Load balancing for massively-parallel soft real-time systems

An approach to decentralized load balancing based on statistical time-series analysis is proposed. Each site estimates the system-wide average load using information about past loads of individual sites and attempts to equal that average. This estimation process is practical because the soft-real-time systems of interest naturally exhibit loads that are periodic, in a statistical sense akin to seasonality in econometrics. It is shown how this load-characterization technique can be the foundation for a load-balancing system in an architecture using cut-through routing and an efficient multicast protocol. A simple stochastic model is presented along with heuristic approximations and a load-balancing scheme using a load-characterization methodology