CFP last date
20 December 2024
Reseach Article

Performance Enhancement of Distributed System through Load Balancing and Task Scheduling

by Abhijit A. Rajguru, Sulabha S. Apte
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 181 - Number 3
Year of Publication: 2018
Authors: Abhijit A. Rajguru, Sulabha S. Apte
10.5120/ijca2018917391

Abhijit A. Rajguru, Sulabha S. Apte . Performance Enhancement of Distributed System through Load Balancing and Task Scheduling. International Journal of Computer Applications. 181, 3 ( Jul 2018), 20-26. DOI=10.5120/ijca2018917391

@article{ 10.5120/ijca2018917391,
author = { Abhijit A. Rajguru, Sulabha S. Apte },
title = { Performance Enhancement of Distributed System through Load Balancing and Task Scheduling },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2018 },
volume = { 181 },
number = { 3 },
month = { Jul },
year = { 2018 },
issn = { 0975-8887 },
pages = { 20-26 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume181/number3/29697-2018917391/ },
doi = { 10.5120/ijca2018917391 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:04:53.387787+05:30
%A Abhijit A. Rajguru
%A Sulabha S. Apte
%T Performance Enhancement of Distributed System through Load Balancing and Task Scheduling
%J International Journal of Computer Applications
%@ 0975-8887
%V 181
%N 3
%P 20-26
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Task scheduling & load balancing in distributed network are the most challenging research area in computer science. In distributed systems, scheduling mechanism has more issues as there is no centralized authority to allocate the workload among multiple processors. Further, handling both load balancing and scheduling at hand is a daunting task. In this paper, we propose a fuzzy based load balancing and task scheduling technique to optimize the performance of distributed system. Initially, clusters are formed and node with larger buffer availability and high CPU speed is elected as cluster head. Tasks are prioritized into flexible and non-flexible using task prioritizing strategy. Non-Flexible tasks are prioritized over flexible tasks. For non-flexible tasks, essential information of nodes such as CPU speed, work load and distance from cluster head are made pass through the fuzzy system. Node state is obtained as output. Based on states of node, non-flexible tasks are allocated. Our technique dynamically handles scheduling and load balancing at hand. We use simulation results to prove efficiency of our technique.

References
  1. Daniel Grosu, Anthony T. Chronopoulos and Ming-Ying Leung, “Load Balancing in Distributed Systems: An Approach Using Cooperative Games”, Proceedings of the 16th International Parallel and Distributed Processing Symposium, (IPDPS’02), 2002.
  2. Veeravalli Bharadwaj, Depasish Ghose and Thomas G. Robertazzi, “Divisible Load Theory: A New Paradigm for Load Scheduling in Distributed Systems”, Journal of Cluster computing, Vol-6, Issue-1, 2003.
  3. Krishna Nadiminti, Marcos Dias de Assunção, and Rajkumar Buyya, “Distributed Systems and Recent Innovations: Challenges and Benefits”, Grid Computing and Distributed Systems Laboratory, 2006.
  4. www.wikipedia.org.
  5. Sandeep Sharma, Sarabjit Singh, and Meenakshi Sharma, “Performance Analysis of Load Balancing Algorithms”, World Academy of Science, Engineering and Technology, 2008
  6. Hisao Kameda, El-Zoghdy Said Fathyy and Inhwan Ryuz Jie Lix, “A Performance Comparison of Dynamic vs. Static Load Balancing Policies in a Mainframe { Personal Computer Network Model”, Proceedings of the 39th IEEE Conference on Decision and Control, 2000.
  7. Daniel Grosua, Anthony T. and Chronopoulosb,”Non-cooperative load balancing in distributed systems”, Elsevier, Journal of Parallel and Distributed Computing, 2005.
  8. Chow KP and Kwok YK, “On load balancing for distributed multiagent computing”, IEEE Transactions on Parallel and Distributed Systems, Vol- 13, pp- 787-801, 2002.
  9. Jiani Guo and Laxmi Narayan Bhuyan, “Load Balancing in a Cluster-Based Web Server for Multimedia Applications”, IEEE Transactions On Parallel and Distributed Systems, Vol-17, 2006
  10. M. Nikravan and M. H. Kashani, “A Genetic Algorithm for Process Scheduling in Distributed Operating Systems Considering Load balancing”, Proceedings 21st European Conference on Modelling and Simulation (ECMS), 2007.
  11. Sivakumar Viswanathan, Bharadwaj Veeravalli and Thomas G. Robertazzi, “Resource-Aware Distributed Scheduling Strategies for Large-Scale Computational Cluster/Grid Systems”, IEEE Transactions on Parallel and Distributed Systems, Vol-18,
  12. Michael Isard, Vijayan Prabhakaran, Jon Currey, Udi Wieder, Kunal Talwar and Andrew Goldberg, “Quincy: Fair Scheduling for Distributed Computing Clusters”, Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles (SOSP '09), 2009.
  13. Xuan Lin, Ying Lu, Deogun, J. and Goddard, “Real-Time Divisible Load Scheduling for Cluster Computing”, Proceedings of 13th IEEE Real Time and Embedded Technology and Applications Symposium, (RTAS '07), 2007.
  14. Kento Aida and Henri Casanova, “Scheduling Mixed-Parallel Applications with AdvanceReservations”, Proceedings of the 17th international symposium on High performance distributed computing (HPDC '08), 2008.
  15. Ananda Basu1, Saddek Bensalem, Doron Peled, and Joseph Sifakis,” Priority Scheduling of Distributed Systems Based on Model Checking”, Proceedings of the 21st International Conference on Computer Aided Verification (CAV ‘09), 2009.
  16. Zuo Jing, Chi Xuefen, Lin Guan and Li Hongxia, “Service-aware Multi-constrained Routing Protocol with QoS Guarantee Based on Fuzzy Logic”, IEEE 22nd International Conference on Advanced Information Networking and Applications - Workshops, (AINAW), pp- 762 - 767, 2008.
  17. NetworkSimulator:http:///www.isi.edu/nsnam/ns
Index Terms

Computer Science
Information Sciences

Keywords

Distributed Computing Fuzzy Logic Load Balancing Task Scheduling.