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Green Task Scheduling Algorithms with Speeds Optimization on Heterogeneous Cloud Servers

Authors:

Luna Mingyi Zhang,

Yan-Qing ZhangAuthors Info & Claims

GREENCOM-CPSCOM '10: Proceedings of the 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing

Pages 76 - 80

https://doi.org/10.1109/GreenCom-CPSCom.2010.70

Published: 18 December 2010 Publication History

Abstract

Currently, a large number of cloud computing servers waste a tremendous amount of energy and emit a considerable amount of carbon dioxide. Thus, it is necessary to significantly reduce pollution and substantially lower energy usage. This paper seeks to implement six innovative green task scheduling algorithms that have two main steps: assigning as many tasks as possible to a cloud server with lowest energy, and setting the same optimal speed for all tasks assigned to each cloud server. A newly proven theorem can determine the optimal speed for all tasks assigned to a computer. These novel green algorithms are developed for heterogeneous cloud servers with adjustable speeds and parameters to effectively reduce energy consumption and finish all tasks before a deadline. Based on sufficient simulations, three green algorithms that allocate a task to a cloud server with minimum energy are more effective than three others that assign a task to a randomly selected cloud server. Sufficient simulation results indicate that the best algorithm among the six algorithms is Shortest Task First for Computer with Minimum Energy algorithm.

References

[1]

K. Li, "Performance Analysis of Power-Aware Task Scheduling Algorithms on Multiprocessor Computers with Dynamic Voltage and Speed," IEEE Trans. Parallel and Distributed Systems, vol. 19, issue 11, pp. 1484-1497, 2008.

Digital Library

[2]

Technical Area of Green Computing, IEEE Technical Committee on Scalable Computing (TCSC), http://sites.google.com/site/greencomputingproject/.

[3]

Report to Congress on Server and Data Center Energy Efficiency: Public Law 109-431, U.S. Environmental Protection Agency ENERGY STAR Program, August 2, 2007.

[4]

Efficient Computing, http://www.google.com/corporate/green/datacenters/.

[5]

Save energy. Save money. Make a difference. http://www.google.com/powermeter/about/index.html.

[6]

S. Ryan, "General Electric Saves nearly $6.5 M with Computer Power Management Features," US EPA ENERGY STAR, Program 202-343-9123.

[7]

http://www.cosn.org/Initiatives/GreenComputing/InterestingFacts/tabid/4639/Default.aspx.

[8]

http://www.springerlink.com/content/j6185q2t22u483h8/.

[9]

http://www.powersavesoftware.com/Download/PS_WP_GreenComputing_EN.pdf.

[10]

S. Albers, "Energy-Efficient Algorithms," Communications of the ACM, vol. 53, no. 5, pp. 86-96, May 2010.

Digital Library

[11]

V.T.D. Truong, Y. Sato, Y. Inoguchi, "Performance evaluation of a Green Scheduling Algorithm for energy savings in Cloud computing," Proc. 2010 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW), pp. 1-8, April 2010.

[12]

A. Kamthe and S.-Y. Lee, "Stochastic Approach to Scheduling Multiple Divisible Tasks on a Heterogeneous Distributed Computing System," Proc. IEEE International Parallel and Distributed Processing Symposium, pp. 1-11, 2007.

[13]

L.K. Goh, B. Veeravalli, and S. Viswanathan, "Design of Fast and Efficient Energy-Aware Gradient-Based Scheduling Algorithms for Heterogeneous Embedded Multiprocessor Systems," IEEE Trans. Parallel and Distributed Systems, vol. 20, no. 1, pp. 1-12, 2009.

Digital Library

[14]

N.B. Nizvandi, J. Taheri, A.Y. Zomaya, and Y.C. Lee, "Linear Combinations of DVFS -enabled Processor Frequencies to Modify the Energy-Aware Scheduling Algorithms," Proc. 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, pp. 388-397, 2010.

Digital Library

[15]

Y. Shin, K. Chois, and T. Sakurait, "Power Optimization of Real-Time Embedded Systems on Variable Speed Processors," Proc. IEEE/ACM International Conference on Computer Aided Design, pp. 365-368, 2000.

