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The Scientific World Journal
Volume 2014, Article ID 259139, 12 pages
http://dx.doi.org/10.1155/2014/259139
Research Article

A Distributed Parallel Genetic Algorithm of Placement Strategy for Virtual Machines Deployment on Cloud Platform

1College of Computer Science and Technology, Jilin University, Changchun 130012, China
2Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China

Received 10 March 2014; Revised 17 June 2014; Accepted 17 June 2014; Published 3 July 2014

Academic Editor: Su Fong Chien

Copyright © 2014 Yu-Shuang Dong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. L. Wang, J. Tao, M. Kunze, A. C. Castellanos, D. Kramer, and W. Karl, “Scientific cloud computing: early definition and experience,” in Proceedings of the 10th IEEE International Conference on High Performance Computing and Communications (HPCC '08), pp. 825–830, Dalian, China, September 2008. View at Publisher · View at Google Scholar · View at Scopus
  2. J. Nakajima, Q. Lin, S. Yang et al., “Optimizing virtual machines using hybrid virtualization,” in Proceedings of the 26th Annual ACM Symposium on Applied Computing (SAC '11), pp. 573–578, March 2011. View at Publisher · View at Google Scholar · View at Scopus
  3. N. Regola and J. Ducom, “Recommendations for virtualization technologies in high performance computing,” in Proceedings of the 2nd IEEE International Conference on Cloud Computing Technology and Science, pp. 409–416, December 2010. View at Publisher · View at Google Scholar · View at Scopus
  4. P. Barham, B. Dragovic, K. Fraser et al., “Xen and the art of virtualization,” in Proceedings of the 19th ACM Symposium on Operating Systems Principles (SOSP '03), pp. 164–177, New York, NY, USA, October 2003. View at Scopus
  5. M. R. Garey and D. S. Johnson, Computers and Intractability: A Guide to the Theory of NP-Completeness, W. H. Freeman, San Francisco, Calif, USA, 1979.
  6. G. M. Amdahl, “Validity of the single processor approach to achieving large scale computing capabilities,” in Proceedings of the Spring Joint Computer Conference, pp. 483–485, ACM, Atlantic City, NJ, USA, April 1967. View at Publisher · View at Google Scholar
  7. J. L. Gustafson, “Reevaluating Amdahl's law,” Communications of the ACM, vol. 31, no. 5, pp. 532–533, 1988. View at Publisher · View at Google Scholar · View at Scopus
  8. H. X. Sun and L. M. Ni, Scalable Problems and Memory-Bounded Speedup, Institute for Computer Applications in Science and Engineering, Hampton, Va, USA, 1992.
  9. J. Tordsson, R. S. Montero, R. Moreno-Vozmediano, and I. M. Llorente, “Cloud brokering mechanisms for optimized placement of virtual machines across multiple providers,” Future Generation Computer Systems, vol. 28, no. 2, pp. 358–367, 2012. View at Publisher · View at Google Scholar · View at Scopus
  10. M. Steiner, B. G. Gaglianello, V. Gurbani et al., “Network-aware service placement in a distributed cloud environment,” ACM SIGCOMM Computer Communication Review, vol. 42, no. 4, pp. 73–74, 2012. View at Publisher · View at Google Scholar
  11. W. Wang, H. Chen, and X. Chen, “An availability-aware virtual machine placement approach for dynamic scaling of cloud applications,” in Proceedings of the 9th International Conference on Ubiquitous Intelligence & Computing and 9th International Conference on Autonomic & Trusted Computing (UIC/ATC '12), pp. 509–516, 2012.
  12. Z. I. M. Yusoh and M. Tang, “A penalty-based genetic algorithm for the composite SaaS placement problem in the cloud,” in Proceedings of the 6th IEEE World Congress on Computational Intelligence (WCCI '10) and IEEE Congress on Evolutionary Computation (CEC '10), pp. 1–8, July 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. B. Li, J. Li, J. Huai, T. Wo, Q. Li, and L. Zhong, “EnaCloud: an energy-saving application live placement approach for cloud computing environments,” in Proceedings of the IEEE International Conference on Cloud Computing (CLOUD '09), pp. 17–24, Bangalore, India, September 2009. View at Publisher · View at Google Scholar
  14. Z. W. Ni, X. F. Pan, and Z. J. Wu, “An ant colony optimization for the composite SaaS placement problem in the cloud,” Applied Mechanics and Materials, vol. 130–134, pp. 3062–3067, 2012. View at Publisher · View at Google Scholar · View at Scopus
