontal scaling and failure handling, are automatically supported by the MSA Deployment (based on Apache Mesos), which uses the Automatic Scaling Service (based on EC3/CLUES Footnote 35 ) to ensure the elasticity of the cluster, Marathon Footnote 36 and Chronos Footnote 37 frameworks in order to handle Long-Running Services (LRS) and application jobs, respectively. Marathon can also migrate services if problems occur. Despite different DevOps practises being adopted for both the core services and user applications (e.g., automated builds of each application image are triggered once a new change is committed to its repository), hot changes in application deployments are not supported out of the box.

MiCADO (Microservices-based Cloud Application-level Dynamic Orchestrator) [ 57 ] is an open-source multi-cloud orchestration and auto-scaling framework for Docker containers, orchestrated by Kubernetes (or alternatively by Docker Swarm). The full MiCADO framework has been investigated and implemented in the COLA (Cloud Orchestration at the Level of Application) project funded by the European Commission [ 66 ].

MiCADO core services are deployed on MiCADO Master , which is configured as the Kubernetes Master Node and provides the Docker Engine , Occopus [ 67 ] (to scale VMs), Prometheus Footnote 38 (for monitoring), Policy Keeper (to perform decision on scaling), and Submitter (to provide submission endpoint) microservices. During operation, MiCADO workers are instantiated on demand and join the cluster managed by the MiCADO Master.

MiCADO supports multi-cloud and cross-cloud deployments on various public and private cloud infrastructures. It also provides interoperability and portability by means of a TOSCA-based Application Description Template (ADT) , which comprises three sections: a) the definition of the individual applications making up a Kubernetes Deployment , b) the specification of the VM and c) the implementation of scaling policies for both VM and Kubernetes scaling levels. ADTs can be consumed by means of a web-based dashboard or a REST API.

Cloud resources are manually selected when configuring VMs. The application life-cycle is handled by MiCADO itself, which leverages Occopus and Kubernetes for managing VMs and containers, respectively. Only automatic workflows are supported. MiCADO allows automated scaling depending on VM and container metrics gathered by two built-in exporters on each MiCADO worker: Prometheus Node Exporter Footnote 39 and CAdvisor Footnote 40 . Scaling policies can be defined specifically for the applications. Lastly, continuous delivery capabilities are supported via “rolling updates” on Kubernetes Deployments.

MODAClouds (MOdel-Driven Approach for the design and execution of applications on multiple Clouds) [ 58 , 59 ] is an open-source design-time and run-time platform for developing and operating multi-cloud applications with guaranteed QoS. The MODAClouds framework has been developed within the homonymous project funded by the European Commission [ 68 ].

The MODAClouds Toolbox consists of three main components: Creator4Clouds , Venues4Clouds , and Energizer4Clouds . Creator4Clouds is a design-time platform which allows to design multi-cloud applications, carry out performance and cost evaluation, and plan the deployment strategy by choosing the service providers that best suit all business and QoS requirements. Venue4Clouds is a decision support system (DSS) to choose the most suitable cloud providers depending on different aspects such as application architecture, business risk, quality and cost. Energizer4Clouds is a run-time platform to deploy, manage, monitor and assure operations of multi-cloud services. Specifically, Tower4Clouds sub-component is responsible for collecting, analysing, and storing monitoring information, whereas SpaceOps4Clouds sub-component enacts application self-adaptation in order to meet predefined objectives and/or constraints whenever changes happen.

MODAClouds supports multi-cloud and cross-cloud deployments on both IaaS and PaaS providers. It leverages an MDE approach in order to support interoperability between cloud providers. In particular, MODACloudML is a set of UML extensions enabling developers to model multi-cloud applications through three level of abstractions: Cloud-enabled Computation Independent Models (CCIM), Cloud-Provider Independent Models (CPIM), and Cloud-Provider Specific Models (CPSM). These models facilitate portability, since they are mostly reusable. Cloud resources can be automatically selected and optimised via Venues4Clouds and SpaceDev4Clouds , and managed through either shell scripts or Puppet. Only automatic workflows are supported.

Within the MODAClouds runtime environment, the Models@Runtime engine is responsible for enacting adaptation actions such as application scaling and bursting, data and application migration, and continuous delivery on both infrastructure and component levels. Failure recovery is partially supported for data migration and scaling/bursting scenarios.

SeaClouds (SEamless Adaptive multi-Cloud management of service-based applicationS) [ 60 , 61 ] is an open-source platform for deploying and managing multi-component applications over heterogeneous clouds. The SeaClouds framework has been investigated and implemented within the homonymous project funded by the European Commission [ 69 ].

The SeaClouds architecture comprises six main components: Dashboard , Discoverer , Planner , Deployer , Monitor , and SLA Service . The Dashboard allows to model applications (topology and requirements). The Discoverer identifies the available capabilities offered by cloud providers. The Planner receives the AAM (Abstract Application Model) from the Dashboard and creates a set of ADP (Abstract Deployment Plan) meeting the application requirements. From the selected plan a Deployable Application Model (DAM) is to be generated, containing the information needed by the Deployer (based on Apache Brooklyn [ 47 ]) to deploy, configure and run the application. The Monitor collects infrastructure and application level metrics from the targeted cloud providers in order to verify that QoS requirements are met. And if not, reconfiguration actions can be triggered. The SLA Service enforces business-oriented policies and business actions to apply in case of violation.

SeaClouds supports multi-cloud and cross-cloud deployments on both IaaS and PaaS providers. It also promotes interoperability and portability by adopting a TOSCA-based representation for AAMs and ADPs, as well as a CAMP-based description for DAMs. Cloud resources are automatically selected and optimised by the Planner. Changes to the binding can also occur in case of reconfiguration actions. Only automatic workflows are supported.

SeaClouds allows repairing actions, such as scaling horizontally and vertically cloud resources, or restarting and replacing failed components. It also supports replanning in order to handle the cases that cannot be solved by repairing. A migration of application modules may happen in this process. Continuous delivery is not supported out of the box.

Other initiatives

In this section, we briefly review a number of other research approaches derived from related EU projects which address, to varying degrees, multi-cloud orchestration, interoperability and portability. Specifically, a few works target semantic interoperability (i.e., moSAIC, cloud4SOA), some explore the benefits of federated cloud networks (BEACON, ATMOSPHERE), whereas others focus on application portability via non-standard (i.e, Claudia, OPTIMIS, ASCETiC, HARNESS), partially-standard (i.e, soCloud) and fully-standard (i.e., CELAR, CloudLightning) cloud modelling.

mOSAIC [ 70 , 71 ] is an open-source API and platform for multiple clouds designed and developed within the homonymous project [ 72 ]. Application deployment and portability across multiple clouds are facilitated by means of a common API and a high-level abstraction of cloud resources. mOSAIC also enables application developers to specify resource requirements in terms of a cloud ontology, whereas the platform, using a brokering mechanism, performs a matchmaking process in order to find the best-fitting cloud services. In so doing, developers can postpone their decision on the procurement of cloud services until runtime. However, even though a platform-independent component-based programming model is used, applications need to be implemented by leveraging one of the supported language-dependent APIs (Java, Python).

Cloud4SOA [ 73 , 74 ] is a multi-cloud broker-based solution developed under the homonymous project [ 75 ], which addresses semantic interoperability and portability challenges at the PaaS layer. It supports multi-platform matchmaking, management, monitoring and migration of applications by semantically interconnecting heterogeneous PaaS offerings. Similar to mOSAIC, Cloud4SOA introduces a cloud ontology establishing a set of abstractions among different PaaS offerings while exposing a multi-PaaS standardised API for the seamless application deployment and management across different cloud platforms. Despite being independent of specific APIs offered by the underlying PaaS offerings, adapters acting as a middleware between the Cloud4SOA API and native PaaS APIs are still needed.

The main goal of the BEACON project [ 76 ] is to develop techniques to federate cloud network resources, and to enable an efficient and secure deployment of federated cloud applications. Specifically, the proposed approach is to build a homogeneous virtualisation layer on top of heterogeneous underlying physical networks, computing and storage infrastructures. By leveraging the combination of Cloud federation, Software Defined Networking (SDN), and Network Function Virtualization (NFV) technologies, the project has delivered an innovative design of a Federation Management system acting as an external service provider dealing with federated networking services among multiple federated OpenStack Clouds [ 77 ].

ATMOSPHERE [ 78 ] aims to design and implement a framework and platform relying on lightweight virtualisation, hybrid resources and Europe and Brazil federated infrastructures to develop, build, deploy, measure and evolve trustworthy, cloud-enabled applications. Orchestration and deployment of complex application topologies is achieved through the TOSCA standard. In the context of the project, partners developed a federated network architecture [ 79 ] by creating multi-tenant overlay networks across different sites. The developed framework offers services such as distribution and inter-site migration of VMs, resource management, and network management.

Claudia [ 80 ] is a service management system implementing an abstraction layer that allows for the automatic service deployment and scaling depending on both infrastructure and service status. Conversely to mOSAIC and Cloud4SOA, each service in Claudia is defined by its corresponding Service Description File (SDF) whose syntax is based on the OVF standard, thereby providing vendor and platform portability. However, special OVF extensions must be defined in order to support automatic scalability, deployment-time customisation and external connectivity specification.

