Enhancing Performance And Reliability of Rule Management Platforms

Abstract

ABSTRACT RulE Management Platforms (REMPs) enable software engineers to represent programming logic as conditional sentences that relate statements of facts. A key benefit of REMPs is that they make software adaptable by burying the complexity of rule invocation in their engines, so that programmers can concentrate on business aspects of highly modular rules. Naturally, rule-driven applications are expected to have excellent performance, since REMP engines should be able to invoke highly modular rules in parallel in response to asserting different facts. In reality, it is very difficult to parallelize rule executions, since it leads to the loss of reliability and adaptability of rule-driven applications. We created a novel solution that is based on obtaining a rule execution model that is used at different layers of REMPs to enhance the performance of rule-driven applications while maintaining their reliability and adaptability. First, using this model, possible races are detected statically among rules, and we evaluate an implementation of our abstraction of algorithms for automatically preventing races among rules. Next, we use the sensitivity analysis to find better schedules among simultaneously executing rules to improve the overall performance of the application. We implemented our solution for JBoss Drools and we evaluated it on three applications. The results suggest that our solution is effective, since we achieved over 225% speedup on average