TOSCA Light: Bridging the Gap between the TOSCA Specification and Production-ready Deployment Technologies

Wurster, Michael; Breitenbücher, Uwe; Harzenetter, Lukas; Leymann, Frank; Soldani, Jacopo; Yussupov, Vladimir

doi:10.5220/0009794302160226

Cited by 14 publications

(18 citation statements)

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“…In case of xOpera this would mean that new technology-specific executor plugins need to be introduced, for example, an executor for Terraform. 26 Another alternative is to employ existing approaches 52,53 for transforming TOSCA models into formats consumable by the chosen target technology, for example, TOSCA to Terraform transformations.…”

Section: Discussion and Limitationsmentioning

confidence: 99%

“…Finally, to enable using other deployment automation technologies, we plan to combine our method with existing approach for transforming TOSCA models into deployable, technology-specific models, for example, in Ansible or Terraform. 52,53 With this extension, we will also enable deploying the produced models without using TOSCA deployment automation technologies.…”

Section: Discussionmentioning

confidence: 99%

“…supported target infrastructures, for example, enabling the support for Kubernetes‐based modeling or the deployment of function orchestrations using Apache Openwhisk and Apache Openwhisk Composer. Finally, to enable using other deployment automation technologies, we plan to combine our method with existing approach for transforming TOSCA models into deployable, technology‐specific models, for example, in Ansible or Terraform 52,53 . With this extension, we will also enable deploying the produced models without using TOSCA deployment automation technologies.…”

Section: Discussionmentioning

confidence: 99%

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Standards‐based modeling and deployment of serverless function orchestrations using BPMN and TOSCA

et al. 2022

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Function-as-a-Service (FaaS) is a cloud service model enabling to implement serverless applications for a variety of use cases. These range from scheduled calls of single functions to complex function orchestrations executed using orchestration services such as AWS step functions. However, since the available function orchestration technologies vary in functionalities, supported modeling languages, and APIs, modeling such function orchestrations and their deployment require significant technology-specific expertise. Moreover, the resulting models are typically not portable due to provider-and technology-specific details, and major efforts are required when exchanging an orchestrator or provider due to such lock-ins. To tackle this issue, we introduce a vendorand technology-agnostic method for the modeling and deployment of serverless function orchestrations, which relies on the business process model and notation (BPMN) and topology and orchestration specification for cloud applications (TOSCA) standards for modeling function orchestrations and their deployment, respectively. We also present a toolchain for modeling serverless function orchestrations in BPMN, generating proprietary models supported by different function orchestration technologies from BPMN models, specifying their actual deployment in TOSCA, and then enacting such deployment. Finally, we illustrate a case study applying our method and toolchain in practice.

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Section: Discussion and Limitationsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Standards‐based modeling and deployment of serverless function orchestrations using BPMN and TOSCA

et al. 2022

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“…Wurster et al [46] describe the Essential Deployment Metamodel (EDMM), a least denominator metamodel of IaC solutions. TOSCA Light [47] is the EDMM-compatible TOSCA subset that was shown to be deployable with 13 IaC solutions using TOSCA Lightning [45].…”

Section: Related Workmentioning

confidence: 99%

Deployment coordination for cross-functional DevOps teams

Sokolowski

2021

Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Softw

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Software stability and reliability are the core concerns of DevOps. They are improved by tightening the collaboration between developers and operators in cross-functional teams on the one hand and by automating operations through continuous integration (CI) and infrastructure as code (IaC) on the other hand. Ideally, teams in DevOps are fully independent. Still, their applications often depend on each other in practice, requiring them to coordinate their deployment through centralization or manual coordination.With this work, we propose and implement the novel IaC solution µs ([mju:z] łmusež ), which automates deployment coordination in a decentralized fashion. µs is the first approach that is compatible with the DevOps goals as it enables truly independent operations of the DevOps teams. We define our research problem through a questionnaire survey with IT professionals and evaluate the solution by comparing it to other modern IaC approaches, assessing its performance, and applying it to existing IaC programs. CCS CONCEPTS• Computer systems organization → Cloud computing; • Software and its engineering → Orchestration languages; Cloud computing; Architecture description languages.

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“…Carrasco et al [143][144][145] combine TOSCA with CAMP by transforming TOSCA topologies into a CAMP-compliant YAML format allowing for an automated migration of cloud applications with the proposal of a unified interface for IaaS and PaaS. Another approach is TOSCA Light [146], a subset of TOSCA, which can be coupled with production-ready deployment technologies, e.g., Kubernetes [147], using TOSCA Lightning [148]. Several further frameworks exist that built upon the benefits of TOSCA focusing, e.g., on a modeldriven approach to deploy big data cloud applications [149] or on deployment techniques to increase the portability of topologies [150].…”

Section: Tosca Related Approachesmentioning

confidence: 99%

Scientific Workflow Execution Using a Dynamic Runtime Model

Erbel¹

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Research projects consist of several kinds of steps covering, e.g., individual procedures to gather data, as well as different ways to process and analyze it. Moreover, the individual steps of the project consist of a sequence of tasks that together form a workflow. With the continuous advancements in computer science, scientists have more and more access to different kinds of infrastructures and tools which are suited for different types of experiments. Cloud computing is one of the premier infrastructures to perform experiments on, as it provides flexible, on-demand computing resources that are off-premise. Still, a uniform and platform-independent orchestration of these resources remains challenging, especially when various infrastructures and human interactions are required throughout the execution of a scientific workflow.This thesis provides an approach to allow scientists to define infrastructural resources for individual tasks within their workflows and dynamically shift them throughout the workflow execution. To reach this goal, we couple recent advancements in cloud orchestration with runtime models and open cloud standards. Combined, we aim for highly tailored workflows while fostering the reuse of already existing methodologies built around the standards. To realize this objective, we build a cloud runtime model orchestration process based on the Open Cloud Computing Interface (OCCI) standard. We extend the OCCI data model with workflow elements and corresponding capabilities to model cloud deployments for individual workflow tasks. This allows forming a runtime workflow model that can be coupled to different systems such as production clouds or simulation environments. To demonstrate the feasibility of the approach, we perform several experiments to validate the standard conform orchestration process and assess the applicability of the runtime workflow model coupled to cloud infrastructures.Our studies show that runtime models are a suitable knowledge base for adaptive behavior including the modeling and runtime representation of highly tailored workflows. We observe that the orchestration of cloud deployments and the execution of workflows follow a reoccurring pattern which can be described via a sequence of runtime states. Especially the uniform interface of OCCI allows for an automatic management and reuse of existing systems and standards. By monitoring and reflecting operational properties, the runtime model fosters decision-making processes not only for self-adaptive systems but also for human users. We show that the runtime model enables human-in-the-loop activities, allowing, e.g., to influence the control flow or the parallelization of workflow tasks at runtime. Furthermore, the runtime model can be attached to different environments allowing to test adaptation and workflow behavior. I especially want to thank Prof. Dr. Jens Grabowski who took me into his group and allowed me to study the wonders of distributed computing. He always provided me with feedback on how to improve and guided me th...

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TOSCA Light: Bridging the Gap between the TOSCA Specification and Production-ready Deployment Technologies

Cited by 14 publications

References 0 publications

Standards‐based modeling and deployment of serverless function orchestrations using BPMN and TOSCA

Standards‐based modeling and deployment of serverless function orchestrations using BPMN and TOSCA

Deployment coordination for cross-functional DevOps teams

Scientific Workflow Execution Using a Dynamic Runtime Model

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