An Application of Bayesian Decision Theory to Decentralized Control of Job Scheduling

There is a wide spectrum of techniques that can be aptly named decentralized control. However, certain functions in distributed operating systems, e.g., scheduling, operate under such demanding requirements that no known optimal control solutions exist. It has been shown that heuristics are necessary. This paper presents a heuristic for the effective cooperation of multiple decentralized components of a job scheduling function. An especially useful feature of the heuristic is that it can dynamically adapt to the quality of the state information being processed. Extensive simulation results show the utility of this heuristic. The simulation results are compared to several analytical models and a baseline simulation model. The heuristic itself is based on the application of Bayesian decision theory. Bayesian decision theory was used because its principles can be applied as a systematic approach to complex decision making under conditions of imperfect knowledge, and it can run relatively cheaply in real time.

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