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Journal Articles Transactions on Large-Scale Data- and Knowledge-Centered Systems Year : 2017

Scientific Workflow Scheduling with Provenance Data in a Multisite Cloud

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Abstract

Recently, some Scientific Workflow Management Systems (SWfMSs) with provenance support (e.g. Chiron) have been deployed in the cloud. However, they typically use a single cloud site. In this paper, we consider a multisite cloud, where the data and computing resources are distributed at different sites (possibly in different regions). Based on a multisite architecture of SWfMS, i.e. multisite Chiron, and its provenance model, we propose a multisite task scheduling algorithm that considers the time to generate provenance data. We performed an extensive experimental evaluation of our algorithm using Microsoft Azure multisite cloud and two real-life scientific workflows (Buzz and Montage). The results show that our scheduling algorithm is up to 49.6% better than baseline algorithms in terms of total execution time.
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Dates and versions

lirmm-01620224 , version 1 (20-10-2017)

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Ji Liu, Esther Pacitti, Patrick Valduriez, Marta Mattoso. Scientific Workflow Scheduling with Provenance Data in a Multisite Cloud. Transactions on Large-Scale Data- and Knowledge-Centered Systems, 2017, 33, pp.80-112. ⟨10.1109/IPDPS.2007.370305⟩. ⟨lirmm-01620224⟩
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