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Implementation of Improved Multi-Queue Job Scheduling Algorithm (IMQJSA) for Load Balancing in Cloud Computing

by Inderpal Singh
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 179 - Number 12
Year of Publication: 2018
Authors: Inderpal Singh
10.5120/ijca2018916128

Inderpal Singh . Implementation of Improved Multi-Queue Job Scheduling Algorithm (IMQJSA) for Load Balancing in Cloud Computing. International Journal of Computer Applications. 179, 12 ( Jan 2018), 9-13. DOI=10.5120/ijca2018916128

@article{ 10.5120/ijca2018916128,
author = { Inderpal Singh },
title = { Implementation of Improved Multi-Queue Job Scheduling Algorithm (IMQJSA) for Load Balancing in Cloud Computing },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2018 },
volume = { 179 },
number = { 12 },
month = { Jan },
year = { 2018 },
issn = { 0975-8887 },
pages = { 9-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume179/number12/28850-2018916128/ },
doi = { 10.5120/ijca2018916128 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:55:08.667309+05:30
%A Inderpal Singh
%T Implementation of Improved Multi-Queue Job Scheduling Algorithm (IMQJSA) for Load Balancing in Cloud Computing
%J International Journal of Computer Applications
%@ 0975-8887
%V 179
%N 12
%P 9-13
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this research paper, authors describe how to manage number of tasks simultaneously at a same time on virtual machines on cloud by utilizing enhanced MQS (Multi-Queue Scheduling). This proposed enhanced multi-queue scheduling method is works on Fuzzy Logic. The main significance to utilize this proposed method on fuzzy logic is to achieve maximum accuracy in results. This enhanced multi-queue scheduling method on fuzzy logic at first grouped the number of jobs according to burst time calculated by some formula. Membership Function plays an important role in enhanced multi-queue job scheduling. It gives more efficient results with decreased execution time as well as overhead. This new designed methodology helps organizations or enterprises to reduce the burden of load balancing when run their applications on cloud. Hence, the use of fuzzy logic in enhanced multi-queue job scheduling is an effective way to handle simultaneous jobs at a same time that also helpful to reduce overhead and provide live

References
  1. AV.Karthick, E.Ramaraj, R.Ganapathy Subramanian, 2014. An Efficient Multi Queue Job Scheduling for Cloud Computing”. World Congress on Computing and Communication Technologies.
  2. Vikas Kumar and Shiva Prakash, 2014. A Load Balancing Based Cloud Computing Techniques and Challenges. International Journal of scientific research and management.
  3. YatendraSahu,R.K. Pateriya, March 2013. Cloud Computing Overview with Load Balancing Techniques” International Journal of Computer Applications.
  4. Rajwinder Kaur and PawanLuthra “Load balancing in Cloud Computing”. Proc. of Int. Conf. on Recent Trends in Information, Telecommunication and Computing, ITC.
  5. Soumya Ray and Ajanta DeSarkar, October 2012. Execution analysis of load balancing algorithms in cloud computing environment. International Journal on Cloud Computing: Services and Architecture.
  6. Geetha C. Megha raj, Dr. Mohan K.G., October 2013. Two Levels Hierarchical Model of Load Balancing in Cloud, International Journal of Emerging Technologyand Advanced Engineering.
  7. Monika Arya, security Challenges in different delivery model specifically SaaS, International Journal of Advanced Engineering Research andStudies.
  8. KalpanaParsi andM.Laharika, May 2013. A Comparative Study of Different Deployment Models in a Cloud, International Journal of Advanced Research in Computer Science and Software Engineering.
  9. MayankaKatyal and Atul Mishra, December 2013. A Comparative Study of Load Balancing Algorithms in Cloud Computing Environment”. International Journal of Distributed and Cloud Computing.
  10. RatanMishra andAnantJaiswal , April 2012. Ant colony Optimization: A Solution of Load balancing in Cloud, International Journal of Web & Semantic Technology.
  11. Amandeep, VandanaYadav, Faz Mohammad, April 2014. Different Strategies for Load Balancing in Cloud Computing Environment: a critical Study, International Journal of Scientific Research Engineering & Technology
  12. Nikita Haryani, DhanammaJagli, July-Aug 2014. Dynamic Method for Load Balancing in Cloud Computing, IOSR Journal of Computer Engineering.
Index Terms

Computer Science
Information Sciences

Keywords

Cloud computing Load balancing parameters and techniques Membership function Load MQS scheduling cloud server throughput and execution time.

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