LoI: Efficient relevance evaluation and filtering for distributed simulation

Abstract

In large-scale distributed simulation, thousands of objects keep moving and interacting in a virtual environment, which produces a mass of messages. High level architecture (HLA) is the prevailing standard for modeling and simulation. It specifies two publish-subscribe mechanisms for message filtering: class-based and value-based. However, the two mechanisms can only judge whether a message is relevant to a subscriber or not. Lacking of the ability to evaluate the relevance, all relevant messages are delivered with the same priority even when congestion occurs. It significantly limits the scalability and performance of distributed simulation. Aiming to solve the relevance evaluation problem, speed up message filtering, and filter more unnecessary messages, a new relevance evaluation mechanism Layer of Interest (LoI) was proposed by this paper. LoI defines a relevance classifier based on the impact of spatial distance on receiving attributes and attribute values. An adaptive publish-subscribe scheme was built on the basis of LoI. This scheme can abandon most irrelevant messages directly. Run-time infrastructure (RTI) can also apply congestion control by reducing the frequency of sending or receiving object messages based on each objects’ LoI. The experiment results verify the efficiency of message filtering and RTI congestion control.