Load balancing in distributed computing systems using fuzzy expert systems

Distributed computing systems have come about due to the rapid increase in processor and/or memory hungry applications coupled with the advent of low-cost powerful workstations. We present fuzzy functions to model load balancing in distributed computing systems. Due to lack of communication delays in message passing, a complete and consistent view of the entire system may never be available to a node of the system. Most of the previous work in load balancing, and distributed decision making in general, does not effectively take into account the uncertainty and inconsistency in state information. Also, both the load patterns and task length descriptions are based on crisp values, which may be inconvenient. Most of the research that uses fuzzy descriptions considers fixed fuzzy function parameter values that can only match in fixed load patterns. The aim of this paper is to solve the uncertainty problem in state information and task selection for migration using proposed fuzzy functions of variable membership values, and to propose an expert system that uses these fuzzy functions in decision making. The proposed approach has been studied by means of simulation. Performance, measured in terms of the mean response time, is improved in all cases.