Abstract

Server Virtualization is a growing trend in almost all the critical IT infrastructures all over the world. Apart from the cost savings involved with such approach, it is even useful in increasing the infrastructure operational efficiency as it speeds up the operation, enhances the services availability and minimizes the downtimes. But it is actually worthless if the available resources are not well managed, that's why data center management is really crucial to ensure that the virtualization applied is beneficial. In this paper, we propose a new representation for the problem of finding the best allocation for the virtual machines on the physical hosts. We also compare the performance of four types of Genetic algorithms that were used to solve this problem. These are: Steady State (ssGA), Generational (genGA), Cellular (cGA) and distributed (dGA).

References

• Kusnetzky, D. 2007. Virtualization is More than Virtual Machine Software.
• Cerling, T. , et al. 2009. Mastering Microsoft Virtualization: Sybex.
• Ruest, D. and Ruest, N. 2009. Virtualization: A Beginner's Guide: McGraw-Hill.
• Kusnetzky, D. 2011. Virtualization: A Manager's Guide. First Edition ed, United States of America: O'Reilly. 60.
• Jin, H. , et al. 2011. Dynamic Processor Resource Configuration in Virtualized Environments, in Proceedings of the SCC '11 IEEE International Conference on Services Computing.
• Jinhua, H. , et al. 2010. A Scheduling Strategy on Load Balancing of Virtual Machine Resources in Cloud Computing Environment, in Proceedings of the PAAP '10 3rd International Symposium on Parallel Architectures, Algorithms and Programming.
• Sawant, S. 2011. A Genetic Algorithm Scheduling Approach for Virtual Machine Resources in a Cloud Computing Environment, in Department of Computer Science, San Jose State University SJSU ScholarWorks.
• Campegiani, P. 2009. A Genetic Algorithm to Solve the Virtual Machines Resources Allocation Problem in Multi-tier Distributed Systems. In Proceedings of VPACT'09 Second International Workshop on Virtualization Performances: Analysis, Characterization and Tools.
• Urgaonkar, R. , et al. 2010. Dynamic Resource Allocation and Power Management in Virtualized Data Centers. In Proceedings of NOMS'10 IEEE Network Operations and Management Symposium
• Georgiadis, L. , Neely, M. , and Tassiulas, L. 2006. Resource allocation and crosslayer control in wireless networks. Foundations and Trends in Networking, Hanover, MA, USA: Now Publishers Inc.
• Kalyvianaki, E. and Charalambous, T. 2008. On Dynamic Resource Provisioning for Consolidated Servers in Virtualized Data Centers. In Proceedings of PMCCS'08 the 8th Int. Workshop on Performability Modeling of Computer and Communication Systems.
• Vavak, F. and Fogarty, T. C. 1996. A Comparative Study of Steady State and Generational Genetic Algorithms for Use in Nonstationary Environments. Evolutionary Computing (Lecture Notes in Computer Science), Brighton, UK: Springer.
• Ma, T. and Abdulhai, B. 2002. Genetic Algorithm-Based Combinatorial Parametric Optimization for the Calibration of Microscopic Traffic Simulation Models. ieee.
• Alba, E. and Dorronsoro, B. 2008. Cellular Genetic Algorithms: Springer.
• Noever, D. and Baskaran, S. 1992. Steady-state vs. generational genetic algorithms: A comparison of time complexity and convergence properties, Santa Fe Institute.
• Sivanandam, S. N. and Deepa, S. N. 2008. Introduction to Genetic Algorithms: Springer.
• Dorronosoro, B. 2004. Cellular Evolutionary Algorithms Site. 2004 [cited Access; Available from: http://neo. lcc. uma. es/cEA-web/index. htm.
• Yi, W. , Liu, Q. , and He, Y. 2000. Dynamic Distributed Genetic Algorithms In Proceedings of Congress on Evolutionary Computation. IEEE.
• Belding, T. C. 1994. The Distributed Genetic Algorithm revisited. In Proceedings of the 6th International Conference on Genetic Algorithms.
• Mcmahon, M. T. 1998. A Distributed Genetic Algorithm With migration for the design of composite laminate structures, in Computer Science and Applications, the Faculty of the Virginia Polytechnic Institute and State University: Blacksburg, Virginia.
• Alba, E. and Troya, J. M. 1999. A Survey of Parallel Distributed Genetic Algorithms.
• VMware ESXi 5. 2012 [cited Access; Available from: http://www. vmware. com/products/vsphere/esxi-and-esx/index. html.
• JCell Framework. [cited Access; Available from: http://jcell. gforge. uni. lu/.