Intelligent Traffic Adaptive Resource Allocation for Edge Computing-based 5G Networks

Published

June 10, 2020

Author(s)

Hamid Gharavi

Abstract

The popularity of smart mobile devices has led to a tremendous increase in mobile traffic, which has put a considerable strain on the fifth generation of mobile communication networks (5G). Among the three application scenarios covered by 5G, ultra-high reliability and ultra-low latency (uRLLC) communication can best be realized with the assistance of artificial intelligence. For a combined 5G, edge computing and IoT-Cloud (a platform that integrates the Internet of Things and cloud) in particular, there remains many challenges to meet the uRLLC latency and reliability requirements despite a tremendous effort to develop smart data-driven methods. Therefore, this paper mainly focuses on artificial intelligence for controlling mobile-traffic flow. In our approach, we first develop a traffic-flow prediction algorithm that is based on long short-term memory (LSTM) with an attention mechanism to train mobile-traffic data in single-site mode. The algorithm is capable of effectively predicting the peak value of the traffic flow. For a multi- site case, we present an intelligent IoT-based mobile traffic prediction-and-control architecture capable of dynamically dispatching communication and computing resources. In our experiments, we demonstrate the effectiveness of the proposed scheme in reducing communication latency and its impact on lowering packet-loss ratio. Finally, we present future work and discuss some of the open issues.

Citation

IEEE Transactions on Cognitive Communications and Networking

Volume

6

Issue

2

Pub Type

Journals

Download Paper

https://doi.org/10.1109/TCCN.2019.2953061

Local Download

Keywords

5G, artificial intelligence, LSTM, mobile traffic, uRLLC

Citation

Gharavi, H. (2020), Intelligent Traffic Adaptive Resource Allocation for Edge Computing-based 5G Networks, IEEE Transactions on Cognitive Communications and Networking, [online], https://doi.org/10.1109/TCCN.2019.2953061, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=929216 (Accessed December 26, 2024)