EVALUATION OF CPU SCHEDULING ALGORITHM WITH GENETIC APPROACH

Amit Gupta

Vol. 1, Jan-Dec 2015

Page Number: 23 - 31

Abstract:

There are many factors that affecting the efficiency of data processing systems. Understanding the operating system performance is a big issue. Operating system performance issues commonly involve process management, memory management, and scheduling. In computer System, Processor scheduling divides a computer processor's work between multiple programs so that it is continually switching from one opened application to another. This gives the appearance that the computer is running a number of different programs simultaneously as the whole process are very fast. Our study is an effort to develop an algorithm (genetic algorithm based) for obtaining optimal solution or near to optimal schedules for CPU Scheduling Problems with minimum computation effort even for large sized problems. Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover. In this paper we compare the result of average waiting time of general algorithms with the genetic algorithm

References

• J.D. Ullman, NP-complete scheduling problems, published in Journal of Computer and System Sciences.
• Wemke van der Weij, Sandjai Bhulai, Rob van der Mei. “Optimal scheduling policies for the limited processor sharing queue”.
• Back, T. (1996). “Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms”. Oxford University Press US. [On-line]. Available: http://books.google.de/books?id=EaN7kvl5coYC [2010].
• Sivanandam, S. N. & Deepa, S. N. “Introduction to Genetic Algorithms”. Springer.2008
• Omar, M., Baharum, A., & Hasan, Y. Abu. “A Job-Shop Scheduling Problem (JSSP) Using Genetic Algorithm“. In Proceedings of 2nd IMT-GT Regional Conference on Mathematics and Statistics, 2006.
• Snehal Kamalapur. ”Efficient CPU Scheduling: A Genetic Algorithm based Approach”. In conference proceeding IEEE, 2006, pp.206-207
• Mitchell, Melanie. “An Introduction to Genetic Algorithms”, 1st ed., MIT Press, 1996.
• Lau, T. L. & Tsang, E. P. K. “Guided genetic algorithm and its application to the generalized assignment problem”. Submitted to Computers and Operations Research, 1998.
• Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Boston: Addison-Wesley
• Davis, L. (1991). Handbook of Genetic Algorithm. Von Nostrand Reinhold, Newyork
• Tomassini, M. (1999). Parallel and Distributed Evolutionary Algorithms: A Review. In Miettinen, K., Makela, M., & Periaux, J. (Eds.), Evolutionary Algorithms in Engineering and Computer Science (pp. 113 - 133). Chichester: J. Wiley and Sons

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