OPTIMIS [ 81 ] is a toolkit which addresses and optimises the whole service lifecycle on the basis of aspects such as trust, risk, eco-efficiency and cost, taking into consideration a number of cloud scenarios, namely, cloud federation, multi-cloud, hybrid cloud. However, regarding multi-cloud, interoperability with non-OPTIMIS providers can only be achieved by using APIs and adapters externally to the OPTIMIS components. According to OPTIMIS programming model, each service is defined as a collection of core elements being packed along with any external software components into VM images. Similar to Claudia, these VM images are configured by means of a service manifest based on the OVF standard, but a set of OVF extensions are required in order to specify the functional and non-functional requirements of the service.

ASCETiC [ 82 ] is an open architecture and approach to multi-cloud optimising energy efficiency, designed within the homonymous EU project [ 83 ]. Analogous to OPTIMIS, the OVF specification is employed to define a complete set of VMs to be deployed at an IaaS provider. Nevertheless, OVF extensions are necessary in order to support SLA negotiation and self-adaptation rules.

The HARNESS project [ 84 ] develops a cloud computing platform incorporating non-traditional and heterogeneous computational, networking and storage resources into the data centre stack to provide high performance at low cost. HARNESS envisions an enhanced cloud PaaS software stack that not only supports existing commodity technologies, but also incorporates heterogeneous technologies such as Dataflow Engines (DFEs), programmable routers and different types of storage devices [ 85 ]. The project demonstrated its results via extensions to OpenStack.

soCloud [ 86 ] is a service-oriented component-based PaaS for managing portability, elasticity, provisioning, and high availability across multiple clouds. Application descriptors are based on the OASIS Service Component Architecture (SCA) standard [ 87 ]. However, since the SCA model doesn’t allow to define non-functional requirements, special SCA extensions are required. A custom DSL is also used in order to describe elasticity. Additionaly, not only does soCloud support only SCA-based applications, but maintaining the mappings to various cloud providers and keeping up with recent features of supported clouds are a concern.

CELAR [ 88 , 89 ] is a resource management platform able to automatically deploy, monitor and scale applications over a cloud infrastructure. Applications are described using TOSCA, which ensures the portability of application descriptions across different IaaS platforms. However, every time a new application is to be deployed, users need to issue the request to the appropriate CELAR Server instance inside the cloud they want to deploy their application to. In contrast to mOSAIC, cloud4SOA and ASCETiC, no brokering mechanism is defined in order to best fit cloud resource requirements. Furthermore, cross-cloud is not supported.

CloudLightning [ 90 ] is a heterogeneous cloud service management and delivery model developed within the homonymous EU project [ 91 ]. Based on the principles of self-organisation and self-management, CloudLightning allows users to design and deploy their applications without the need for selecting the most suitable resources. This separation of concerns is made possible using a CloudLightning-specific service description language (CL-SDL), which extends TOSCA in order to capture specific attributes. The declarative approach is enriched with resource discovery mechanisms allowing easier identification and consumption of a variety of heterogeneous resources. CloudLightning proposes a solution based on a Gateway Service , which relies on two open-source tools: Alien4Cloud acting as the Gateway Service UI and Brooklyn-TOSCA Footnote 41 acting as the deployment orchestrator. In view of the above, the same remarks made in “ Brooklyn ” section are applicable to CloudLightning.

Critical discussion

Tables  2 , 3 , 4 and 5 summarise the CROFs presented in “ Review of cROFs ” section by outlining the features debated in “ Analysis framework ” section. We discuss the main characteristics of these frameworks next.

Most of the reviewed CROFs provide different access modes, including web-based dashboards and APIs, and allow both multi-cloud and cross-cloud deployments, except for Heat and CloudFormation which, as infrastructure-centric services, only support their own IaaS providers (i.e., OpenStack and Amazon, respectively). Besides, some of them natively support deployments on BYON (e.g., Cloudify, Brooklyn, Roboconf, SeaClouds). Interoperability between cloud providers is mainly achieved by means of open standards and open libraries/abstraction layers (e.g. jclouds). Open standards appear to be gaining ground, especially in academic scenarios. As such, a number of academic CROFs provide interoperability via OCCI (e.g., Roboconf) or CDMI (e.g., INDIGO-DataCloud) support, while others do via TOSCA (MiCADO, SeaClouds). Despite being the focus of previous research efforts (e.g., mOSAIC, Cloud4SOA), semantic approaches seem to be no longer a priority compared to the adoption of open standards. Of all the initiatives, MODAClouds is the only one to employ model-driven methodologies.

With regard to application portability, CROFs from both industry and academia are placing ever-increasing importance on modelling standards. However, while taking TOSCA (e.g., Cloudify, MiCADO) and CAMP (e.g., Brooklyn, SeaClouds) as reference models, they happen to customise and extend standard types. Thus, a further effort would be appropriate in order to ensure greater compliance with the aforementioned specifications. Aside from the adoption of standard models, model reusability is encouraged by means of modules shared either locally or remotely. Besides, since containers provide improved application encapsulation and abstraction from resources, most of the CROFs support containers as well as container orchestration.

As regards resource provisioning, there are different aspects of the matter that need to be considered, such as: selection, configuration and deployment of resources. In multi-cloud scenarios, selection is far from being a trivial task due to the diversity of cloud services’ characteristics and QoS. While manual selection is supported in the majority of CROFs, automatic and optimised selections are almost exclusively supported by academic CROFs. The optimised selection leverages QoS and technical requirements, and is carried out either based on static information on the service quality provided by cloud providers or through dynamic negotiation of SLAs. A few multi-cloud projects (e.g., INDIGO-DataCloud, MODAClouds, SeaClouds) provide support for SLA management, even though multi-cloud SLAs are not covered. Limited support is currently available for dynamic selection (i.e., SeaClouds).

Resource deployment can be manual or automatic. While most commercial CROFs support both manual and automatic workflows, academic CROFs exclusively support automatic ones. Using standard models such as TOSCA, where applicable, proves useful both for defining a custom workflow and for automatically generating one. However, since current standards lack support for modelling the semantics related to the instantiation of relationships between component instances, the actual wiring of component instances depends on the capabilities offered by the CROF enacting the deployment. On that note, standard extensions in support of sophisticated wiring on instance level would be desirable. As for resource configuration, on the one hand scripts are extensively supported, but on the other hand configuration management tools are mostly supported by commercial CROFs. Nonetheless, a few academic projects (e.g., INDIGO-DataCloud and MODAClouds) exploit these tools in order to enact DevOps practices as well.

Monitoring plays a key role in keeping track of the status of applications as well as physical and virtual resources. Monitoring metrics at different abstraction levels (e.g., infrastructure and application ones) and capturing dependencies between these levels allow to perform root cause analysis, such that any issues at infrastructure level can automatically lead to run-time infrastructure adaptation which best fits run-time application requirements. While infrastructure metrics are widely supported by both commercial and academic CROFs, application and custom metrics necessitate further investigation. Metric aggregation mechanisms are available for a large majority of CROFs. Nevertheless, in light of multi-cloud scenarios, where applications and resources may be largely distributed, metric collection and aggregation from heterogeneous cloud environments are necessary. As a result, standardised interfaces and formats should be inspected.

Monitoring data allows for different purposes such as enforcing SLAs, enabling elasticity, ensuring QoS. SLAs can be used as a basis for cloud services and respective applications to be managed during their life-cycle. Multi-cloud management requires specific mechanisms for run-time adaptation across a diversity of cloud set-ups, including scalability, migration, fault-tolerance, continuous delivery. While reactive approaches to run-time adaptation are fairly consolidated among all CROFs, predictive approaches (based on workload prediction models and machine learning optimisation) are only supported in some commercial CROFs (e.g., AWS CloudFormation) and should be more explored.

Both academic and commercial CROFs largely provide support for threshold-based horizontal scaling. Policy-based approaches, especially in the academic landscape, are gaining in importance as well. Migration support is still limited in both industry and academia, as it is closely linked to portability in all its facets, i.e., VM portability, application portability, data portability. Although platform-independent standards (TOSCA) and virtualisation techniques (containers) have improved application encapsulation and abstraction from resources, platform-independent data representation and standardisation of data import and export across diverse and heterogeneous clouds need to be inspected. In this regard, MODAClouds provides a solution to the data migration issue, albeit in the context of scalable NoSQL databases.

Both academic and commercial CROFs support failure recovery mechanisms based on restarting/replacing failed components or, in a worst-case scenario, rolling back entire application stacks. Of all academic CROFs, Cloudiator, MODAClouds and SeaClouds allow to identify abnormal and undesirable states of the system and apply a limited set of autonomic actions. However, the emergence of decentralised multi-cloud setups connecting a wider variety of entities and resources requires autonomic management systems that consider self-organisation, self-management and self-healing across a diversity of cloud deployments. Continuous delivery is well supported in the commercial landscape, and it is also gaining ground in the academic one because of the ever-growing use of DevOps methodologies.

Cloud computing technology has greatly evolved over the past few years, transforming the traditional infrastructure, platform and software resources into elastic and on-demand virtual components. However, heterogeneous and multi-layer resources have to be orchestrated in an effective way in order to ensure that end-users are provided with acceptable quality levels.

