Abstract

Problem statement: We present development of neural network based fuzzy inference system for scheduling of parallel Jobs with the help of a real life workload data. The performance evaluation of a parallel system mainly depends on how the processes are co scheduled? Various co scheduling techniques available are First Come First Served, Gang Scheduling, Flexible Co Scheduling and Agile Algorithm Approach: In order to use a wide range of objective functions, we used a rule bases scheduling strategy. The rule system depends on scheduling results of the agile algorithm and classifies all possible scheduling states and assigns an appropriate scheduling strategy based on actual state. The rule bases were developed with the help of a real workload data. Results: With the help of rule base results, scheduling was done again, which is compared with the first come first served, gang scheduling, flexible co scheduling and agile algorithm. The results of scheduling showed the optimized results of agile algorithm with the help of neuro fuzzy optimization technique. Conclusion: The study confirmed that the Neuro Fuzzy Technique can be used as a better optimization tool for optimizing any scheduling algorithm, This optimization tool is used for agile algorithm which is further used for process grain scheduling of parallel jobs.

Highlights

  • Related threads is scheduled to run on a set of processors at the same time on a one to one basis

  • Scheduling strategy based on neuro fuzzy system: The neural networks and fuzzy systems are dynamic, parallel processing systems that estimate input output functions

  • The neuro fuzzy optimization technique is used, the algorithm is again implemented and the results show that the results of the neuro fuzzy are very close to the agile algorithm

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Summary

Introduction

Related threads is scheduled to run on a set of processors at the same time on a one to one basis. The study optimizes the agile algorithm with the help of neuro fuzzy classifier. The rule bases scheduled properly, it will harm the performance of the are generated with the help of the scheduling results of parallel algorithm. Neuro fuzzy system are fuzzy systems that are trained by a learning algorithm derived from neural network theory. The process of learning minimizes the differences between the networks output and the rule base for each pattern in the training set, a rule which best classifies it. Fuzzy systems: Within this study, we aim to generate rule based scheduling system. The study concentrates on defining strict boundaries for all the features used as scheduling metrics and the rule assigns an appropriate scheduling algorithm. The generation of an appropriate situation classification is to be generated during the generation of the rule based scheduling system (Moratori, 2010)

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