Scalable Load Balancing Approach for Cloud Environment
Cloud computing is a combination of parallel and distributed system which aims at effective resource utilization, providing uninterrupted services all the time which adapts itself with varying number of users without much capital investment. Ubiquitous, scalability and elasticity are some of the important features of cloud computing. To maintain essential characteristics, there is a need of mechanism which distributes the load efficiently among the available resources. Load balancing means the distribution of tasks among different available resources so that no one is over or under-utilized. Scalable, adaptable, efficient and reliable are some of the desirable features of a load balancing approach.
In this paper, authors have proposed a new load balancing approach named “Weighted Biased Random Walk” for the cloud environment using the concept of biased random walk. Weighted biased random walk approach has been analytically & experimentally analyzed. It has been compared with other load balancing approaches based on biased random walk found in literature. It has been found that weighted biased random walk approach is self-adjustable, distributed, dynamic, scalable and efficient in nature. It outperforms the other load balancing approaches based upon biased random walk. Collective presence of all the desirable features makes the weighted biased random walk approach perfect load balancing approach for the cloud environment.
R. Buyya, C. S. Yeo, S. Venugopal, J. Broberg, and I. Brandic, “Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility,” Future Generation Computer Systems, vol. 25, no. 6, pp. 599-616, June 2009.
A. Jain and R. Kumar, “A taxonomy of cloud computing,” International Journal of Scientific and Research Publications, vol. 4, no. 7, pp. 1-5, July 2014.
M. Khari, S. Gupta and M. Kumar, “Security outlook for cloud computing: A proposed architectural-based security classification for cloud computing,” Proc. IEEE Conference. Computing for Sustainable Global Development (INDIACom), March 2016, pp. 2153-2158.
A. Khiyaita, H. El Bakkali, M. Zbakh, and D. El Kettani, “Load balancing cloud computing: state of art,” Proc. IEEE Conf. Network Security and Systems (JNS2), April 2012, pp. 106-109.
K. Al Nuaimi, N. Mohamed, M. Al Nuaimi, and J. Al-Jaroodi, “A survey of load balancing in cloud computing: challenges and algorithms,” Proc. IEEE Symp. Network Cloud Computing and Applications (NCCA), December 2012, pp. 137-142.
A. Jain and R. Kumar, “A multi stage load balancing technique for cloud environment,” Proc. IEEE Conf. Information Communication and Embedded Systems (ICICES), February 2016, pp. 1-7.
R. Armstrong, D. Hensgen, and T. Kidd, “The relative performance of various mapping algorithms is independent of sizable variances in run-time predictions,” Proc. IEEE Conf. Heterogeneous Computing Workshop (HCW 98), March 1998, pp. 79-87.
Y. Lu, Q. Xie, G. Kliot, A. Geller, J. R. Larus and A. Greenberg, “Join-Idle-Queue: A novel load balancing algorithm for dynamically scalable web services,” Performance Evaluation, vol. 68, no. 11, pp. 1056-1071, November 2011.
G. Xu, J. Pang, and X. Fu, “A load balancing model based on cloud partitioning for the public cloud,” Tsinghua Science and Technology. vol. 18, no. 1, February 2013, pp. 34-39.
V. Tyagi and T. Kumar, “ORT broker policy: reduce cost and response time using throttled load balancing algorithm,” Proc. Conf. Intelligent Computing, Communication and Convergence (ICCC), 2015, pp. 217-221.
A. Zaouch and F. Benabbou, “Load balancing for improved quality of service in the cloud,” International Journal of Advanced Computer Science and Applications, vol. 6, no. 7, pp. 184-189, 2015.
S. Nakrani and C. Tovey, “On honey bees and dynamic server allocation in internet hosting centers,” International Society for Adaptive Behavior, vol. 12, no. 3, pp. 223-240, December 2004.
D. Babu and P. V. Krishna, “Honey bee behavior inspired load balancing of tasks in cloud computing environments,” Applied Soft Computing, vo. 13, no. 5, pp. 292-2303, May 2013.
X. D. Xue, B. Xu, H. L. Wang, and C. P. Jiang, “The basic principle and application of ant colony optimization algorithm,” Proc. IEEE Conf. Artificial Intelligence and Education (ICAIE), October 2010, pp. 358-360.
Y. Gao, H. Guan, Z. Qi, Y. Hou, and L. Liu, “A multi-objective ant colony system algorithm for virtual machine placement in cloud computing,” Journal of Computer and System Sciences, vol. 79, no. 8, pp. 1230-1242, December 2013.
M. Mitchell, An introduction to genetic algorithms. MIT press; 1998.
J. Gu, J. Hu, T. Zhao, and G. Sun, “A new resource scheduling strategy based on genetic algorithm in cloud computing environment,” Journal of Computers, vol. 7, no. 1, pp. 42-52, January 2012.
M. Fleischer, “Simulated annealing: past, present, and future,” Proc. IEEE Conf. Winter Simulation, December 1995, pp. 155-161.
S. Zhan and H. Huo, “Improved PSO-based task scheduling algorithm in cloud computing,” Journal of Information and Computational Science, vol. 9, no. 13, November 2012, pp. 3821-3829
“Random Walk,” https://en.wikipedia.org/wiki/Random_walk, October 12, 2016.
O. A. Rahmeh, P. Johnson, and A. Taleb-Bendiab, “A dynamic biased random sampling scheme for scalable and reliable grid networks,” INFOCOMP Journal of Computer Science, vol. 7, no. 4, pp. 1-10, December 2008.
M. Randles, O. Abu-Rahmeh, P. Johnson, and A. Taleb-Bendiab, “ Biased random walks on resource network graphs for load balancing,” The Journal of Supercomputing. vol. 53, no. 1, pp. 138-162, July 2010.
S. S. Manakattu and S. D. Kumar, “An improved biased random sampling algorithm for load balancing in cloud-based systems,” Proc. ACM Conference. Advances in Computing, Communications, and Informatics, August 2012, pp. 459-462.
N. Kumar and S. Agarwal, “Self-regulatory graph-based model for managing VM migration in cloud data centers,” Proc. IEEE Conf. Advance Computing Conference (IACC), February 2014, pp. 731-734.
V. Ariharan and S. S. Manakattu, “Neighbor aware random Sampling (NARS) algorithm for load balancing in cloud computing,” Proc. IEEE Conf. Electrical, Computer and Communication Technologies (ICECCT), March 2015, pp. 1-5.
P. Erdos and A. Renyi, “On random graphs I,” Publicationes Mathematicae, vol. 6, pp. 290-297, 1959.
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