Scientific Workflow Scheduling with Provenance Data in a Multisite Cloud

Ji Liu (1) , Esther Pacitti (1, 2) , Patrick Valduriez (1, 2) , Marta Mattoso (3)

1 ZENITH - Scientific Data Management
2 IBC - Institut de Biologie Computationnelle
3 COPPE-UFRJ - Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia

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.

Domaines: Base de données [cs.DB]; Calcul parallèle, distribué et partagé [cs.DC]

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⟩