New parallel processing strategies in complex event processing systems with data streams

Abstract

Sensor network–based application has gained increasing attention where data streams gathered from distributed sensors need to be processed and analyzed with timely responses. Distributed complex event processing is an effective technology to handle these data streams by matching of incoming events to persistent pattern queries. Therefore, a well-managed parallel processing scheme is required to improve both system performance and the quality-of-service guarantees of the system. However, the specific properties of pattern operators increase the difficulties of implementing parallel processing. To address this issue, a new parallelization model and three parallel processing strategies are proposed for distributed complex event processing systems. The effects of temporal constraints, for example, sliding windows, are included in the new parallelization model to enable the processing load for the overlap between windows of a batch induced by each input event to be shared by the downstream machines to avoid events that may result in wrong decisions. The proposed parallel strategies can keep the complex event processing system working stably and continuously during the elapsed time. Finally, the application of our work is demonstrated using experiments on the StreamBase system regardless of the increased input rate of the stream or the increased time window size of the operator.