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1 IBC - Institut de Biologie Computationnelle 2 MSR - INRIA - Microsoft Research - Inria Joint Centre 3 ZENITH - Scientific Data Management LIRMM - Laboratoire d-Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée 4 COPPE-UFRJ - COPPE - Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia

Abstract : Clouds appear as appropriate infrastructures for executing Scientific Workflows SWfs. A cloud is typically made of several sitesor data centers, each with its own resources and data. Thus, it becomes important to be able to execute some SWfs at more than one cloud site because of the geographical distribution of data or available resources among di erent cloud sites. Therefore, a major problem is how to execute a SWf in a multisite cloud, while reducing execution time and monetary costs. In this paper, we propose a general solution based on multi-objective scheduling in order to execute SWfs in a multisite cloud. The solution includes a multiobjective cost model including execution time and monetary costs, a Single Site Virtual Machine VM Provisioning approach SSVP and ActGreedy, a multisite scheduling approach. We present an experimental evaluation, based on the execution of the SciEvol SWf in Microsoft Azure cloud. The results reveal that our scheduling approach significantly outperforms two adapted baseline algorithms which we propose by adapting two existing algorithms and the scheduling time is reasonable compared with genetic and brute-force algorithms. The results also show that our cost model is accurate and that SSVP can generate better VM provisioning plans compared with an existing approach.

Keywords : multisite cloud parallel execution multi-objective scheduling Scientific workflow scientific workflow management system





Autor: Ji Liu - Esther Pacitti - Patrick Valduriez - Daniel De Oliveira - Marta Mattoso -

Fuente: https://hal.archives-ouvertes.fr/



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