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Scientific Programming
Volume 2016, Article ID 6208358, 13 pages
http://dx.doi.org/10.1155/2016/6208358
Research Article

Task Classification Based Energy-Aware Consolidation in Clouds

1Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea
2IT Convergence Education Center, Dongguk University, Seoul, Republic of Korea
3Department of Computer Science, Dongduk Women’s University, Seoul, Republic of Korea

Received 22 January 2016; Accepted 3 August 2016

Academic Editor: Zhihui Du

Copyright © 2016 HeeSeok Choi 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. W. Ai, K. Li, S. Lan et al., “On elasticity measurement in cloud computing,” Scientific Programming, vol. 2016, Article ID 7519507, 13 pages, 2016. View at Publisher · View at Google Scholar
  2. J. Lim, T. Suh, J. Gil, and H. Yu, “Scalable and leaderless Byzantine consensus in cloud computing environments,” Information Systems Frontiers, vol. 16, no. 1, pp. 19–34, 2014. View at Publisher · View at Google Scholar
  3. S. K. Choi, K. S. Chung, and H. Yu, “Fault tolerance and QoS scheduling using CAN in mobile social cloud computing,” Cluster Computing, vol. 17, no. 3, pp. 911–926, 2014. View at Publisher · View at Google Scholar · View at Scopus
  4. M. Armbrust, A. Fox, R. Griffith et al., “A view of cloud computing,” Communications of the ACM, vol. 53, no. 4, pp. 50–58, 2010. View at Publisher · View at Google Scholar · View at Scopus
  5. Y. Wen, X. Zhu, J. J. P. C. Rodrigues, and C. W. Chen, “Cloud mobile media: reflections and outlook,” IEEE Transactions on Multimedia, vol. 16, no. 4, pp. 885–902, 2014. View at Publisher · View at Google Scholar · View at Scopus
  6. M. Dayarathna, Y. Wen, and R. Fan, “Data center energy consumption modeling: a survey,” IEEE Communications Surveys & Tutorials, vol. 18, no. 1, pp. 732–794, 2015. View at Publisher · View at Google Scholar
  7. N. Boumkheld, M. Ghogho, and M. E. Koutbi, “Energy consumption scheduling in a smart grid including uding renewable energy,” Journal of Information Processing Systems, vol. 11, no. 1, pp. 116–124, 2015. View at Publisher · View at Google Scholar · View at Scopus
  8. P. Barham, B. Dragovic, K. Fraser et al., “Xen and the art of virtualization,” ACM SIGOPS Operating Systems Review, vol. 37, no. 5, pp. 164–177, 2003. View at Publisher · View at Google Scholar
  9. I. Habib, “Virtualization with KVM,” Linux Journal, vol. 2008, no. 166, article 8, 2008. View at Google Scholar
  10. X. Ruan and H. Chen, “Performance-to-power ratio aware Virtual Machine (VM) allocation in energy-efficient clouds,” in Proceedings of the IEEE International Conference on Cluster Computing (CLUSTER '15), pp. 264–273, Chicago, Ill, USA, September 2015. View at Publisher · View at Google Scholar
  11. D. Sood, H. Kour, and S. Kumar, “Survey of computing technologies: distributed, utility, cluster, grid and cloud computing,” Journal of Network Communications and Emerging Technologies, vol. 6, no. 5, pp. 99–102, 2016. View at Google Scholar
  12. Y. Gao, H. Guan, Z. Qi, B. Wang, and L. Liu, “Quality of service aware power management for virtualized data centers,” Journal of Systems Architecture, vol. 59, no. 4-5, pp. 245–259, 2013. View at Publisher · View at Google Scholar · View at Scopus
  13. W. Guo, W. Sun, W. Hu, and Y. Jin, “Resource allocation strategies for data-intensive workflow-based applications in optical grids,” in Proceedings of the 10th IEEE Singapore International Conference on Communications Systems (ICCS '06), pp. 1–5, IEEE, Singapore, November 2006. View at Publisher · View at Google Scholar · View at Scopus
  14. O. Shai, E. Shmueli, and D. G. Feitelson, “Heuristics for resource matching in Intel's compute farm,” in Job Scheduling Strategies for Parallel Processing, vol. 8429, pp. 116–135, Springer, 2013. View at Google Scholar
