Abstract
Problem statement: In this study, we present the development of genetic algorithm based neuro fuzzy technique for process grain sized in scheduling of parallel jobs with the help of real lIfe workload data. Approach: The study uses the rule based scheduling strategy for the scheduling and classIfies all possible scheduling strategies. The rule bases are developed with the help of the neuro fuzzy system and with the genetic fuzzy system. From the comparison of the two classIfiers of the fuzzy systems, it is found that the neuro fuzzy system results higher error rate when compared to the genetic fuzzy system. Hence the study concentrates on reducing the error rate of the results of the neuro fuzzy system by using the genetic algorithm for improving the parameters to the neuro fuzzy system. Results: The study shows that improving parameter like weights in the layers of the neuro fuzzy system using genetic algorithm reduces the error rate and comparative results of the neuro fuzzy, genetic fuzzy and the genetic based neuro fuzzy technique are shown for the parallel job scheduling. Conclusion: The study confirmed that the Genetic Based Neuro Fuzzy Technique can be used as a better optimization tool for optimizing any scheduling algorithm and this optimization tool is used in this study for agile algorithm which is used for process grain scheduling of parallel jobs.
Highlights
The agile algorithm and the results shows that the genetic algorithm was compared with the traditional algorithms fuzzy technique gives better results than the neuro like first come first served, gang scheduling, flexible co fuzzy
Genetic fuzzy algorithm is proved to perform better when compared to the neuro fuzzy algorithm
It has been shown that the error percentage is least in genetic fuzzy algorithms than the neuro fuzzy algorithms for running time results and the objective value analysis
Summary
A good way of characterizing a parallel job scheduling is to consider the synchronization the process grain is fine grain sized jobs and these jobs cannot be schedules separately. When scheduling the parallel jobs in a parallel system, we need to consider all the grain sizes .The agile algorithm concentrates on the detailed scheduling of the parallel jobs by classIfying the grain size in detail. Soft computing is an innovative approach to grain size refers to the size of an independent set of construct computationally intelligent systems. It is Corresponding Author: Sadasivam Vijayakumar Sudha, Department of Information Technology, Kalaignar Karunanidhi Institute of Technology, Anna University of Technology, Coimbatore, 641402, India. If awt is medium and tat is medium the computing consists of computing paradigms
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