Efficient Scheduling of Scientific Workflows using Hot Metadata in a Multisite Cloud

Ji Liu (1, 2) , Luis Pineda (3) , Esther Pacitti (1, 2) , Alexandru Costan (3) , Patrick Valduriez (1, 2) , Gabriel Antoniu (3) , Marta Mattoso (4)

1 ZENITH - Scientific Data Management
2 IBC - Institut de Biologie Computationnelle
3 KerData - Scalable Storage for Clouds and Beyond
4 COPPE-UFRJ - Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia

Large-scale, data-intensive scientific applications are often expressed as scientific workflows (SWfs). In this paper, we consider the problem of efficient scheduling of a large SWf in a multisite cloud, i.e. a cloud with geo-distributed cloud data centers (sites). The reasons for using multiple cloud sites to run a SWf are that data is already distributed , the necessary resources exceed the limits at a single site, or the monetary cost is lower. In a multisite cloud, metadata management has a critical impact on the efficiency of SWf scheduling as it provides a global view of data location and enables task tracking during execution. Thus, it should be readily available to the system at any given time. While it has been shown that efficient metadata handling plays a key role in performance, little research has targeted this issue in multisite cloud. In this paper, we propose to identify and exploit hot metadata (frequently accessed metadata) for efficient SWf scheduling in a multisite cloud, using a distributed approach. We implemented our approach within a scientific workflow management system, which shows that our approach reduces the execution time of highly parallel jobs up to 64% and that of the whole SWfs up to 55%.

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

Ji Liu, Luis Pineda, Esther Pacitti, Alexandru Costan, Patrick Valduriez, et al.. Efficient Scheduling of Scientific Workflows using Hot Metadata in a Multisite Cloud. IEEE Transactions on Knowledge and Data Engineering, 2019, 31 (10), pp.1940-1953. ⟨10.1109/TKDE.2018.2867857⟩. ⟨lirmm-01867717⟩