In this work we thoroughly analysed the cloud orchestration landscape: after presenting a taxonomy of relevant features and dimensions, we mapped and evaluated several cloud resource orchestration frameworks against it, especially focusing on multi-cloud capabilities. This systematic analysis has allowed to identify key open research issues, also proposing a set of future research directions in the cloud orchestration scenario.

Availability of data and materials

Not applicable.

http://www.scopus.com/

https://dl.acm.org/

http://ieeexplore.ieee.org/

https://www.sciencedirect.com/

https://link.springer.com/

https://occi-wg.org/about/specification/

https://www.dmtf.org/standards/cmwg

https://www.dmtf.org/standards/ovf

https://www.snia.org/cdmi

https://www.w3.org/TR/owl-syntax/

https://www.w3.org/TR/sparql11-query/

https://www.w3.org/Submission/SWRL/

https://jclouds.apache.org/

http://libcloud.apache.org/

https://docs.docker.com/engine/swarm/

https://kubernetes.io/

http://mesos.apache.org/

https://www.chef.io/

https://puppet.com/

https://www.ansible.com/

https://wiki.openstack.org/wiki/Heat-Translator

https://wiki.openstack.org/wiki/Zun

https://clojure.org/

http://riemann.io/

http://www.clocker.io/

https://www.drools.org/

https://github.com/tliron/puccini

https://docs.aws.amazon.com/rds/index.html

https://docs.microsoft.com/en-us/azure/azure-monitor/

https://docs.aws.amazon.com/cloudwatch/index.html

https://docs.aws.amazon.com/ecs/index.html

https://docs.aws.amazon.com/eks/

https://aws.amazon.com/autoscaling/

https://www.grycap.upv.es/clues/eng/index.php

https://mesosphere.github.io/marathon/

https://mesos.github.io/chronos/

https://prometheus.io/

https://github.com/prometheus/node_exporter

https://github.com/google/cadvisor

https://github.com/cloudsoft/brooklyn-tosca

Abbreviations

Cloud Resource Orchestration Framework

Model Driven Engineering

Infrastructure as a Service

Platform as a Service

Cloud Management Platform

Service Level Agreement

Topology and Orchestration Specification for Cloud Applications

Virtual Machine

Bring Your Own Node

Continuous Integration/Continuous Delivery

RightScale (2019) RightScale 2019 State of the Cloud Report. https://info.flexera.com/SLO-CM-WP-State-of-the-Cloud-2019 . Accessed 12 Oct 2019.

Ranjan R, Benatallah B, Dustdar S, Papazoglou MP (2015) Cloud resource orchestration programming: Overview, issues, and directions. IEEE Internet Comput 19(5):46–56. https://doi.org/10.1109/MIC.2015.20 .

Article   Google Scholar  

Weerasiri D, Barukh MC, Benatallah B, Sheng QZ, Ranjan R (2017) A Taxonomy and Survey of Cloud Resource Orchestration Techniques. ACM Comput Surv 50(2):26–12641. https://doi.org/10.1145/3054177 .

Baur D, Seybold D, Griesinger F, Tsitsipas A, Hauser CB, Domaschka J (2015) Cloud Orchestration Features: Are Tools Fit for Purpose?, In: 2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC), 95–101. https://doi.org/10.1109/UCC.2015.25 .

Bousselmi K, Brahmi Z, Gammoudi MM (2014) Cloud services orchestration: A comparative study of existing approaches In: 28th International Conference on Advanced Information Networking and Applications Workshops, 410–416. https://doi.org/10.1109/WAINA.2014.72 .

Grozev N, Buyya R (2014) Inter-cloud architectures and application brokering: taxonomy and survey. Softw Pract Experience 44(3):369–390. https://doi.org/10.1002/spe.2168 .

Ferry N, Rossini A, Chauvel F, Morin B, Solberg A (2013) Towards Model-Driven Provisioning, Deployment, Monitoring, and Adaptation of Multi-cloud Systems In: IEEE Sixth International Conference on Cloud Computing, 887–894. https://doi.org/10.1109/CLOUD.2013.133 .

Petcu D (2014) Consuming resources and services from multiple clouds. J Grid Comput 12(2):321–345. https://doi.org/10.1007/s10723-013-9290-3 .

Petcu D, Vasilakos A (2014) Portability in Clouds: Approaches and Research Opportunities. Scalable Comput Pract Experience 15(3):251–270. https://doi.org/10.12694/scpe.v15i3.1019 .

Google Scholar  

Ferry N, Rossini A (2018) CloudMF: Model-Driven Management of Multi-Cloud Applications. ACM Trans Internet Technol 18(2):16–24. https://doi.org/10.1145/3125621 .

Ferrer AJ (2016) Inter-cloud research: Vision for 2020. Procedia Comput Sci 97:140–143. https://doi.org/10.1016/j.procs.2016.08.292 .

Buyya R, Srirama SN, Casale G, Calheiros R, Simmhan Y, Varghese B, Gelenbe E, Javadi B, Vaquero LM, Netto MAS, Toosi AN, Rodriguez MA, Llorente IM, Vimercati SDCD, Samarati P, Milojicic D, Varela C, Bahsoon R, Assuncao MDD, Rana O, Zhou W, Jin H, Gentzsch W, Zomaya AY, Shen H (2018) A Manifesto for Future Generation Cloud Computing: Research Directions for the Next Decade. ACM Comput Surv 51(5):105–110538. https://doi.org/10.1145/3241737 .

Lewis GA (2013) Role of standards in cloud-computing interoperability In: 46th Hawaii International Conference on System Sciences, 1652–1661. https://doi.org/10.1109/HICSS.2013.470 .

Badger L, Bohn R, Chandramouli R, Grance T, Karygiannis T, Patt-Corner R, Voas E (2010) Cloud Computing Use Cases. https://www.nist.gov/itl/use-cases . Accessed 12 Oct 2019.

Ahronovitz M, et al. (2010) Cloud Computing Use Cases White Paper Version 4.0. http://www.cloud-council.org/Cloud_Computing_Use_Cases_Whitepaper-4_0.pdf . Accessed 12 Oct 2019.

Distributed Management Task Force (2010) Use Cases and Interactions for Managing Clouds. https://www.dmtf.org/sites/default/files/standards/documents/DSP-IS0103_1.0.0.pdf . Accessed 12 Oct 2019.

Zhang Z, Wu C, Cheung DWL (2013) A survey on cloud interoperability: Taxonomies, standards, and practice. SIGMETRICS Perform Eval Rev 40(4):13–22. https://doi.org/10.1145/2479942.2479945 .

Stravoskoufos K, Preventis A, Sotiriadis S, Petrakis EGM (2014) A Survey on Approaches for Interoperability and Portability of Cloud Computing Services In: Proceedings of the 4th International Conference on Cloud Computing and Services Science (CLOSER2014), 112–117. https://doi.org/10.5220/0004856401120117 .

García ÁL, del Castillo EF, Fernández PO (2016) Standards for enabling heterogeneous IaaS cloud federations. Comput Stand Interfaces 47:19–23. https://doi.org/10.1016/j.csi.2016.02.002 .

Gartner (2018) Competitive Landscape: Cloud Service Brokerage. https://www.gartner.com/en/documents/3889023/competitive-landscape-cloud-service-brokerage . Accessed 12 Oct 2019.

Liu F, Tong J, Mao J, Bohn RB, Messina JV, Badger ML, Leaf DM (2011) NIST Cloud Computing Reference Architecture. https://www.nist.gov/publications/nist-cloud-computing-reference-architecture . Accessed 12 Oct 2019.

Elhabbash A, Samreen F, Hadley J, Elkhatib Y (2019) Cloud brokerage: A systematic survey. ACM Comput Surv 51(6):119–111928. https://doi.org/10.1145/3274657 .

Bernstein D, Ludvigson E, Sankar K, Diamond S, Morrow M (2009) Blueprint for the Intercloud - Protocols and Formats for Cloud Computing Interoperability In: Proceedings of the 2009 Fourth International Conference on Internet and Web Applications and Services. ICIW ’09, 328–336. https://doi.org/10.1109/ICIW.2009.55 .

Global Inter-cloud Technology Forum (2010) Use Cases and Functional Requirements for Inter-Cloud Computing: A white paper. http://www.gictf.jp/doc/GICTF_Whitepaper_20100809.pdf . Accessed 12 Oct 2019.

Petcu D (2013) Multi-cloud: Expectations and current approaches In: Proceedings of the 2013 International Workshop on Multi-cloud Applications and Federated Clouds. MultiCloud ’13, 1–6.. ACM, New York. https://doi.org/10.1145/2462326.2462328 .

Toosi AN, Calheiros RN, Buyya R (2014) Interconnected cloud computing environments: Challenges, taxonomy, and survey. ACM Comput Surv 47(1):7–1747. https://doi.org/10.1145/2593512 .

Nogueira E, Moreira A, Lucrédio D, Garcia V, Fortes R (2016) Issues on developing interoperable cloud applications: definitions, concepts, approaches, requirements, characteristics and evaluation models. J Softw Eng Res Dev 4(1):7. https://doi.org/10.1186/s40411-016-0033-6 .

