• Sanjay Gupta

Load balancing is an important factor in public cloud computing. The process of load balancing increases the utilisation of dedicated resources in public cloud computing. Load balancing is approached in two ways: static and dynamic. Dynamic load balancing applies swarm intelligence-based algorithms to the job scheduling process. instead of conventional load balancing approaches such as FCFS, SJF, and partition of load. This paper proposes a hybrid load balancing algorithm for public cloud computing. The hybrid algorithm is a combination of two swarm intelligence algorithms. The algorithms of swarm intelligence are ant colony optimization and genetic algorithms. The diversity of ant colony optimization and the guided approach of genetic algorithms control virtual machine allocation and make the best use of a dedicated system's resources. The proposed algorithm was tested on the cloud-Sim simulator and applied to different sets of user groups and data centres. The experimental analysis of the results suggests that the proposed algorithm is efficient in place of other load balancing algorithms such as GA and ACO.

Cloud Computing, Load Balancing, Swarm intelligence, ACO, GA, Cloud-Sim

"Enhance the Performance of Public Cloud Computing using Hybrid Swarm Intelligence Algorithms.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.3, Issue 2, page no.15-20, February-2016, Available :http://www.jetir.org/papers/JETIR1701844.pdf

"Enhance the Performance of Public Cloud Computing using Hybrid Swarm Intelligence Algorithms.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.3, Issue 2, page no. pp15-20, February-2016, Available at : http://www.jetir.org/papers/JETIR1701844.pdf