An adaptive parallel processing strategy in complex event processing systems over data streams

TL;DR

This paper introduces an adaptive parallel processing strategy for complex event processing systems over data streams, aiming to optimize performance by dynamically estimating event splitting policies based on workload conditions.

Contribution

It proposes a novel parallelization model and an adaptive strategy using histograms and probability theory to improve CEP system efficiency under varying workloads.

Findings

Reduces event processing waiting time

Maintains high throughput under workload variations

Effective in dynamic stream environments

Abstract

Efficient matching of incoming events of data streams to persistent queries is fundamental to event stream processing systems. These applications require dealing with high volume and continuous data streams with fast processing time on distributed complex event processing (CEP) systems. Therefore, a well-managed parallel processing technique is needed for improving the performance of the system. However, the specific properties of pattern operators in the CEP systems increase the difficulties of the parallel processing problem. To address these issues, a parallelization model and an adaptive parallel processing strategy are proposed for the complex event processing by introducing a histogram, and utilizing the probability and queue theory. The proposed strategy can estimate the optimal event splitting policy, which can suit the most recent workloads conditions such that the selected policy has the least expected waiting time for further processing the arriving events. The proposed strategy can keep the CEP system running fast under the variation of the time window sizes of operators and input rates of streams. Finally, the utility of our work is demonstrated through the experiments on the StreamBase system.