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

TL;DR

This paper presents an optimization approach for complex event processing in IoT edge devices, balancing execution costs to improve latency and throughput through a Python library that adaptively optimizes code and data placement.

Contribution

It introduces a combined optimization method for data and code placement in CEP tasks on IoT edge devices, enhancing performance and resource utilization.

Findings

Optimizing critical path improves throughput and reduces delay.

The Python library enables adaptive code and I/O optimization on IoT devices.

Performance gains are demonstrated across multiple devices during CEP operations.

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.