Efficient Complex Event Processing in Information-centric Networking at the Edge

TL;DR

This paper introduces INetCEP, a novel architecture that enhances in-network complex event processing within Information-centric Networking, significantly improving response times and processing speeds for IoT applications.

Contribution

The paper presents INetCEP, a new network architecture that supports continuous data streams and efficient CEP in ICN, addressing limitations of existing systems.

Findings

Response times up to 73 microseconds under high load

Over 15 times faster event forwarding than Flink

32 times faster complex query processing than Flink

Abstract

Information-centric Networking (ICN) is an emerging Internet architecture that offers promising features, such as in-network caching and named data addressing, to support the edge computing paradigm, in particular Internet-of-Things (IoT) applications. ICN can benefit from Complex Event Processing (CEP), which is an in-network processing paradigm to specify and perform efficient query operations on data streams. However, integrating CEP into ICN is a challenging task due to the following reasons: (1) typical ICN architectures do not provide support for forwarding and processing continuous data streams; (2) IoT applications often need short response times and require robust event detection, which both are hard to accomplish using existing CEP systems. In this article, we present a novel network architecture, called INetCEP, for efficient CEP-based in-network processing as part of ICN. INetCEP enables efficient data processing in ICN by means of (1) a unified communication model that supports continuous data streams, (2) a meta query language for CEP to specify data processing operations in the data plane, and (3) query processing algorithms to resolve the specified operations. Our experimental results for two IoT use cases and datasets show that INetCEP offers very short response times of up to 73 {\mu}s under high workload and is more than 15X faster in terms of forwarding events than the state-of-the-art CEP system Flink. Furthermore, the delivery and processing of complex queries is around 32X faster than Flink and more than 100X faster than a naive pull-based reference approach, while maintaining 100% accuracy.