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A Two-Phase Energy-Aware Scheduling Approach for CPU-Intensive Jobs in Mobile Grids

Authors:

Matías Hirsch,

Juan Manuel Rodríguez,

Cristian Mateos,

Alejandro ZuninoAuthors Info & Claims

Journal of Grid Computing, Volume 15, Issue 1

Pages 55 - 80

https://doi.org/10.1007/s10723-016-9387-6

Published: 01 March 2017 Publication History

Abstract

The profusion of mobile devices over the world and their evolved computational capabilities promote their inclusion as resource providers in traditional Grid environments. However, their efficient exploitation requires adapting current schedulers to operate with computing capabilities limited by energy supply and mobile devices that cannot be assumed to be dedicated, among other concerns. We propose a two-phase scheduling approach for running CPU-intensive jobs on mobile devices that combines novel energy-aware criteria with job stealing techniques. The approach was evaluated through an event-based simulator that uses battery consumption profiles extracted from real mobile devices. CPU usage derived from non-Grid processes was also modelled. For evaluating the first phase we compared the number of finalized jobs by all energy-aware criteria, while for the second phase we analyzed the performance boost introduced by job stealing. While the best first phase criteria finalized up to 90 % of submitted jobs, job stealing increased this percentage by up to 9 %.

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Cited By

Hosseinzadeh MGhafour MHama HVo BKhoshnevis A(2020)Multi-Objective Task and Workflow Scheduling Approaches in Cloud Computing: a Comprehensive ReviewJournal of Grid Computing10.1007/s10723-020-09533-z18:3(327-356)Online publication date: 1-Sep-2020
https://dl.acm.org/doi/10.1007/s10723-020-09533-z
Aryania AAghdasi HKhanli L(2018)Energy-Aware Virtual Machine Consolidation Algorithm Based on Ant Colony SystemJournal of Grid Computing10.1007/s10723-018-9428-416:3(477-491)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1007/s10723-018-9428-4
Gallidabino APautasso C(2018)Decentralized Computation Offloading on the Edge with Liquid WebWorkersWeb Engineering10.1007/978-3-319-91662-0_11(145-161)Online publication date: 5-Jun-2018
https://dl.acm.org/doi/10.1007/978-3-319-91662-0_11
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A Two-Phase Energy-Aware Scheduling Approach for CPU-Intensive Jobs in Mobile Grids

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Published In

cover image Journal of Grid Computing

Journal of Grid Computing Volume 15, Issue 1

March 2017

133 pages

ISSN:1570-7873

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Copyright © Copyright © 2017 Springer Science+Business Media Dordrecht.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 March 2017

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Cited By

Hosseinzadeh MGhafour MHama HVo BKhoshnevis A(2020)Multi-Objective Task and Workflow Scheduling Approaches in Cloud Computing: a Comprehensive ReviewJournal of Grid Computing10.1007/s10723-020-09533-z18:3(327-356)Online publication date: 1-Sep-2020
https://dl.acm.org/doi/10.1007/s10723-020-09533-z
Aryania AAghdasi HKhanli L(2018)Energy-Aware Virtual Machine Consolidation Algorithm Based on Ant Colony SystemJournal of Grid Computing10.1007/s10723-018-9428-416:3(477-491)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1007/s10723-018-9428-4
Gallidabino APautasso C(2018)Decentralized Computation Offloading on the Edge with Liquid WebWorkersWeb Engineering10.1007/978-3-319-91662-0_11(145-161)Online publication date: 5-Jun-2018
https://dl.acm.org/doi/10.1007/978-3-319-91662-0_11
Rodrigues VDa Rosa Righi RRostirolla GVictória Barbosa JAndré Da Costa CAlberti AChang V(2017)Towards Enabling Live Thresholding as Utility to Manage Elastic Master-Slave Applications in the CloudJournal of Grid Computing10.1007/s10723-017-9405-315:4(535-556)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1007/s10723-017-9405-3

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