Design of a Scalable Reasoning Engine for Distributed, Real-Time and Embedded Systems

Effective and efficient knowledge dissemination and reasoning in distributed, real-time, and embedded (DRE) systems remains a hard problem due to the need for tight time constraints on evaluation of rules and scalability in dissemination of knowledge events. Limitations in satisfying the tight timing properties stem from the fact that most knowledge reasoning engines continue to be developed in managed languages like Java and Lisp, which incur performance overhead in their interpreters due to wasted precious clock cycles on managed features like garbage collection and indirection. Limitations in scalable dissemination stem from the presence of ontologies and blocking network communications involving connected reasoning agents. This paper addresses the existing problems with timeliness and scalability in knowledge reasoning and dissemination by presenting a C++-based knowledge reasoning solution that operates over a distributed and anonymous publish/subscribe transport mechanism provided by the OMG’s Data Distribution Service (DDS). Experimental results evaluating the performance of the C++based reasoning solution illustrate microsecond-level evaluation latencies, while the use of the DDS publish/subscribe transport illustrates significant scalability in dissemination of knowledge events while also tolerating joining and leaving of system entities. keywords: knowledge reasoning and dissemination; mission-critical systems; fault tolerance; scalability