A neural network approach for dynamic load balancing in homogeneous distributed systems

A novel neural-based solution to the problem of dynamic load balancing in homogeneous distributed systems is proposed. The winner-take-all (WTA) neural network model is used for implementing the selection and location policies of a typical dynamic load balancing algorithm. Tasks with interprocess communication requirements are considered. All delays due to any usage of the communications network resource are taken into account. Performance studied by simulation is compared against the no load balancing case. The results suggest that it is always beneficial to use load balancing than not at all.