Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2014, Article ID 215016, 11 pages
http://dx.doi.org/10.1155/2014/215016
Research Article

Coarse-Grain QoS-Aware Dynamic Instance Provisioning for Interactive Workload in the Cloud

School of Information Engineering, Inner Mongolia University of Technology, Hohhot 010080, China

Received 22 November 2013; Revised 22 January 2014; Accepted 23 January 2014; Published 25 March 2014

Academic Editor: Huiping Li

Copyright © 2014 Jianxiong Wan 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. D. Meisner, B. T. Gold, and T. F. Wenisch, “PowerNap: Eliminating server idle power,” in Proceedings of the 14th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS '09), pp. 205–216, Washington, DC, USA, March 2009. View at Publisher · View at Google Scholar · View at Scopus
  2. “A. E. instance launch time,” http://aws.amazon.com/ec2/faqs/#Howquicklywillsystemsberunning.
  3. W. E. Leland, M. S. Taqqu, W. Willinger, and D. V. Wilson, “On the self-similar nature of Ethernet traffic (extended version),” IEEE/ACM Transactions on Networking, vol. 2, no. 1, pp. 1–15, 1994. View at Publisher · View at Google Scholar · View at Scopus
  4. Y. Chen, A. Das, W. Qin, A. Sivasubramaniam, Q. Wang, and N. Gautam, “Managing server energy and operational costs in hosting centers,” in Proceedings of the ACM International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS), Banff, Canada, 2005.
  5. D. Gmach, J. Rolia, L. Cherkasova, and A. Kemper, “Workload analysis and demand prediction of enterprise data center applications,” in roceedings of the IEEE 10th International Symposium on Workload Characterization (IISWC '07), Boston, Mass, USA, 2007.
  6. E. Caron and F. Desprez, “Forecasting for grid and cloud computing ondemand resources based on pattern matching,” in Proceedings of the 2nd IEEE International Conference on Cloud Computing Technology and Science (CloudCom '10), pp. 456–463, Indianapolis, Ind, USA, 2010.
  7. D. Niu, H. Xu, B. Li, and S. Zhao, “Quality-assured cloud bandwidth auto-scaling for video-on-demand applications,” in Proceedings of the IEEE INFOCOM, pp. 460–468, Orlando, Fla, USA, 2012.
  8. F. Ahmad and T. N. Vijaykumar, “Joint optimization of idle and cooling power in data centers while maintaining response time,” in Proceedings of the 15th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS '10), pp. 243–256, March 2010. View at Publisher · View at Google Scholar · View at Scopus
  9. J. Leverich and C. Kozyrakis, “On the energy (in)efficiency of hadoop clusters,” ACM SIGOPS Operating Systems Review, vol. 44, no. 1, pp. 61–65.
  10. S. Shen, K. Deng, A. Iosup, and D. Epema, “Scheduling jobs in the cloud using on-demand and reserved instances,” in Euro-Par 2013 Parallel Processing, vol. 8097 of Lecture Notes in Computer Science, pp. 242–254, Springer, Berlin, Germany, 2013. View at Google Scholar
  11. W. Ming, Y. Jian, and R. Yongyi, “Dynamic instance provisioning strategy in an iaas cloud,” in Proceedings of the 32nd Chinese Control Conference (CCC '13), pp. 6670–6675, Xi'an, China, 2013.
  12. D. Deng, Z. Lu, W. Fang, and J. Wu, “Cloudstreammedia: a cloud assistant global video on demand leasing scheme,” in Proceedings of the IEEE 10th International Conference on Services Computing (SCC '13), pp. 486–493, Washington, DC, USA, 2013.
  13. W. Fang, Z. Lu, J. Wu, and Z. Cao, “Rpps: a novel resource prediction and provisioning scheme in cloud data center,” in IEEE Ninth International Conference on Services Computing (SCC '12), pp. 609–616, Honolulu, Hawaii, USA, 2012.
  14. J. Jiang, J. Lu, G. Zhang, and G. Long, “Optimal cloud resource auto-scaling for web applications,” in Proceedings of the 13th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), pp. 58–65, Delft, The Netherlands, 2013.
  15. M. E. Crovella and A. Bestavros, “Self-similarity in world wide web traffic: evidence and possible causes,” IEEE/ACM Transactions on Networking, vol. 5, no. 6, pp. 835–846, 1997. View at Publisher · View at Google Scholar · View at Scopus
  16. T. Yoshihara, S. Kasahara, and Y. Takahashi, “Practical time-scale fitting of self-similar traffic with markov-modulated poisson process,” Telecommunication Systems, vol. 17, no. 1-2, pp. 185–211, 2001. View at Google Scholar · View at Scopus
  17. P. Salvador, R. Valadas, and A. Pacheco, “Multiscale Fitting Procedure Using Markov Modulated Poisson Processes,” Telecommunication Systems, vol. 23, no. 1-2, pp. 123–148, 2003. View at Publisher · View at Google Scholar · View at Scopus
  18. A. T. Andersen and B. F. Nielsen, “A Markovian approach for modeling packet traffic with long-range dependence,” IEEE Journal on Selected Areas in Communications, vol. 16, no. 5, pp. 719–732, 1998. View at Publisher · View at Google Scholar · View at Scopus
  19. M. Guazzone, C. Anglano, and M. Canonico, “Exploiting VM migration for the automated power and performance management of green cloud computing systems,” in Energy Efficient Data Centers, vol. 7396 of Lecture Notes in Computer Science, pp. 81–92, Springer, Berlin, Germany, 2012. View at Google Scholar
  20. D. Bruneo, “A stochastic model to investigate data center performance and qos in iaas cloud computing systems,” IEEE Transactions on Parallel and Distributed Systems, vol. 25, no. 3, pp. 560–569, 2013. View at Publisher · View at Google Scholar
  21. T. H. Szymanski, “Low latency energy efficient communications in global-scale cloud computing systems,” in Proceedings of the ACM Workshop on Energy Efficient High Performance Parallel and Distributed Computing (EEHDPC '13), pp. 13–22, 2013.
  22. J. Tai, J. Zhang, J. Li, W. Meleis, and N. Mi, “Ara: Adaptive resource allocation for cloud computing environments under bursty workloads,” in Proceedings of the IEEE 30th International Performance Computing and Communications Conference (IPCCC '11), pp. 1–8, 2011.
  23. Y. J. Hong, J. Xue, and M. Thottethodi, “Selective commitment and selective margin: techniques to minimize cost in an iaas cloud,” in Proceedings of the IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS '12), pp. 99–109, 2012.
  24. A. O. Allen, Probability, Statistics, and Queueing Theory with Computer Science Applications, Academic Press, Boston, Mass, USA, 1990. View at MathSciNet
  25. G. Bolch, S. Greiner, H. de Meer, and K. S. Trivedi, Queueing Networks and Markov Chains, Wiley-Interscience, New York, NY, USA, 1998. View at Publisher · View at Google Scholar · View at MathSciNet