CFP last date
20 May 2024
Reseach Article

A Novel Bee Colony Approach to Distributed Systems Scheduling

by Raheleh Sarvizadeh, Mostafa Haghi Kashani, Fahimeh Sadat Zakeri, Seyed Mahdi Jameii
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 42 - Number 10
Year of Publication: 2012
Authors: Raheleh Sarvizadeh, Mostafa Haghi Kashani, Fahimeh Sadat Zakeri, Seyed Mahdi Jameii
10.5120/5726-7792

Raheleh Sarvizadeh, Mostafa Haghi Kashani, Fahimeh Sadat Zakeri, Seyed Mahdi Jameii . A Novel Bee Colony Approach to Distributed Systems Scheduling. International Journal of Computer Applications. 42, 10 ( March 2012), 1-6. DOI=10.5120/5726-7792

@article{ 10.5120/5726-7792,
author = { Raheleh Sarvizadeh, Mostafa Haghi Kashani, Fahimeh Sadat Zakeri, Seyed Mahdi Jameii },
title = { A Novel Bee Colony Approach to Distributed Systems Scheduling },
journal = { International Journal of Computer Applications },
issue_date = { March 2012 },
volume = { 42 },
number = { 10 },
month = { March },
year = { 2012 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume42/number10/5726-7792/ },
doi = { 10.5120/5726-7792 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:32:10.457861+05:30
%A Raheleh Sarvizadeh
%A Mostafa Haghi Kashani
%A Fahimeh Sadat Zakeri
%A Seyed Mahdi Jameii
%T A Novel Bee Colony Approach to Distributed Systems Scheduling
%J International Journal of Computer Applications
%@ 0975-8887
%V 42
%N 10
%P 1-6
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The problem of task scheduling in distributed systems is known as an NP-hard problem, and methods based on heuristic or metaheuristic search have been proposed to obtain optimal and suboptimal solutions. The scheduling problem is a key factor for distributed systems to gain better performance. In this paper, an efficient method based on memetic algorithm is developed to solve the problem of distributed systems scheduling. With regard to load balancing efficiently, Bee Colony Optimization (BCO) has been applied as local search in the proposed memetic algorithm. The proposed method has been compared to existing GA-based method and two memetic-Based methods in which Tabu method and Learning Automata method have been used as local search. The results demonstrated that the proposed method outperform the above mentioned methods in terms of CPU Utilization, communication cost and Makespan.

