A mixture of HMM, GA, and Elman network for load prediction in cloud-oriented data centers

Da-yu XU; Shan-lin YANG; Ren-ping LIU

doi:10.1631/jzus.C1300109

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A mixture of HMM, GA, and Elman network for load prediction in cloud-oriented data centers

Computer & Automation | Updated：2022-05-19

- A mixture of HMM, GA, and Elman network for load prediction in cloud-oriented data centers
- A mixture of HMM, GA, and Elman network for load prediction in cloud-oriented data centers
- Frontiers of Information Technology & Electronic Engineering Vol. 14, Issue 11, Pages: 845-858(2013)
- Affiliations：
  
  1.MOE Key Laboratory of Process Optimization and Intelligent Decision-Making, Hefei University of Technology, Hefei 230009, China
  2.Information and Communication Technologies Centre, CSIRO, Sydney 2122, Australia
- Author bio：
  
  [ "Da-yu XU, E-mail: xdyhfut@163.com" ]
- Funds：
- DOI：10.1631/jzus.C1300109
  CLC： TP391
- Received：25 April 2013，
  
  Revised：2013-10-15，
  
  Published：2013-11
- Accepted：
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Da-yu XU, Shan-lin YANG, Ren-ping LIU. A mixture of HMM, GA, and Elman network for load prediction in cloud-oriented data centers[J]. Frontiers of Information Technology & Electronic Engineering, 2013, 14(11): 845-858.
DOI：

Da-yu XU, Shan-lin YANG, Ren-ping LIU. A mixture of HMM, GA, and Elman network for load prediction in cloud-oriented data centers[J]. Frontiers of Information Technology & Electronic Engineering, 2013, 14(11): 845-858. DOI： 10.1631/jzus.C1300109.

摘要

Abstract

The rapid growth of computational power demand from scientific

business

and Web applications has led to the emergence of cloud-oriented data centers. These centers use pay-as-you-go execution environments that scale transparently to the user. Load prediction is a significant cost-optimal resource allocation and energy saving approach for a cloud computing environment. Traditional linear or nonlinear prediction models that forecast future load directly from historical information appear less effective. Load classification before prediction is necessary to improve prediction accuracy. In this paper

a novel approach is proposed to forecast the future load for cloud-oriented data centers. First

a hidden Markov model (HMM) based data clustering method is adopted to classify the cloud load. The Bayesian information criterion and Akaike information criterion are employed to automatically determine the optimal HMM model size and cluster numbers. Trained HMMs are then used to identify the most appropriate cluster that possesses the maximum likelihood for current load. With the data from this cluster

a genetic algorithm optimized Elman network is used to forecast future load. Experimental results show that our algorithm outperforms other approaches reported in previous works.

关键词

Keywords

references

S. Andersson , , , J. Yamagishi , , , R.A.J. Clark . . Synthesis and evaluation of conversational characteristics in HMM-based speech synthesis . . Speech Commun. , , 2012 . . 54 ( ( 2 ): ): 175 - - 188 . . DOI: 10.1016/j.specom.2011.08.001 http://doi.org/10.1016/j.specom.2011.08.001 . .

A. Arasu , , , G.S. Manku . . Approximate Counts and Quantiles over Sliding Windows . . 23rd ACM SIGMOD-SIGACT-SIGART Symp. on Principles of Database Systems , , 2004 . . p.286 - - 296 . . DOI: 10.1145/1055558.1055598 http://doi.org/10.1145/1055558.1055598 . .

D. Ardagna , , , S. Casolari , , , M. Colajanni , , , B. Panicucci . . Dual time-scale distributed capacity allocation and load redirect algorithms for cloud systems . . J. Parall. Distr. Comput. , , 2012 . . 72 ( ( 6 ): ): 796 - - 808 . . DOI: 10.1016/j.jpdc.2012.02.014 http://doi.org/10.1016/j.jpdc.2012.02.014 . .

M. Armbrust , , , A. Fox , , , R. Griffith , , , A.D. Joseph , , , R.H. Katz , , , A. Konwinski , , , G. Lee , , , D.A. Patterson , , , I.S.A. Rabkin , , , M. Zaharia . . Above the Clouds: a Berkeley View of Cloud Computing . . Available from , , 2009 . . http://www.eecs.berkeley.edu/Pubs/TechRpts/2009/EECS-2009-28.pdf http://www.eecs.berkeley.edu/Pubs/TechRpts/2009/EECS-2009-28.pdf , , . .

