Comparative study on load balancing and service broker algorithms in Cloud computing using cloud analyst tool

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Published Mar 1, 2021
https://doi.org/10.47164/ijngc.v12i1.189
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Volume 12, Issue 1, March 2021

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Payaswini P
Assistant Professor of Computer Science & Technology, Goa University, India

Abstract

In recent years, there has been a large increase in the number of cloud users as it provides an easy and flexible way to manage user data and applications. With the emerging technologies such as the Internet of things, cloud computing being the backbone, the load on the cloud servers has increased. The cloud data centers consist of servers hosting multiple virtual machines. One of the main challenges in cloud computing is to efficiently distribute the user service requests to different virtual machines in order to reduce the request processing time and to provide more user satisfaction. Load balancing algorithms basically address two issues: the selection of the data center and the distribution of the load on different virtual machines. Data center selection is handled by service broker policy and distribution of the load on virtual machines handled by VM load balancer. Load balancing techniques play a vital role in minimizing the response time and maximizing throughput and also ensure scalability and reliability. Hence, it has become an important research topic in the field of cloud computing. Cloud-analyst, a java based open source toolkit, is useful to simulate and analyze the load balancing algorithms. In this paper, a comparative study on different service broker policies and VM load balancing algorithms for cloud computing is presented with simulation results. The aim of this comparative study is to find the performance of different service broker policies and the load balancing algorithm tested on different scenarios.

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How to Cite
Payaswini P. (2021). Comparative study on load balancing and service broker algorithms in Cloud computing using cloud analyst tool. International Journal of Next-Generation Computing, 12(1), 49–61. https://doi.org/10.47164/ijngc.v12i1.189
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