Archivio Istituzionale della RicercaScientific and industrial applications have become more complex over the past decades and nowadays include multi-domain workflows (e.g. ingestion of large (unstructured) datasets, high-performance computing simulations, machine learning tasks). This chapter discusses the EU-funded H2020 LEXIS project approach to the effective management of such multi-domain workflows and their execution on heterogeneous and geographically distributed computing resources. The LEXIS project solution aims at providing users with a tool to describe all the steps involved in the execution of the application (i.e. the workflow) and their execution on the most appropriate pool of resources.
Distributed HPC Resources Orchestration for Supporting Large-Scale Workflow Execution / Scionti, A.; Viviani, P.; Vitali, G.; Vercellino, C.; Terzo, O.; Hachinger, S.; Vojacek, L.; Svaton, V.; Krenek, J.; Donnat, F.; Exertier, F. - In: HPC, Big Data, and AI Convergence Towards Exascale: Challenge and VisionSTAMPA. - [s.l] : CRC Press, 2022. - ISBN 9781003176664. - pp. 81-103 [10.1201/9781003176664-5]
Distributed HPC Resources Orchestration for Supporting Large-Scale Workflow Execution
Scionti A.;Vitali G.;Vercellino C.;
2022
Abstract
Scientific and industrial applications have become more complex over the past decades and nowadays include multi-domain workflows (e.g. ingestion of large (unstructured) datasets, high-performance computing simulations, machine learning tasks). This chapter discusses the EU-funded H2020 LEXIS project approach to the effective management of such multi-domain workflows and their execution on heterogeneous and geographically distributed computing resources. The LEXIS project solution aims at providing users with a tool to describe all the steps involved in the execution of the application (i.e. the workflow) and their execution on the most appropriate pool of resources.
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https://hdl.handle.net/11583/2974769