A Self-Adaptive Model to Improve Average Response Time of Multiple-Event Filtering for Pub/Sub System

Publish/subscribe system captures the dynamic aspect of the specified information by notifying users of interesting events as soon as possible. Fast response time is important for event filtering which requires multiple step processing and is also one of important factors to provide good service for subscribers. Generally the event arrival rate is time varying and unpredictable. It is very possible that no event arrives in one unit time and multiple events arrive in another unit time. When multiple events with different workloads arrive at the same time, the average response time of multiple-event filtering depends on the sequence of event by event filtering. As far as we know, significant research efforts have been dedicated to the techniques of single event filtering, they can not efficiently filter multiple events in fast response time. In this paper, we first propose a multipleevent filtering algorithm based on R-tree. By calculating relative workload of each event, event by event filtering can be executed with short-job first policy so as to improve average response time of multiple-event filtering. Furthermore, a self-adaptive model is proposed to filter multiple events in dynamically changing environment. The multiple-event filtering algorithm and the self-adaptive model are evaluated in a simulated environment. The results show that the average response time can be improved maximum up to nearly 50%. With the self-adaptive model, multiple events can be filtered with average response time always same as or close to the possible best time in the dynamically changing environment.