Kaur K, Sharma DS, Kahlon DKS (2017) Interoperability and portability approaches in inter-connected clouds: A review. ACM Comput Surv 50(4):49–14940. https://doi.org/10.1145/3092698 .

Bellendorf J, Mann ZÁ (2018) Cloud Topology and Orchestration Using TOSCA: A Systematic Literature Review. In: Kritikos K, Plebani P, de Paoli F (eds)Service-Oriented and Cloud Computing, 207–215. https://doi.org/10.1007/978-3-319-99819-0_16 .

Domaschka J, Griesinger F, Baur D, Rossini A (2015) Beyond Mere Application Structure Thoughts on the Future of Cloud Orchestration Tools. Procedia Comput Sci 68:151–162. https://doi.org/10.1016/j.procs.2015.09.231 .

Khoshkbarforoushha A, Wang M, Ranjan R, Wang L, Alem L, Khan SU, Benatallah B (2016) Dimensions for evaluating cloud resource orchestration frameworks. Computer 49(2):24–33. https://doi.org/10.1109/MC.2016.56 .

Clusters of European Projects on Cloud (2015) Inter-cloud Challenges, Expectations and Issues Cluster Position Paper: Initial Research Roadmap and Project’s Classification. https://eucloudclusters.wordpress.com/future-cloud . Accessed 12 Oct 2019.

Clusters of European Projects on Cloud (2016) Inter-cloud Challenges, Expectations and Issues Cluster Position Paper: Research Roadmap Update. https://eucloudclusters.wordpress.com/future-cloud . Accessed 12 Oct 2019.

Clusters of European Projects on Cloud (2017) Future Cloud Cluster Vision for 2030. https://eucloudclusters.wordpress.com/future-cloud . Accessed 12 Oct 2019.

GigaSpaces Research CloudifyTeam (2016) Cloud Management in the Enterprise - An Overview of Orchestration vs. PaaS vs. CMP. https://cloudify.co/blog/cloud-management-roundup-orchestration-paas-cmp/ . Accessed 12 Oct 2019.

OASIS (2013) Topology and Orchestration Specification for Cloud Applications Version 1.0. http://docs.oasis-open.org/tosca/TOSCA/v1.0/os/TOSCA-v1.0-os.html . Accessed 6 July 2020.

Buyya R, Vecchiola C, Selvi ST (2013) Chapter 10 - Cloud Applications. In: Buyya R, Vecchiola C, Selvi ST (eds)Mastering Cloud Computing, 353–371.. Morgan Kaufmann, Boston. https://doi.org/10.1016/B978-0-12-411454-8.00010-3 .

Chapter   Google Scholar  

Costache S, Dib D, Parlavantzas N, Morin C (2017) Resource management in cloud platform as a service systems: Analysis and opportunities. J Syst Softw 132:98–118. https://doi.org/10.1016/j.jss.2017.05.035 .

Kolb S, Wirtz G (2014) Towards Application Portability in Platform as a Service In: IEEE 8th International Symposium on Service Oriented System Engineering, 218–229. https://doi.org/10.1109/SOSE.2014.26 .

Oberle K, Fisher M (2010) ETSI CLOUD - Initial Standardization Requirements for Cloud Services In: Proceedings of the 7th International Conference on Economics of Grids, Clouds, Systems, and Services. GECON’10, 105–115.. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15681-6_8 .

OASIS (2014) Cloud Application Management for Platforms Version 1.1. http://docs.oasis-open.org/camp/camp-spec/v1.1/camp-spec-v1.1.html . Accessed 12 Oct 2019.

Soltesz S, Pötzl H, Fiuczynski ME, Bavier A, Peterson L (2007) Container-based Operating System Virtualization: A Scalable, High-performance Alternative to Hypervisors In: Proceedings of the 2Nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007. EuroSys ’07, 275–287.. ACM, New York. https://doi.org/10.1145/1272996.1273025 .

Singh S, Singh N (2016) Containers & Docker: Emerging roles & future of Cloud technology In: 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), 804–807. https://doi.org/10.1109/ICATCCT.2016.7912109 .

Komarek A, Pavlik J, Sobeslav V (2017) Hybrid System Orchestration with TOSCA and Salt. J Eng Appl Sci 12(9):2396–2401. https://doi.org/10.36478/jeasci.2017.2396.2401 .

OpenStack (2016) OpenStack Heat. https://wiki.openstack.org/wiki/Heat . Accessed 12 Oct 2019.

Cloudify (2019) Cloudify. http://cloudify.co/ . Accessed 12 Oct 2019.

The Apache Software Foundation (2016) The Apache Brooklyn project. https://brooklyn.apache.org/ . Accessed 12 Oct 2019.

Apache (2015) Apache Stratos. https://stratos.apache.org/ . Accessed 12 Oct 2019.

FastConnect (2018) Alien4Cloud. https://alien4cloud.github.io . Accessed 12 Oct 2019.

HashiCorp (2019) HashiCorp Terraform. https://www.terraform.io/ . Accessed 12 Oct 2019.

Amazon (2016) Amazon CloudFormation. https://aws.amazon.com/cloud-for-ma-tion/ . Accessed 12 Oct 2019.

Baur D, Domaschka J (2016) Experiences from building a cross-cloud orchestration tool In: Proceedings of the 3rd Workshop on CrossCloud Infrastructures & Platforms. CrossCloud ’16, 4–146. https://doi.org/10.1145/2904111.2904116 .

Baur D, Seybold D, Griesinger F, Masata H, Domaschka J (2018) A Provider-Agnostic Approach to Multi-cloud Orchestration Using a Constraint Language In: 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), 173–182. https://doi.org/10.1109/CCGRID.2018.00032 .

Pham LM, Tchana A, Donsez D, de Palma N, Zurczak V, Gibello P (2015) Roboconf: A Hybrid Cloud Orchestrator to Deploy Complex Applications In: IEEE 8th International Conference on Cloud Computing, 365–372. https://doi.org/10.1109/CLOUD.2015.56 .

Salomoni D, Campos I, Gaido L, et al. (2016) Indigo-datacloud: foundations and architectural description of a platform as a service oriented to scientific computing. CoRR abs/1603.09536. http://arxiv.org/abs/1603.09536. Accessed 6 July 2020.

Salomoni D, Campos I, Gaido L, et al. (2018) INDIGO-DataCloud: a Platform to Facilitate Seamless Access to E-Infrastructures. J Grid Comput 16(3):381–408. https://doi.org/10.1007/s10723-018-9453-3 .

Kiss T, Kacsuk P, Kovacs J, Rakoczi B, Hajnal A, Farkas A, Gesmier G, Terstyanszky G (2019) MiCADO—Microservice-based Cloud Application-level Dynamic Orchestrator. Futur Gener Comput Syst 94:937–946. https://doi.org/10.1016/j.future.2017.09.050 .

Ardagna D, Di Nitto E, Mohagheghi P, Mosser S, Ballagny C, D’Andria F, Casale G, Matthews P, Nechifor C, Petcu D, Gericke A, Sheridan C (2012) MODAClouds: A model-driven approach for the design and execution of applications on multiple Clouds In: 4th International Workshop on Modeling in Software Engineering (MISE), 50–56. https://doi.org/10.1109/MISE.2012.6226014 .

Nitto ED, Matthews P, Petcu D, Solberg A (2017) Model-Driven Development and Operation of Multi-Cloud Applications: The MODAClouds Approach. https://doi.org/10.1007/978-3-319-46031-4 .

Brogi A, Carrasco J, Cubo J, D’Andria F, Ibrahim A, Pimentel E, Soldani J (2014) SeaClouds: Seamless adaptive multi-cloud management of service-based applications In: 17th Conferencia Iberoamericana en Software Engineering (CIbSE 2014), 95–108.. Curran Associates, Inc., Pucon.

Brogi A, Fazzolari M, Ibrahim A, Soldani J, Wang P, Carrasco J, Cubo J, Durán F, Pimentel E, Di Nitto E, D’Andria F (2015) Adaptive management of applications across multiple clouds: The SeaClouds Approach. CLEI Electron J 18:2–2. https://doi.org/10.19153/cleiej.18.1.1 .

University of Ulm (2015) Cloudiator. http://cloudiator.org/ . Accessed 12 Oct 2019.

Linagora (2013) Roboconf. http://roboconf.net . Accessed 12 Oct 2019.

Caballer M, Zala S, García ÁL, Moltó G, Fernández PO, Velten M (2018) Orchestrating Complex Application Architectures in Heterogeneous Clouds. J Grid Comput 16(1):3–18. https://doi.org/10.1007/s10723-017-9418-y .

INDIGO consortium (2017) The INDIGO-DataCloud project. https://www.indigo-datacloud.eu/ . Accessed 12 Oct 2019.

COLA consortium (2017) The COLA Project. https://project-cola.eu/ . Accessed 12 Oct 2019.

Kovács J, Kacsuk P (2018) Occopus: a multi-cloud orchestrator to deploy and manage complex scientific infrastructures. J Grid Comput 16(1):19–37. https://doi.org/10.1007/s10723-017-9421-3 .

