CFP last date
20 May 2024
Reseach Article

Aggregation of EDF and ACO for Enhancing Real Time System Performance

by Jashweeni Nandanwar, Urmila Shrawankar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 73 - Number 17
Year of Publication: 2013
Authors: Jashweeni Nandanwar, Urmila Shrawankar
10.5120/12834-9974

Jashweeni Nandanwar, Urmila Shrawankar . Aggregation of EDF and ACO for Enhancing Real Time System Performance. International Journal of Computer Applications. 73, 17 ( July 2013), 24-33. DOI=10.5120/12834-9974

@article{ 10.5120/12834-9974,
author = { Jashweeni Nandanwar, Urmila Shrawankar },
title = { Aggregation of EDF and ACO for Enhancing Real Time System Performance },
journal = { International Journal of Computer Applications },
issue_date = { July 2013 },
volume = { 73 },
number = { 17 },
month = { July },
year = { 2013 },
issn = { 0975-8887 },
pages = { 24-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume73/number17/12834-9974/ },
doi = { 10.5120/12834-9974 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:40:22.497751+05:30
%A Jashweeni Nandanwar
%A Urmila Shrawankar
%T Aggregation of EDF and ACO for Enhancing Real Time System Performance
%J International Journal of Computer Applications
%@ 0975-8887
%V 73
%N 17
%P 24-33
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Time constraint is the main factor in real time operating system and it affects the deadline of the process. To achieve deadline, proper scheduling algorithm is required to schedule the task. In this paper an Adaptive scheduling algorithm is developed which is the combination of Earliest Deadline First (EDF) and Ant Colony Optimization (ACO). The EDF algorithm places the process in a priority queue and executed using the deadline. The priority of the processes depends upon the deadline and handles the under loaded condition. The limitation of EDF algorithm is that it cannot handle the overloaded condition. The execution of ACO algorithm is based on the execution time. Process which contains the minimum execution time is executed first. The limitation of ACO algorithm is, it takes more time for execution than EDF. Therefore, to remove the limitation of both the algorithms an Adaptive scheduling algorithm is developed. It increases performance of the system and decreases the system failure. Also the percentage of missing deadline is reduced. The advantage of an Adaptive scheduling algorithm is, it handles over-loaded and under-loaded condition simultaneously. The performance of an Adaptive scheduling algorithm is calculated in terms of Success Ratio that is the number of process scheduled and CPU Utilization. The result of execution time is compared with the EDF and ACO scheduling algorithm. The goal of an Adaptive scheduling algorithm is to show the switching between the scheduling algorithms and to decrease the system failure and increase the system performance.

