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Mobile Information Systems
Volume 2016, Article ID 4859862, 9 pages
http://dx.doi.org/10.1155/2016/4859862
Research Article

Power-Aware Resource Reconfiguration Using Genetic Algorithm in Cloud Computing

Li Deng,1,2 Yang Li,1,2 Li Yao,1,2 Yu Jin,1,2 and Jinguang Gu1,2

1College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, China
2Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, Wuhan, China

Received 23 September 2016; Accepted 12 December 2016

Academic Editor: Qingchen Zhang

Copyright © 2016 Li Deng 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. Armbrust, A. Fox, R. Griffith et al., “Above the clouds: a Berkeley view of cloud computing,” Tech. Rep. UCB/EECS-2009-28, Electrical Engineering and Computer Sciences Department, University of California, Berkeley, 2009. View at Google Scholar
  2. G. Copil, D. Moldovan, H. Truong, and S. Dustdar, “rSYBL: a framework for specifying and controlling cloud services elasticity,” ACM Transactions on Internet Technology, vol. 16, no. 3, 2016. View at Google Scholar
  3. H. Jin, L. Deng, S. Wu, X. H. Shi, H. H. Chen, and X. D. Pan, “MECOM: live migration of virtual machines by adaptively compressing memory pages,” Future Generation Computer Systems, vol. 38, pp. 23–35, 2014. View at Publisher · View at Google Scholar · View at Scopus
  4. A. Rai, R. Bhagwan, and S. Guha, “Generalized resource allocation for the cloud,” in Proceedings of the ACM 3rd Symposium on Cloud Computing (SOCC '12), San Jose, Calif, USA, 2012.
  5. F. Hermenier, X. Lorca, J. M. Menaud, G. Muller, and J. Lawall, “Entropy: a consolidation manager for clusters,” in Proceedings of the ACM/Usenix International Conference on Virtual Execution Environments (VEE '09), pp. 41–50, Washington, DC, USA, March 2009. View at Publisher · View at Google Scholar
  6. L. Chen and H. Shen, “Consolidating complementary VMs with spatial/temporal-awareness in cloud datacenters,” in Proceedings of the 33rd IEEE Conference on Computer Communications (INFOCOM '14), pp. 1033–1041, IEEE, Toronto, Canada, May 2014. View at Publisher · View at Google Scholar · View at Scopus
  7. M. Dabbagh, B. Hamdaoui, M. Guizani, and A. Rayes, “An energy-efficient VM prediction and migration framework for overcommitted clouds,” IEEE Transactions on Cloud Computing, 2016. View at Publisher · View at Google Scholar
  8. L. Zhang, Z. Li, and C. Wu, “Dynamic resource provisioning in cloud computing: a randomized auction approach,” in Proceedings of the 33rd IEEE Conference on Computer Communications ('INFOCOM '14), pp. 433–441, Ontario, Canada, May 2014. View at Publisher · View at Google Scholar · View at Scopus
  9. Z. Zhou, F. Liu, Z. Li, and H. Jin, “When smart grid meets geo-distributed cloud: an auction approach to datacenter demand response,” in Proceedings of the IEEE Conference on Computer Communications (INFOCOM '15), pp. 2650–2658, IEEE, May 2015. View at Publisher · View at Google Scholar · View at Scopus
  10. L. Deng and L. Yao, “Dynamic allocation of virtual resources based on genetic algorithm in the cloud,” in Proceedings of the Asia-Pacific Services Computing Conference (APSCC '15), pp. 153–164, 2015.
  11. S. Nathan, U. Bellur, and P. Kulkarni, “Towards a comprehensive performance model of virtual machine live migration,” in Proceedings of the 6th ACM Symposium on Cloud Computing (SoCC '15), pp. 288–301, Kohala Coast, Hawaii, USA, August 2015. View at Publisher · View at Google Scholar
  12. Z.-H. Zhan, X.-F. Liu, Y.-J. Gong, J. Zhang, H. S.-H. Chung, and Y. Li, “Cloud computing resource scheduling and a survey of its evolutionary approaches,” ACM Computing Surveys, vol. 47, no. 4, article 63, 2015. View at Publisher · View at Google Scholar · View at Scopus
  13. T. Kaur and I. Chana, “Energy efficiency techniques in cloud computing: a survey and taxonomy,” ACM Computing Surveys, vol. 48, no. 2, article 22, 2015. View at Publisher · View at Google Scholar · View at Scopus
  14. J. Liu, K. L. Li, D. K. Zhu, J. J. Han, and K. Q. Li, “Minimizing cost of scheduling tasks on heterogeneous multicore embedded systems,” ACM Transactions on Embedded Computing Systems, vol. 16, no. 2, 2016. View at Google Scholar
