DLBEM: Dynamic load balancing using expectation-maximization

This paper proposes a dynamic load balancing strategy called DLBEM based on maximum likelihood estimation methods for parallel and distributed applications. A mixture Gaussian model is employed to characterize workload in data- intensive applications. Using a small subset of workload information in systems, the DLBEM strategy reduces considerable communication overheads caused by workload information exchange and job migration. In the meantime, based on the Expectation-Maximization algorithm, DLBEM achieves near accurate estimation of the global system state with significantly less communication overheads and results in efficient workload balancing. Simulation results for some representative cases on a two-dimensional 16*16 grid demonstrate that DLBEM approach achieves even resource utilization and over 90% accuracy in the estimation of the global system state information with over 70% reduction on communication overheads compared to a baseline strategy.