References
  1. M. Kaladevi and Dr. S. Sathiyabama "A Comparative Study of Scheduling Algorithms for Real Time Task" International Journal of Advances in Science and Technology, Vol. 1, No. 4, 2010.
  2. A. F. M. Suaib Akhter, Mahmudur Rahman Khan,Shariful Islam " Overload Avoidance Algorithm for Real-Time Distributed System" IJCSN International Journal of Computer Science and Network Security, Vol. 12 no. 9,September 2012.
  3. Marko Bertogna and Sanjoy Baruah "Limited Preemption EDF Scheduling of Sporadic Task Systems" IEEE Transactions on Industrial Informatics, Vol. 6, no. 4, November 2010.
  4. Fengxiang Zhang and Alan Burns "Schedulability Analysis for Real-Time Systems with EDF Scheduling" IEEE Transactions on Computers, Vol. 58, no. 9, September 2009.
  5. Lalatendu Behera Durga Prasad Mohapatra "Schedulability Analysis of Task Scheduling in Multiprocessor Real-Time Systems Using EDF Algorithm" 2012 International Conference on Computer Communication and Informatics Coimbatore, INDIA
  6. Shuhui Li, Shangping Re, Yue Yu, Xing Wang, Li Wang, and Gang Quan, " Profit and Penalty Aware Scheduling for Real-Time Online Services" IEEE Transactions on Industrial Informatics, Vol. 8, no. 1, February 2012.
  7. Sachin R. Sakhare and Dr. M. S. Ali "Genetic Algorithm Based Adaptive Scheduling Algorithm for Real Time Operating Systems" International Journal of Embedded Systems and Applications (IJESA) Vol. 2, No. 3, September 2012.
  8. Jashweeni Nandanwar, Urmila Shrawankar "An Adaptive Real Time Task Scheduler" IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 6, No 1, November 2012.
  9. Ching-Chih Han, Member and Kwei-Jay Lin, "Distance constraint scheduling and its application to real time system" IEEE Transactions On Computers, Vol. 45, no. 7, July 1996.
  10. C. Liu and J. Layland, "Scheduling Algorithms for Multiprogramming in a Hard Real-Time Environment," J. ACM, vol. 20, pp. 46–61, 1973.
  11. Giorgio C. Buttazzo, Marko Bertogna and Gang Yao "Limited Preemptive Scheduling for Real-Time Systems" IEEE Transactions On Industrial Informatics, Vol. 9, no. 1, February 2013.
  12. Xuefeng Piao, Sangchul Han, Heeheon Kim, Minkyu Park, Yookun Cho "Predictability of Earliest Deadline Zero Laxity Algorithm for Multiprocessor Real-Time Systems" Proceedings of the Ninth IEEE International
  13. Symposium on Object and Component-Oriented Real-Time Distributed Computing 2006
  14. W. N. Chen and J. Zhang, "An Ant Colony Optimization Approach to a Grid Workflow Scheduling Problem with Various QoS Requirements," IEEE Trans. System, Man, and Cybernetics- Part C, vol. 39, no. 1, pp. 29-43, Jan. 2009.
  15. W. N. Chen, J. Zhang, H. S. H. Chung, R. Z. Huang, and O. Liu, "Optimizing Discounted Cash Flows in Project Scheduling—An Ant Colony Optimization Approach," IEEE Trans. Systems, Man, and Cybernetics-Part C, vol. 40, no. 1, pp. 64-77, Jan. 2010.
  16. Ketan Kotecha and Apurva Shah "Scheduling Algorithm for Real-Time Operating Systems using ACO" 2010 International Conference on Computational Intelligence and Communication Networks.
  17. Yuren Zhou, Xinsheng Lai, Yuanxiang Li, and Wenyong Dong "Ant Colony Optimization with Combining Gaussian Eliminations for Matrix Multiplication" IEEE Transactions On Cybernetics, Vol. 43, no. 1, February 2013.
  18. Michael A. Palis "The Granularity Metric for Fine-Grain Real-Time Scheduling" IEEE Transactions on Computers, Vol. 54, no. 12, December 2005.
  19. Ya-Shu Chen, Han Chiang Liao, and Ting-Hao Tsai " Online Real-Time Task Scheduling in Heterogeneous Multicore System-on-a-Chip" IEEE Transactions On Parallel And Distributed Systems, Vol. 24, no. 1, January 2013.
  20. Joon-Woo Lee and Ju-Jang Lee "Ant-Colony-Based Scheduling Algorithm for Energy-Efficient Coverage of WSN" IEEE Sensors Journal, Vol. 12, no. 10, October 2012.
  21. Wei-Neng Chen, Member, IEEE, and Jun Zhang, Senior Member, IEEE "Ant Colony Optimization for Software Project Scheduling and Staffing with an Event-Based Scheduler" IEEE Transactions on Software Engineering, Vol. 39, no. 1, January 2013
  22. Krithi Ramamritham, John A Stankovik and Perng Fei Shiah, "Efficient scheduling algorithms for real-time multiprocessor systems", IEEE Transaction on Parallel and Distributed Systems, vol. 1(2), April 1990.
  23. Silviu S. Craciunas, Christoph M. Kirsch Ana Sokolova "Response Time versus Utilization in Scheduler Overhead Accounting" 2010 16th IEEE Real-Time and Embedded Technology and Applications Symposium. Bowman, M. , Debray, S. K. , and Peterson, L. L. 1993. Reasoning about naming systems.
Index Terms

Computer Science
Information Sciences

Keywords

Real-Time Scheduling algorithm Earliest Deadline First Ant Colony Optimization Load balancing Adaptive Scheduling Algorithm