References
  1. Shen, C. C. and Tsai, W. H. 1985. A Graph Matching Approach to Optimal Task Assignment in Distributed Computing Using a Minimax Criterion. IEEE Trans. On Computers, Vol. 34, 197-203.
  2. Ma, P. Y. R. , Lee, E. Y. S. and Tsuchiya J. 1982. A Task Allocation Model for Distributed Computing Systems. IEEE Trans. On Computers, Vol. 31, 41-47.
  3. Park, G. L. 2004. Performance Evaluation of a List Scheduling Algorithm In Distributed Memory Multiprocessor Systems. International Journal of Future Generation Computer Systems, Vol. 20, 249-256.
  4. Park, C. I. and Choe, T. Y. 2002. An optimal scheduling algorithm based on task duplication. IEEE Trans. on Computers, Vol. 51, 444–448.
  5. Woodside, C. M. and Monforton, G. G. 1993. Fast Allocation of Processes in Distributed and Parallel Systems. IEEE Trans. On Parallel and Distributed Systems, Vol. 4, 164-174.
  6. Page, A. J. , Keane, T. M. and Naughton, T. J. 2010. Multi-heuristic dynamic task allocation using genetic algorithms in a heterogeneous distributed system. Journal of Parallel and Distributed Computing, Vol. 70, 758-766.
  7. Sarje, A. K. and Sagar, G. 1991. Heuristic Model for Task Allocation in Distributed Computer Systems. Proc. of the IEEE, Vol. 138, 313-318.
  8. Chow, Y. and Kohler, W. H. 1979. Models for Dynamic Load Balancing in a Heterogeneous Multiple Processor System. IEEE Transactions on Computers, Vol. 28, 354-361.
  9. Lin, M. and Yang, L. T. 1999. Hybrid Genetic Algorithms for Scheduling Partially Ordered Tasks in a Multi-processor Environment. Proc. of the 6th IEEE Conf. on Real-Time Computer Systems and Applications, 382–387.
  10. Yao, W. , Yao, J. and Li, B. 2004. Main Sequences Genetic Scheduling For Multiprocessor Systems Using Task Duplication," International Journal of Microprocessors and Microsystems, Vol. 28, 85-94.
  11. Moore, M. 2003. An Accurate and Efficient Parallel Genetic Algorithm to Schedule Tasks on a Cluster. Proc. of the IEEE International Parallel and Distributed Processing Symposium.
  12. Martino, V. D. 2003. Sub Optimal Scheduling in a Grid using Genetic Algorithms. Proc. of the IEEE International Parallel and Distributed Processing Symposium.
  13. Zomaya, A. Y. , Ward, C. and Macey, B. 1999. Genetic Scheduling for Parallel Processor Systems: Comparative Studies and Performance Issues. IEEE Trans. On Parallel and Distributed Systems, Vol. 10, 795-812.
  14. Woo, S. H. , Yang, S. , Kim, S. and Han, T. 1997. Task scheduling in distributed computing systems with a genetic algorithm. High-Performance Computing on the Information Superhighway, HPC-Asia '97, 301-307.
  15. Hou, E. S. H. , Ansari, N. and Ren, H. 1994. A Genetic Algorithm for Multiprocessor Scheduling. IEEE Trans. On Parallel and Distributed Systems, Vol. 5, 113-120.
  16. Lan, Y. and Yu, T. 1995. A Dynamic Central Scheduler Load-Balancing Mechanism. Proc. of the 14th IEEE Ann. Int'l Phoenix Conf. on Computers and Communication, 734-740.
  17. Bonomi, F. and Kumar, A. 1990. Adaptive Optimal Load-Balancing in a Heterogeneous Multiserver System with a Central Job Scheduler. IEEE Trans. on Computers, Vol. 39, 1232-1250.
  18. Teodorovic, D. , Davidovic, T. and Selmic, M. 2011. Bee Colony Optimization: The Applications Survey. ACM Transactions on Computational Logic, 1-20.
  19. Karaboga, D. and Basturk, B. 2008. On the performance of arti?cial bee colony (ABC)," Algorithm. Appl. Soft Comput, Vol. 8, 687–697.
  20. Hanani, A. , Nourossana, S. , Haj seyed javadi, H. and Rahmani, A. 2010. Solving the Scheduling Problem in Multi-Processor Systems with Communication Cost and Precedence using Bee Colony System. in Proc. of the 3rd International Conference on Advanced Computer Theory and Engineering, V5-464-V5-469.
  21. Nikravan, M. and Kashani, M. H. 2007. A Genetic Algorithm For Process Scheduling In Distributed Operating Systems Considering Load balancing. in Proceedings of the 21th European Conference on Modeling and Simulation, 645-650.
  22. Kashani, M. H. , Jamei, M. , Akbari, M. and Moosavi Tayebi, R. 2011. Utilizing Bee Colony to Solve Task Scheduling Problem in Distributed Systems. in Proceedings of the Third International Conference on Computational Intelligence, Communication Systems and Networks, 298-303.
  23. Jahanshahi, M. , Gholipour, M. , Kordafshari, M. S. and Rahmani, A. M. 2009. A Novel Method for Task Scheduling in Distributed Systems Using Memetic. in proceeding of the Second International Conference on Communication Theory, Reliability, and Quality of Service, 58-62.
  24. Jahanshahi, M. , Meybodi, M. R. and Dehghan, M. 2009. A New Approach for Task Scheduling in Distributed Systems Using Learning Automata. Proceedings of the IEEE International Conference on Automation and Logistics Shenyang, 62-67.
Index Terms

Computer Science
Information Sciences

Keywords

Scheduling Memetic Algorithm Bee Colony Optimization