MODAClouds consortium (2012) The MODAClouds project. http://multiclouddevops.com/ . Accessed 12 Oct 2019.

SeaClouds consortium (2013) The SeaClouds project. http://www.seaclouds-project.eu/ . Accessed 12 Oct 2019.

Petcu D, Macariu G, Panica S, Crăciun C (2013) Portable Cloud applications—From theory to practice. Futur Gener Comput Syst 29(6):1417–1430. https://doi.org/10.1016/j.future.2012.01.009 .

Petcu D, Martino BD, Venticinque S, Rak M, Máhr T, Lopez GE, Brito F, Cossu R, Stopar M, Šperka S, Stankovski V (2013) Experiences in building a mOSAIC of clouds. J Cloud Comput Adv Syst Appl 2(1):12. https://doi.org/10.1186/2192-113X-2-12 .

mOSAIC consortium (2010) The mOSAIC project. http://www.mosaic-cloud.eu/ . Accessed 12 Oct 2019.

DAndria F, Bocconi S, Cruz JG, Ahtes J, Zeginis D (2012) Cloud4SOA: Multi-cloud Application Management Across PaaS Offerings In: 14th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, 407–414. https://doi.org/10.1109/SYNASC.2012.65 .

Kamateri E, Loutas N, Zeginis D, Ahtes J, D’Andria F, Bocconi S, Gouvas P, Ledakis G, Ravagli F, Lobunets O, Tarabanis KA (2013) Cloud4SOA: A Semantic-Interoperability PaaS Solution for Multi-cloud Platform Management and Portability. In: Lau K-K, Lamersdorf W, Pimentel E (eds)Service-Oriented and Cloud Computing, ESOCC 2013, 64–78. https://doi.org/10.1007/978-3-642-40651-5_6 .

Cloud, 4SOA consortium (2010) The Cloud4SOA project. http://www.cloud4soa.eu/ . Accessed 12 Oct 2019.

BEACON consortium (2015) The BEACON project: Enabling Federated Cloud Networking. http://www.beacon-project.eu/ . Accessed 12 Oct 2019.

Celesti A, Levin A, Massonet P, Schour L, Villari M (2016) Federated networking services in multiple openstack clouds, 338–352. https://doi.org/10.1007/978-3-319-33313-7_26 .

ATMOSPHERE consortium (2017) The ATMOSPHERE project: Adaptive, Trustworthy, Manageable, Orchestrated, Secure, Privacy-assuring Hybrid, Ecosystem for Resilient Cloud Computing. https://www.atmosphere-eubrazil.eu/ . Accessed 12 Oct 2019.

Castañeda IA, Blanquer I, de Alfonso C (2019) Easing the deployment and management of cloud federated networks across virtualised clusters In: Proceedings of the 9th International Conference on Cloud Computing and Services Science, CLOSER 2019, Heraklion, Crete, Greece, May 2-4, 2019, 601–608. https://doi.org/10.5220/0007877406010608 .

Rodero-Merino L, Vaquero LM, Gil V, Galán F, Fontán J, Montero RS, Llorente IM (2010) From infrastructure delivery to service management in clouds. Futur Gener Comput Syst 26(8):1226–1240. https://doi.org/10.1016/j.future.2010.02.013 .

Ferrer AJ, Hernández F, Tordsson J, Elmroth E, Ali-Eldin A, Zsigri C, Sirvent R, Guitart J, Badia RM, Djemame K, Ziegler W, Dimitrakos T, Nair SK, Kousiouris G, Konstanteli K, Varvarigou T, Hudzia B, Kipp A, Wesner S, Corrales M, Forgó N, Sharif T, Sheridan C (2012) OPTIMIS: A holistic approach to cloud service provisioning. Futur Gener Comput Syst 28(1):66–77. https://doi.org/10.1016/j.future.2011.05.022 .

Ferrer AJ, Lordan F, Ortiz D, Guitart J, Macias M, Panuccio P, M. Badia R, Ponsard C, Temporale C, García D, Sirvent R, Deprez J, Sommacampagna D, Djemame K, Armstrong D, Agiatzidou E, Ejarque J, Blasi L, Kammer M (2014) Ascetic - adapting service lifecycle towards efficient clouds In: European Project Space on Information and Communication Systems - EPS Barcelona, 89–106.. SciTePress, Barcelona. https://doi.org/10.5220/0006183400890106 .

ASCETiC consortium (2013) The ASCETiC project. http://ascetic-project.eu/ . Accessed 12 Oct 2019.

HARNESS consortium (2012) The HARNESS project: Hardware and Network-Enhanced Software Systems for Cloud Computing. http://www.harness-project.eu/ . Accessed 12 Oct 2019.

Coutinho JGF, Pell O, O’Neill E, Sanders P, McGlone J, Grigoras P, Luk W, Ragusa C (2014) Harness project: Managing heterogeneous computing resources for a cloud platform. In: Goehringer D, Santambrogio MD, Cardoso JMP, Bertels K (eds)Reconfigurable Computing: Architectures, Tools, and Applications, 324–329.. Springer. https://doi.org/10.1007/978-3-319-05960-0_36 .

Paraiso F, Merle P, Seinturier L (2016) soCloud: a service-oriented component-based PaaS for managing portability, provisioning, elasticity, and high availability across multiple clouds. Computing 98(5):539–565. https://doi.org/10.1007/s00607-014-0421-x .

Article   MathSciNet   Google Scholar  

OASIS (2011) Service Component Architecture (SCA). http://www.oasis-opencsa.org/sca . Accessed 12 Oct 2019.

Giannakopoulos I, Papailiou N, Mantas C, Konstantinou I, Tsoumakos D, Koziris N (2014) CELAR: Automated application elasticity platform In: IEEE International Conference on Big Data (Big Data), 23–25. https://doi.org/10.1109/BigData.2014.7004481 .

CELAR consortium (2012) The CELAR project. http://www.celarcloudproject.eu/ . Accessed 12 Oct 2019.

Selea T, Drăgan I, Fortiş T-F (2017) The CloudLightning Approach to Cloud-user Interaction In: Proceedings of the 1st International Workshop on Next Generation of Cloud Architectures. CloudNG:17, 4–145. https://doi.org/10.1145/3068126.3068130 .

CloudLightning consortium (2018) The CloudLightning project. https://cloudlightning.eu/ . Accessed 12 Oct 2019.

Download references

Acknowledgements

Author information.

Orazio Tomarchio, Domenico Calcaterra and Giuseppe Di Modica contributed equally to this work.

Authors and Affiliations

Department of Electrical, Electronic and Computer Engineering, University of Catania, Catania, Italy

Orazio Tomarchio, Domenico Calcaterra & Giuseppe Di Modica

You can also search for this author in PubMed   Google Scholar

Contributions

All authors contributed equally to the article. The author(s) read and approved the final manuscript.

Ethics declarations

Competing interests.

The authors declare that they have no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Tomarchio, O., Calcaterra, D. & Modica, G.D. Cloud resource orchestration in the multi-cloud landscape: a systematic review of existing frameworks. J Cloud Comp 9 , 49 (2020). https://doi.org/10.1186/s13677-020-00194-7

Download citation

Received : 14 April 2020

Accepted : 04 August 2020

Published : 10 September 2020

DOI : https://doi.org/10.1186/s13677-020-00194-7

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Cloud computing
• Multi-cloud

To read this content please select one of the options below:

Please note you do not have access to teaching notes, strategy, operations, and profitability: the role of resource orchestration.

International Journal of Operations & Production Management

ISSN : 0144-3577

Article publication date: 20 February 2018

Issue publication date: 21 March 2018

Developing and implementing strategies to maximize profitability is a fundamental challenge facing manufacturers. The complexity of orchestrating resources in practice has been overlooked in the operations field and it is now necessary to go beyond the direct effects of individual resources and uncover different resource configurations that maximize profitability. The paper aims to discuss these issues.

Design/methodology/approach

Drawing on a sample of US manufacturing firms, multiple regression analysis (MRA) and fuzzy set qualitative comparative analysis (fsQCA) are performed to examine the effects of resource orchestration on firm profitability over time. By comparing the findings between analyses, the study represents a move away from examining the net effects of resource levers on performance alone.

The findings characterize the resource conditions for manufacturers’ high performance, and also for absence of high performance. Pension and retirement expense is a core resource condition with R&D and SG&A as consistent peripheral conditions for profitability. Moreover, although workforce size was found to have a significant negative effect under MRA, this plays a role in manufacturers’ performance as a peripheral resource condition under fsQCA.

Originality/value

Accounting for different resource deployment configurations, this study deepens knowledge of resource orchestration and presents findings that enable manufacturers to maximize profitability. An empirical contribution is offered by the introduction of a new method for examining manufacturing strategy configurations: fsQCA.

• Performance
• Manufacturing strategy
• Configuration analysis
• Resource orchestration

Hughes, P. , Hodgkinson, I.R. , Elliott, K. and Hughes, M. (2018), "Strategy, operations, and profitability: the role of resource orchestration", International Journal of Operations & Production Management , Vol. 38 No. 4, pp. 1125-1143. https://doi.org/10.1108/IJOPM-10-2016-0634

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited

Related articles

We’re listening — tell us what you think, something didn’t work….

