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The scheduling and mapping of the precedence-constrained task graphs of parallel programs to processors is considered one of the most crucial NP-complete problems in parallel and distributed computing systems. In this paper, a dynamic task scheduling model based on fuzzy logic is proposed. The main objective of this technique is to improve the fuzzy decision which is used in task scheduling on a network of processing elements by introducing new input parameters to an existing fuzzy model and, in the same time, improving the load balance on the network in a dynamic environment. The proposed Fuzzy Model is capable of processing inputs from on the fly data that arises from the current state of the processors. According to the proposed model, tasks are generated randomly and are served based on the First-Come-First-Serve rule. When the task is ready to be assigned, its information is passed to the processors for bidding. Each processor has a local scheduler for managing its own activities, which supplies information on its current state and follows whatever decision is given where the fuzzy logic mechanism is used in making decision on the task assignment. A comparative study between the existed fuzzy model and our modified fuzzy model has been done. The comparative results show that our modified fuzzy model outperforms the existed one.
Date of Conference: 28-30 March 2010