M. Atique , , , M.S. Ali . . A Novel Adaptive Neuro Fuzzy Inference System Based CPU Scheduler for Multimedia Operating System . . Int. Conf. on Neural Networks , , 2007 . . p.1002 - - 1007 . . DOI: 10.1109/IJCNN.2007.4371095 http://doi.org/10.1109/IJCNN.2007.4371095 . .

E. Bauer , , , R. Adams . . Reliability and Availability of Cloud Computing . . Wiley-IEEE Press, New Jersey, USA , , 2012 . . .

M.N. Bennani , , , D.A. Menasce . . Resource Allocation for Autonomic Data Centers Using Analytic Performance Models . . 2nd IEEE Int. Conf. on Autonomic Computing , , 2005 . . p.229 - - 240 . . DOI: 10.1109/ICAC.2005.50 http://doi.org/10.1109/ICAC.2005.50 . .

T. Benson , , , A. Akella , , , D.A. Maltz . . Network Traffic Characteristics of Data Centers in the Wild . . 10th ACM SIGCOMM Conf. on Internet Measurement , , 2010 . . p.267 - - 280 . . DOI: 10.1145/1879141.1879175 http://doi.org/10.1145/1879141.1879175 . .

J. Bilmes . . A Gentle Tutorial on the EM Algorithm and Its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models . . Technical Report ICSI-TR-97-02, University of Berkeley, CA , , 1997 . . .

H. Bozdogan . . Model selection and Akaike's information criterion (AIC): the general theory and its analytical extensions . . Psychometrika , , 1987 . . 52 ( ( 3 ): ): 345 - - 370 . . DOI: 10.1007/BF02294361 http://doi.org/10.1007/BF02294361 . .

K.P. Burnham , , , D.R. Anderson . . Multimodel inference understanding AIC and BIC in model selection . . Sociol. Methods Res. , , 2004 . . 33 ( ( 2 ): ): 261 - - 304 . . DOI: 10.1177/0049124104268644 http://doi.org/10.1177/0049124104268644 . .

R.N. Calheiros , , , R. Ranjan , , , R. Buyya . . Virtual Machine Provisioning Based on Analytical Performance and QoS in Cloud Computing Environments . . Int. Conf. on Parallel Processing , , 2011 . . p.295 - - 304 . . DOI: 10.1109/ICPP.2011.17 http://doi.org/10.1109/ICPP.2011.17 . .

E. Caron , , , F. Desprez , , , A. Muresan . . Forecasting for Grid and Cloud Computing On-Demand Resources Based on Pattern Matching . . 2nd IEEE Int. Conf. on Cloud Computing Technology and Science , , 2010 . . p.456 - - 463 . . DOI: 10.1109/CloudCom.2010.65 http://doi.org/10.1109/CloudCom.2010.65 . .

S. Di , , , D. Kondo , , , W. Cirne . . Characterization and Comparison of Cloud versus Grid Workloads . . IEEE Int. Conf. on Cluster Computing , , 2012a . . p.230 - - 238 . . DOI: 10.1109/CLUSTER.2012.35 http://doi.org/10.1109/CLUSTER.2012.35 . .

S. Di , , , D. Kondo , , , W. Cirne . . Host Load Prediction in a Google Compute Cloud with a Bayesian Model . . IEEE Int. Conf. on High Performance Computing, Networking, Storage and Analysis , , 2012b . . p.1 - - 11 . . DOI: 10.1109/SC.2012.68 http://doi.org/10.1109/SC.2012.68 . .

T.V.T. Duy , , , Y. Sato , , , Y. Inoguchi . . Performance Evaluation of a Green Scheduling Algorithm for Energy Savings in Cloud Computing . . IEEE Int. Symp. on Parallel & Distributed Processing, Workshops and PhD Forum , , 2010 . . p.1 - - 8 . . DOI: 10.1109/IPDPSW.2010.5470908 http://doi.org/10.1109/IPDPSW.2010.5470908 . .

T.V.T. Duy , , , Y. Sato , , , Y. Inoguchi . . Improving accuracy of host load predictions on computational grids by artificial neural networks . . Int. J. Parall. Emerg. Distr. Syst. , , 2011 . . 26 ( ( 4 ): ): 275 - - 290 . . DOI: 10.1080/17445760.2010.481786 http://doi.org/10.1080/17445760.2010.481786 . .

