DePo: Dynamically Offload Expensive Event Processing to the Edge of Cyber-Physical Systems

Event processing is one of the cornerstones to manage massive data streams in Cyber-Physical Systems (CPS). Due to CPS applications' increasing complexity, detecting highly complicated events (aka. 'expensive' events) leads to significant performance degradation, particularly harmful to mission-critical systems. To tackle this challenge, we define a new task - dynamic event processing offloading to CPS-edges. This paper proves the problem NP-hard and proposes a solution - DePo. DePo splits the expensive events into sub-models and offloads them to CPS edges. We design a long and short-term event memory mechanism in DePo that enables the edges and server to process expensive events collaboratively within their capabilities. Besides, we propose a concept called Edge Utility to measure the optimality of offloading schemes. A heuristic algorithm is presented in this study to guide how to dispatch events to edges, thereby helping DePo generate a sub-optimal solution in polynomial computational complexity. Our extensive experiments show that the performance gap between DePo and the optimal benchmark is less than 5%. DePo effectively reduces more than 40% redundant states and provides over 100% higher throughput than state-of-the-art approaches. Experimental results verified the high stability and scalability of DePo, especially when dealing with a large number of expensive events.