Machine Learning Approach to Tuning Distributed Operating System Load Balancing Algorithms

This work concerns the use of machine learning techniques (genetic algorithms) to optimize load balancing policies in the openMosix distributed operating system. Parameters/alternative algorithms in the openMosix kernel were dynamically altered/selected based on the results of a genetic algorithm fitness function. In this fashion optimal parameter settings and algorithms choices were sought for the loading scenarios used as the test cases.