The performance of a distributed system can be enhanced by using the capacity of idle or under-utilized workstations. These workstations are ideal candidates to migrate processes from workstations that do not have enough capacity. Most of the existing load balancing algorithms are fixed in the operating system and cannot be modified or adapted. The purpose of this paper is to show that some advantages can be obtained by using a neuro-fuzzy system as an adaptive component of a load balancing algorithm. Following this proposal, the system administrator of a network of workstations can define the policies through fuzzy rules. These fuzzy rules are embedded into a neural network that can be used to take load balancing decisions.
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