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A Reliability Model for the Task Scheduling in Distributed Systems based on Fuzzy Theory

Sagar Gulati, P.K. Yadav, K. Bhatia


Distributed Systems is a mean to run multiple transactions simultaneously. In distributed systems, while dealing with real-time assignments, tasks have to be scheduled correctly and timely. All practical real-time scheduling algorithms in distributed systems present a trade-off between performance and computational complexity. This is mainly due to the complex nature of Mathematical models. In comparison, fuzzy is a simplified approach that optimizes the complete system that too with less time complexity. Further Fuzzy approach produces results that are closer to real world problems. The most innovative part of the research is that reliability metrics have been taken as the major parameter for decision for scheduling. The priority is computed based on the values of Failure rate, CPU time and Reliability. The problem has been simulated on MATLAB 7.0.4 Mamdani Fuzzy Inference Engine to evaluate the performance of the proposed methodology. Experimental results have shown that the proposed fuzzy scheduler creates feasible schedules for homogeneous and heterogeneous tasks..


Fuzzy Scheduling, CPU Time, Reliability, Failure Rate, System Utilization, Feasible Schedule

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Ross Anderson, Security Engineering – A Guide to Building Dependable Distributed Systems, 2nd edition, John Wiley & Sons 2008.

Yadav, P. K., Bhatia, K., Gulati, Sagar, Reliability driven Soft Real time task scheduling in Distributed Computing Environment, Proceedings of International conference of SocPros 2011, AISC 130, pp. 207 – 214.

Ramamritham K., Stankovic J. A., 1994. Scheduling algorithms and operating systems support for real-time systems, Proceedings of the IEEE, Vol. 82(1), pp55-67.

Goossens J., Richard P., Overview of real-time scheduling problems, Euro Workshop on Project Management and Scheduling, 2004

Ramamritham K., Stankovic J. A., Scheduling algorithms and operating systems support for real-time systems, Proceedings of the IEEE, Vol. 82(1), pp55- 67, January 1994.

Wang Lie-Xin, A course in fuzzy systems and control, Prentice Hall, Paperback, Published August 1996.

P. K. Yadav, P. Pradhan, Preet Pal Singh, A fuzzy clustering method to minimize the inter task communication effect for optimal utilization of processor‟s capacity in distributed real time systems, Advances In Intelligent And Soft Computing, Volume 130, 2012, pp 159-168

Goossens J., Richard P., 2004. Overview of real-time scheduling problems. Euro Workshop on Project Management and Scheduling

Liu C. L., Layland J. W., 1973. Scheduling Algorithms for Multiprogramming in a Hard Real-Time Environment. Journal of the ACM, Vol. 20, No.1, pp 46-61.

Hong J., Tan X., Towsley D., 1989. A Performance Analysis of Minimum Laxity and Earliest Deadline Scheduling in a Real-Time System. IEEE Trans. on Comp., Vol. 38, No. 12.

Sha, L. and Goodenough, J. B., 1990. Real-Time Scheduling Theory and Ada. IEEE Computer, Vol. 23, No. 4, pp. 53-62.

Andersson B., Jonsson J., 2000. Fixed-priority preemptive multiprocessor scheduling: to partition or not to partition. Seventh International Conference on Real-Time Computing Systems and Applications (RTCSA'00)

Sabeghi M., Naghibzadeh M., Taghavi T., 2005. A Fuzzy Algorithm for Scheduling Soft Periodic Tasks in Preemptive Real-Time Systems. International Joint Conferences on Computer, Information, and Systems Sciences, and Engineering (CISSE).

Sabeghi M., Naghibzadeh M., Taghavi T., 2006. Scheduling Non-Preemptive Periodic Tasks in Soft Real- Time Systems Using Fuzzy Inference. 9th IEEE International Symposium on Object and component-oriented Real-time distributed Computing (ISORC).

L. A. Zadeh, "Fuzzy sets versus probability," Proc. IEEE, vol. 68, pp. 421-421, March 1980.

L. A. Zadeh, "Fuzzy logic, neural networks, and soft computing," Commun. ACM, vol. 37, pp. 77-84, March 1994.

W. Pedrycz and F. Gomide, An introduction to fuzzy sets: analysis and design: The MIT Press, 1998.

E. H. Mamdani, "Application of fuzzy algorithms for the control of a dynamic plant," Proc. IEE, vol. 121, pp. 1585-1588, Dec 1974.

T. Takagi and M.Sugeno, "Fuzzy identification of systems and its applications to modeling and control," IEEE Trans. Syst., Man, Cybern., vol. 15, pp. 116-132, 1985.

H. Surmann and A. P. Ungering, "Fuzzy rule-based systems on general-purpose processors," IEEE Micro, vol. 15, pp. 40-48, Aug 1995.

G. Ascia and V. Catania, "A general purpose processor oriented to fuzzy reasoning," in Proc. 10th IEEE International Conf. Fuzzy Systems, Melbourne, Australia, 2001, pp. 352-355.

Mahdi Hamzeh, Sied Mehdi Fakhraie, and Caro Lucas, Soft real-time fuzzy task scheduling for multiprocessor systems, International journal of intelligent technology Vol. 2 No. 4, 2007, pp 211-216

Siar, Seyedeh Habibe Nabavi, Shahaboddin, Shamshirband, Static task scheduling in cooperative distributed systems based on soft computing techniques, Australian journal of basic and applied sciences, Vol. 4, No. 6, 2010, pp. 1518-1526.


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