An ensemble multiscale wavelet-GARCH hybrid SVR algorithm for mobile cloud computing workload prediction

  • Saeed SharifianEmail author
  • Masoud Barati
Original Article


Dynamic resource allocation and auto scalability are important aspects in mobile cloud computing environment. Predicting the cloud workload is a crucial task for dynamic resource allocation and auto scaling. Accuracy of workload prediction algorithm has significant impact on cloud quality of service and total cost of provided service. Since, existing prediction algorithms have competition for better accuracy and faster run time, in this paper we proposed a hybrid prediction algorithm to address both of these concerns. First we apply three level wavelet transform to decompose the workload time series into different resolution of time–frequency scales. An approximate and three details components. Second, we use support vector regression (SVR) for prediction of approximate and two low frequency detail components. The SVR parameters are tuned by a novel chaotic particle swarm optimization algorithm. Since the last detail component of time series has high frequency and is more likely to noise, we used generalized autoregressive conditional heteroskedasticity (GARCH) model to predict it. Finally, an ensemble method is applied to recompose these predicted samples from four multi scale predictions to achieve workload prediction for the next time step. The proposed method named wavelet decomposed 3 PSO optimized SVR plus GARCH (W3PSG). We evaluate the proposed W3PSG method with three different real cloud workload traces. Based on the results, the proposed method has relatively better prediction accuracy in comparison with competitive methods. According to mean absolute percentage error metric, in best case W3PSG method achieves 29.93%, 29.91%, and 24.53% of improvement in accuracy over three rival methods: GARCH, artificial neural network, and SVR respectively.


