Enhancement of dynamic load balancing using Particle Swarm Algorithm in Cloud Environment
DOI:
https://doi.org/10.24297/ijct.v15i10.4390Keywords:
PSO, Centralized, Decentralized, energy, throughputAbstract
Dynamic load balancing with decentralized load balancer using PSO technique: Cloud consists of multiple resources and various clients request to the cloud for allocation of shared resources. Each request will be allotted to the virtual machines. In different situation different machines get different load. So to balance the load amongst different virtual machines decentralized load balancer is enhanced using particle swarm algorithm. The main objective is reducing the energy and increasing the throughput in comparison to centralized and simple decentralized load balancer using particle swarm optimization.
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References
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[3] Cristian Mateos, Elina Pacini & Carlos Garc Garino, An ACO-inspired algorithm for minimizing weighted flow time in cloud-based parameter sweep experiments, 2013.
[4] Hongsheng Su, Ying Qi and Xi Song, "The Available Transfer Capability Based On a Chaos Cloud Particle Swarm Algorithm ", IEEE ninth International Conference on Natural Computation (ICNC), vol 13, pp.574-579, 2013.
[5] Rajkumar Buyya,“A Particle Swarm
Optimization-based Heuristic for Scheduling Workflow A", Cloud Computing and Distributed Systems Laboratory, Department of Computer.
[6] Madhurima Rana, Saurabh Bilgaiyan and Utsav Kar, “A Study on load balancing in cloud computing environment using evolutionary and swarm based algorithmsâ€, IEEE International Conference on Control, Instrumentation, Communication and Computational Technologies, vol.14, pp. 245-250, 2014.
[7] Pooja Samal and Pranati Mishra, "Analysis of variants in Round Robin Algorithms for load balancing in Cloud Computing", International Journal of Computer Science and Information Technologies, vol.4(3), pp. 416-419,, 2013.
[8] Wang Yonggui, Han Ruilian. Study on cloud computing task schedule strategy based on MACO algorithm [J]. Computer Measurement & Control, vol.19 (5), pp.1203- 1211, 2011.
[9] Jun Zhang and Wei-Neng Chen, “A Set-Based Discrete PSO for cloud Workflow Scheduling with User-Defined Qos Constraints, IEEE International conference on Systems, Man And Cybernetics, vol.12,pp. 773 – 778, 2012.
[10] J. Kennedy and R. Eberhart, Particle swarms optimization In IEEE International Conference on Neural Networks, vol. 4, pp 1942–1948, 1995.
[11] Andrew J. Page and Thomas J. Naughton, "Dynamic task scheduling using genetic algorithms for heterogeneous distributed computing", 19th IEEE International Conference on Parallel and Distributed Processing Symposium, pp. 189a, 2012.
[12] Akhil Goyal,Bharti, "A Study of Load Balancing in Cloud Computing using Soft Computing Techniques", International Journal of Computer Applications, 2014.
[13] Zhanghui Liu and Xiaoli Wang," A PSO-Based Algorithm for Load Balancing in Virtual Machines of Cloud Computing Environment", Advances in Swarm Intelligence Lecture Notes in Computer Science ", pp 142-147, 2012.
[14] Dr. M.Sridhar and Dr. G..Rama Mohan Babu , " Hybrid Particle Swarm Optimization scheduling for Cloud Computing " , IEEE International Advance Computing Conference, vol. 15, pp 1196-1200, 2015.
[15] Zehua Zhang, Xuejie Zhang, "A Load Balancing Mechanism Based on Ant Colony and Complex Network Theory in Open Cloud Computing Federation", 2nd IEEE International Conference on Industrial Mechatronics and Automation, pp. 240-243, 2010.
[16] Kun Li, Gaochao Xu, Guangyu Zhao, Yushuang Dong, Dan Wang, "Cloud Task scheduling based on Load Balancing Ant Colony Optimization", Sixth IEEE Annual ChinaGrid Conference, pp. 3-9, 2011.
[17] Azade Khalili and Seyed Morteza Babamir, "Makespan Improvement of PSO-based Dynamic Scheduling in cloud environment", 23rd IEEE Iranian Conference on Electrical Engineering (ICEE), vol.15, pp.613-617, 2015.
[18] Hongwei Zhao and Wang Chenyu, “A Dynamic Dispatching Method of Resource based on Particle swarm optimizationâ€,10th Web Information System and Application Conference
for Cloud Computing Environment, vol.13, pp.351-354,2013.
[19] Sung-Soo Kim, Ji-Hwan Byeon, Hongbo Liu, Ajith Abraham and Seán McLoone, "Optimal job scheduling in grid computing using efficient binary artificial bee colony optimization", Soft Computing, Springer, vol. 17, No. 5, pp. 867-882,2013.
[20] Marco Dorigo, Gianni Di Caro Luca and M. Gambardella, "Ant Algorithms for Discrete Optimization", Artificial Life, Massachusetts Institute of Technology, pp. 137-172, 1999.
[21] Nidhi Jain Kansal and Inderveer Chana, "Cloud Load Balancing Techniques:A Step Towards Green Computing", International Journal of Computer Science Issues, vol.9, No.1, pp.238-246, 2012.
[22] Qinghai Bai, "Analysis of Particle Swarm Optimization Algorithm", Computer and Information Science, vol. 3, No. 1, pp. 180-184, 2010.
[23] Particle Swarm Optimization, "http://en.wikipedia.org/wiki/Particle_swarm_optimization"
[24] Geng Yushui and Yuan Jiaheng, “Cloud data migration method based on PSO algorithmâ€, 14th International Symposium on Distributed Computing and Applications for Business Engineering and Science, pp.143-146, 2015.
[25] Salim Bitam, "Bees Life Algorithm for Job Scheduling in Cloud Computing",Proceedings of The Third International Conference on Communications and Information Technology, pp. 186-191,2012.
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Published
2016-07-23
How to Cite
Bansal, G., & Kaur, A. (2016). Enhancement of dynamic load balancing using Particle Swarm Algorithm in Cloud Environment. INTERNATIONAL JOURNAL OF COMPUTERS &Amp; TECHNOLOGY, 15(10), 7164–7168. https://doi.org/10.24297/ijct.v15i10.4390
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