Report bugs here

All feedback is valuable

Please share your general feedback

Join us on our journey

Platform update page.

Visit emeraldpublishing.com/platformupdate to discover the latest news and updates

Questions & More Information

Answers to the most commonly asked questions here

• Help & FAQ

Entrepreneurial orientation as the basis for resource orchestration, business resilience and family livelihood in turbulent, resource-scarce contexts: a study among non-farm entrepreneurs in Ethiopia

• Business Management & Organisation

Research output : Thesis › internal PhD, WU

Small and medium enterprises (SMEs) are viewed as key to family livelihood and poverty reduction in developing countries, but their resilience in such turbulent, resource-scarce contexts depends on the strategic actions taken by entrepreneurs in acquiring, bundling, and leveraging scarce resources. These strategic actions and practices are categorized, in this thesis, as resource orchestration (RO) processes. From a strategic entrepreneurship perspective, the firm’s entrepreneurial orientation (EO) is a key driver of these orchestration processes. However, business resilience is not an end goal in itself and must be linked to family livelihood, thus requiring the investigation of supportive survival strategies used in this context. This thesis, therefore, focuses on advancing the debates on business resilience, resource orchestration processes driven by EO, and survival strategies that support non-farm entrepreneurs in improving family livelihood in the context of a developing country (i.e., Ethiopia).

The results of a survey of 408 non-farm entrepreneurs show that EO drives resource orchestration processes and explains the differences between firms’ resilience (Chapters 4 and 5). Supportive strategies are also identified that confirm entrepreneurial efforts to transform business resilience into a contribution to family livelihood (Chapter 6). This empirical research is based on two preparatory studies: conducting an extensive literature review of the state of the art of SME-related resilience studies (Chapter 2), building, and testing a resilience measurement instrument in resource-scarce disruptive environments (Chapter 3). Both studies contribute new insights to the resilience literature.

This thesis informs theory and practice related to increasing the business resilience of SMEs and turning this resilience into a family livelihood in resource-scarce disruptive business environments. This thesis makes empirical and theoretical contributions that critically engage with these issues in the context of developing countries. Based on the results, this thesis suggests further research and discusses implications for entrepreneurs, researchers, policymakers, and business practitioners.

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

• 10.18174/498297

Files and links

• Download PDF

Fingerprint

• Enterprises Social Sciences 100%
• Family Social Sciences 100%
• Entrepreneurial Orientation Social Sciences 100%
• Resources Psychology 100%
• Resilience Psychology 100%
• Developing Countries Economics, Econometrics and Finance 60%
• Theses Social Sciences 45%
• Process Social Sciences 36%

Projects per year

Determinants for Resilient Non-Farm Entrepreneurship: A Comparison of Men and Women Entrepreneurs in Ethiopia

Saad, M., Omta, O., Hagelaar, G. & van der Velde, G.

1/04/15 → 11/09/19

Project : PhD

• Family 100%
• Enterprises 100%
• Entrepreneurial Orientation 100%
• Resources 100%
• Resilience 100%

T1 - Entrepreneurial orientation as the basis for resource orchestration, business resilience and family livelihood in turbulent, resource-scarce contexts

T2 - a study among non-farm entrepreneurs in Ethiopia

AU - Saad, Muhammedamin Hussen

N1 - WU thesis 7300 Includes bibliographical references. - With summary in English

PY - 2019/9/11

Y1 - 2019/9/11

N2 - Small and medium enterprises (SMEs) are viewed as key to family livelihood and poverty reduction in developing countries, but their resilience in such turbulent, resource-scarce contexts depends on the strategic actions taken by entrepreneurs in acquiring, bundling, and leveraging scarce resources. These strategic actions and practices are categorized, in this thesis, as resource orchestration (RO) processes. From a strategic entrepreneurship perspective, the firm’s entrepreneurial orientation (EO) is a key driver of these orchestration processes. However, business resilience is not an end goal in itself and must be linked to family livelihood, thus requiring the investigation of supportive survival strategies used in this context. This thesis, therefore, focuses on advancing the debates on business resilience, resource orchestration processes driven by EO, and survival strategies that support non-farm entrepreneurs in improving family livelihood in the context of a developing country (i.e., Ethiopia). The results of a survey of 408 non-farm entrepreneurs show that EO drives resource orchestration processes and explains the differences between firms’ resilience (Chapters 4 and 5). Supportive strategies are also identified that confirm entrepreneurial efforts to transform business resilience into a contribution to family livelihood (Chapter 6). This empirical research is based on two preparatory studies: conducting an extensive literature review of the state of the art of SME-related resilience studies (Chapter 2), building, and testing a resilience measurement instrument in resource-scarce disruptive environments (Chapter 3). Both studies contribute new insights to the resilience literature. This thesis informs theory and practice related to increasing the business resilience of SMEs and turning this resilience into a family livelihood in resource-scarce disruptive business environments. This thesis makes empirical and theoretical contributions that critically engage with these issues in the context of developing countries. Based on the results, this thesis suggests further research and discusses implications for entrepreneurs, researchers, policymakers, and business practitioners.

AB - Small and medium enterprises (SMEs) are viewed as key to family livelihood and poverty reduction in developing countries, but their resilience in such turbulent, resource-scarce contexts depends on the strategic actions taken by entrepreneurs in acquiring, bundling, and leveraging scarce resources. These strategic actions and practices are categorized, in this thesis, as resource orchestration (RO) processes. From a strategic entrepreneurship perspective, the firm’s entrepreneurial orientation (EO) is a key driver of these orchestration processes. However, business resilience is not an end goal in itself and must be linked to family livelihood, thus requiring the investigation of supportive survival strategies used in this context. This thesis, therefore, focuses on advancing the debates on business resilience, resource orchestration processes driven by EO, and survival strategies that support non-farm entrepreneurs in improving family livelihood in the context of a developing country (i.e., Ethiopia). The results of a survey of 408 non-farm entrepreneurs show that EO drives resource orchestration processes and explains the differences between firms’ resilience (Chapters 4 and 5). Supportive strategies are also identified that confirm entrepreneurial efforts to transform business resilience into a contribution to family livelihood (Chapter 6). This empirical research is based on two preparatory studies: conducting an extensive literature review of the state of the art of SME-related resilience studies (Chapter 2), building, and testing a resilience measurement instrument in resource-scarce disruptive environments (Chapter 3). Both studies contribute new insights to the resilience literature. This thesis informs theory and practice related to increasing the business resilience of SMEs and turning this resilience into a family livelihood in resource-scarce disruptive business environments. This thesis makes empirical and theoretical contributions that critically engage with these issues in the context of developing countries. Based on the results, this thesis suggests further research and discusses implications for entrepreneurs, researchers, policymakers, and business practitioners.

UR - https://edepot.wur.nl/498297

U2 - 10.18174/498297

DO - 10.18174/498297

M3 - internal PhD, WU

SN - 9789463950848

PB - Wageningen University

CY - Wageningen

May 15, 2024

Tips and Resources for a Successful Summer of Dissertation Writing

By Yana Zlochistaya

Summer can be a strange time for graduate students. Gone are the seminars and workshops, the student clubs, and the working group, that structured the semester and provided us with a sense of community. Instead, we’re faced with a three-month expanse of time that can feel equal parts liberating and intimidating. This double-edged freedom is only exacerbated for those of us in the writing stage of our dissertation, when isolation and a lack of discipline can have a particularly big impact. For those hoping not to enter another summer with lofty plans, only to blink and find ourselves in August disappointed with our progress, we’ve compiled some tips and resources that can help.

According to Graduate Writing Center Director Sabrina Soracco, the most important thing you can do to set yourself up for writing success is to clarify your goals. She recommends starting this process by looking at departmental requirements for a completed dissertation. Consider when you would like to file and work backwards from that point, determining what you have to get done in order to hit that target. Next, check in with your dissertation committee members to set up an accountability structure. Would they prefer an end-of-summer update to the whole committee? A monthly check-in with your chair or one of your readers? Setting up explicit expectations that work for you and your committee can cut through the aimlessness that comes with a major writing project.

For those early on in their dissertation-writing process, a committee meeting is also a valuable opportunity to set parameters. “One of the problems with the excitement for the discipline that happens post-quals is that it results in too many ideas,” says Director. Soracco. Your committee members should give you input on productive research directions so that you can begin to hone in on your project. It is also important to remember that your dissertation does not have to be the end-all-and-be-all of your academic research. Ideas that do not fit into its scope can end up becoming conference papers or even book chapters.

