StreamFlow: Cross-Breeding Cloud With HPC

Iacopo Colonnelli,Barbara Cantalupo,Marco Aldinucci,Ivan Merelli

doi:10.1109/tetc.2020.3019202

Iacopo Colonnelli, Barbara Cantalupo + Show 2 more

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https://doi.org/10.1109/tetc.2020.3019202

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Abstract

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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">entire</i> 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.

Highlights

Both in the HPC and cloud realms, workflows play an essential role for applications coordination because they provide means to model and formalise complex processes in multiple steps, e.g. tasks, jobs, OS containers or even Virtual Machines, depending on the target system
Workflows are used in different execution environments, such as HPC, cloud and edge, all of these environments continue their path toward greater specialisation in term of typical features and workloads
Being the literature in workflows massive, we focus on the aspects of interest for this work, inviting the reader to refer to existing surveys for a more general comparison among workflow systems

Summary

Introduction

Both in the HPC and cloud realms, workflows play an essential role for applications coordination because they provide means to model and formalise complex processes in multiple steps, e.g. tasks, jobs, OS containers or even Virtual Machines, depending on the target system. Workflows are used in different execution environments, such as HPC, cloud and edge, all of these environments continue their path toward greater specialisation in term of typical features and workloads. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

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