Performance Comparison of Deep VM Workload Prediction Approaches for Cloud

Conference paper
First Online:
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 710)

Abstract

With the exponential growth of distributed devices, the era of cloud computing is continued to expand and the systems are required to be more and more energy-efficient with time. The virtualization in cloud manages a large-scale grid-of-servers to efficiently process the demands while optimizing power consumption and energy efficiency. However, to ensure the overall performance, it is critical to predict and extract the high-level features of the future virtual machines (VMs). To predict its load deeply, this paper investigates the methods of a revolutionary machine-learning technique, i.e., deep learning. It extracts the multiple correlation among VMs based on its past workload trace and predicts their future workload with high accuracy. The VM workload prediction helps the decision makers for capacity planning and to apply the suitable VM placement and migration technique with a more robust scaling decision. The effectiveness of deep learning approaches is extensively evaluated using real workload traces of PlanetLab and optimized with selection of model, granularity of training data, number of layers, activation functions, epochs, batch size, the type of optimizer, etc.

Keywords

Cloud computing Deep learning Energy efficiency Physical machine (PM) Virtual machine (VM) Workload prediction 
This is a preview of subscription content, log in to check access.

References

  1. 1.
  2. 2.
    F. Tao, L. Zhang, V.C. Venkatesh, Y. Luo, and Y. Cheng, Cloud manufacturing: A computing and service-oriented manufacturing model, in Proc. Inst. Mech. Eng. B—J. Eng. Manuf., 225 (10), (2011) 1969–1976.Google Scholar
  3. 3.
    C.C. Lin, P. Liu, and J.J. Wu, Energy-efficient virtual provision algorithms for cloud systems, 4th IEEE International Conference on Utility and Cloud Computing (2011) 81–88.Google Scholar
  4. 4.
    M. Mishra, A. Das, P. Kulkarni, and A. Sahoo, Dynamic resource management using virtual machine migrations, IEEE Communications Magazine (2012) 34–40.Google Scholar
  5. 5.
    A. Beloglazov and R. Buyya, “Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers,” Concurrency and Computation: Practice and Experience, 24 (13), (2012) 1397– 1420.Google Scholar
  6. 6.
    R. N. Calheiros, E. Masoumi, R. Ranjan and R. Buyya, Workload Prediction Using ARIMA Model and Its Impact on Cloud Applications’ QoS, in IEEE Transactions on Cloud Computing, 3(4), (2015) 449–458.Google Scholar
  7. 7.
    N. Bobroff, A. Kochut, and K. Beaty, Dynamic placement of virtual machines for managing sla violations, in Integrated Network Management, IM’07. 10th IFIP/IEEE International Symposium (2007) 119–128.Google Scholar
  8. 8.
    A. Murtazaev and S. Oh, Sercon: Server consolidation algorithm using live migration of virtual machines for green computing, TE Technical Review, 28(3), (2011) 212–231.Google Scholar
  9. 9.
    D. Minarolli and B. Freisleben, “Distributed Resource Allocation to Virtual Machines via Artificial Neural Networks,” 22nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, Torino (2014) 490–499.Google Scholar
  10. 10.
    R. Bitirgen, E. Ipek, and J.F. Martinez. Coordinated management of multiple interacting resources in chip multiprocessors: A machine learning approach. In Proc. 41st Annual IEEE/ACM International Symposium on Microarchitecture (2008) 318–329.Google Scholar
  11. 11.
    Z. Xiao, W. Song and Q. Chen. Dynamic resource allocation using virtual machines for cloud computing environment, IEEE Transactions on Parallel and Distributed Systems, 24(6), (2013) 1107–1117.Google Scholar
  12. 12.
    G. Kousiouris, A. Menychtas, D. Kyriazis, et al. Parametric design and performance analysis of a decoupled service-oriented prediction framework based on embedded numerical software, IEEE Transactions on Services Computing, 6(4), (2013) 511–524.Google Scholar
  13. 13.
    R. Calheiros, E. Masoumi, R. Ranjan and R. Buyya. Workload Prediction Using ARIMA Model and Its Impact on Cloud Applications’ QoS, IEEE Transactions On Cloud Computing, 3 (4), (2015) 449–458.Google Scholar
  14. 14.
    F. Farahnakian, T. Pahikkala, P. Liljeberg, J. Plosila and H. Tenhunen, Utilization Prediction Aware VM Consolidation Approach for Green Cloud Computing, IEEE 8th International Conference on Cloud Computing, New York City, NY, (2015) 381–388.Google Scholar
  15. 15.
    F. Qiu, B. Zhang and J. Guo, “A deep learning approach for VM workload prediction in the cloud, 17th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), Shanghai (2016) 319–324.Google Scholar
  16. 16.
    Ken-ichi Funahashi, Yuichi Nakamura, Approximation of dynamical systems by continuous time recurrent neural networks, Neural Networks, 6(6), (1993) 801–806.Google Scholar
  17. 17.
    H. Wang and B. Raj, A survey: Time travel in deep learning space: An introduction to deep learning models and how deep learning models evolved from the initial ideas, arXiv preprint arXiv:1510.04781 (2015).
  18. 18.
    Sepp Hochreiter and Jürgen Schmidhuber, Long short-term memory. Neural computation 9(8), (1997) 1735–1780.Google Scholar
  19. 19.
    LeCun, Yann, et al., Gradient-based learning applied to document recognition, Proceedings of the IEEE, 86(11), (1998) 2278–2324.Google Scholar
  20. 20.
    K. Park and V. Pai, CoMon: a mostly-scalable monitoring system for PlanetLab, ACM SIGOPS Operating Systems Review, 40, (2006) 65–74.Google Scholar
  21. 21.
    R.N. Calheiros, R. Ranjan, A. Beloglazov, C. Rose and R. Buyya, CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms, Software: Practice and Experience, 41(1), (2011) 23–50.Google Scholar
  22. 22.
    Beloglazov A, Energy-efficient management of virtual machines in data centers for cloud computing, PhD thesis (2013).Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringIndian Institute of TechnologyPatnaIndia

Personalised recommendations