J. Elman . . Finding structure in time . . Cogn. Sci. , , 1990 . . 14 ( ( 2 ): ): 179 - - 211 . . DOI: 10.1207/s15516709cog1402_1 http://doi.org/10.1207/s15516709cog1402_1 . .

M.R. Hassan , , , B. Nath , , , M. Kirley . . A fusion model of HMM, ANN and GA for stock market forecasting . . Expert Syst. Appl. , , 2007 . . 33 ( ( 1 ): ): 171 - - 180 . . DOI: 10.1016/j.eswa.2006.04.007 http://doi.org/10.1016/j.eswa.2006.04.007 . .

R. Hassan , , , B. Nath , , , M. Kirley , , , J. Kamruzzaman . . A hybrid of multiobjective evolutionary algorithm and HMM-fuzzy model for time series prediction . . Neurocomputing , , 2012 . . 81 1 - - 11 . . DOI: 10.1016/j.neucom.2011.09.012 http://doi.org/10.1016/j.neucom.2011.09.012 . .

A. Hirose . . Complex-Valued Neural Networks, Vol. 400 . . Springer, New York, Dordrecht, Heidelberg, London , , 2012 . . .

S. Islam , , , J. Keung , , , K. Lee , , , A. Liu . . Empirical prediction models for adaptive resource provisioning in the cloud . . Future Gener. Comput. Syst. , , 2012 . . 28 ( ( 1 ): ): 155 - - 162 . . DOI: 10.1016/j.future.2011.05.027 http://doi.org/10.1016/j.future.2011.05.027 . .

P. Kaur , , , A.K. Soni , , , A. Gosain . . A robust kernelized intuitionistic fuzzy c-means clustering algorithm in segmentation of noisy medical images . . Pattern Recogn. Lett. , , 2013 . . 34 ( ( 2 ): ): 163 - - 175 . . DOI: 10.1016/j.patrec.2012.09.015 http://doi.org/10.1016/j.patrec.2012.09.015 . .

A. Khan , , , X. Yan , , , S. Tao , , , N. Anerousis . . Workload Characterization and Prediction in the Cloud: a Multiple Time Series Approach . . 3rd IEEE Int. Workshop on Cloud Management , , 2012 . . p.1287 - - 1294 . . DOI: 10.1109/NOMS.2012.6212065 http://doi.org/10.1109/NOMS.2012.6212065 . .

M. Khashei , , , A. Zeinal Hamadani , , , M. Bijari . . A novel hybrid classification model of artificial neural networks and multiple linear regression models . . Expert Syst. Appl. , , 2012 . . 39 ( ( 3 ): ): 2606 - - 2620 . . DOI: 10.1016/j.eswa.2011.08.116 http://doi.org/10.1016/j.eswa.2011.08.116 . .

A. Li , , , X. Yang , , , S. Kandula , , , M. Zhang . . CloudCmp: Comparing Public Cloud Providers . . 10th ACM SIGCOMM Conf. on Internet Measurement , , 2010 . . p.1 - - 14 . . DOI: 10.1145/1879141.1879143 http://doi.org/10.1145/1879141.1879143 . .

K.C. Liang , , , X.D. Wang , , , D. Anastassiou . . Bayesian basecalling for DNA sequence analysis using hidden Markov models . . IEEE Trans. Comput. Biol. Bioinf. , , 2007 . . 4 ( ( 3 ): ): 430 - - 440 . . DOI: 10.1109/tcbb.2007.1027 http://doi.org/10.1109/tcbb.2007.1027 . .

S. Marston , , , Z. Li , , , S. Bandyopadhyay , , , J. Zhang , , , A. Ghalsasi . . Cloud computing: the business perspective . . Decis. Support Syst. , , 2011 . . 51 ( ( 1 ): ): 176 - - 189 . . DOI: 10.1016/j.dss.2010.12.006 http://doi.org/10.1016/j.dss.2010.12.006 . .

A.K. Mishra , , , J.L. Hellerstein , , , W. Cirne , , , C.R. Das . . Towards characterizing cloud backend workloads: insights from Google compute clusters . . ACM SIGMETRICS Perform. Eval. Rev. , , 2010 . . 37 ( ( 4 ): ): 34 - - 41 . . DOI: 10.1145/1773394.1773400 http://doi.org/10.1145/1773394.1773400 . .

