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.