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Reseach Article

Response Time Minimization Task Scheduling Algorithm

by M. Hemamalini, M. V. Srinath
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 145 - Number 1
Year of Publication: 2016
Authors: M. Hemamalini, M. V. Srinath
10.5120/ijca2016910532

M. Hemamalini, M. V. Srinath . Response Time Minimization Task Scheduling Algorithm. International Journal of Computer Applications. 145, 1 ( Jul 2016), 9-14. DOI=10.5120/ijca2016910532

@article{ 10.5120/ijca2016910532,
author = { M. Hemamalini, M. V. Srinath },
title = { Response Time Minimization Task Scheduling Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2016 },
volume = { 145 },
number = { 1 },
month = { Jul },
year = { 2016 },
issn = { 0975-8887 },
pages = { 9-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume145/number1/25240-2016910532/ },
doi = { 10.5120/ijca2016910532 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:47:36.227579+05:30
%A M. Hemamalini
%A M. V. Srinath
%T Response Time Minimization Task Scheduling Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 145
%N 1
%P 9-14
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Computational grid has been measured as the best model for managing large scale distributed system having geographically owed resources. Load balancing algorithms are important in the research of network applications. The proposed algorithm minimizes the average execution time and response time. The proposed Response Time Minimization algorithm is implemented with Cloudanalyst toolkit which can simulate a decentralized module. The Cloudanalyst toolkit abstracts the features and behavior of complex fundamental elements such as tasks, resources and users. The proposed algorithm presents service namely resource discovery. The proposed Response Time Minimization Approach is compared with Round Robin Algorithm and equally spread execution algorithm. The main objective of the Response time minimization algorithm is to share the load to the available Virtual Machine (VM) efficiently in order to improve the response time, data processing time and minimize delay time. The performance analysis indicates that the response time of the proposed task scheduling algorithm is much better than the Round Robin Algorithm and equally spread execution algorithm. Results support the proposed approach.

References
  1. B.Santhosh Kumar , Latha Parthiban (2015). An Energy Efficient Data Center Selection Framework for virtualized Cloud Computing Environment. Indian Journal of Science and Technology, vol 8(35). ISSN (Print) : 0974-6846
  2. Pushtikant Malviya, Swapnamukta Agrawal, Shailendra Singh, “An Effective Approach for Allocating VMs to Reduce the Power Consumption of Virtualized Cloud Environment,” Fourth International Conference on Communication Systems and Network Technologies, IEEE, 2014.
  3. Akhil Goyal, Bharti, “A Study of Load Balancing in Cloud Computing using soft Computing Techniques,” International Journal of Computer Applications © 2014 by IJCA Journal, Vol. 92, No. 9, 2014.
  4. Hemamalini M. (2012). Review on grid task scheduling algorithm in a distributed heterogeneous environment. International Journal of Computer Applications. 40(2):24-30.
  5. M.Hemamalini, Dr.M.V.Srinath “State of the Art: Task Scheduling Algorithms in Heterogeneous Grid Computing Environment”, Elysium Journal of Engineering Research and Management, Volume-1, Issue-1, August 2014.
  6. Kokilavani T, Amalarethinam GDI. Load balanced min-min static meta-task scheduling algorithm in a grid computing environment. International Journal of Computer Applications. 2011; 20(2):43-9.
  7. Hemamalini M. Dr.M.V.Srinath. (2015).Memory Constrained Load Shared Minimum Execution Time Grid Task Scheduling Algorithm in a Heterogeneous Environment. Indian Journal of Science and Technology, Vol 8(15), ISSN (Print) : 0974-6846 , ISSN (Online) : 0974-5645.
  8. Anton Beloglozov, Jemal Abawajy, Rajkumar Buyya,“Energy-aware Resource Allocation Heuristics for Efficient management of Data centers for Cloud Computing,” Future Generation Computer Systems, vol.28, pp. 755-768, ELSEVIER 2012.
  9. Bharti Wadhwa, Amandeep Verma, “Energy Saving approaches for Green Cloud Computing: A Review,” Proceedings of RAECS UIET Panjab University Chandigarh, IEEE, 06 – 08 March, 2014.
  10. Jens Buysse, Konstantinos Georgakilas, Anna Tzanakaki,Marc De Leenheer, Bart Dhoedt, Chris Develder, “Energy- Efficient Resource-Provisioning Algorithm for Optical Clouds,” Journal of Optical Communication Network, Vol. 5, No. 3, pp. 226-239, March 2013.
  11. An-ping Xiong, Chun-xiang Xu, ”Energy Efficient Multiresource Allocation of Virtual Machine Based on PSO in Cloud Data Center,” Hindawi Publishing Corporation, Mathematical Problems in Engineering,Vol. 2014, Article ID 816518, June, 2014.
  12. Wanneng Shu, Wei Wang, Yunji Wang, “A Novel Energy-Efficient Resource Allocation Algorithm based on Immune Clonal Optimization for Green Cloud Computing,” EURASIP Journal on Wireless Communications and Networking, Springer, 2014.
  13. Yanwen Xiao, Jinbao Wang, Yaping li, Hong Gao, “An Energy-Efficient Data Placement Algorithm and Node Scheduling Strategies in Cloud Computing Systems,” 2nd International Conference on Advances in Computer Science and Engineering, Atlantis Press (CSE 2013).
  14. Meenakshi Sharma , Pankaj Sharma ,”Performance Evaluation of Adaptive Virtual Machine Load Balancing Algorithm “, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 3, No.2, 2012.
  15. Raj, G. (2012). Comparative Analysis of Load Balancing Algorithms in Cloud Computing, 1(3), 120–124.
  16. Bhathiya Wickremasinghe, Rodrigo N. Calheiros, and Rajkumar Buyya. “Cloud Analyst: A Cloud Sim-based Visual Modeller for Analysing Cloud Computing Environments and Applications”, 20-23 April 2010 Page No 446-452.
  17. Tanveer Ahmed, Yogendra Singh (2012), Analytic Study of Load Balancing Technique Using Tool Cloud Analyst. International Journal of Engineering Research and Applications (IJERA). Vol. 2, Issue 2,Mar-Apr 2012, pp.1027-1030. ISSN: 2248-9622 .
Index Terms

Computer Science
Information Sciences

Keywords

Task scheduling Response Time virtual Machine Data center and Load balancing.