Evolutionary algorithms that use runtime migration of detector processes to reduce latency in event-based systems

Event-based systems (EBS) are widely used to efficiently process massively parallel data streams. In distributed event processing the allocation of event detectors to machines is crucial for both the latency and efficiency, and a naive allocation may even cause a system failure. But since data streams, network traffic, and event loads cannot be predicted sufficiently well the optimal detector allocation cannot be found a-priori and must instead be determined at runtime. This paper describes how evolutionary algorithms (EA) can be used to minimize both network and processing latency by means of runtime migration of event detectors. The paper qualitatively evaluates the algorithms on synthetical data streams in a distributed event-based system. We show that some EAs work efficiently even with large numbers of event detectors and machines and that a hybrid of Cuckoo Search and Particle Swarm Optimization outperforms others.