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The Journal of Supercomputing

Erschienen in:

01.06.2014

A new method based on PSR and EA-GMDH for host load prediction in cloud computing system

verfasst von: Qiangpeng Yang, Chenglei Peng, He Zhao, Yao Yu, Yu Zhou, Ziqiang Wang, Sidan Du

Erschienen in: The Journal of Supercomputing | Ausgabe 3/2014

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Abstract

Host load prediction is one of the most effective measures for improving resource utilization in cloud computing systems. Due to the drastic fluctuation of the host load in the Cloud, accurately predicting the host load remains a challenge. In this paper, we propose a new prediction method that combines the Phase Space Reconstruction method and the Group Method of Data Handling based on an Evolutionary Algorithm. The performance of our proposed method is evaluated using two real-world load traces. The first is the load trace in a traditional distributed system, whereas the second is in a Google data center. The results show that the proposed method achieves a better prediction performance than some state-of-the-art methods.

Vorheriger Artikel The BonaFide C Analyzer: automatic loop-level characterization and coverage measurement

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Load traces on unix systems (1997) http://www.cs.northwestern.edu/pdinda/LoadTraces/

More google cluster data. Google reach blog (2011) http://googleresearch.blogspot.com/2011/11/more-google-cluster-data.html

Akioka S, Muraoka Y (2004) Extended forecast of cpu and network load on computational grid. In: IEEE international symposium on cluster computing and the grid, 2004. CCGrid 2004. IEEE, pp 765–772

Bobroff N, Kochut A, Beaty K (2007) Dynamic placement of virtual machines for managing sla violations. In: 10th IFIP/IEEE international symposium on integrated network management, 2007. IM’07. IEEE, pp 119–128

Cleveland WS, Devlin SJ (1988) Locally weighted regression: an approach to regression analysis by local fitting. J Am Stat Assoc 83(403):596–610CrossRefMATH

Dabrowski C, Hunt F (2009) Using markov chain analysis to study dynamic behaviour in large-scale grid systems. In: Proceedings of the seventh Australasian symposium on grid computing and e-research, vol 99. Australian Computer Society, Inc., pp 29–40

Di S, Kondo D, Cirne W (2012) Characterization and comparison of cloud versus grid workloads. In: 2012 IEEE international conference on cluster computing (CLUSTER). IEEE, pp 230–238

Di S, Kondo D, Cirne W (2012) Host load prediction in a google compute cloud with a bayesian model. In: Proceedings of the international conference on high performance computing, networking, storage and analysis. IEEE Computer Society Press, p 21

Dinda PA, O’Hallaron DR (2000) Host load prediction using linear models. Clust Comput 3(4):265–280CrossRef

10.

Duy TVT, Sato Y, Inoguchi Y (2011) Improving accuracy of host load predictions on computational grids by artificial neural networks. Int J Parallel Emerg Distrib Syst 26(4):275–290CrossRef

11.

Guenter B, Jain N, Williams C (2011) Managing cost, performance, and reliability tradeoffs for energy-aware server provisioning. In: INFOCOM, 2011 Proceedings IEEE. IEEE, pp 1332–1340

12.

Han M, Xi J, Xu S, Yin FL (2004) Prediction of chaotic time series based on the recurrent predictor neural network. IEEE Trans Signal Process 52(12):3409–3416CrossRefMathSciNet

13.

Kim H, Eykholt R, Salas J (1999) Nonlinear dynamics, delay times, and embedding windows. Phys D Nonlinear Phenom 127(1):48–60CrossRefMATH

14.

Nariman-Zadeh N, Darvizeh A, Jamali A, Moeini A (2005) Evolutionary design of generalized polynomial neural networks for modelling and prediction of explosive forming process. J Mater Process Technol 164:1561–1571CrossRef

15.

Osman S, Subhraveti D, Su G, Nieh J (2002) The design and implementation of zap: a system for migrating computing environments. ACM SIGOPS Oper Syst Rev 36(SI):361–376

16.

Packard NH, Crutchfield JP, Farmer JD, Shaw RS (1980) Geometry from a time series. Phys Rev Lett 45:712–716

17.

Takens F (1981) Detecting strange attractors in turbulence. In: Dynamical systems and turbulence, Warwick 1980. Springer, Berlin, pp 366–381

18.

Urgaonkar B, Shenoy P, Chandra A, Goyal P (2005) Dynamic provisioning of multi-tier internet applications. In: Proceedings, second international conference on autonomic computing, 2005. ICAC 2005. IEEE, pp 217–228

19.

Wolf A, Swift JB, Swinney HL, Vastano JA (1985) Determining lyapunov exponents from a time series. Phys D Nonlinear Phenom 16(3):285–317CrossRefMATHMathSciNet

20.

Wu Y, Yuan Y, Yang G, Zheng W (2007) Load prediction using hybrid model for computational grid. In: 2007 8th IEEE/ACM international conference on grid computing. IEEE, pp 235–242

21.

Yang D, Cao J, Yu C, Xiao J (2012) A multi-step-ahead cpu load prediction approach in distributed system. In: 2012 second international conference on cloud and green computing (CGC). IEEE, pp 206–213

22.

Zhang Q, Cheng L, Boutaba R (2010) Cloud computing: state-of-the-art and research challenges. J Internet Serv Appl 1(1):7–18CrossRef

23.

Zhang Q, Zhani MF, Zhang S, Zhu Q, Boutaba R, Hellerstein JL (2012) Dynamic energy-aware capacity provisioning for cloud computing environments. In: Proceedings of the 9th international conference on Autonomic computing. ACM, pp 145–154

Titel: A new method based on PSR and EA-GMDH for host load prediction in cloud computing system
verfasst von: Qiangpeng Yang
Chenglei Peng
He Zhao
Yao Yu
Yu Zhou
Ziqiang Wang
Sidan Du
Publikationsdatum: 01.06.2014
Verlag: Springer US
Erschienen in: The Journal of Supercomputing / Ausgabe 3/2014
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-014-1097-x

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