Digital Library

[16]

Y.C. Lee and A.Y. Zomaya, "On Effective Slack Reclamation in Task Scheduling for Energy Reduction," Journal of Information Processing Systems, vol. 5, no. 4, pp. 175-186, 2009.

[17]

D. Zhu, R. Melhem, and B.R. Childers, "Scheduling with Dynamic Voltage/Speed Adjustment Using Slack Reclamation in Multiprocessor Real-Time Systems," IEEE Trans. Parallel and Distributed Systems, vol. 14, no. 7, pp. 686-700, 2003.

Digital Library

[18]

MATH2640 Introduction to Optimisation: 4. Inequality Constraints, Complementary slackness condition, Maximisation and Minimisation, Kuhn-Tucker method: summary, http://www.maths.leeds.ac.uk/~cajones/math2640/notes4.pdf

Cited By

(2017)An energy-aware task consolidation algorithm for cloud computing data centreInternational Journal of High Performance Computing and Networking10.5555/3141079.314109010:4-5(352-358)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.5555/3141079.3141090
Koutsandria GSkevakis ESayegh AKoutsakis P(2016)Can everybody be happy in the cloud? Delay, profit and energy-efficient scheduling for cloud servicesJournal of Parallel and Distributed Computing10.1016/j.jpdc.2016.05.01396:C(202-217)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.1016/j.jpdc.2016.05.013
Xiong YWan SShe JWu MHe YJiang K(2016)An energy-optimization-based method of task scheduling for a cloud video surveillance centerJournal of Network and Computer Applications10.1016/j.jnca.2015.06.01759:C(63-73)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1016/j.jnca.2015.06.017
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Green Task Scheduling Algorithms with Speeds Optimization on Heterogeneous Cloud Servers

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cover image Guide Proceedings

GREENCOM-CPSCOM '10: Proceedings of the 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing

December 2010

974 pages

ISBN:9780769543314

Publisher

IEEE Computer Society

United States

Publication History

Published: 18 December 2010

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Cited By

(2017)An energy-aware task consolidation algorithm for cloud computing data centreInternational Journal of High Performance Computing and Networking10.5555/3141079.314109010:4-5(352-358)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.5555/3141079.3141090
Koutsandria GSkevakis ESayegh AKoutsakis P(2016)Can everybody be happy in the cloud? Delay, profit and energy-efficient scheduling for cloud servicesJournal of Parallel and Distributed Computing10.1016/j.jpdc.2016.05.01396:C(202-217)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.1016/j.jpdc.2016.05.013
Xiong YWan SShe JWu MHe YJiang K(2016)An energy-optimization-based method of task scheduling for a cloud video surveillance centerJournal of Network and Computer Applications10.1016/j.jnca.2015.06.01759:C(63-73)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1016/j.jnca.2015.06.017
Cabrera GPérez-Roses HJuan AMarquès JFaulin JHill RKuhl M(2013)Promoting green internet computing throughout simulation-optimization scheduling algorithmsProceedings of the 2013 Winter Simulation Conference: Simulation: Making Decisions in a Complex World10.5555/2675983.2676222(1917-1925)Online publication date: 8-Dec-2013
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Kasae YOguchi M(2013)Proposal for an optimal job allocation method for data-intensive applications based on multiple costs balancing in a hybrid cloud environmentProceedings of the 7th International Conference on Ubiquitous Information Management and Communication10.1145/2448556.2448561(1-8)Online publication date: 17-Jan-2013
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Zapater MAyala JMoya J(2012)Leveraging Heterogeneity for Energy Minimization in Data CentersProceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)10.1109/CCGrid.2012.34(752-757)Online publication date: 13-May-2012
https://dl.acm.org/doi/10.1109/CCGrid.2012.34
Shen GZhang Y(2011)A shadow price guided genetic algorithm for energy aware task scheduling on cloud computersProceedings of the Second international conference on Advances in swarm intelligence - Volume Part I10.5555/2026282.2026354(522-529)Online publication date: 12-Jun-2011
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Wang ZZhang Y(2011)Energy-Efficient Task Scheduling Algorithms with Human Intelligence Based Task Shuffling and Task RelocationProceedings of the 2011 IEEE/ACM International Conference on Green Computing and Communications10.1109/GreenCom.2011.15(38-43)Online publication date: 4-Aug-2011
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