Stkm on Sca: A Unified Framework with Components, Workflows and Algorithmic Skeletons

Aldinucci, Marco; Bouziane, Hinde Lilia; Danelutto, Marco; Pérez, Christian

doi:10.1007/978-3-642-03869-3_64

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…to retrieve temporary results. While the execution of independent steps in the cloud means they can be executed in any temporal order in a single processing element, in the HPC platforms the need for co-allocating at the same time multiple processing elements to execute a single job is the rule [5]. This complementarity is the cornerstone of a computing continuum that appears emerging in data-driven applicative domains.…”

Section: Introductionmentioning

confidence: 99%

StreamFlow: cross-breeding cloud with HPC

Colonnelli,

Cantalupo,

Merelli

et al. 2020

Preprint

Self Cite

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Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g. Kubernetes and batch clusters. We present a novel approach to workflow execution, called StreamFlow, that complements the workflow graph with the declarative description of potentially complex execution environments, and that makes it possible the execution onto multiple sites not sharing a common data space. StreamFlow is then exemplified on a novel bioinformatics pipeline for single-cell transcriptomic data analysis workflow.

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Section: Introductionmentioning

confidence: 99%

StreamFlow: cross-breeding cloud with HPC

Colonnelli,

Cantalupo,

Merelli

et al. 2020

Preprint

Self Cite

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“…Most research contributions on component models focus on the component structure , its control and evolution , or the specification of adequate coordination/communication modes between components, for example, parallel communications between components and orchestration of services exposed by components for SOA . Other works in the component community aim to dig into efficient ways to implement the component model in specific contexts leveraging well chosen supporting environment or middleware for deploying and running the components.…”

Section: Introductionmentioning

confidence: 99%

Programming distributed and adaptable autonomous components-the GCM/ProActive framework

Baude

Henrio

Ruz

2014

Softw. Pract. Exper.

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SUMMARYComponent oriented software has become a useful tool to build larger and more complex systems by describing the application in terms of encapsulated, loosely-coupled entities called components. At the same time, asynchronous programming patterns allow for the development of efficient distributed applications. While several component models and frameworks have been proposed, most of them tightly integrate the component model with the middleware they run upon. This intertwining is generally implicit and not discussed, leading to entangled, hard to maintain code. This article describes our efforts in the development of the GCM/ProActive framework for providing distributed and adaptable autonomous components. GCM/ProActive integrates a component model designed for execution on large scale environments, with a programming model based on active objects allowing a high degree of distribution and concurrency. This new integrated model provides a more powerful development, composition, and execution environment than other distributed component frameworks. We illustrate that GCM/ProActive is particularly adapted to the programming of autonomic component systems, and to the integration into a service oriented environment.

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Scientific workflow execution in the cloud using a dynamic runtime model

Erbel

Grabowski

2023

Softw Syst Model

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To explain specific phenomena, scientists perform a sequence of tasks, e.g., to gather, analyze and interpret data, forming a scientific workflow. Depending on the complexity of the workflow, scientists require access to various kinds of tools, applications and infrastructures for individual tasks. Current approaches are often limited to managing these resources at design time, requiring the scientist to preemptively set up applications essential for their workflow. Therefore, a dynamic provisioning and configuration of computing resources are required that fulfills these needs at runtime. In this paper, we present a dynamic runtime model that couples workflow tasks with their individual applications and infrastructure requirements. This runtime model is used as a knowledge base by a model-driven workflow execution engine orchestrating the sequence of tasks and their infrastructure. We exhibit that the simplicity of the runtime model supports the creation of highly tailored infrastructures, the integration of self-developed applications, as well as a human-in-the-loop allowing scientists to monitor and interact with the workflow at runtime. To tackle the heterogeneity of cloud provider interfaces, we implement the workflow runtime model by extending the Open Cloud Computing Interface cloud standard, which provides an extensible data model as well as a uniform interface to manage cloud resources. We demonstrate the applicability of our approach using three case studies and discuss the benefits of the runtime model from a user and system perspective.

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Stkm on Sca: A Unified Framework with Components, Workflows and Algorithmic Skeletons

Cited by 4 publications

References 16 publications

StreamFlow: cross-breeding cloud with HPC

StreamFlow: cross-breeding cloud with HPC

Programming distributed and adaptable autonomous components-the GCM/ProActive framework

Scientific workflow execution in the cloud using a dynamic runtime model

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