Complex Event Processing in the Edge: A Combined Optimization Approach for Data and Code Placement

TL;DR

This paper addresses the challenge of running complex event processing (CEP) on resource-constrained edge IoT devices by formulating CEP execution as a directed acyclic graph (DAG) and optimizing the critical path through constrained programming. It introduces a Python-based CEP management library with a virtual shared memory (VSM) layer and code/data migration mechanisms to dynamically distribute CEP components across multiple devices, periodically re-optimizing every 30 seconds. The key contributions include a DAG-based problem formulation, a CP-SAT optimization approach, and a comparative study against heuristic baselines in a smart-vehicle scenario, showing improvements in throughput and reduced latency on the critical path. The work demonstrates practical impact by enabling low-latency CEP on edge devices via adaptive placement, data sharing, and lightweight cross-device execution, while outlining future directions for ML-assisted optimization and larger-scale deployments.

Abstract

The increasing variety of input data and complexity of tasks that are handled by the devices of internet of things (IoT) environments require solutions that consider the limited hardware and computation power of the edge devices. Complex event processing (CEP), can be given as an example, which involves reading and aggregating data from multiple sources to infer triggering of important events. In this study, we balance the execution costs between different paths of the CEP task graph with a constrained programming optimization approach and improve critical path performance. The proposed approach is implemented as a Python library, allowing small-scale IoT devices to adaptively optimize code and I/O assignments and improve overall latency and throughput. The implemented library abstracts away the communication details and allows virtualization of a shared memory between IoT devices. The results show that optimizing critical path performance increases throughput and reduces delay across multiple devices during CEP operations.

Figures (10)

• Figure 1: Example dependency relationship between CEP tasks for the smart vehicle scenario.
• Figure 2: Path and step definitions that compose the CEP.
• Figure 3: IoT edge infrastructure with CEP worker and manager applications.
• Figure 4: CEP steps between distribution and code execution.
• Figure 5: CEP data collection and script execution flow.