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Power and performance management of virtualized computing environments via lookahead control

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Abstract

There is growing incentive to reduce the power consumed by large-scale data centers that host online services such as banking, retail commerce, and gaming. Virtualization is a promising approach to consolidating multiple online services onto a smaller number of computing resources. A virtualized server environment allows computing resources to be shared among multiple performance-isolated platforms called virtual machines. By dynamically provisioning virtual machines, consolidating the workload, and turning servers on and off as needed, data center operators can maintain the desired quality-of-service (QoS) while achieving higher server utilization and energy efficiency. We implement and validate a dynamic resource provisioning framework for virtualized server environments wherein the provisioning problem is posed as one of sequential optimization under uncertainty and solved using a lookahead control scheme. The proposed approach accounts for the switching costs incurred while provisioning virtual machines and explicitly encodes the corresponding risk in the optimization problem. Experiments using the Trade6 enterprise application show that a server cluster managed by the controller conserves, on average, 22% of the power required by a system without dynamic control while still maintaining QoS goals. Finally, we use trace-based simulations to analyze controller performance on server clusters larger than our testbed, and show how concepts from approximation theory can be used to further reduce the computational burden of controlling large systems.

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References

  1. Li, Q., Bauer, M.: Understanding the performance of enterprise applications. In: Proc. of IEEE Conference on Systems, Man and Cybernetics, June 2005, pp. 2825–2829

  2. Smith, R.: Power companies consider options for energy sources. The Wall Street J. A. 10, Oct. 29 (2007)

  3. Darema, F.: Grid computing and beyond: The context of dynamic data driven applications systems. Proc. IEEE 93(3), 692–697 (2005)

    Article  Google Scholar 

  4. Menascé, D.A., Almeida, V.A.F.: Capacity Planning for Web Services. Prentice Hall, Upper Saddle River (2002)

    Google Scholar 

  5. Welsh, M., Culler, D.: Adaptive overload control for busy internet servers. In: Proc. of USENIX Symp. on Internet Technologies and Systems (USITS), March 2003

  6. Grit, L., Irwin, D., Yumerefendi, A., Chase, J.: Virtual machine hosting for networked clusters: Building the foundations for “autonomic” orchestration. In: Proc. of the IEEE Wkshp. on Virtualization Technology in Dist. Sys., p. 7, Nov. 2006

  7. Garbacki, P., Naik, V.: Efficient resource virtualization and sharing strategies for heterogeneous grid environments. In: Proc. of the IEEE Symp. on Integrated Network Management, pp. 40–49, May 2007

  8. Nathuji, R., Isci, C., Gorbatov, E.: Exploiting platform heterogeneity for power efficient data centers. In: Proc. IEEE Intl. Conf. on Autonomic Computing (ICAC), p. 5, Jun. 2007

  9. Lin, B., Dinda, P.: Vsched: Mixing batch and interactive virtual machines using periodic real-time scheduling. In: Proc. of the IEEE/ACM Conf. on Supercomputing, p. 8, Nov. 2005

  10. Nathuji, R., Schwan, K.: Virtualpower: coordinated power management in virtualized enterprise systems. In: Proc. of the ACM SIGOPS Symp. on Op. Sys. Principles, pp. 265–278, Oct. 2005

  11. Govindan, S., Nath, A., Das, A., Urgaonkar, B., Sivasubramaniam, A.: I/o scheduling and xen and co.: communication-aware cpu scheduling for consolidated xen-based hosting platforms. In: Proc. of the ACM SIGOPS Symp. on Op. Sys. Principles, pp. 126–136, Jun. 2007

  12. Khanna, G., Beaty, K., Kar, G., Kochut, A.: Application performance management in virtualized server environments. In: Proc. of the IEEE Network Ops. and Mgmt. Symp., pp. 373–381, Apr. 2006

  13. Tsai, C., Shin, K., Reumann, J., Singhal, S.: Online web cluster capacity estimation and its application to energy conservation. IEEE Trans. Parallel Distrib. Syst. 18(7), 932–945 (2007)

    Article  Google Scholar 

  14. Steinder, M., Whalley, I., Carrera, D., Gaweda, I., Chess, D.: Server virtualization in autonomic management of heterogeneous workloads. In: Proc. of the IEEE Symp. on Integrated Network Management, pp. 139–148, May 2007

