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Many distributed scheduling algorithms have been developed and reported in the current literature. However, very few of them explicitly treat stability issues. This paper first discusses stability issues for distributed scheduling algorithms in general terms. Two very different distributed scheduling algorithms which contain explicit mechanisms for stability are then presented and evaluated with respect to individual specific stability issues. One of the algorithms is based on stochastic learning automata and the other on bidding. The results indicate how very specific the treatment of stability is to the algorithm and environment under consideration. [ABSTRACT FROM AUTHOR]

This paper describes a heuristic approach for solving the problem of dynamically scheduling tasks in a real-time system where tasks have deadlines and general resource requirements. The crux of our approach lies in the heuristic function used to select the task to be scheduled next. The heuristic function is composed of three weighted factors. These factors explicitly consider information about real-time constraints of tasks and their utilization of resources. Simulation studies show that the weights for the various factors in the heuristic function have to be fine-tuned in order to obtain a degree of success in the range of 75-88 percent of that obtained via exhaustive search. However, modifying the approach to use limited backtracking improves the degree of success substantially to as high as 99.5 percent. This improvement is observed even when the initial set of weights are not tailored for a particular set of tasks. Simulation studies also show that in most cases the schedule determined by the heuristic algorithm is optimal or close to optimal. [ABSTRACT FROM AUTHOR]