Constrained Markov control model and online stochastic optimization algorithm for power conservation in multimedia server cluster systems

Regular Paper Control Applications
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Abstract

This paper presents a novel Markov switching state space control model for dynamically switching resource configuration scheme to achieve power conservation for multimedia server cluster systems. This model exploits the hierarchical dynamic structure of network system and its construction is flexible and scalable. Using this analytical model, the problem of power conservation is posed as a constrained stochastic optimization problem with the goal of minimizing the average power consumption subject to the constraint on the average blocking ratio. Applying Lagrange approach and online estimation of the performance gradient, a policy iteration algorithm is proposed to search the optimal policy online. This algorithm does not depend on any prior knowledge of system parameters, and converges to the optimal solution. Simulation results demonstrate the convergence of the proposed algorithm and effectiveness to different access workloads.

Keywords

Markov decision process online optimization performance potential policy iteration power conservation 
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References

  1. [1]
    Report to Congress on Server and Data Center Energy Efficiency, U.S. Environmental Protection Agency, ENERGY STAR Program, Aug. 2007.Google Scholar
  2. [2]
    Gartner, “Gartner estimates ICT industry accounts for 2 percent of global CO 2 emissions,” Apr. 2007. http://www.gartner.com/it/page.jsp?id=503867.
  3. [3]
    T. Wauters, W. V. D. Meerssche, F. D. Turck, B. Dhoedt, P. Demeester, T. V. Caenegem, and E. Six, “Co-operation proxy caching algorithms for timeshifted IPTV services,” Proc. of the 32nd EUROMICRO Conf. on Software Engineering and Advanced Applications, pp. 379–386, Aug. 2006.Google Scholar
  4. [4]
    L. Benini, A. Bogliolo, and G. D. Micheli, “A survey of design techniques for system level dynamic power management,” IEEE Trans. Very Large Scale Integr. Syst., vol. 8, no. 3, pp. 299–316, Jun. 2000.CrossRefGoogle Scholar
  5. [5]
    G. Semeraro, G. Magklis, R. Balasubramonian, D. H. Albonesi, S. Dwarkadas, and M. L. Scott, “Energy efficient processor design using multiple clock domains with dynamic voltage and frequency scaling,” Proc. of the 8th Int’l Symp. High Performance Computer Architecture, pp. 29–40, Feb. 2002.Google Scholar
  6. [6]
    M. E. Salehi, M. Samadi, M. Najibi, A. Afzali-Kusha, M. Pedram, and S. M. Fakhraie, “Dynamic voltage and frequency scheduling for embedded processors considering power/performance tradeoffs,” IEEE Trans. on Very Large Scale Intergr. Syst., vol. 19, no. 10, pp. 1931–1935, Oct. 2011.CrossRefGoogle Scholar
  7. [7]
    X. R. Wang, K. Ma, and Y. F. Wang, “Adaptive power control with online model estimation for chip multiprocessors,” IEEE Trans. on Parallel and Distributed Systems, vol. 22, no. 10, pp. 1681–1696, Oct. 2011.CrossRefGoogle Scholar
  8. [8]
    W. Dargie, “Dynamic power management in wireless sensor networks: state-of-the-art,” IEEE Sensors Journal, vol. 12, no. 5, pp. 1519–1528, May 2012.CrossRefGoogle Scholar
  9. [9]
    C. Xian, Y. Lu, and Z. Li, “Dynamic voltage scaling for multitasking real time systems with uncertain execution time,” IEEE Trans. on Computer-Aided Design Integr. Circuits and Syst., vol. 27, no. 8, pp. 1476–1478, Aug. 2008.Google Scholar
  10. [10]
    R. Bianchini and R. Rajamony, “Power and energy management for server systems,” Computer, vol. 37, no. 11, pp. 68–76, Nov. 2004.CrossRefGoogle Scholar
  11. [11]
    E. Pinheiro, R. Bianchini, E. V. Carrera, and T. Heath, “Dynamic cluster reconfiguration for power and performance,” Compliers and Operating Systems for Low Power, Kluwer Academic Publishers, pp. 75–93, 2003.Google Scholar
  12. [12]
    J. Slegers, N. Thomas, and I. Mitrani, “Dynamic server allocation for power and performance,” Proc. of the SPEC International Performance Evaluation Workshop on Performance Evaluation: Metrics, Models and Benchmarks, pp. 247–261, Jan. 2008.Google Scholar
  13. [13]
    C. Santana, J. C. B. Leite, and D. Mosse, “Power management by load forecasting in web server clusters,” Cluster Computing, vol. 14, no. 4, pp. 471–481, 2011.CrossRefGoogle Scholar
  14. [14]
    Reduce Energy Costs and Go Green with VMware Green IT Solutions, http://www.vmware.com/files/pdf/VMware-GREEN-IT-OVERVIEW_SB_EN.pdf.
  15. [15]
    A. Beloglazov, R. Buyya, Y. C. Lee, and A. Zomaya, “A taxonomy and survey of energy efficient data centers and cloud computing systems,” Advances in Computers, vol. 82, pp. 47–111, Mar. 2011.CrossRefGoogle Scholar
  16. [16]
    W. W. Zhu, C. Luo, J. F. Wang, and S. P. Li, “Multimedia cloud computing,” IEEE Signal Processing Magazine, vol. 28, no. 3, pp. 59–69, May 2011.CrossRefGoogle Scholar
  17. [17]
    C. Joonho, A. S. Reaz, and B. Mukherjee, “A survey of user behavior in VoD service and bandwidth-saving multicast streaming schemes,” IEEE Communications Surveys & Tutorials, vol. 14, no. 1, pp. 156–169, 2012.CrossRefGoogle Scholar
  18. [18]
    A. Gosavi, “Target-sensitive control of Markov and semi-Markov processes,” International Journal of Control, Automation and Systems, vol. 9, no. 5, pp. 941–951, Oct. 2011.MathSciNetCrossRefGoogle Scholar
  19. [19]
    S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge, Cambridge University Press, U.K., 2004.Google Scholar
  20. [20]
    D. Bertsekas, Nonlinear Programming, Athena Scientific, Belmont, MA, 2000.Google Scholar
  21. [21]
    X. R. Cao, Stochastic Learning and Optimization: A Sensitivity-based View, Springer, 2007.Google Scholar
  22. [22]
    L. Jung, “Analysis of recursive stochastic algorithms,” IEEE Trans. on Automatic Control, vol. 22, no. 4, pp. 551–575, Aug. 1977.CrossRefGoogle Scholar

Copyright information

© Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of AutomationUniversity of Science and Technology of ChinaHefeiP. R. China
  2. 2.Cable Network InstituteAcademy of Broadcasting Science, SARFTBeijingP. R. China

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