Scalable Parallel and Distributed Expert Database Systems with Predictive Load Balancing

Much prior work in AI on various attempts to speed up rule-based systems by parallel processing has been reported. Unfortunately, many of these results indicate that there is limited parallelism to be found when rules are applied to relatively small amounts of data. Thus, one can predict that much greater parallelism can be extracted when rules are applied to large amounts of data. However, traditional compile-time parallelization strategies as developed for main-memory based systems do not scale to large databases. We propose a scalable strategy for the efficient parallel implementation of rule-based systems operating upon large databases. We concentrate on load balancing techniques in a synchronous model of rule execution, where the variance in runtime of the distributed sites is minimized per cycle of rule processing, thus increasing utilization and speedup. We demonstrate that static load balancing techniques are insufficient, and thus low overhead dynamic load balancing is the key to successful scaling. We present a form of dynamic load balancing that is based upon predicting future system loads, rather than conventional demand-driven approaches that monitor current system state. We analyze a number of possible predictive dynamic load balancing protocols by isoefficiency analysis to guide the design of a parallel database rule processing system.