Open Access

ARTICLE

Efficient Virtual Resource Allocation in Mobile Edge Networks Based on Machine Learning

by Li Li, Yifei Wei, Lianping Zhang, Xiaojun Wang

1 Beijing University of Posts and Telecommunications, Beijing, China
2 Alibaba Cloud Computing, Beijing, China
3 Dublin City University, Dublin, Ireland

* Corresponding Author: Li Li. Email: sigurlily

Journal of Cyber Security 2020, 2(3), 141-150. https://doi.org/10.32604/jcs.2020.010764

Received 26 March 2020; Accepted 07 September 2020; Issue published 14 September 2020

Abstract

The rapid growth of Internet content, applications and services require more computing and storage capacity and higher bandwidth. Traditionally, internet services are provided from the cloud (i.e., from far away) and consumed on increasingly smart devices. Edge computing and caching provides these services from nearby smart devices. Blending both approaches should combine the power of cloud services and the responsiveness of edge networks. This paper investigates how to intelligently use the caching and computing capabilities of edge nodes/cloudlets through the use of artificial intelligence-based policies. We first analyze the scenarios of mobile edge networks with edge computing and caching abilities, then design a paradigm of virtualized edge network which includes an efficient way of isolating traffic flow in physical network layer. We develop the caching and communicating resource virtualization in virtual layer, and formulate the dynamic resource allocation problem into a reinforcement learning model, with the proposed self-adaptive and self-learning management, more flexible, better performance and more secure network services with lower cost will be obtained. Simulation results and analyzes show that addressing cached contents in proper edge nodes through a trained model is more efficient than requiring them from the cloud.

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Keywords

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