Dynamic resource allocation through workload prediction for energy efficient computing

Adeel Ahmed; David Brown; Alexander Gegov

doi:10.1007/978-3-319-46562-3_3

Dynamic resource allocation through workload prediction for energy efficient computing

Adeel Ahmed, David Brown, Alexander Gegov

School of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

206 Downloads (Pure)

Abstract

Rapid and continuous increase in online information exchange and data based services has led to an increase in enterprise data centres. Energy efficient computing is key to a cost effective operation for all such enterprise IT systems. In this paper we propose dynamic resource allocation in server based IT systems through workload prediction for energy efficient computing. We use CPU core as a dynamic resource that can be allocated and deallocated based on predicted workload. We use online workload prediction as opposed to offline statistical analysis of workload characteristics. We use online learning and workload prediction using neural network for online dynamic resource allocation for energy efficient computing. We also analyse the effect of dynamic resource allocation on clients by measuring the request response time to clients for variable number of cores in operation. We show that dynamic resource allocation through workload prediction in server based IT systems can provide a cost effective, energy efficient and reliable operation without effecting quality of experience for clients.

Original language	English
Title of host publication	Advances in Computational Intelligence Systems
Subtitle of host publication	Contributions Presented at the 16th UK Workshop on Computational Intelligence, September 7–9, 2016, Lancaster, UK
Editors	P. Angelov, A. Gegov, C. Jayne, Q. Shen
Publisher	Springer
Pages	35-44
Number of pages	10
ISBN (Electronic)	978-3-319-46562-3
ISBN (Print)	978-3-319-46561-6
DOIs	https://doi.org/10.1007/978-3-319-46562-3_3
Publication status	Published - 1 Jan 2017
Event	16th Annual UK Workshop on Computational Intelligence - Lancaster University, Lancaster, United Kingdom Duration: 7 Sept 2016 → 9 Sept 2016 http://wp.lancs.ac.uk/ukci2016/ http://wp.lancs.ac.uk/ukci2016/

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	513
ISSN (Print)	2194-5357

Conference

Conference	16th Annual UK Workshop on Computational Intelligence
Abbreviated title	UKCI 2016
Country/Territory	United Kingdom
City	Lancaster
Period	7/09/16 → 9/09/16
Internet address	http://wp.lancs.ac.uk/ukci2016/ http://wp.lancs.ac.uk/ukci2016/

Keywords

energy efficient computing
dynamic resource
allocation
workload prediction
neural network

Access to Document

10.1007/978-3-319-46562-3_3

Dynamic Resource Allocation Through Workload Prediction for Energy Efficient ComputingAccepted author manuscript (Post-print), 811 KB

http://www.springer.com/us/book/9783319465616

Cite this

Ahmed, A., Brown, D., & Gegov, A. (2017). Dynamic resource allocation through workload prediction for energy efficient computing. In P. Angelov, A. Gegov, C. Jayne, & Q. Shen (Eds.), Advances in Computational Intelligence Systems: Contributions Presented at the 16th UK Workshop on Computational Intelligence, September 7–9, 2016, Lancaster, UK (pp. 35-44). (Advances in Intelligent Systems and Computing; Vol. 513). Springer. https://doi.org/10.1007/978-3-319-46562-3_3

Ahmed, Adeel ; Brown, David ; Gegov, Alexander. / Dynamic resource allocation through workload prediction for energy efficient computing. Advances in Computational Intelligence Systems: Contributions Presented at the 16th UK Workshop on Computational Intelligence, September 7–9, 2016, Lancaster, UK. editor / P. Angelov ; A. Gegov ; C. Jayne ; Q. Shen. Springer, 2017. pp. 35-44 (Advances in Intelligent Systems and Computing).

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abstract = "Rapid and continuous increase in online information exchange and data based services has led to an increase in enterprise data centres. Energy efficient computing is key to a cost effective operation for all such enterprise IT systems. In this paper we propose dynamic resource allocation in server based IT systems through workload prediction for energy efficient computing. We use CPU core as a dynamic resource that can be allocated and deallocated based on predicted workload. We use online workload prediction as opposed to offline statistical analysis of workload characteristics. We use online learning and workload prediction using neural network for online dynamic resource allocation for energy efficient computing. We also analyse the effect of dynamic resource allocation on clients by measuring the request response time to clients for variable number of cores in operation. We show that dynamic resource allocation through workload prediction in server based IT systems can provide a cost effective, energy efficient and reliable operation without effecting quality of experience for clients.",

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Ahmed, A, Brown, D & Gegov, A 2017, Dynamic resource allocation through workload prediction for energy efficient computing. in P Angelov, A Gegov, C Jayne & Q Shen (eds), Advances in Computational Intelligence Systems: Contributions Presented at the 16th UK Workshop on Computational Intelligence, September 7–9, 2016, Lancaster, UK. Advances in Intelligent Systems and Computing, vol. 513, Springer, pp. 35-44, 16th Annual UK Workshop on Computational Intelligence, Lancaster, United Kingdom, 7/09/16. https://doi.org/10.1007/978-3-319-46562-3_3

Dynamic resource allocation through workload prediction for energy efficient computing. / Ahmed, Adeel; Brown, David; Gegov, Alexander.
Advances in Computational Intelligence Systems: Contributions Presented at the 16th UK Workshop on Computational Intelligence, September 7–9, 2016, Lancaster, UK. ed. / P. Angelov; A. Gegov; C. Jayne; Q. Shen. Springer, 2017. p. 35-44 (Advances in Intelligent Systems and Computing; Vol. 513).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Ahmed A, Brown D, Gegov A. Dynamic resource allocation through workload prediction for energy efficient computing. In Angelov P, Gegov A, Jayne C, Shen Q, editors, Advances in Computational Intelligence Systems: Contributions Presented at the 16th UK Workshop on Computational Intelligence, September 7–9, 2016, Lancaster, UK. Springer. 2017. p. 35-44. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-319-46562-3_3

Dynamic resource allocation through workload prediction for energy efficient computing

Abstract

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Keywords

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