Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Simulated Annealing Load Balancing Algorithm for Cloud Data Centres through Ant Colony Optimization

Author : Anu V.R. 1 Elizabeth Sherly 2

Date of Publication :28th April 2018

Abstract: Load balancing optimization strategies on cloud data centers are active research area in the cloud environment. Through energy efficient load balancing techniques, VMs can be packed into the fewer number of servers and reduce the power dissipation and CO2 footprint. However allocating too many VMs to a physical server can cause interference issues, hotspot and SLA violations. Here we present a simulated annealing load balancing strategy through ant colony optimization to keep a strong tradeoff between these above mentioned issues and provide an energy efficient, SLA guaranteed load balancing algorithm to map VMs effectively between physical servers. Experimental results show that the proposed algorithm simulated annealing through ant colony optimization performs well and achieve better load balancing results as compared with other algorithms.

Reference :

    1. Varasteh, A., & Goudarzi, M.,”Server consolidation techniques in virtualized data centres: A survey”, IEEE Systems Journal, 11(2), 772-783, 2017.
    2. Choudhary, A., Rana, S., & Matahai, K. J.,” A critical analysis of energy efficient virtual machine placement techniques and its optimization in a cloud computing environment”. Procedia Computer Science, 78, 132-138,2016.
    3. Fan, Z., Shen, H., Wu, Y., & Li, Y., “Simulatedannealing load balancing for resource allocation in cloud environments”. In Parallel and Distributed Computing, Applications and Technologies (PDCAT), 2013 International Conference on (pp. 1-6). IEEE, December 2013.
    4. Cardoso, L. P., Mattos, D. M., Ferraz, L. H. G., Duarte, O. C. M., & Pujolley, G., “An efficient energy-aware mechanism for virtual machine migration”. In Global Information Infrastructure and Networking Symposium (GIIS), 2015 (pp. 1-6). IEEE, October 2015.
    5. Tian, W., Zhao, Y., Xu, M., Zhong, Y., & Sun, X., “A toolkit for modeling and simulation of real-time virtual machine allocation in a cloud data center”. IEEE Transactions on Automation Science and Engineering, 12(1), 153-161, 2015.
    6. ÇaÄŸlar, Ä°., & Altilar, D. T., “An energy efficient VM allocation approach for data centers”, In Big Data Security on Cloud (Big Data Security), IEEE International Conference on High Performance and Smart Computing (HPSC), and IEEE International Conference on Intelligent Data and Security (IDS), 2016 IEEE 2nd International Conference on (pp. 240-244), IEEE, April 2016.

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