Stateful complex event detection on event streams using parallelization of event stream aggregations and detection tasks

Saeed Fathollahzadeh, Kia Teymourian, Mohsen Sharifi

Abstract: Detection of stateful complex event patterns using parallel programming features is a challenging task because of statefulness of event detection operators. Parallelization of event detection tasks needs to be implemented in a way that keeps track of state changes by new arriving events. In this paper, we describe our implementation for a customized complex event detection engine by using Open Multi-Processing (OpenMP), a shared memory programming model. In our system event detection is implemented using Deterministic Finite Automata (DFAs). We implemented a data stream aggregator that merges 4 given event streams into a sequence of C++ objects in a buffer used as source event stream for event detection in a next processing step. We describe implementation details and 3 architectural variations for stream aggregation and parallelized of event processing. We conducted performance experiments with each of the variations and report some of our experimental results. A comparison of our performance results shows that for event processing on single machine with multi cores and limited memory, using mutli-threads with shared buffer has better stream processing performance than an implementation with multi-processes and shared memory.