  15. V. Ebrahimirad, M. Goudarzi, and A. Rajabi, “Energy-aware scheduling for precedence-constrained parallel virtual machines in virtualized data centers,” Journal of Grid Computing, vol. 13, no. 2, pp. 233–253, 2015. View at Publisher · View at Google Scholar · View at Scopus
  16. J. Huang, K. Wu, and M. Moh, “Dynamic Virtual Machine migration algorithms using enhanced energy consumption model for green cloud data centers,” in Proceedings of the International Conference on High Performance Computing & Simulation (HPCS '14), pp. 902–910, Bologna, Italy, July 2014. View at Publisher · View at Google Scholar · View at Scopus
  17. P. Xiao, Z. Hu, D. Liu, X. Zhang, and X. Qu, “Energy-efficiency enhanced virtual machine scheduling policy for mixed workloads in cloud environments,” Computers & Electrical Engineering, vol. 40, no. 5, pp. 1650–1665, 2014. View at Publisher · View at Google Scholar · View at Scopus
  18. A. Paya and D. C. Marinescu, “Energy-aware load balancing policies for the cloud ecosystem,” in Proceedings of the 28th IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW '14), pp. 823–832, IEEE, Phoenix, Ariz, USA, May 2014. View at Publisher · View at Google Scholar · View at Scopus
  19. P. Xiao, Z.-G. Hu, and Y.-P. Zhang, “An energy-aware heuristic scheduling for data-intensive workflows in virtualized datacenters,” Journal of Computer Science and Technology, vol. 28, no. 6, pp. 948–961, 2013. 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 2009 IEEE International Conference on Cluster Computing and Workshops (CLUSTER '09), pp. 1–10, IEEE, New Orleans, La, USA, September 2009. View at Publisher · View at Google Scholar · View at Scopus
  21. C.-M. Wu, R.-S. Chang, and H.-Y. Chan, “A green energy-efficient scheduling algorithm using the DVFS technique for cloud datacenters,” Future Generation Computer Systems, vol. 37, pp. 141–147, 2014. View at Publisher · View at Google Scholar · View at Scopus
  22. L. Luo, W. Wu, W. Tsai, D. Di, and F. Zhang, “Simulation of power consumption of cloud data centers,” Simulation Modelling Practice and Theory, vol. 39, pp. 152–171, 2013. View at Publisher · View at Google Scholar
  23. G. Katsaros, J. Subirats, J. O. Fitó, J. Guitart, P. Gilet, and D. Espling, “A service framework for energy-aware monitoring and VM management in Clouds,” Future Generation Computer Systems, vol. 29, no. 8, pp. 2077–2091, 2013. View at Publisher · View at Google Scholar · View at Scopus
  24. I. Hwang, T. Kam, and M. Pedram, “A study of the effectiveness of CPU consolidation in a virtualized multi-core server system,” in Proceedings of the ACM/IEEE International Symposium on Low Power Electronics and Design (ISLPED '12), pp. 339–344, Redondo Beach, Calif, USA, August 2012. View at Publisher · View at Google Scholar · View at Scopus
  25. K. Maurya and R. Sinha, “Energy conscious dynamic provisioning of virtual machines using adaptive migration thresholds in cloud data center,” International Journal of Computer Science and Mobil Computing, vol. 2, no. 3, pp. 74–82, 2013. View at Google Scholar
  26. A. Beloglazov, J. Abawajy, and R. Buyya, “Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing,” Future Generation Computer Systems, vol. 28, no. 5, pp. 755–768, 2012. View at Publisher · View at Google Scholar · View at Scopus
  27. W. Lin, J. Z. Wang, C. Liang, and D. Qi, “A threshold-based dynamic resource allocation scheme for cloud computing,” Procedia Engineering, vol. 23, pp. 695–703, 2011. View at Publisher · View at Google Scholar
  28. Z. Xiao, J. Jiang, Y. Zhu, Z. Ming, S. Zhong, and S. Cai, “A solution of dynamic VMs placement problem for energy consumption optimization based on evolutionary game theory,” Journal of Systems and Software, vol. 101, pp. 260–272, 2015. View at Publisher · View at Google Scholar · View at Scopus