Intelligent Workload Management of Computing Resource Allocation For Mobile Cloud Computing

  IJCOT-book-cover
 
International Journal of Computer & Organization Trends  (IJCOT)          
 
© 2015 by IJCOT Journal
Volume - 5 Issue - 2
Year of Publication : 2015
AuthorsMuzammil H Mohammed,Faiz Baothman
  10.14445/22492593/IJCOT-V19P302

MLA

Muzammil H Mohammed,Faiz Baothman"Intelligent Workload Management of Computing Resource Allocation For Mobile Cloud Computing", International Journal of Computer & organization Trends (IJCOT), V5(2):8-19 Mar - Apr 2015, ISSN:2249-2593, www.ijcotjournal.org. Published by Seventh Sense Research Group.

Abstract - Mobile cloud computing (MCC) allows mobile devices to source their computing, storage and alternative tasks onto the cloud to realize a lot of capacities and better performance. one in all the foremost important analysis problems is however the cloud will expeditiously handle the attainable overwhelming requests from mobile users once the cloud resource is proscribed. during this paper, a unique MCC adaptative resource allocation model is projected to realize the optimum resource allocation in terms of the greatest overall system reward by considering each cloud and mobile devices. to realize this goal, we have a tendency to model the adaptative resource allocation as a semi-Markov decision process (SMDP) to capture the dynamic arrivals and departures of resource requests. Intensive simulations square measure conducted to demonstrate that our projected model can do higher system reward and lower service obstruction likelihood compared to ancient approaches supported greedy resource allocation algorithmic program. Performance comparisons with numerous MCC resource allocation schemes are provided.

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,EECS Department, University of California, Berkeley, Calif, USA, 2009.
2. M. Walshy, “Gartner: Mobile to outpace desktop web by 2013,” Online Media Daily.
3. D. Huang, X. Zhang, M. Kang, and J. Luo, “Mobicloud: a secure mobile cloud frame-work for pervasive mobile computing and communication,” in Proceedings of 5th IEEE International Symposium on Service-Oriented System Engineering, 2010.
4. X. H. Li, H. Zhang, and Y. F. Zhang, “Deploying mobile computation in cloud service,” in Proceedings of the 1st International Conference for Cloud Computing (CloudCom '09), p. 301, 2009.
5. B. Chun and P. Maniatis, “Augmented smartphone applications through clone cloud execution,” in Proceedings of the 12th USENIX HotoS, 2009.
6. X. Zhang, J. Schiffman, S. Gibbs, A. Kunjithapatham, and S. Jeong, “Securing elastic applications on mobile devices for cloud computing,” in Proceedings of the ACM workshop on Cloud Computing Security, pp. 127–134, 2009.
7. X. Meng, V. Pappas, and L. Zhang, “Improving the scalability of data center networks with traffic-aware virtual machine placement,” in Proceedings of the IEEE INFOCOM, San Diego, Calif, USA, March 2010.
8. L. X. Cai, L. Cai, X. Shen, and J. W. Mark, “Resource management and QoS provisioning for IPTV over mmWave-based WPANs with directional antenna,” ACM Mobile Networks and Applications, vol. 14, no. 2, pp. 210–219, 2009.
9. H. T. Cheng and W. Zhuang, “Novel packet-level resource allocation with effective QoS provisioning for wireless mesh networks,” IEEE TransacTions on Wireless Communications, vol. 8, no. 2, pp. 694–700, 2009.
10. L. X. Cai, X. Shen, and J. W. Mark, “Efficient MAC protocol for ultra-wideband networks,” IEEE Communications Magazine, vol. 47, no. 6, pp. 179–185, 2009.
11. H. Liang, D. Huang, and D. Peng, “On economic mobile cloud computing model,” in Proceedings of the International Workshop on Mobile Computing and Clouds (MobiCloud '10), 2010.
12. G. Wei, A. V. Vasilakos, Y. Zheng, and N. Xiong, “A game-theoretic method of fair resource allocation for cloud computing services,” The Journal of Supercomputing, vol. 54, no. 2, pp. 252–269, 2009.
13. K. Lorincz, B. R. Chen, J. Waterman, G. Werner-Allen, and M. Welsh, “Resource aware programming in the pixie os,” in Proceedings of the SenSys, Raleigh, NC, USA, November 2008.
14. K. Lorincz, B. Chen, J. Waterman, G. Werner-Allen, and M. Welsh, “A stratified approach for supporting high throughput event processing applications,” in Proceedings of the DEBS, Nashville, Tenn, USA, July 2009.
15. K. Boloor, R. Chirkova, Y. Viniotis, and T. Salo, “Dynamic request allocation and scheduling for context aware applications subject to a percentile response time sla in a distributed cloud,” in Proceedings of the 2nd IEEE International Conference on Cloud Computing Technology and Science, Indianapolis, Ind, USA, November 2010.
16. R. Ramjee, D. Towsley, and R. Nagarajan, “On optimal call admission control in cellular networks,” Wireless Networks, vol. 3, no. 1, pp. 29–41, 1997.
17. S. O. H. Mine and M. L. Puterman, Markovian Decision Process, Elsevier, Amsterdam, The Netherlands, 1970.
18. M. Puterman, Markov Decision Processes: Discrete Stochastic Dynamic Programming, John Wiley & Sons, New York, NY, USA, 2005.MathWorks, “Matlab,” http://www.mathworks.com/.
19. Hongbin Liang,State Key Laboratory of Information Security, Institute of Information Engineering, The Chinese Academy of Sciences, Beijing 100093, China
20. Tianyi Xing, School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, Sichuan 610031, China
21. Lin X. Cai Arizona State University, 699 S Mill Avenue, Suite 464, Tempe, AZ 85281, USA

Keywords-
Cloud Computing, mobile cloud computing, semi-Markov decision process, QoS, cloud service supplier.