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Scientific Programming
Volume 2018, Article ID 9471356, 10 pages
https://doi.org/10.1155/2018/9471356
Research Article

Energy-Aware VM Initial Placement Strategy Based on BPSO in Cloud Computing

1School of Computer Science and Technology, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
2Beijing Institute of Control Engineering, Beijing 100190, China

Correspondence should be addressed to Xiong Fu; nc.ude.tpujn@xuf

Received 22 September 2017; Revised 21 December 2017; Accepted 10 January 2018; Published 14 February 2018

Academic Editor: Emiliano Tramontana

Copyright © 2018 Xiong Fu 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. M. Stillwell, D. Schanzenbach, F. Vivien, and H. Casanova, “Resource allocation algorithms for virtualized service hosting platforms,” Journal of Parallel and Distributed Computing, vol. 70, no. 9, pp. 962–974, 2010. View at Publisher · View at Google Scholar · View at Scopus
  2. M. Cardosa, A. Singh, H. Pucha, and A. Chandra, “Exploiting spatio-temporal tradeoffs for energy-aware MapReduce in the cloud,” IEEE Transactions on Computers, vol. 61, no. 12, pp. 1737–1751, 2012. View at Publisher · View at Google Scholar · View at Scopus
  3. C. C. T. Mark, D. Niyato, and T. Chen-Khong, “Evolutionary optimal virtual machine placement and demand forecaster for cloud computing,” in Proceedings of the 25th IEEE International Conference on Advanced Information Networking and Applications, AINA 2011, pp. 348–355, Singapore, March 2011. View at Publisher · View at Google Scholar · View at Scopus
  4. T. Cerling, J. Buller, C. Enstall, and R. Ruiz, Mastering Microsoft® Virtualization, Wiley Publishing, Inc., Indianapolis, IN, USA, 2009. View at Publisher · View at Google Scholar
  5. M. Chen, H. Zhang, Y. Y. Su, X. Wang, G. Jiang, and K. Yoshihira, “Effective VM sizing in virtualized data centers,” in Proceedings of the 12th IFIP/IEEE International Symposium on Integrated Network Management, pp. 594–601, Dublin, Ireland, May 2011. View at Publisher · View at Google Scholar · View at Scopus
  6. B. Moores, “Autonomic virtual machine placement in the data center[C]//,” in Proceedings of the IEEE 33rd International Conference on Distributed Computing Systems Workshops, pp. 220–225, 2013.
  7. H. Nakada, T. Hirofuchi, H. Ogawa, and S. Itoh, “Toward virtual machine packing optimization based on genetic algorithm,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface, vol. 5518, no. 2, pp. 651–654, 2009. View at Publisher · View at Google Scholar · View at Scopus
  8. A. Beloglazov and R. Buyya, “Adaptive threshold-based approach for energy-efficient consolidation of virtual machines in cloud data centers,” in Proceedings of the ACM 8th International Workshop on Middleware for Grids, Clouds and e-Science (MGC '10), pp. 1–6, Bangalore, India, December 2010. View at Publisher · View at Google Scholar
  9. X. Li, J. Wu, S. Tang, and S. Lu, “Let's stay together: Towards traffic aware virtual machine placement in data centers,” in Proceedings of the 33rd IEEE Conference on Computer Communications, IEEE INFOCOM 2014, pp. 1842–1850, can, May 2014. View at Publisher · View at Google Scholar · View at Scopus
  10. J. Xu and J. A. B. Fortes, “Multi-Objective Virtual Machine Placement in Virtualized Data Center Environments,” in Ieee/acm International Conference on Green Computing and Communications & 2010 Ieee/acm International Conference on Cyber, Physical and Social Computing, pp. 179–188, IEEE, Hangzhou, China, December 2010. View at Publisher · View at Google Scholar · View at Scopus
  11. Q. Li, Q. Hao, L. Xiao, and Z. Li, “Adaptive management and multi-objective optimization for virtual machine placement in cloud computing,” Chinese Journal of Computers, vol. 34, no. 12, pp. 2253–2264, 2011. View at Publisher · View at Google Scholar · View at Scopus
  12. F. Farahnakian, A. Ashraf, T. Pahikkala et al., “Using Ant Colony System to Consolidate VMs for Green Cloud Computing,” IEEE Transactions on Services Computing, vol. 8, no. 2, pp. 187–198, 2015. View at Publisher · View at Google Scholar · View at Scopus
  13. S. Srikantaiah, A. Kansal, and F. Zhao, “Energy aware consolidation for cloud computing[C]//,” in Proceedings of the Conference on Power Aware Computing and Systems, pp. 10–10, 2008.
  14. R. C. Eberhart and J. Kennedy, “A new optimizer using particle swarm theory,” in Proceedings of the 6th International Symposium on Micromachine and Human Science, pp. 39–43, IEEE, Nagoya, Japan, October 1995. View at Scopus
  15. J. Kennedy and R. Eberhart, “Particle swarm optimizatio,” in Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948, IEEE, Perth, Australia, December 1995. View at Scopus
  16. X. Zhang, Y. Du, Z. Qin, G. Qin, and J. Lu, “A Modified Particle Swarm Optimizer,” in Advances in Natural Computation, vol. 3612 of Lecture Notes in Computer Science, pp. 592–601, Springer, Berlin, Germany, 2005. View at Publisher · View at Google Scholar
  17. W. Qinghong U, J. Zhang H, and U. Xin He X, AN ANT COLONY ALGORITHM WITH MUTATION FEATURES[J]. Journal of Computer Research, 1999.
  18. P.-Y. Yin and J.-Y. Wang, “Ant colony optimization for the nonlinear resource allocation proble,” Applied Mathematics and Computation, vol. 174, no. 2, pp. 1438–1453, 2006. View at Publisher · View at Google Scholar · View at Scopus
  19. H. Wang, D. Lin, and M.-Q. Li, “A competitive genetic algorithm for resource-constrained project scheduling problem,” in Proceedings of the International Conference on Machine Learning and Cybernetics, ICMLC 2005, vol. 5, pp. 2945–2949, August 2005. View at Scopus
  20. S. Jang H, T. Kim Y, and J. Kim K, “The Study of Genetic Algorithm-based Task Scheduling for Cloud Computing[J],” in Proceedings of the International Journal of Control Automation, pp. 157–162, 2012.
  21. J. Kennedy and R. C. Eberhart, “A discrete binary version of the particle swarm algorithm,” vol. 5, pp. 4104–4108, 1997.