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Reseach Article

Modified Active Monitoring Ant Clustering based Load Balancing over Public Clouds

by Sonam Raghuwanshi, Rashmi Nigoti
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 167 - Number 1
Year of Publication: 2017
Authors: Sonam Raghuwanshi, Rashmi Nigoti
10.5120/ijca2017914105

Sonam Raghuwanshi, Rashmi Nigoti . Modified Active Monitoring Ant Clustering based Load Balancing over Public Clouds. International Journal of Computer Applications. 167, 1 ( Jun 2017), 29-34. DOI=10.5120/ijca2017914105

@article{ 10.5120/ijca2017914105,
author = { Sonam Raghuwanshi, Rashmi Nigoti },
title = { Modified Active Monitoring Ant Clustering based Load Balancing over Public Clouds },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2017 },
volume = { 167 },
number = { 1 },
month = { Jun },
year = { 2017 },
issn = { 0975-8887 },
pages = { 29-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume167/number1/27737-2017914105/ },
doi = { 10.5120/ijca2017914105 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:13:40.951244+05:30
%A Sonam Raghuwanshi
%A Rashmi Nigoti
%T Modified Active Monitoring Ant Clustering based Load Balancing over Public Clouds
%J International Journal of Computer Applications
%@ 0975-8887
%V 167
%N 1
%P 29-34
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Cloud computing is a technique of sharing resources over Internet, where Users Can Share or Store Resources and Data over Data Centers processed by Virtual Machines. During the Access of Resources over Data Centers requires load to be balanced by Virtual machines. The Existing Naïve Bayes Clustering is an efficient technique for Load Balancing over Cloud, but the existing methodology takes low Throughput and Make Span Time. Hence a new and efficient technique is implemented for Load Balancing over Public Clouds using Modified Active Monitoring based Ant Clustering. The Proposed Methodology implemented provides High Throughput and Make Span time as well as low Standard Deviation in Comparison with the Existing Naïve Bayes Load Balancing.

References
  1. R. Yu, Y. Zhang, S. Gjessing,W. Xia, and K. Yang, “Toward cloud based vehicular networks with efficient resource management,” IEEE Netw.Mag., vol. 27, no. 5, pp. 48–55, Sep./Oct. 2013.
  2. Rahman, Mazedur, Samira Iqbal, and Jerry GAO. "Load Balancer as a Service in Cloud Computing." In Service Oriented System Engineering (SOSE), 2014 IEEE 8th International Symposium on, pp. 204-211. IEEE, 2014.
  3. Rajkumar Buyya. Market-oriented cloud computing: Vision, hype, and reality of delivering computing as the 5th utility. In CCGRID, page 1, 2009
  4. Paul Barham, Boris Dragovic, Keir Fraser, Steven Hand, Tim Harris, Alex Ho, Rolf Neugebauer, Ian Pratt, and AndrewWarfield. Xen and the art of virtualization. ACM SIGOPS Operating Systems Review, 37(5):164–177, 2003
  5. VMware .VMware virtualization software, 2012.
  6. Younggyun Koh, Rob Knauerhase, Paul Brett, Mic Bowman, ZhihuaWen, and Calton Pu. An analysis of performance interference effects in virtual environments. In Proceedings of the IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS, 2007.
  7. Xing Pu, Ling Liu, Yiduo Mei, Sankaran Sivathanu, Younggyun Koh, and Calton Pu. Understanding performance interference of i/o workload in virtualized cloud environments. Pages 51–58, 2010.
  8. M. Armbrust, A. Fox, R. Griffith, A. D. Joseph, R. Katz, A. Konwinski, G. Lee, D. Patterson, A. Rabkin, I. Stoica, and M. Zaharia, “A view of cloud computing,” Commun. ACM, vol. 53, no. 4, pp. 50–58, 2010.
  9. R. Moreno-Vozmediano, R. S. Montero, and I. M. Llorente, “Key challenges in cloud computing: enabling the future internet of services,” IEEE Int. Comput., vol. 17, no. 4, pp. 18–25, Jul./Aug. 2013.
  10. A. Gulati, A. Holler, M. Ji, G. Shanmuganathan, C. Waldspurger, and X. Zhu, “VMware distributed resource management: Design, implementation and lessons learned,” VMware Tech. J., vol. 1, no. 1, pp. 45–64,Mar. 2012.
  11. J. T. Piao and J. Yan, “A network-aware virtual machine placement and migration approach in cloud computing,” in Proc. 9th IEEE Int. Conf. Grid Cooperative Comput., 2010, pp. 87–92
  12. Jia Zhao, Kun Yang,” A Heuristic Clustering-Based Task Deployment Approach for Load Balancing Using Bayes Theorem in Cloud Environment”, IEEE Transaction on Parallel and Distributed Systems, Volume 27, Issue 2, February 2016.
  13. S. Girdzijauskas, G. Chockler, Y. Vigfusson, Y. Tock, and R. Melamed, “Magnet: practical subscription clustering for Internet-scale publish/subscribe,” in Proceedings of the Fourth ACM International Conference on Distributed Event-Based Systems, ACM, 2010.
  14. J. Alveirinho, J. Paiva, J. Leitao, and L. Rodrigues, “Flexible and Efficient Resource Location in Large-Scale Systems,” The 4th ACM SIGOPS/SIGACT Workshop on Large Scale Distributed Systems and Middleware, 2010.
  15. Q. Wei, G. Xu, and Y. Li, “Research on cluster and load balance based on Linux virtual server,” in Proc. Inf. Comput. Appl., 2011, pp. 169–176
  16. S. M. Lau, Q. Lu, and K. S. Leung, “Adaptive load distribution algorithms for heterogeneous distributed systems with multiple task classes”, J. Parallel Distributed Computing. vol. 66, no. 2, pp. 163–180, 2006.
  17. J. Sonnek, J. Greensky, R. Reutiman, and A. Chandra, “Starling: Minimizing communication overhead in virtualized computing platforms using decentralized affinity-aware migration,” in Proc. 39th IEEE Int. Conf. Parallel Process., Sep. 2010, pp. 228–237.
Index Terms

Computer Science
Information Sciences

Keywords

Cloud Computing Virtual Machines Load Balancing Naïve Bayes Clustering Ant based Clustering Active Monitoring.