Space-Time Matching Algorithms for Interest Management in Distributed Virtual Environments

Abstract

Interest management in Distributed Virtual Environments (DVEs) is a data-filtering technique designed to reduce bandwidth consumption and therefore enhances the scalability of the system. This technique usually involves a process called interest matching, which determines what data should be sent to the participants as well as what data should be filtered. Although most of the existing interest matching approaches have been shown to meet their runtime performance requirements, they have a fundamental disadvantage-they perform interest matching at discrete time intervals. As a result, they would fail to report events between discrete timesteps. If participants of the DVE ignore these missing events, they would most likely perform incorrect simulations. This article presents a new approach called space-time interest matching, which aims to capture the missing events between discrete timesteps. Although this approach requires additional matching effort, a number of novel algorithms are developed to significantly improve its runtime efficiency.

An accurate interest matching algorithm based on prediction of the space–time intersection of regions for the distributed virtual environment

“…The missing event refers to the overlap event that failed to be detected by the IM algorithms [2]. In case the IM algorithm performs region matching at discrete timesteps and overlaps occur when the regions have been moving continuously between time steps, the algorithm cannot report the event and this leads to an incorrect simulation result.…”

“…The space-time algorithm (called STIM in this paper) is proposed by Elvis et al [2]. Space-time interest matching tackles the missing event problem in the space-time dimension.…”

“…And all-to-all data exchange between computational nodes, on which virtual entities are hosted, will consume a large amount of network bandwidth and decrease the runtime performance of the system significantly. Therefore, efficient data distribution becomes crucial to reduce the exchange of irrelevant data between nodes and to meet performance and usability requirements [1][2].…”

“…A subscription entity only needs to receive attribute updates from the other entities whose update regions overlap with subscription regions. The process of checking the overlap state of update-subscription region pairs is referred to Interest Matching (IM) [2].…”

“…Four sets of experiments are conducted to evaluate event-capturing ability, redundant matching avoidance, runtime efficiency and scalability of our algorithm. Three IM algorithms are chosen as baselines: the Frequent Interest Matching algorithm (FIM) [15], the Space-Time Interest Matching algorithm (STIM) [2] and the Predictive Interest Matching algorithm using Brute Force approach (PIM-BF).…”