  15. Z. I. M. Yusoh and M. Tang, “A cooperative coevolutionary algorithm for the composite SaaS Placement Problem in the Cloud,” in Proceedings of the 17th International Conference on Neural Information Processing, pp. 618–625, Springer, 2010.
  16. H. Wang, W. Xu, F. Wang, and C. Jia, “A cloud-computing-based data placement strategy in high-speed railway,” Discrete Dynamics in Nature and Society, vol. 2012, Article ID 396387, 15 pages, 2012. View at Publisher · View at Google Scholar · View at Scopus
  17. D. Yuan, Y. Yang, X. Liu, and J. Chen, “A data placement strategy in scientific cloud workflows,” Future Generation Computer Systems, vol. 26, no. 8, pp. 1200–1214, 2010. View at Publisher · View at Google Scholar · View at Scopus
  18. L. Guo, Z. He, S. Zhao, N. Zhang, J. Wang, and C. Jiang, “Multi-objective optimization for data placement strategy in cloud computing,” in Information Computing and Applications, vol. 308 of Communications in Computer and Information Science, Springer, Berlin, Germany, 2012. View at Google Scholar
  19. J. Ding, H. Y. Han, and A. H. Zhou, “A data placement strategy for data-intensive cloud storage,” Advanced Materials Research, vol. 354, pp. 896–900, 2012. View at Publisher · View at Google Scholar · View at Scopus
  20. G. von Laszewski, L. Wang, A. J. Younge, and X. He, “Power-aware scheduling of virtual machines in DVFS-enabled clusters,” in Proceedings of the IEEE International Conference on Cluster Computing and Workshops (CLUSTER '09), pp. 1–10, September 2009. View at Publisher · View at Google Scholar · View at Scopus
  21. W. Fang, X. Liang, S. Li, L. Chiaraviglio, and N. Xiong, “VMPlanner: optimizing virtual machine placement and traffic flow routing to reduce network power costs in cloud data centers,” Computer Networks, vol. 57, no. 1, pp. 179–196, 2013. View at Publisher · View at Google Scholar · View at Scopus
  22. R. Ge, X. Feng, and K. W. Cameron, “Performance-constrained distributed DVS scheduling for scientific applications on power-aware clusters,” in Proceedings of the ACM/IEEE Conference on Supercomputing (SC '05), November 2005. View at Publisher · View at Google Scholar · View at Scopus
  23. Y. Gao, H. Guan, Z. Qi, Y. Hou, and L. Liu, “A multi-objective ant colony system algorithm for virtual machine placement in cloud computing,” Journal of Computer and System Sciences, vol. 79, no. 8, pp. 1230–1242, 2013. View at Publisher · View at Google Scholar · View at Scopus
  24. B. Kantarci, L. Foschini, A. Corradi, and H. T. Mouftah, “Inter-and-intra data center VM-placement for energy-efficient large-Scale cloud systems,” in Proceedings of the IEEE Globecom Workshops (GC Wkshps '12), pp. 708–713, Anaheim, Calif, USA, December 2012. View at Publisher · View at Google Scholar
  25. S. Chaisiri, B. Lee, and D. Niyato, “Optimal virtual machine placement across multiple cloud providers,” in Proceedings of the IEEE Asia-Pacific Services Computing Conference (APSCC '09), pp. 103–110, December 2009. View at Publisher · View at Google Scholar · View at Scopus
  26. J. T. Piao and J. Yan, “A network-aware virtual machine placement and migration approach in cloud computing,” in Proceedings of the 9th International Conference on Grid and Cloud Computing (GCC '10), pp. 87–92, Nanjing, China, November 2010. View at Publisher · View at Google Scholar · View at Scopus
  27. C. H. Hsu, U. Kremer, and M. Hsiao, “Compiler-directed dynamic frequency and voltage scheduling,” in Power-Aware Computer Systems, pp. 65–81, Springer, Berlin, Germany, 2001. View at Google Scholar
  28. M. Srinivas and L. M. Patnaik, “Adaptive probabilities of crossover and mutation in genetic algorithms,” IEEE Transactions on Systems, Man and Cybernetics, vol. 24, no. 4, pp. 656–667, 1994. View at Publisher · View at Google Scholar · View at Scopus
  29. R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. F. de 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, vol. 41, no. 1, pp. 23–50, 2011. View at Publisher · View at Google Scholar · View at Scopus
  30. 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 Computation Practice and Experience, vol. 24, no. 13, pp. 1397–1420, 2012. View at Publisher · View at Google Scholar · View at Scopus
  31. MathWorks T. Matlab, The MathWorks, Natick, Mass, USA, 2004.