D.X. Niu , , , B.E. Kou , , , Y.Y. Zhang . . Mid-long Term Load Forecasting Using Hidden Markov Model . . 3rd Int. Symp. on Intelligent Information Technology Application , , 2009 . . p.481 - - 483 . . DOI: 10.1109/ⅡTA.2009.422 http://doi.org/10.1109/ⅡTA.2009.422 . .

A.K. Palit , , , D. Popovic . . Computational Intelligence in Time Series Forecasting: Theory and Engineering Applications. In : Advances in Industrial Control . . Springer-Verlag New York, Inc., Secaucus, NJ , , 2005 . . .

J.J. Prevost , , , K. Nagothu , , , B. Kelley , , , M. Jamshidi . . Prediction of Cloud Data Center Networks Loads Using Stochastic and Neural Models . . 6th Int. Conf. on System of Systems Engineering , , 2011 . . p.276 - - 281 . . DOI: 10.1109/SYSOSE.2011.5966610 http://doi.org/10.1109/SYSOSE.2011.5966610 . .

L.R. Rabiner . . A tutorial on hidden Markov models and selected applications in speech recognition . . Proc. IEEE , , 1989 . . 77 ( ( 2 ): ): 257 - - 286 . . DOI: 10.1109/5.18626 http://doi.org/10.1109/5.18626 . .

C. Reiss , , , A. Tumanov , , , G.R. Ganger , , , R.H. Katz , , , M.A. Kozuch . . Towards Understanding Heterogeneous Clouds at Scale: Google Trace Analysis . . Proc. 3rd ACM Symp. on Cloud Computing , , 2012 . . p.7 . .

P. Saripalli , , , G.V.R. Kiran , , , R.R. Shankar , , , H. Narware , , , N. Bindal . . Load Prediction and Hot Spot Detection Models for Autonomic Cloud Computing . . 4th IEEE Int. Conf. on Utility and Cloud Computing , , 2011 . . p.397 - - 402 . . DOI: 10.1109/UCC.2011.66 http://doi.org/10.1109/UCC.2011.66 . .

L.M. Vaquero , , , L. Rodero-Merino , , , J. Caceres , , , M. Lindner . . A break in the clouds: towards a cloud definition . . ACM SIGCOMM Comput. Commun. Rev. , , 2009 . . 39 ( ( 1 ): ): 50 - - 55 . . DOI: 10.1145/1496091.1496100 http://doi.org/10.1145/1496091.1496100 . .

P. Wang , , , H. Wang , , , W. Wang . . Finding Semantics in Time Series . . ACM SIGMOD Int. Conf. on Management of Data , , 2011 . . p.385 - - 396 . . DOI: 10.1145/1989323.1989364 http://doi.org/10.1145/1989323.1989364 . .

D.L. Weakliem . . A critique of the Bayesian information criterion for model selection . . Sociol. Methods Res. , , 1999 . . 27 ( ( 3 ): ): 359 - - 397 . . DOI: 10.1177/0049124199027003002 http://doi.org/10.1177/0049124199027003002 . .

L. Yu , , , S. Wang , , , K.K. Lai . . Credit risk assessment with a multistage neural network ensemble learning approach . . Expert Syst. Appl. , , 2008 . . 34 ( ( 2 ): ): 1434 - - 1444 . . DOI: 10.1016/j.eswa.2007.01.009 http://doi.org/10.1016/j.eswa.2007.01.009 . .

G.P. Zhang . . Time series forecasting using a hybrid ARIMA and neural network model . . Neurocomputing , , 2003 . . 50 159 - - 175 . . DOI: 10.1016/S0925-2312(01)00702-0 http://doi.org/10.1016/S0925-2312(01)00702-0 . .

Y. Zhang . . Prediction of Financial Time Series with Hidden Markov Models . . PhD Thesis, Simon Fraser University, Burnaby, Canada , , 2004 . . .

Y. Zhang , , , W. Sun , , , Y. Inoguchi . . CPU Load Predictions on the Computational Grid . . 6th IEEE Int. Symp. on Cluster Computing and the Grid , , 2006 . . p.321 - - 326 . . DOI: 10.1109/CCGRID.2006.27 http://doi.org/10.1109/CCGRID.2006.27 . .

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