Overload Avoidance for Dynamic Virtual Machine Resource Allocation Environment

Authors(4) :-Pillapakam Sridharan Srivatsan, M Manimaran, V Manikandan, M. Murugesan

Cloud computing allows business customers to scale up and down their resource usage based on needs. Many of the touted gains in the cloud model come from resource multiplexing through virtualization technology. In this paper, we present a system that uses virtualization technology to allocate data center resources dynamically based on application demands and support green computing by optimizing the number of servers in use. We introduce the concept of skewness to measure the unevenness in the multidimensional resource utilization of a server. By minimizing skewness, we can combine different types of workloads nicely and improve the overall utilization of server resources. We develop a set of heuristics that prevent overload in the system effectively while saving energy used. Trace driven simulation and experiment results demonstrate that our algorithm achieves good performance.

Authors and Affiliations

Pillapakam Sridharan Srivatsan
Dhanalakshmi College of Engineering, Chennai, Tamilnadu, India
M Manimaran
Dhanalakshmi College of Engineering, Chennai, Tamilnadu, India
V Manikandan
Dhanalakshmi College of Engineering, Chennai, Tamilnadu, India
M. Murugesan
Dhanalakshmi College of Engineering, Chennai, Tamilnadu, India

multi cloud storage, cloud user, skewness, disaster recovery, reencryption, Green Computing, CMS QoS, TTP, CPDP

