Advance Approach for Load Balancing in Cloud Computing Using (HMSO) Hybrid Multi Swarm Optimization

Authors(1) :-Akshita Jain

Cloud computing is a technology that simplifies tasks by assigning virtual machine (VM) dynamically. Users demand resources as they require, creates difficulties. There are so numerous complexities faced by cloud provider. One of the further most tasks for him is load balancing. There are many algorithms which are accessible for appropriate load balancing but the main purpose of the cloud system is that its client can operate the resources to have economic profits. A resource distribution supervision process is essential to avoid under utilization or over utilization of the resources which might affect the services of the cloud. Equally load distributing acquires improved performance by transferring load from an over loaded server. Resourceful scheduling and resource distribution is an important characteristic of cloud computing based on which the performance of the system is predictable. In this paper I have proposed advance approach for load balancing in cloud computing using hybrid multi swarm optimization (HMSO) and compared the number of load balancing algorithm in terms of cost optimization, response time and processing time.

Authors and Affiliations

Akshita Jain
Computer Science Department, RGPV, Bhopal, Madhya Pradesh, India

Cloud Computing, load balancing, Virtual Machines, Cloud Provider, HMSO

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Publication Details

Published in : Volume 3 | Issue 5 | May-June 2018
Date of Publication : 2018-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 457-463
Manuscript Number : CSEIT183568
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Akshita Jain, "Advance Approach for Load Balancing in Cloud Computing Using (HMSO) Hybrid Multi Swarm Optimization", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 5, pp.457-463, May-June-2018.
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