Once you have a clear goal that you have discussed with your committee, the hard part begins: you have to actually write. The Graduate Writing Center offers several resources to make that process easier:

• The Graduate Writing Community. This is a totally remote, two-month program that is based on a model of “gentle accountability.” When you sign up, you are added to a bCourses site moderated by a Graduate Writing Consultant. At the beginning of the week, everyone sets their goals in a discussion post, and by the end of the week, everyone checks in with progress updates. During the week, the writing consultants offer nine hours of remote synchronous writing sessions. As a writing community member, you can attend whichever sessions work best for your schedule. All that’s required is that you show up, set a goal for that hour, and work towards that goal for the length of two 25-minute Pomodoro sessions . This year’s summer writing community will begin in June. Keep your eye on your email for the registration link!
• Writing Consultations : As a graduate student, you can sign up for an individual meeting with a Graduate Writing Consultant. They can give you feedback on your work, help you figure out the structure of a chapter, or just talk through how to get started on a writing project. 
• Independent Writing Groups: If you would prefer to write with specific friends or colleagues, you can contact Graduate Writing Center Director Sabrina Soracco at [email protected] so that she can help you set up your own writing group. The structure and length of these groups can differ; often, members will send each other one to five pages of writing weekly and meet the next day for two hours to provide feedback and get advice. Sometimes, groups will meet up not only to share writing, but to work in a common space before coming together to debrief. Regardless of what the groups look like, the important thing is to create a guilt-free space. Some weeks, you might submit an outline; other weeks, it might be the roughest of rough drafts; sometimes, you might come to a session without having submitted anything. As long as we continue to make progress (and show up even when we don’t), we’re doing what we need to. As Director Soracco puts it, “it often takes slogging through a lot of stuff to get to that great epiphany.”

Yana Zlochistaya is a fifth-year graduate student in the Department of Comparative Literature and a Professional Development Liaison with the Graduate Division. She previously served as a co-director for Beyond Academia.

School of Social Work

• Academic Programs
• Student Resources
• News & Events
• Faculty & Staff
• Request Info

Highfill defends dissertation on military domestic abuse policies

Thursday, May 16, 2024 • Jaelon Jackson :

By Jaelon Jackson School of Social Work

In her remarkable academic journey at The University of Texas at Arlington, Christine Highfill was supported by a dedicated team of mentors and collaborators. 

Her dissertation committee, chaired by Social Work Associate Professor Dr. Rachel Voth Schrag and Social Work Assistant Professor Dr. Donna Schuman, provided invaluable guidance and expertise throughout her research process. Other esteemed members of her committee included Social Work Assistant Professor Dr. Rebecca Mauldin, Social Work Associate Professor Dr. Ling Xu, and Sociology and Anthropology Professor Dr. Beth Anne Shelton.

Highfill's dissertation, under the guidance of her committee, examines the historical changes in laws regarding domestic violence, particularly within the military context. She investigates the current policies within the military and their real-world impact on individuals and families affected by domestic abuse. 

Drawing from her own experiences surviving domestic abuse within the military, Highfill's research is deeply personal and driven by a passion to make a difference in the lives of others facing similar challenges.

"Dr. Highfill's work highlights the importance of considering the unique needs of military families in domestic violence research. She is working to translate what we have learned in civilian survivorship research into the context of military connected families. 

“Her approach can serve as a crucial foundation to build and tailor effective interventions for military connected survivors of intimate partner violence in both civilian and military contexts," Voth Schrag said.

Her dedication to research is evident in her extensive publication record. Highfill has authored 11 articles and two reports, with one article recently accepted for publication and four others currently under review. 

Highfill has also registered six scoping review protocols and is contracted with Oxford Press to write the Encyclopedia of Social Work entry on scoping reviews—a methodology she has mastered to systematically analyze literature on diverse topics.

In addition to her academic pursuits, Highfill is a Licensed Master Social Worker actively engaged in community service. She serves as the data and evaluation coordinator for a local nonprofit agency, facilitating communication between the agency and a national program evaluation firm. 

Moreover, Highfill provides mental health therapy services to community members in North Texas, contributing to the well-being of those she serves.

Highfill reflects on her journey. 

She said Antwan Williams, the School of Social Work’s assistant director of communications, marketing and recruiting and an adjunct assistant professor, was the first person she talked to when looking at if social work was right for her before deciding to start the Master of Social Work program here at UTA. Now she has not only earned her MSW degree but also a Ph.D. in Social Work from UTA.

“You start as one person and end up changed, with the Ph.D. title it represents not just academic success but personal growth," Highfill said.

Schuman says she has worked with Highfill on several research projects focused on improving health and well-being outcomes in military-connected populations. 

“She is a dedicated and intrepid researcher whose passion and commitment to her work shine through in every project she tackles. 

“I look forward to the next chapter of her professional journey and the outstanding contributions she will make toward improving the lives of our military and veteran families," Schuman said.

Highfill's expertise in military-connected domestic violence and abuse has earned her national recognition from The Institute for Military and Veteran Family Wellness. Her commitment to improving policies and support systems for survivors underscores her dedication to creating positive change in both academic and real-world contexts.

In addition to earning her MSW and PhD in Social Work degrees from UTA, Highfill received a Bachelor of Arts degree from Southwest Baptist University majoring in psychology, and a master's degree in human services counseling: military resilience cognate from Liberty University.

Dr. Highfill officially graduated on May 10, marking the culmination of her academic journey and the next step of what surely will be an impactful career.

Stay Connected

• SSW Communications & Marketing Office
• SSW in the Media
• Monthly Newsletters
• Monthly Video News Update
• Courtyard Conversations - Monthly Talk Show
• Marketing Products
• Video Gallery
• Photo Gallery
• SSW Upcoming Events

Address School of Social Work Box 19129 501 W. Mitchell Street Arlington, TX 76019-0129

Phone (Local): 817-272-3181 | (Toll Free): 866-272-3181 Fax : 817-272-5229

The School of Social Work Admissions office is located in Suite 203 in the Social Work and Smart Hospital Building (501 W. Mitchell Street, Arlington, TX 76019).

Follow us on social media

• Faculty/Staff Resources
• Parking Pass Request
• Request Information
• Conference Room/Classroom Reservation
• Special Event Request
• Faculty Academic & Student Affairs Resources

Research from the perspective of resource orchestration on digital ecosystem

• Published: 19 June 2017
• Volume 21 , pages 827–835, ( 2018 )

Cite this article

• Zhengyan Cui 1 &
• Taohua-Ouyang 1  

873 Accesses

Explore all metrics

Since digital technologies have brought opportunities for marginal enterprises to rise, it deserves attentions from the academia about how marginal enterprises reconfigure ecosystems. This thesis has a case study of Duch Group (Duch) in 3D printing from the perspective of resource orchestration. It deeply investigates the dynamic process of how marginal enterprises reconfigure the ecosystem by digital technologies and attain the core position. It is found that the marginal enterprises, adhering to the “point-line-surface” principle, successively reconfigure the manufacturing process, industry chain and ecosystem by implementing actions of structuring, bundling and leveraging on the key resources such as digital technologies to attain the core position. This research benefits both academics and practitioners by contributing to cumulative theoretical developments and by offering practical insights.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Digitalisation as Enabler Towards a Sustainable Circular Economy in Germany

A Modelling Method for Digital Service Design and Intellectual Property Management Towards Industry 4.0: CAxMan Case

Examining the development of a digital ecosystem in an Industry 4.0 context: a sociotechnical perspective

Luo, M., Li, L.: The age of the internet business model innovation: value creation perspective. China Ind. Econ. 57 (1), 95–107 (2015)

Google Scholar  

Adner, R., Kapoor, R.: Innovation ecosystems and the pace of substitution: re-examining technology S-curves. Strateg. Manag. J. 3 , 1–24 (2015)

Selander, L., Henfridsson, O., Svahn, F.: Capability search and redeem across digital ecosystems. J. Inf. Technol. 28 (3), 183–197 (2013)

Article   Google Scholar  

Christensen, C.M., Bower, J.L.: Customer power, strategic investment, and the failure of leading firms. Strateg. Manag. J. 17 , 197–218 (1996)

Liu, L., Tan, C., Jiang, S., Lei, H.: Li Feng’s process of platform leadership achievement: a resource dependence perspective. China Ind. Econ. 1 , 134–146 (2015)

Wareham, J., Fox, P.B., Cano Giner, J.: Technology ecosystem governance. Organ. Sci. 25 (4), 1195–1215 (2014)

Yoo, Y., Henfridsson, O., Lyytinen, K.: Research commentary—the new organizing logic of digital innovation: an agenda for information systems research. Inf. Syst. Res. 21 (4), 724–735 (2010)

Thomas, L.D.W., Autio, E.: Modeling the Ecosystem: A Meta-synthesis of Ecosystem and Related Literatures. DRUID, Copenhagen (2012)

Teece, D.J.: Explicating dynamic capabilities: the nature and microfoundations of (sustainable) enterprise performance. Strateg. Manag. J. 28 (13), 1319–1350 (2007)

Perrigot, R., Pénard, T.: Determinants of E-commerce strategy in franchising: a resource-based view. Int. J. Electron. Commer. 17 (3), 109–130 (2013)

Sirmon, D.G., Hitt, M.A., Ireland, R.D., et al.: Resource orchestration to create competitive advantage breadth, depth, and life cycle effects. J. Manag. 37 (5), 1390–1412 (2011)

Adner, R., Kapoor, R.: Value creation in innovation ecosystems: how the structure of technological interdependence affects firm performance in new technology generations. Strateg. Manag. J. 31 (3), 306–333 (2010)

Gawer, A., Cusumano, M.A.: Platform Leadership: How Intel, Microsoft, and Cisco Drive Industry Innovation. Harvard Business School Press, Boston (2002)

Moore, J.F.: Predators and prey: a new ecology of competition. Harv. Bus. Rev. 71 (3), 75–83 (1993)

