In today’s computing world scheduling of real time task in a multiprocessor environment is very crucial. To do the scheduling, how the scheduler is implemented? what parameters are considered ? and how those parameters affect? Is also very important. Using the realistic parameters of the task the scheduling can be done and predict the resource requirement and analysis of the resource utilization factor can be done. Based on the tasks parameter it is necessary to classify them into dependent and independent, which is very important for the scheduler to assign them to the processors. For this prediction process machine learning algorithms are applied like logistic regression, decision tree, K-means and k-NN. In this paper initially classification of tasks into two categories dependent and independent is done later the same sets can be assigned to the processors for their execution.

Multiprocessor scheduling,Global scheduling, Partitioned scheduling, Machine Learning

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