Co-evolving fuzzy rule sets for job exchange in computational grids

In our work, we utilize a competitive Co-evolutionary Algorithm in order to optimize the parameter set of a Fuzzy System for job exchange in Computational Grids. In this domain, the providers of High Performance Computing (HPC) centers strive for minimizing the response time for their own customers by trying to distribute workload to other sites in the Grid environment. The Fuzzy System is used for steering each site's decisions whether to distribute or accept workload in a beneficial, yet egoistic direction. This scenario is particularly suited for the application of a competitive CA: Grid sites' Fuzzy Systems are modeled as species, which evolve in different populations. While each species tries to minimize the response time for locally submitted jobs, their individuals' fitness is determined within the commonly shared ecosystem. Using real workload traces and Grid setups, we show that the opportunistic cooperation leads to significant improvements for both each Grid site and the overall system.