Performance Evaluation of FCFS and EBF in Linear and Non-Linear Gridlet Size

Authors

  • Bhardwaj N Department of Computer Engineering UIET, Kurukshetra University, Haryana, India
  • Karambir Department of Computer Engineering UIET, Kurukshetra University, Haryana, India
  • Jangra A Department of Computer Engineering UIET, Kurukshetra University, Haryana, India

Keywords:

Grid Computing, Workflow, Resource Utilization, Throughput

Abstract

Grid computing define as the infrastructure in which hardware as well as software resources situated at different places; shared and uses by the different organizations which coordinated to provide consistent, pervasive and transparent access. Workflow is a set of task or subtasks having dependency among them. Resource allocation is one the objective of grid computing. Efficiently use of resources to run the workflow tasks in order to achieve maximum utilization of resources. Throughput is amount of information process in given amount of time. This parameter is mainly applied to various phenomenon’s of networking systems. In this paper, first come first serve and easy backfilling algorithm performance evaluated on the basis of linear and non-linear increase in gridlet size and compare the result in both the cases. The results indicate that EBF has better resource utilization and throughput than FCFS.

References

. H. F. Lipson, “Tracking and Tracing” Cyber Attacks: Technical Challenges and Global Policy Issues”, CERT Coordination Centre, Special Report: CMU/SEI-2002-SR-009, 2002

. N. Stephen and N. Judy, Network Intrusion Detection, 3nd ed., New Riders Publishing, United States of America, 2002.

. A. D. BasheerNayef, “Mitigation and traceback countermeasures for DDoS attacks”, Iowa State University, 2005. .

. Chen, Y. Hwang, K., W. S. Ku, “Distributed change-point detection of DDoS attacks over multiple network domains.” Proceedings of the IEEE International Symposium on Collaborative Technologies and Systems, Las Vegas, NV, 14-17 May. IEEE CS, 2006, pp. 543–550.

. L. Limwiwatkul, A. Rungsawang, “ Distributed denial of service detection using TCP/IP header and traffic measurement analysis”, Proceedings of the IEEE International Symposium Communications and Information Technology, Sapporo, Japan, 26-29 October, IEEE CS, 2006, , pp. 605–610.

. Lee, Juhyun Kim, Ki Hoon Kwon, Younggoo Han, Sehun Kim, “DDoS attack detection method using Cluster analysis”, Expert System with Applications 34, 2008, pp.1659-1665.

. K. Reyhaneh, F. Ahmad, “An Anomaly-Based Method for DDoS Attacks Detection using RBF Neural Networks”, International Conference on Network and Electronics Engineering IPCST vol.11, 2011, IACSIT Press, Singapore.

. Cristóbal Romero, Sebastián Ventura, Pedro G. Espejo and César Hervás,” Data Mining Algorithms to Classify Students”.

. UCLA CSD packet traces.http://www.lasr.cs.ucla.edu/ddos/traces/public/usc.

. N. Abirami, T. Kamalakannan and Dr. A. Muthukumaravel ,” A Study on Analysis of Various Data mining Classification Techniques on Healthcare Data” International Journal of Emerging Technology and Advanced Engineering, Volume 3, Issue 7, July 2013,pp.604-607.

. K. Wisaeng,”A Comparison of Different Classification Techniques for Bank Direct Marketing”, International Journal of Soft Computing and Engineering, Volume-3, Issue-4, September 2013, pp. 116-119.

. S. Archana and Dr. K. Elangovan, “Survey of Classification Techniques in Data Mining”, International Journal of Computer Science and Mobile Applications, Vol.2 Issue. 2, February-2014, pg. 65-71.

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Published

2025-11-10

How to Cite

[1]
N. Bhardwaj, Karambir, and A. Jangra, “Performance Evaluation of FCFS and EBF in Linear and Non-Linear Gridlet Size”, Int. J. Comp. Sci. Eng., vol. 3, no. 8, pp. 46–49, Nov. 2025.

Issue

Vol. 3 No. 8 (2015): IJCSE August Edition

Section

Research Article

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