  15. Xu, J., Zhao, M., Fortes, J., Carpenter, R., Yousif, M.: On the use of fuzzy modeling in virtualized data center management. In: Proc. IEEE Intl. Conf. on Autonomic Computing (ICAC), pp. 25–35, Jun. 2007

  16. Kephart, J., Chan, H., Levine, D., Tesauro, G., Rawson, F., Lefurgy, C.: Coordinating multiple autonomic managers to achieve specified power-performance tradeoffs. In: Proc. IEEE Intl. Conf. on Autonomic Computing (ICAC), pp. 145–154, Jun. 2007

  17. Ranganathan, P., Leech, P., Irwin, D., Chase, J.: Ensemble-level power management for dense blade servers. In: Proc. of the IEEE Symp. on Computer Architecture, pp. 66–77, Jun. 2006

  18. Lefurgy, C., Wang, X., Ware, M.: Server-level power control. In: Proc. IEEE Conf. on Autonomic Computing, p. 4, Jun. 2007

  19. Pinheiro, E., Bianchini, R., Heath, T.: Dynamic Cluster Reconfiguration for Power and Performance. Kluwer Academic Publishers, Dordrecht (2003)

    Google Scholar 

  20. Mosberger, D., Jin, T.: httperf: A tool for measuring web server performance. Perf. Eval. Rev. 26, 31–37 (1998)

    Article  Google Scholar 

  21. Arlitt, M., Jin, T.: Workload characterization of the 1998 world cup web site. Hewlett-Packard Labs, Technical Report HPL-99-35R1, Tech. Rep., Sept. (1999)

  22. Abdelwahed, S., Kandasamy, N., Neema, S.: Online control for self-management in computing systems. In: Proc. IEEE Real-Time & Embedded Technology & Application Symp. (RTAS), pp. 368–376 (2004)

  23. Maciejowski, J.M.: Predictive Control with Constraints. Prentice Hall, London (2002)

    Google Scholar 

  24. Harvey, A.C.: Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press, Cambridge (2001)

    Google Scholar 

  25. Enhanced Intel SpeedStep Technology for the Intel Pentium M Processor, Intel Corp. (2004)

  26. Copeland, T., Weston, J.: Financial Theory and Corporate Policy, 3rd, edn. Addison-Wesley, Reading (1988)

    Google Scholar 

  27. Weddle, C., Oldham, M., Qian, J., Wang, A., Reiher, P., Kuenning, G.: Paraid: A gear-shifting power-aware raid. ACM Trans. Storage 3, 13 (2007)

    Article  Google Scholar 

  28. Hughes, G., Murray, J.: Reliability and security of raid storage systems and d2d archives using sata disk drives. ACM Trans. Storage 1, 95–107 (2005)

    Article  Google Scholar 

  29. Kusic, D., Kandasamy, N.: Approximation modeling for the online performance management of distributed computing systems. In: Proc. of IEEE Intl. Conf. on Autonomic Computing (ICAC), p. 23, June 2007

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Authors and Affiliations

  1. Electrical and Computer Engineering Department, Drexel University, Philadelphia, PA, 19104, USA

    Dara Kusic & Nagarajan Kandasamy

  2. Agents and Emergent Phenomena Group, IBM T.J. Watson Research Center, Hawthorne, NY, 10532, USA

    Jeffrey O. Kephart & James E. Hanson

  3. Robust and Secure System Group, NEC Laboratories America, Princeton, NJ, 08540, USA

    Guofei Jiang

Authors
  1. Dara Kusic
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  2. Jeffrey O. Kephart
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  3. James E. Hanson
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  4. Nagarajan Kandasamy
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  5. Guofei Jiang
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Corresponding author

Correspondence to Dara Kusic.

Additional information

A preliminary version of this paper appeared in the 2008 IEEE International Conference on Autonomic Computing.

D. Kusic is supported by NSF grant DGE-0538476 and N. Kandasamy acknowledges support from NSF grant CNS-0643888.

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Kusic, D., Kephart, J.O., Hanson, J.E. et al. Power and performance management of virtualized computing environments via lookahead control. Cluster Comput 12, 1–15 (2009). https://doi.org/10.1007/s10586-008-0070-y

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  • DOI: https://doi.org/10.1007/s10586-008-0070-y

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