@Article{jcs.2020.010764,
AUTHOR = {Li Li, Yifei Wei, Lianping Zhang, Xiaojun Wang},
TITLE = {Efficient Virtual Resource Allocation in Mobile Edge Networks Based on  Machine Learning},
JOURNAL = {Journal of Cyber Security},
VOLUME = {2},
YEAR = {2020},
NUMBER = {3},
PAGES = {141--150},
URL = {http://www.techscience.com/JCS/v2n3/40142},
ISSN = {2579-0064},
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.},
DOI = {10.32604/jcs.2020.010764}
}