Optimizing correlation structure of event services considering time and capacity constraints

Constructing optimal event correlation architecture is crucial to large-scale event services. It plays an instrumental role in detecting composite events requested by different subscribers in scalable and timely manner. However, events generated from different sources might have different time and priority requirements. In addition, the network links and correlation servers might have different bandwidth and processing constraints respectively. In this work, we address the problem of optimizing distributed event correlation to maximize the correlation profit (benefit minus shipping and processing cost) of detecting composite events, while at the same time satisfying the network bandwidth, node capacity, and correlation tasks time constrains. We show that this problem is NP-hard and provide a heuristic approximation algorithm. We evaluate our heuristic approach with different network sizes, topologies under different event delivery and detection requirements. Our simulation study shows that the results obtained by our heuristic are close to the upper bound.