Workload prediction Cloud computing Multi-scale wavelet decomposition 



  1. 1.
    Korpi D, Tamminen J, Turunen M, Huusari T, Choi Y-S, Anttila L, Talwar S, Valkama M (2016) Full-duplex mobile device: pushing the limits. IEEE Commun Mag 54(9):80–87CrossRefGoogle Scholar
  2. 2.
    Li W, Zhao Y, Lu S, Chen D (2015) Mechanisms and challenges on mobility-augmented service provisioning for MCC. IEEE Commun Mag 53(3):89–97CrossRefGoogle Scholar
  3. 3.
    Wang Y, Chen I-R, Wang D-C (2015) A survey of mobile cloud computing applications: perspectives and challenges. Wirel Pers Commun 80(4):1607–1623CrossRefGoogle Scholar
  4. 4.
    Fernando N, Loke SW, Rahayu W (2013) Mobile cloud computing: a survey. Future Gener Comput Syst 29(1):84–106CrossRefGoogle Scholar
  5. 5.
    Zhang Q, Zhani MF, Zhang Sh et al (2012) Dynamic energy-aware capacity provisioning for cloud computing environments. In: Proceedings of the 9th international conference on Autonomic computing. San Jose, California, USA, 18–20 September 2012.
  6. 6.
    Barroso LA, Hölzle U (2007) The case for energy-proportional computing. Computer 40(12):33–37. CrossRefGoogle Scholar
  7. 7.
    Fu Y, Lu Ch, Wang H (2010) Robust control-theoretic thermal balancing for server clusters. In: IEEE international symposium on parallel and distributed processing (IPDPS). Atlanta, GA, USA, 19–23 April 2010.
  8. 8.
    Qureshi A, Weber R, Balakrishnan H, Guttag J, Maggs B (2009) Cutting the electric bill for internet-scale systems. In: Proceedings of the ACM SIGCOMM 2009 conference on data communication, New YorkGoogle Scholar
  9. 9.
    Kusic D, Kephart JO, Hanson JE, Kandasamy N, Jiang G (2009) Power and performance management of virtualized computing environments via lookahead control. Clust Comput 12(1):1–15CrossRefGoogle Scholar
  10. 10.
    Lajevardi B, Haapala KR, Junker JF (2015) Real-time monitoring and evaluation of energy efficiency and thermal management of data centers. J Manuf Syst 37(2):511–516CrossRefGoogle Scholar
  11. 11.
    Mao M, Humphrey M (2012) A performance study on the vm startup time in the cloud. In: 2012 IEEE 5th international conference on cloud computing (CLOUD). Honolulu, HI, USA, 24–29 June 2012.
  12. 12.
    Ghorbani M, Wang Y, Xue Y, Pedram M, Bogdan P (2014) Prediction and control of bursty cloud workloads: a fractal framework. In: Proceedings of the 2014 international conference on hardware/software codesign and system synthesis, New Delhi, IndiaGoogle Scholar
  13. 13.
    Rashidi S, Sharifian S (2017) A hybrid heuristic queue based algorithm for task assignment in mobile cloud. Future Gener Comput Syst 68:31–345CrossRefGoogle Scholar
  14. 14.
    You C, Huang K, Chae H, Kim BH (2016) Energy-efficient resource allocation for mobile-edge computation offloading. IEEE Trans Wirel Commun 16(99):1397–1411Google Scholar
  15. 15.
    Karamoozian A, Hafid A, Boushaba M, Afzali M (2016) QoS-aware resource allocation for mobile media services in cloud environment. In: 13th IEEE annual consumer communications & networking conference (CCNC). Las Vegas, NV, USA, 9–12 January 2016.
  16. 16.
    Valipour M, Banihabib ME, Behbahani SMR (2013) Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in predicting the monthly inflow of Dez dam reservoir. J Hydrol 476(7):433–441CrossRefGoogle Scholar
  17. 17.
    Nourikhah H, Akbari MK, Kalantari M (2015) Modeling and predicting measured response time of cloud-based web services using long-memory time series. J Supercomput 71(2):673–696CrossRefGoogle Scholar
  18. 18.
    Calheiros RN, Masoumi E, Ranjan R, Buyya R (2015) Workload prediction using ARIMA model and its impact on cloud applications. QoS IEEE Trans Cloud Comput 3(4):449–458CrossRefGoogle Scholar
  19. 19.
    Zhang J, Tan Z (2013) Day-ahead electricity price predicting using WT, CLSSVM and EGARCH model. Int J Electr Power Energy Syst 45(1):362–368MathSciNetCrossRefGoogle Scholar
  20. 20.
    Chang BR, Tsai HF (2009) Novel hybrid approach to data-packet-flow prediction for improving network traffic analysis. Appl Soft Comput 9(3):1177–1183CrossRefGoogle Scholar
  21. 21.
    Chenglei H, Kangji L, Guohai L, Lei P (2015) Predicting building energy consumption based on hybrid PSO-ANN prediction model. In: 34th Chinese control conference (CCC). Hangzhou, China, 28–30 July 2015.
  22. 22.
    Islam S, Keung J, Lee K, Liu A (2012) Empirical prediction models for adaptive resource provisioning in the cloud. Future Gener Comput Syst 28(1):155–162CrossRefGoogle Scholar
  23. 23.
    Rashidi S, Sharifian S (2017) Cloudlet dynamic server selection policy for mobile task off-loading in MCC using soft computing techniques. J Supercomput. CrossRefGoogle Scholar
  24. 24.
    Wu CL, Chau KW (2011) Rainfall-runoff modeling using artificial neural network coupled with singular spectrum analysis. J Hydrol 399(3–4):394–409. CrossRefGoogle Scholar
  25. 25.
    Yaseen ZM et al (2019) An enhanced extreme learning machine model for river flow forecasting: state-of-the-art, practical applications in water resource engineering area and future research direction. J Hydrol 569:387–408CrossRefGoogle Scholar
  26. 26.
    Hong WC, Dong Y, Zhang WY, Chen L-Y, Panigrahi BK (2013) Cyclic electric load predicting by seasonal SVR with chaotic genetic algorithm. Int J Electr Power Energy Syst 44(1):604–614CrossRefGoogle Scholar
  27. 27.
    Hu Z, Bao Y, Xiong T (2014) Comprehensive learning particle swarm optimization based memetic algorithm for model selection in short-term load predicting using support vector regression. Appl Soft Comput 25:15–25. CrossRefGoogle Scholar
  28. 28.
    Moazenzadeh R et al (2018) Coupling a firefly algorithm with support vector regression to predict evaporation in northern Iran. Eng Appl Comput Fluid Mech 12(1):584–597Google Scholar
  29. 29.
    Sanaei Z, Abolfazli S, Gani A, Buyya R (2013) Heterogeneity in MCC: taxonomy and open challenges. IEEE Commun Surv Tutor 16(1):369–392CrossRefGoogle Scholar
  30. 30.
    Li C, Liu S, Zhang H, Hu Y (2013) Machinery condition prediction based on wavelet and support vector machine. In: 2013 international conference on quality, reliability, risk, maintenance, and safety engineering (QR2MSE)Google Scholar
  31. 31.
    De Giorgi MG, Campilongo S, Congedo PM (2014) Comparison between wind power prediction models based on wavelet decomposition with least-squares support vector machine (LS-SVM) and artificial neural network (ANN). Energies 7(8):5251–5272. CrossRefGoogle Scholar
  32. 32.
    Sun Y, Leng B, Guan W (2015) A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system. Neurocomputing 166:109–121CrossRefGoogle Scholar
  33. 33.
    Roy N, Dubey A, Gokhale A (2011) Efficient autoscaling in the cloud using predictive models for workload predicting. In: 2011 IEEE international conference on cloud computing (CLOUD)Google Scholar
  34. 34.
    Chen K-Y (2007) Predicting systems reliability based on support vector regression with genetic algorithms. Reliab Eng Syst Saf 92(4):423–432CrossRefGoogle Scholar
  35. 35.
    Zhang WY, Hong W-C, Dong Y, Tsai G, Sung J-T, Fan G-F (2012) Application of SVR with chaotic GASA algorithm in cyclic electric load predicting. Energy 45(1):850–858CrossRefGoogle Scholar
  36. 36.
    Hong W-C (2009) Chaotic particle swarm optimization algorithm in a support vector regression electric load predicting model. Energy Convers Manag 50(1):105–117CrossRefGoogle Scholar
  37. 37.
    Hong W-C (2011) Traffic flow predicting by seasonal SVR with chaotic simulated annealing algorithm. Neurocomputing 74(12–13):2096–2107CrossRefGoogle Scholar
  38. 38.
    Barati M, Sharifian S (2015) A hybrid heuristic-based tuned support vector regression model for cloud load prediction. J Supercomput 71(11):4235–4259CrossRefGoogle Scholar
  39. 39.
    Liang Y, Qiu L (2016) Network traffic prediction based on SVR improved by chaos theory and ant colony optimization. Int J Future Gener Commun Netw 8(1):69–78. CrossRefGoogle Scholar
  40. 40.
    Chen Y, Ganapathi A, Griffith R, Katz RH (2010) Analysis and lessons from a publicly available Google cluster trace. In: EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2010-95 94.
  41. 41.
    Yang Q, Peng C, Zhao H, Yu Y, Zhou Y, Wang Z, Du S (2014) A new method based on PSR and EA-GMDH for host load prediction in cloud computing system. J Supercomput 68(3):1402–1417CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electrical EngineeringAmirkabir University of TechnologyTehranIran

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