A quasi-optimal cluster allocation strategy for parallel program execution in distributed systems using genetic algorithms

Abstract

This paper shows an approach to find quasi-optimal solutions to the first optimization stage of parallel program tasks allocation problem, in an internet distributed system. The user initiates program execution from an arbitrary node in an arbitrary cluster, the parallel tasks comprising the program migrate to quasi-optimal clusters using an strategy that tries to minimize intercluster traffic of the parallel program execution.Genetic algorithms (GAs) [11] are used to provide a set of timely, quasi-optimal solutions.