[1] W. Zhu, C. Luo, J. Wang, and S. Li, “Multimedia cloud computing:An emerging technology for providing multimedia services and applications,” IEEE Signal Processing Magazine, vol. 28, no. 3, pp. 59–69, 2011.
[2] C.F.Lai, Y.M.Huang and H.C. Chao, “DLNA-based multimedia sharing system over OSGI framework with extension to P2P network,”IEEE Systems Journal, vol. 4, no. 2, pp. 262–270, 2010.
[3] W. Hui, H. Zhao, C. Lin, and Y. Yang, “Effective load balancing for cloud-based multimedia system,” in Proceedings of 2011 International Conference on Electronic & Mechanical Engineering and Information Technology. IEEE Press, 2011, pp. 165–168.
[4] C.Y.Chen, H.C.Chao, S.Y.Kuo, and K.D.Chang, “Rule-based intrusion detection mechanism for IP multimedia subsystem,” Journal of Internet Technology, vol. 9, no. 5, pp. 329–336, 2008.
[5] R.Yavatkar, D.Pendarakis, and R. Guerin, “A framework for policy based admission control,” Internet Requests for Comments, RFC Editor, RFC 2753, 2000.
[6] D.Niyato and E.Hossain, “Integration of WiMAX and Wi-Fi: Optimal pricing for bandwidth sharing,” IEEE Communication Magazine, vol. 45, no. 5, pp. 140–146, 2007.
[7] C.Y.Chang, T.Y.Wu, C.C.Huang, A.J.W.Whang, and H.C.Chao, “Robust header compression with load balance and dynamic bandwidth aggregation capabilities in WLAN,” Journal of Internet Technology, vol. 8, no. 3, pp. 365–372, 2007.
[8] J.Sun, X.Wu, and X.Sha, “Load balancing algorithm with multiservice in heterogeneous wireless networks,” in Proceedings of 6th International ICST Conference on Communications and Networking in China (ChinaCom 2011). IEEE Press, 2011, pp. 703–707.
[9] H.Son, S.Lee, S.C.Kim, and Y.S.Shin, “Soft load balancing over heterogeneous wireless networks,” IEEE Transactions on Vehicular Technology, vol. 57, no. 4, pp. 2632–2638, 2008.
[10] L.Zhou, H.C.Chao, and A.V.Vasilakos, “Joint forensics-scheduling strategy for delay-sensitive multimedia applications over heterogeneous networks,” IEEE Journal on Selected Areas of Communications, vol. 29, no. 7, pp. 1358–1367, 2011.
[11] X.Nan, Y.He, and L.Guan, “Optimal resource allocation for multimedia cloud based on queuing model,” in Proceedings of 2011 IEEE 13th International Workshop on Multimedia Signal Processing (MMSP 2011). IEEE Press, 2011, pp. 1–6.
[12] M.Garey and D. Johnson, Computers and Intractability - A Guide to the Theory of NP-Completeness. Freeman, San Francisco, 1979.
[13] S.Kirkpatrik, C.Gelatt, and M.Vecchi, “Optimization by simulated annealing,” Science, vol. 220, pp. 671–680, 1983.
[14] J. H. Holland, Adaptation in Natural and Artificial Systems, University of Michigan Press, 1975.
[15] J.Kennedy and R.Eberhart, “Particle swarm optimization,” in Proceedings of IEEE International Conference on Neural Networks. IEEE Press, 1995, p. 1942V1948.
[16] Y.Shi and R.Eberhart, “A modified particle swarm optimizer,” in Proceedings of IEEE International Conference on Evolutionary Computation. IEEE Press, 1998, pp. 69–73.
[17] X.Zhang, S.Hu, D.Chen, and X.Li, “Fast covariance matching with fuzzy genetic algorithm,” IEEE Transactions on Industrial Engineering, vol. 8, no. 1, pp. 148–157, 2012.
[18] W.Ip, D.Wang, and V.Cho, “Aircraft ground service scheduling problems and their genetic algorithm with hybrid assignment and sequence encoding scheme,” IEEE Systems Journal, 2012, to appear.
[19] F.Gonzalez-Longatt, P.Wall, P.Regulski, and V.Terzija, “Optimal electric network design for a large offshore wind farm based on a modified genetic algorithm approach,” IEEE Systems Journal, vol. 6, no. 1, pp. 164–172, 2012.
[20] H.Cheng and S.Yang, “Genetic algorithms with immigrants schemes for dynamic multicast problems in mobile ad hoc networks,” Engineering Applications of Artificial Intelligence, vol. 23, no. 5, pp. 806–819, 2010.
[21] R.Van den Bossche, K.Vanmechelen, and J.Broeckhove, “Cost-optimal scheduling in hybrid IaaS clouds for deadline constrained workloads,” in Proceedings of 2010 IEEE 3rd International Conference on Cloud Computing. IEEE Press, 2010, pp. 228–235.
[22] K.P.Chow and Y.K.Kwok, “On load balancing for distributed Multi agent computing,” IEEE Transactions on Parallel and Distributed Systems, vol. 13, no. 8, pp. 787–801, 2002.
[23] X.Qin, H.Jiang, A.Manzanares, X.Ruan, and S.Yin, “Communication aware load balancing for parallel applications on clusters,” IEEE Transactions on Computers, vol. 59, no. 1, pp. 42–52, 2010.
[24] A.Y.Zomaya and Y.H.Teh, “Observations on using genetic algorithms for dynamic load-balancing,” IEEE Transactions on Parallel and Distributed Systems, vol. 12, no. 9, pp. 899–911, 2001.
[25] Y.M.Huang, M.Y.Hsieh, H.C.Chao, S.H.Hung, and J.H.Park, “Pervasive, secure access to a hierarchical-based healthcare monitoring architecture in wireless heterogeneous sensor networks,” IEEE Journal on Selected Areas of Communications, vol. 27, no. 4, pp. 400–411, 2009.
[26] L.Yang and M.Guo, High-performance Computing: Paradigm and Infrastructure John Wiley and Sons, 2006.
[27] T.Y.Wu, H.C.Chao, and C.Y.Huang, “A survey of mobile IP in cellular and mobile ad-hoc network environments,” Ad Hoc Networks Journal, vol. 3, no. 3, pp. 351–370, 2005.
[28] Q.Yuan, F.Qian, and W.Du, “A hybrid genetic algorithm with the Baldwin effect,” Information Sciences, vol. 180, no. 5, pp. 640–652, 2010.
[29] S.Ross, Introduction to Probability Models, 10th ed. Academic Press, 2009.

Publication Details

Published in : Volume 1 | Issue 2 | March-April 2015
Date of Publication : 2015-04-25
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 221-229
Manuscript Number : IJSRSET152273
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Pillapakam Sridharan Srivatsan, M Manimaran, V Manikandan, M. Murugesan, " Overload Avoidance for Dynamic Virtual Machine Resource Allocation Environment, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 1, Issue 2, pp.221-229, March-April-2015.
Journal URL : http://ijsrset.com/IJSRSET152273

Article Preview

Follow Us

Contact Us