A Generic Approach to Achieve Optimal Server Consolidation by Using Existing Servers in Virtualized Data Center

Virtualization-based server consolidation has been proven to be an ideal technique to solve the server sprawl problem by consolidating multiple virtualized servers onto a few physical servers leading to improved resource utilization and return on investment. In this paper, we solve this problem by using existing servers, which are heterogeneous and diversely preferred by IT managers. Five practical consolidation rules are introduced, and a decision model is proposed to optimally allocate source services to physical target servers while maximizing the average resource utilization and preference value. Our model can be regarded as a multi-objective multi-dimension bin-packing (MOMDBP) problem with constraints, which is strongly NP-hard. An improved grouping generic algorithm (GGA) is introduced for the problem. Extensive simulations were performed and the results are given.

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References:
[1] K. Parent, "Server Consolidation Improves IT-s Capacity Utilization.",
Court Square Data Group, 2005.
[2] J. Koomey, "Estimating total power consumption by servers in the US
and the world.", Final Report, 2007.
[3] GTSI White Paper, "Reducing Data Center-s Power and Energy
Consumption: Saving Money and Go Green.", 2008.
[4] R. Gupta, S. K. Bose, S. Sundarrajan, et al., "A two stage heuristic
algorithm for solving the server consolidation problem with item-item
and bin-item incompatibility constraints.", In Proceedings of IEEE
International Conference on Service Computing, Hawaii, USA, 2008,
vol. 2, pp. 39-46.
[5] S. Agrawal, S. K. Bose, S. Sundarrajan, "Grouping genetic algorithm for
solving the server consolidation problem with conflicts.", In Proceedings
of the first ACM/SIGEVO Summit on Genetic and Evolutionary
Computation, Shanghai, China, 2009, pp. 1-8.
[6] Y. C. Lee, A. Y. Zomaya, "Energy efficient utilization of resources in
cloud computing systems.", Journal of Supercomputing, 2010.
[7] P. Padala, X. Y. Zhu, Z. K. Wang, et al., "Performance evaluation of
virtualization technologies for server consolidation.", HP Lab, 2007.
[8] VMWare White Paper, www.vmware.com
[9] P. Barham, B. Dragovic, K. Fraser, et al., "Xen and the Art
Virtualization.", In Proceedings of the 9th Symposium on Operating
Systems Principles, 2003, pp. 164-177.
[10] A. Spellmann, K. Erickson, J. Reynolds, "Server consolidation using
performance modeling.", IT Professional, Vol. 5, Issue 5, pp. 31-36,
2003.
[11] K. Parent, "Server Consolidation Improves IT-s Capacity Utilization.",
Court Square Data Group, 2005.
[12] Y. Song, Y. W. Zhang, Y. Zh. Sun, "Utility analysis for Internet-oriented
server consolidation in VM-based data centers.", In Proceedings of IEEE
International Conference on Cluster Computing and Workshop, 2009,
pp. 1-10.
[13] C. Chekuri, S. Khanna, "On Multi-Dimensional Packing Problems.", In
Proceedings of the 10th Annual ACM-SIAM Symposium on Discrete
Algorithms, 1999, pp. 185-194.
[14] M. R. Garey, D. S. Johnson, "Computer and Interactability: A Guide to
the Theory of NP-Completeness", W.H. Freeman and Company, 1979.
[15] A. L. Corcoran, R. L. Wainwright, "A generic algorithm for packing in
three dimensions.", In proceedings of the 1992 ACM/SIGAPP
Symposium on Applied Computing, 1992, pp. 1021-1030.
[16] F. Benevenuto, C. Fernandes, "Performance Models for Virtualized
Applications.", ISPA 2006 Workshops, LNCS, pp. 427-439.
[17] D. Menasce, "Performance by Design: Computer Capacity Planning.",
Prentice Hall, 2004.
[18] Microsoft Virtual Server, Microsoft Corporation,
http://www.microsoft.com/windowsserversystem/ virtualserver/
[19] G. Somani, S. Chandhary, "Application Performance Isolation in
Virtualization.", In Proceedings of IEEE International Conference on
Cloud Computing, 2009, vol. 2, pp. 39-46.
[20] Gartner Research, "Server Consolidation: Benefits & Challenges", 2002.
[21] Y. Aijiro, A. Tanaka, "A Combinational Optimization Algorithm for
Server Consolidation.", the 21st Annual Conference of Japanese Society
for Artificial Intelligence, 2007.
[22] M. Bichler, "Capacity Planning for Virtualized Servers.", 16th Workshop
on Information Technologies and System, Milwaukee, USA, 2006.
[23] Liang Liu, Hao Wang, Xue Liu, et al., "GreenCloud: a new architecture
for green data center", In Proceedings of the 6th International conference
industry session on Autonomic computing and communications industry
session, 2009, pp. 29-38.
[24] E. Falkenauer, "A Hybrid Group Generic Algorithm for Bin Packing",
Journal of Heuristic, vol. 2 pp. 5-30, 2004.
[25] Sung Young Jung, Jeong-Hee Hong, Taek-Soo Kim, "A Statistic Model
for User Preference", IEEE Trans. on Knowledge and data engineering,
vol.17, No. 6, 2005.
[26] S. Y. Jing, K. She, "A Rough Sets Approach to User Preference
Modeling", RSKT2010, LANI, Beijing, China, 2010.
[27] D. Goldberg, "Genetic Algorithms in Search, Optimization and Machine
Learning", Reading, MA: Addison Wesley, 1989.
[28] T. Back, "Evolutionary Algorithms in Theory and Practice. Evolution
Strategies", Evolutionary Programming. Genetic Algorithms. Oxford
University Press, 1996.