QoS Parameters for Cloud Using Swarm Optimization Dynamic Load Balancing Algorithm

Sanjay G. Patel¹ , S.D. Panchal²

Section:Research Paper, Product Type: Journal
Vol.7 , Issue.3 , pp.33-39, Jun-2019

Online published on Jul 30, 2019

Copyright © Sanjay G. Patel, S.D. Panchal . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Due to popularity of Cloud computing environment, the cloud computing users are increasing day by day and that has become one of the important challenges for the cloud providers in terms of load balancing. Load balancing distributes the traffic evenly over multiple paths. In this research work, we have proposed the Dynamic Improved PSO Load balancing algorithm and implement it over CloudSim toolkit. This toolkit assisted the modeling and generation of virtual machines in a simulated manner such that datacenters, jobs and their mapping to VMs can be done on a same node whereas provide the desirable result. Therefore, the results are compared with the existing load balancing algorithms namely Modified Throttled, FCFS and Particle Swam Optimization based on their performance using CloudSim Simulator. Simulation outcomes are recorded in terms of the Response time and datacenter processing time of these algorithms along with its performance and cost details.

Key-Words / Index Term :
Cloud Computing, Load Balancing, Virtual Machine, Scheduling, Particle Swarm Optimization, Modified Throttled, FCFS, CloudSim, Response time, Data Center Processing Time, Cost

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