ExPERT: Pareto-Efficient Task Replication on Grids and a Cloud

Many scientists perform extensive computations by executing large bags of similar tasks (BoTs) in mixtures of computational environments, such as grids and clouds. Although the reliability and cost may vary considerably across these environments, no tool exists to assist scientists in the selection...

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Bibliographic Details
Published in:2012 IEEE 26th International Parallel and Distributed Processing Symposium pp. 167 - 178
Main Authors: Ben-Yehuda, O. A., Schuster, A., Sharov, A., Silberstein, M., Iosup, A.
Format: Conference Proceeding
Language:English
Published: IEEE 01.05.2012
Subjects:
bags-of-tasks
cloud
Cloud computing
Computational modeling
Equations
grid
Mathematical model
Pareto-frontier
Processor scheduling
Reliability
Throughput
ISBN:1467309753, 9781467309752
ISSN:1530-2075
Online Access:Get full text
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Summary:Many scientists perform extensive computations by executing large bags of similar tasks (BoTs) in mixtures of computational environments, such as grids and clouds. Although the reliability and cost may vary considerably across these environments, no tool exists to assist scientists in the selection of environments that can both fulfill deadlines and fit budgets. To address this situation, we introduce the Expert BoT scheduling framework. Our framework systematically selects from a large search space the Pareto-efficient scheduling strategies, that is, the strategies that deliver the best results for both make span and cost. Expert chooses from them the best strategy according to a general, user-specified utility function. Through simulations and experiments in real production environments, we demonstrate that Expert can substantially reduce both make span and cost in comparison to common scheduling strategies. For bioinformatics BoTs executed in a real mixed grid + cloud environment, we show how the scheduling strategy selected by Expert reduces both make span and cost by 30%-70%, in comparison to commonly-used scheduling strategies.
ISBN:1467309753
9781467309752
ISSN:1530-2075
DOI:10.1109/IPDPS.2012.25