  15. Q. Li, Q.-F. Hao, L.-M. Xiao, and Z.-J. Li, “Adaptive management and multi-objective optimization for virtual machine placement in cloud computing,” Chinese Journal of Computer, vol. 34, no. 12, pp. 2253–2264, 2011. View at Publisher · View at Google Scholar · View at Scopus
  16. W. Wang, B. Li, and B. Liang, “Dominant resource fairness in cloud computing systems with heterogeneous servers,” in Proceedings of the IEEE Conference on Computer Communications (INFOCOM '14), pp. 583–591, IEEE, Toronto, Canada, May 2014. View at Publisher · View at Google Scholar · View at Scopus
  17. J. Guo, F. Liu, J. C. S. Lui, and H. Jin, “Fair network bandwidth allocation in IaaS datacenters via a cooperative game approach,” IEEE/ACM Transactions on Networking, vol. 24, no. 2, pp. 873–886, 2016. View at Publisher · View at Google Scholar
  18. D. Lo, L. Cheng, R. Govindaraju, P. Ranganathan, and C. Kozyrakis, “Improving resource efficiency at scale with heracles,” ACM Transactions on Computer Systems, vol. 34, no. 2, 2016. View at Google Scholar
  19. S. Singh and I. Chana, “QoS-aware autonomic resource management in cloud computing: a systematic review,” ACM Computing Surveys, vol. 48, no. 3, article 42, 2016. View at Publisher · View at Google Scholar · View at Scopus
  20. K. H. Park, W. Hwang, H. Seok et al., “MN-MATE: elastic resource management of manycores and a hybrid memory hierarchy for a cloud node,” ACM Journal on Emerging Technologies in Computing Systems, vol. 12, no. 1, article 5, 2015. View at Publisher · View at Google Scholar · View at Scopus
  21. R. C. Chiang, S. Rajasekaran, N. Zhang, and H. H. Huang, “Swiper: exploiting virtual machine vulnerability in third-party clouds with competition for I/O resources,” IEEE Transactions on Parallel and Distributed Systems, vol. 26, no. 6, pp. 1732–1742, 2015. View at Publisher · View at Google Scholar · View at Scopus
  22. N. Jain, I. Menache, J. Naor, and J. Yaniv, “Near-optimal scheduling mechanisms for deadline-sensitive jobs in large computing clusters,” ACM Transactions on Parallel Computing, vol. 2, no. 1, 2015. View at Google Scholar
  23. J. Ghaderi, S. Shakkottai, and R. Srikant, “Scheduling storms and streams in the cloud,” ACM Transactions on Modeling and Performance Evaluation of Computing Systems, vol. 1, no. 4, 2016. View at Google Scholar
  24. T. Wu, W. Dou, F. Wu, S. Tang, C. Hu, and J. Chen, “A deployment optimization scheme over multimedia big data for large-scale media streaming application,” ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 12, no. 5, article 73, 2016. View at Publisher · View at Google Scholar
  25. J. Xu, C. Liu, X. Zhao, S. Yongchareon, and Z. Ding, “Resource management for business process scheduling in the presence of availability constraints,” ACM Transactions on Management Information Systems, vol. 7, no. 3, article 9, 2016. View at Publisher · View at Google Scholar
  26. E. Falkenauer and A. Delchambre, “A genetic algorithm for bin packing and line balancing,” in Proceedings of the IEEE International Conference on Robotics and Automation, pp. 1186–1192, Nice, France, May 1992. View at Publisher · View at Google Scholar
  27. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182–197, 2002. View at Publisher · View at Google Scholar · View at Scopus
  28. L. Chen, H. Shen, and K. Sapra, “Distributed autonomous virtual resource management in datacenters using finite-Markov decision process,” in Proceedings of the 5th ACM Symposium on Cloud Computing (SOCC '14), pp. 1–13, ACM, Seattle, Wash, USA, November 2014. View at Publisher · View at Google Scholar · View at Scopus
  29. CloudSim: A Framework for Modeling and Simulation of Cloud Computing Infrastructures and Services, 2015, http://www.cloudbus.org/cloudsim/.
  30. H. Liu, C.-Z. Xu, H. Jin, J. Gong, and X. Liao, “Performance and energy modeling for live migration of virtual machines,” in Proceedings of the 20th ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC '11), pp. 171–181, ACM, San Jose, Calif, USA, June 2011. View at Publisher · View at Google Scholar · View at Scopus