A Two Level Load Balancing Approach for Cloud Environment

Authors: Anurag Jain, Rajneesh Kumar

Abstract:

Cloud computing is the outcome of rapid growth of internet. Due to elastic nature of cloud computing and unpredictable behavior of user, load balancing is the major issue in cloud computing paradigm. An efficient load balancing technique can improve the performance in terms of efficient resource utilization and higher customer satisfaction. Load balancing can be implemented through task scheduling, resource allocation and task migration. Various parameters to analyze the performance of load balancing approach are response time, cost, data processing time and throughput. This paper demonstrates a two level load balancer approach by combining join idle queue and join shortest queue approach. Authors have used cloud analyst simulator to test proposed two level load balancer approach. The results are analyzed and compared with the existing algorithms and as observed, proposed work is one step ahead of existing techniques.

Keywords: Cloud Analyst, Cloud Computing, Join Idle Queue, Join Shortest Queue, Load balancing, Task Scheduling.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1123610

References:

[1] 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.
[2] B. P. Rimal, C. Eumni and I. Lumb, “A Taxonomy and Survey of Cloud Computing Systems”, Fifth International Joint Conference on INC, IMS and IDC, pp. 44-51, Aug. 2009.
[3] P. Sasikala, “Cloud computing: Present Status and Future Implications’ International Journal of Cloud Computing, vol. 1, no. 1, pp. 23-36, 2011.
[4] A. Khiyaita, M. Zbakh, H. El Bakkali, and D. El Kettani, “Load balancing Cloud Computing: State of Art”, National Days of Network Security and Systems (JNS2), pp. 106-109, Apr. 2012.
[5] K. A. Nuaimi, N. Mohamed, M. A. Nuaimi and J. Al-Jaroodi, “A Survey of Load Balancing in Cloud Computing: Challenges and Algorithms”, Second Symposium on Network Cloud Computing and Applications (NCCA), pp. 137-142, Dec. 2012.
[6] Amandeep, V. Yadav and F. Mohammad, “Different Strategies for Load Balancing in Cloud Computing Environment: A Critical Study”, International Journal of Scientific Research Engineering & Technology (IJSRET), vol. 3, no. 1, pp. 85-90, Apr. 2014.
[7] B. Wickremasinghe, R. N. Calheiros and R. Buyya, “Cloudanalyst: A Cloudsim-Based Visual Modeller for Analysing Cloud Computing Environments and Applications”, Proceedings of the 24th IEEE International Conference on Advanced Information Networking and Applications (AINA 2010), pp. 446-452, Apr. 2010.
[8] Y. Lu, Q. Xie, G. Kliot, A. Geller, J. Larus and A. Greenberg, “Join-Idle-Queue: A Novel Load Balancing Algorithm for Dynamically Scalable Web Services”, Elsevier Science Publishers, vol. 68, no. 11, pp. 1056-1071, Nov. 2011.
[9] V. Gupta, M. Harchol-Balter, K. Sigman and W. Whitt, “Analysis of Join-the-Shortest-Queue Routing for Web Server Farms”, International Symposium on Computer Modelling, Measurement and Evaluation, Elsevier Science Publishers, vol. 64, no. 9, pp. 1062-1081, Oct. 2007.
[10] S. C. Wang, K. Q. Yan, W. P. Liao and S. S. Wang, “Towards a Load Balancing in a Three-Level Cloud Computing Network”, 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT), pp. 108-113, July 2010.
[11] http://www.internetworldstats.com as on Aug. 2015.