EdgeFlow: Open-Source Multi-layer Data Flow Processing in Edge Computing for 5G and Beyond

TL;DR

EdgeFlow is an open-source multi-layer data flow framework for edge computing that optimizes task distribution and resource allocation across edge and cloud layers, improving performance and robustness in 5G and beyond.

Contribution

This paper introduces EdgeFlow, a novel multi-layer edge computing framework that balances computation and communication, with an open-source platform for emulation and evaluation.

Findings

Reduces task finish time significantly in face recognition scenarios

Increases tolerance to run-time variations compared to other approaches

Effectively balances resources across multiple network layers

Abstract

Edge computing has evolved to be a promising avenue to enhance the system computing capability by offloading processing tasks from the cloud to edge devices. In this paper, we propose a multi-layer edge computing framework called EdgeFlow. In this framework, different nodes ranging from edge devices to cloud data centers are categorized into corresponding layers and cooperate together for data processing. With the help of EdgeFlow, one can balance the trade-off between computing and communication capability so that the tasks are assigned to each layer optimally. At the same time, resources are carefully allocated throughout the whole network to mitigate performance fluctuation. The proposed open-source data flow processing framework is implemented on a platform that can emulate various computing nodes in multiple layers and corresponding network connections. Evaluated on the face recognition scenario, EdgeFlow can significantly reduce task finish time and perform more tolerance to run-time variation, compared with the pure cloud computing, the pure edge computing and Cloudlet. Potential applications of EdgeFlow, including network function visualization, Internet of Things, and vehicular networks, are also discussed in the end of this work.