A Graph Matching Approach to Optimal Task Assignment in Distributed Computing Systems Using a Minimax Criterion
Issue No.03 - March (1985 vol.34)
null Chien-Chung Shen , Institute of Computer Engineering, National Chiao Tung University
A graph matching approach is proposed in this paper for solving the task assignment problem encountered in distributed computing systems. A cost function defined in terms of a single unit, time, is proposed for evaluating the effectiveness of task assignment. This cost function represents the maximum time for a task to complete module execution and communication in all the processors. A new optimization criterion, called the minimax criterion, is also proposed, based on which both minimization of interprocessor communication and balance of processor loading can be achieved. The proposed approach allows various system constraints to be included for consideration. With the proposed cost function and the minimax criterion, optimal task assignment is defined. Graphs are then used to represent the module relationship of a given task and the processor structure of a distributed computing system. Module assignment to system processors is transformed into a type of graph matching, called weak homomorphism. The search of optimal weak homomorphism corresponding to optimal task assignment is next formulated as a state-space search problem. It is then solved by the well-known A* algorithm in artificial intelligence after proper heuristic information for speeding up the search is suggested. An illustrative example and some experimental results are also included to show the effectiveness of the heuristic search.
weak homomorphism, A* algorithm, distributed computing systems, grapi matching, interprocessor communication, load balancing, minimax criterion, state-space search
null Chien-Chung Shen, "A Graph Matching Approach to Optimal Task Assignment in Distributed Computing Systems Using a Minimax Criterion", IEEE Transactions on Computers, vol.34, no. 3, pp. 197-203, March 1985, doi:10.1109/TC.1985.1676563