Comparison of Various Load Balancing Algorithms in Cloud Computing

Open Access

Year : 2021 | Volume : | Issue : 2 | Page : 1-5
By

    Ayushi Bhargava

  1. Manish Bhardwa

  1. Student, Department of Computer Science & Engineering, Poornima Institute of Engineering & Technology, Jaipur, Rajasthan, India
  2. Assistant Professor, Department of Computer Science & Engineering, Poornima Institute of Engineering & Technology, Jaipur, Rajasthan, India

Abstract

The components associated with distributed computing are customers, datacenter and appropriated server. One of the principal issuesin distributed computing isload adjusting. Adjusting the heap intends to circulate the outstanding task at hand among a few hubs uniformly so no single hub will be over- burden. Burden can be of any kind that is it very well may be CPU load, memory limit or system load. Right now, introduced a design of burden adjusting and calculation which will additionally improve the heap adjusting issue by limiting the reaction time. Right now, have proposed the improved variant of existing managed load adjusting approach for distributed computing by comping the Randomization and covetous burden adjusting calculation. To check the presentation of proposed approach, we have utilized the cloud investigator test system (Cloud Analyst). Through reenactment examination, it has been discovered that proposed improved form of controlled burden adjusting approach has indicated better execution as far as cost, reaction time and information preparing time.

Keywords: CC–Cloud Computing, CA–Cloud Analyst, GA–genetic algorithm, ACO–ant colony optimization, SHC–stochastic algorithm, FCFS–first come first serve

[This article belongs to Recent Trends in Parallel Computing(rtpc)]

How to cite this article: Ayushi Bhargava, Manish Bhardwa Comparison of Various Load Balancing Algorithms in Cloud Computing rtpc 2021; 8:1-5
How to cite this URL: Ayushi Bhargava, Manish Bhardwa Comparison of Various Load Balancing Algorithms in Cloud Computing rtpc 2021 {cited 2021 Nov 20};8:1-5. Available from: https://journals.stmjournals.com/rtpc/article=2021/view=90424

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References

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Regular Issue Open Access Article
Volume 8
Issue 2
Received September 30, 2021
Accepted October 22, 2021
Published November 20, 2021