Tan, B., Pan, S.L., Lu, X., Huang, L.: Leveraging digital business ecosystems for enterprise agility: the tri-logic development strategy of Alibaba.com. In: ICIS 2009 Proceedings, 171 (2009)

Yamakami, T.: 2010 13th International Conference on a Mobile Digital Ecosystem Framework: Lessons from the Evolution of Mobile Data Services, pp. 516–520. IEEE (2010)

Koshutanski, H., Ion, M., Telesca, L.: The International Conference on Distributed Identity Management Model for Digital Ecosystems, pp. 132–138. IEEE (2007)

Iansiti, M., Levien, R.: The keystone advantage: what the new dynamics of business ecosystems mean for strategy, innovation, and sustainability. Pers. Psychol. 20 (2), 88–90 (2004)

Iansiti, M., Levien, R.: Strategy as ecology. Harv. Bus. Rev. 82 (3), 68–81 (2004)

Tiwana, A., Konsynski, B., Bush, A.A.: Research commentary—platform evolution: coevolution of platform architecture, governance, and environmental dynamics. Inf. Syst. Res. 21 (4), 675–687 (2010)

Ghazawneh, A., Henfridsson, O.: Balancing platform control and external contribution in third-party development: the boundary resources model. Inf. Syst. J. 23 (2), 173–192 (2013)

Wright, M., Marlow, S.: Entrepreneurial activity in the venture creation and development process. Int. Small Bus. J. 30 (2), 107–114 (2012)

Kor, Y.Y., Mahoney, J.T., Michael, S.C.: Resources, capabilities and entrepreneurial perceptions. J. Manag. Stud. 44 (7), 1187–1212 (2007)

Cui, M., Pan, S.L.: Developing focal capabilities for E-commerce adoption: a resource orchestration perspective. Inf. Manag. 52 (2), 200–209 (2014)

Bridoux, F., Smith, K.G., Grimm, C.M.: The management of resources temporal effects of different types of actions on performance. J. Manag. 39 (4), 928–957 (2013)

Sirmon, D.G., Gove, S., Hitt, M.A.: Resource management in dyadic competitive rivalry: the effects of resource bundling and deployment. Acad. Manag. J. 51 (5), 919–935 (2008)

Yin, R.K.: Case Study Research: Design and Methods. Sage Publications, London (2013)

Ouyang, T.: Case study method in the field of business administration. Nankai Bus. Rev.: Tianjin, China 7 (2), 100–105 (2004)

Patton, M.Q.: How to Use Qualitative Methods in Evaluation. Sage, Newbury Park (1987)

Glaser, B.G., Strauss, A.L.: The Discovery of Grounded Theory: Strategies for Qualitative Research. Transaction Publishers, Chicago (2009)

Evered, R., Louis, M.R.: Alternative perspectives in the organizational sciences: “inquiry from the inside” and “inquiry from the outside”. Acad. Manag. Rev. 6 (3), 385–395 (1981)

Miles, M.B., Huberman, A.M.: Drawing valid meaning from qualitative data: toward a shared craft. Educ. Res. 13 (5), 20–30 (1984)

Eisenhardt, K.M.: Building theories from case study research. Acad. Manag. Rev. 14 (4), 532–550 (1989)

Walsham, G.: Doing interpretive research. Eur. J. Inf. Syst. 15 (3), 320–330 (2006)

Corbin, J., Strauss, A.: Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory. Sage Publications, Thousand Oaks (2014)

Eisenhardt, K.M., Graebner, M.E.: Theory building from cases: opportunities and challenges. Acad. Manag. J. 50 (1), 25 (2007)

Pan, S.L., Tan, B.: Demystifying case research: a structured-pragmatic-situational (SPS) approach to conducting case studies. Inf. Organ. 21 (3), 161–176 (2011)

Eamonn, K.: Blurring Boundaries, Uncharted Frontiers, pp. 17–28. Deloitte University Press (2015)

Nambisan, S., Baron, R.A.: Interactions in virtual customer environments: implications for product support and customer relationship management. J. Interact. Mark. 21 (2), 42–62 (2007)

Jawahar, I.M., McLaughlin, G.L.: Toward a descriptive stakeholder theory: an organizational life cycle approach. Acad. Manag. Rev. 26 , 397–414 (2001)

Sirmon, D.G., Hitt, M.A., Arregle, J.-L., Campbell, J.T.: Capability strengths and weaknesses in dynamic markets: investigating the bases of temporary competitive advantage. Strateg. Manag. J. 31 (13), 1386–1409 (2010)

Agarwal, R., Gort, M.: Products and firm life cycles and firm survival. Am. Econ. Rev. 92 , 184–190 (2002)

Smith, K.G., Mitchell, T.R., Summer, C.E.: Top level management priorities in different stages of the organizational life cycle. Acad. Manag. J. 28 , 799–820 (1985)

Download references

Acknowledgements

The authors would like to thank the National Natural Science Foundation of China (71172176, 71472012, 71529001, 71632003), the Ministry of Education of Humanities and Social Science Project (14YJA630045), the Planning Project of Beijing Philosophy and Social Sciences (15JGB119) for supporting this work.

Author information

Authors and affiliations.

School of Economics and Management, Beihang University, Beijing, China

Zhengyan Cui &  Taohua-Ouyang

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Taohua-Ouyang .

Rights and permissions

Reprints and permissions

About this article

Cui, Z., Taohua-Ouyang Research from the perspective of resource orchestration on digital ecosystem. Cluster Comput 21 , 827–835 (2018). https://doi.org/10.1007/s10586-017-0906-4

Download citation

Received : 14 January 2017

Revised : 01 May 2017

Accepted : 03 May 2017

Published : 19 June 2017

Issue Date : March 2018

DOI : https://doi.org/10.1007/s10586-017-0906-4

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Digital ecosystem
• Resource orchestration
• Marginal enterprise
• Reconfigure
• 3D printing
• Find a journal
• Publish with us
• Track your research

• The Student Experience
• Financial Aid
• Degree Finder
• Undergraduate Arts & Sciences
• Departments and Programs
• Research, Scholarship & Creativity
• Centers & Institutes
• Geisel School of Medicine
• Guarini School of Graduate & Advanced Studies
• Thayer School of Engineering
• Tuck School of Business

Campus Life

• Diversity & Inclusion
• Athletics & Recreation
• Student Groups & Activities
• Residential Life

Department of Sociology

• [email protected] Contact & Department Info Mail
• Undergraduate
• The Thesis Proposal
• Past Honors Theses
• Course Schedule by Term
• First Year Seminars
• Introductory Courses
• Lower Division
• Upper Division
• Culminating Experience
• Prizes & Awards
• Research Awards
• Transfer Credit
• Alumni Stories
• Tell Us Your Story
• What is Sociology?
• First Year Questions
• Exchange Program
• Costs and Financial Aid
• Student Visa
• Courses, Credits, and Grades
• Faculty Research
• Student Research Spotlight
• Sociology Research Awards
• Research Resources
• A. Wade Smith Research Award
• Media Contacts
• News & Events

Search form

Sociology department faculty response to events of may 1st, 2024.

We, in the Sociology department, join our colleagues in History , Art History , AAAS , NAIS , LALACS , Spanish & Portuguese , English , Religion , and WGSS departments at Dartmouth College, in denouncing the use of militarized police force against those who gathered on May 1, 2024 for peaceful demonstration. We also join fellow sociologists and social scientists across the country in registering our support for academic freedom, free speech, and the right for students, staff, and faculty to participate in peaceful protests.   

As sociologists, we wish to underscore that social movements have been pivotal in the implementation of many of the democratic rights and freedoms we celebrate today. Student protests have been especially important in drawing attention to the global interlinking of injustices, and importantly, in the fight for a more just and equitable world.

As researchers and educators holding a diversity of viewpoints, we write in support of our students' right to peaceful protest without fear of police brutality. Indeed, as research across a number of our fields of expertise demonstrates, inviting police onto college campuses is not only disruptive to educational outcomes , student health, mental health, and well-being , but also afflicts greater violence and harm onto students from minority or marginalized racial, ethnic, gender, citizenship, and class backgrounds. In the two weeks since President Beilock called police to the green, we have witnessed negative impacts to student wellbeing, learning, and ability to engage in productive dialogue. While we recognize that this may have been a difficult decision to make, it was a poor one. We know that it can be difficult to balance students' rights to free speech while simultaneously creating spaces where all students feel welcome. But we also know that bringing militarized police forces to campus only makes matters worse.  

The President's response to the protest has left our community fractured, and has shattered our students' trust and safety on campus. Following our colleagues in other departments and programs, we call on President Beilock and the administration:

• To acknowledge publicly that the deployment of state police and armored vehicles was an excessive and punitive response to peaceful protests, which will not be repeated.
• To announce publicly that you are asking prosecutors to dismiss all charges against the a) faculty, students, and staff who were arrested on May 1, 2024 and b) the two students arrested on October 30, 2023. This request in no way infringes on prosecutorial discretion.
• To make it officially and publicly known that academic freedom includes the right to peacefully express support for Palestinian rights. The college should permit nonviolent protest outdoors and restore faith in fair process and commitment to free speech on campus.