Energy efficient genetic‐based schedulers in computational grids. (23rd April 2012)
- Record Type:
- Journal Article
- Title:
- Energy efficient genetic‐based schedulers in computational grids. (23rd April 2012)
- Main Title:
- Energy efficient genetic‐based schedulers in computational grids
- Authors:
- Kołodziej, Joanna
Khan, Samee Ullah
Wang, Lizhe
Zomaya, Albert Y.
Hussain, Farookh Khadeer
Wyrzykowski, Roman
Tudruj, Marek - Abstract:
- <abstract abstract-type="main" id="cpe2839-abs-0001"> <title>Summary</title> <p>In today's highly parametrized distributed computational environments, such as green grid clusters and clouds, the growing power and cooling rates are becoming the dominant part of the users' and system managers' budgets. Computational grids, owing to their sheer sizes, still require advanced methodologies and strategies for supporting the scheduling of the users' tasks and applications to the distributed resources. The efficient resource allocation becomes even more challenging when energy utilization, beyond the conventional scheduling criteria, such as <italic>Makespan</italic>, is treated as first‐class additional scheduling objective. In this paper, we address the independent batch scheduling in computational grid as a bi‐objective global minimization problem with <italic>Makespan</italic> and <italic>energy consumption</italic> as the main criteria. We apply the <italic>dynamic voltage and frequency scaling</italic> model for the management of the cumulative power energy utilized by the grid resources. We develop three genetic algorithms as energy‐aware grid schedulers, which were empirically evaluated in three grid size scenarios in static and dynamic modes. The simulation results confirmed the effectiveness of the proposed genetic algorithm‐based schedulers in the reduction of the energy consumed by the whole system and in dynamic load balancing of the resources in grid clusters, which is<abstract abstract-type="main" id="cpe2839-abs-0001"> <title>Summary</title> <p>In today's highly parametrized distributed computational environments, such as green grid clusters and clouds, the growing power and cooling rates are becoming the dominant part of the users' and system managers' budgets. Computational grids, owing to their sheer sizes, still require advanced methodologies and strategies for supporting the scheduling of the users' tasks and applications to the distributed resources. The efficient resource allocation becomes even more challenging when energy utilization, beyond the conventional scheduling criteria, such as <italic>Makespan</italic>, is treated as first‐class additional scheduling objective. In this paper, we address the independent batch scheduling in computational grid as a bi‐objective global minimization problem with <italic>Makespan</italic> and <italic>energy consumption</italic> as the main criteria. We apply the <italic>dynamic voltage and frequency scaling</italic> model for the management of the cumulative power energy utilized by the grid resources. We develop three genetic algorithms as energy‐aware grid schedulers, which were empirically evaluated in three grid size scenarios in static and dynamic modes. The simulation results confirmed the effectiveness of the proposed genetic algorithm‐based schedulers in the reduction of the energy consumed by the whole system and in dynamic load balancing of the resources in grid clusters, which is sufficient to maintain the desired quality level(s). Copyright © 2012 John Wiley &amp; Sons, Ltd.</p> </abstract> … (more)
- Is Part Of:
- Concurrency and computation. Volume 27:Number 4(2015:Mar.)
- Journal:
- Concurrency and computation
- Issue:
- Volume 27:Number 4(2015:Mar.)
- Issue Display:
- Volume 27, Issue 4 (2015)
- Year:
- 2015
- Volume:
- 27
- Issue:
- 4
- Issue Sort Value:
- 2015-0027-0004-0000
- Page Start:
- 809
- Page End:
- 829
- Publication Date:
- 2012-04-23
- Subjects:
- Parallel processing (Electronic computers) -- Periodicals
Parallel computers -- Periodicals
004.35 - Journal URLs:
- http://onlinelibrary.wiley.com/ ↗
- DOI:
- 10.1002/cpe.2839 ↗
- Languages:
- English
- ISSNs:
- 1532-0626
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3405.622000
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 4349.xml