Open Access
ARTICLE
Efficient Virtual Resource Allocation in Mobile Edge Networks Based on Machine Learning
Li Li1,*, Yifei Wei1, Lianping Zhang2, Xiaojun Wang3
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.
Keywords
Cite This Article
L. Li, Y. Wei, L. Zhang and X. Wang, "Efficient virtual resource allocation in mobile edge networks based on machine learning,"
Journal of Cyber Security, vol. 2, no.3, pp. 141–150, 2020. https://doi.org/10.32604/jcs.2020.010764