eBPF-based Content and Computation-aware Communication for Real-time Edge Computing

TL;DR

This paper introduces an eBPF-based framework for content and computation-aware communication in edge computing, enabling efficient multi-streaming and resource management for real-time IoT applications.

Contribution

It presents a novel SDN-based framework utilizing eBPF for programmable, resource-efficient multi-stream data transmission at the edge.

Findings

Reduces network bandwidth usage

Improves programmability and flexibility

Saves system resources

Abstract

By placing computation resources within a one-hop wireless topology, the recent edge computing paradigm is a key enabler of real-time Internet of Things (IoT) applications. In the context of IoT scenarios where the same information from a sensor is used by multiple applications at different locations, the data stream needs to be replicated. However, the transportation of parallel streams might not be feasible due to limitations in the capacity of the network transporting the data. To address this issue, a content and computation-aware communication control framework is proposed based on the Software Defined Network (SDN) paradigm. The framework supports multi-streaming using the extended Berkeley Packet Filter (eBPF), where the traffic flow and packet replication for each specific computation process is controlled by a program running inside an in-kernel Virtual Ma- chine (VM). The proposed framework is instantiated to address a case-study scenario where video streams from multiple cameras are transmitted to the edge processor for real-time analysis. Numerical results demonstrate the advantage of the proposed framework in terms of programmability, network bandwidth and system resource savings.