Skip to Main content Skip to Navigation
Conference papers

Managing Hot Metadata for Scientific Workflows on Multisite Clouds

Luis Pineda-Morales 1, 2 Ji Liu 2, 3, 4 Alexandru Costan 1 Esther Pacitti 3, 4 Gabriel Antoniu 1 Patrick Valduriez 3, 4 Marta Mattoso 5
1 KerData - Scalable Storage for Clouds and Beyond
IRISA-D1 - SYSTÈMES LARGE ÉCHELLE, Inria Rennes – Bretagne Atlantique
3 ZENITH - Scientific Data Management
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : Large-scale scientific applications are often expressed as workflows that help defining data dependencies between their different components. Several such workflows have huge storage and computation requirements, and so they need to be processed in multiple (cloud-federated) datacenters. It has been shown that efficient metadata handling plays a key role in the performance of computing systems. However, most of this evidence concern only single-site, HPC systems to date. In this paper, we present a hybrid decentralized/distributed model for handling hot metadata (frequently accessed metadata) in multisite architectures. We couple our model with a scientific workflow management system (SWfMS) to validate and tune its applicability to different real-life scientific scenarios. We show that efficient management of hot metadata improves the performance of SWfMS, reducing the workflow execution time up to 50% for highly parallel jobs and avoiding unnecessary cold metadata operations.
Complete list of metadata

Cited literature [30 references]  Display  Hide  Download
Contributor : Gabriel Antoniu Connect in order to contact the contributor
Submitted on : Friday, November 11, 2016 - 3:14:47 PM
Last modification on : Thursday, November 25, 2021 - 11:28:10 AM
Long-term archiving on: : Thursday, March 16, 2017 - 11:32:17 AM


Files produced by the author(s)



Luis Pineda-Morales, Ji Liu, Alexandru Costan, Esther Pacitti, Gabriel Antoniu, et al.. Managing Hot Metadata for Scientific Workflows on Multisite Clouds. Big Data, Dec 2016, Washington, DC, United States. pp.390-397, ⟨10.1109/BigData.2016.7840628⟩. ⟨hal-01395715⟩



Record views


Files downloads