Label-less Learning for Traffic Control in an Edge Network

TL;DR

This paper introduces LLTC, a label-less learning-based traffic control algorithm for edge clouds that reduces network traffic while maintaining cloud intelligence, validated through experiments.

Contribution

It proposes a novel LLTC algorithm that evaluates data value at the edge to optimize offloading, balancing network load and cloud intelligence.

Findings

LLTC reduces data transmission significantly.

Maintains required cloud intelligence levels.

Effective in resource-constrained edge environments.

Abstract

With the development of intelligent applications (e.g., self-driving, real-time emotion recognition, etc), there are higher requirements for the cloud intelligence. However, cloud intelligence depends on the multi-modal data collected by user equipments (UEs). Due to the limited capacity of network bandwidth, offloading all data generated from the UEs to the remote cloud is impractical. Thus, in this article, we consider the challenging issue of achieving a certain level of cloud intelligence while reducing network traffic. In order to solve this problem, we design a traffic control algorithm based on label-less learning on the edge cloud, which is dubbed as LLTC. By the use of the limited computing and storage resources at edge cloud, LLTC evaluates the value of data, which will be offloaded. Specifically, we first give a statement of the problem and the system architecture. Then, we design the LLTC algorithm in detail. Finally, we set up the system testbed. Experimental results show that the proposed LLTC can guarantee the required cloud intelligence while minimizing the amount of data transmission.