King's College London

TY - CHAP

T1 - Caching as an Image Characterization Problem using Deep Convolutional Neural Networks

AU - Wang, Yantong

AU - Friderikos, Vasilis

PY - 2020/6

Y1 - 2020/6

N2 - Caching of popular content closer to the mobile user can significantly increase overall user experience as well as network efficiency by decongesting backbone network segments in the case of congestion episodes. In order to find the optimal caching locations, many conventional approaches rely on solving a complex optimization problem that suffers from the curse of dimensionality, which may fail to support online decision making. In this paper we propose a framework to amalgamate model based optimization with data driven techniques by transforming an optimization problem to a grayscale image and train a convolutional neural network (CNN) to predict optimal caching location policies. The rationale for the proposed modelling comes from CNN's superiority to capture features in grayscale images reaching human level performance in image recognition problems. The CNN is trained with optimal solutions and numerical investigations reveal that the performance can increase by more than 400 compared to powerful randomized greedy algorithms. To this end, the proposed technique seems as a promising way forward to the holy grail aspect in resource orchestration which is providing high quality decision making in real time.

AB - Caching of popular content closer to the mobile user can significantly increase overall user experience as well as network efficiency by decongesting backbone network segments in the case of congestion episodes. In order to find the optimal caching locations, many conventional approaches rely on solving a complex optimization problem that suffers from the curse of dimensionality, which may fail to support online decision making. In this paper we propose a framework to amalgamate model based optimization with data driven techniques by transforming an optimization problem to a grayscale image and train a convolutional neural network (CNN) to predict optimal caching location policies. The rationale for the proposed modelling comes from CNN's superiority to capture features in grayscale images reaching human level performance in image recognition problems. The CNN is trained with optimal solutions and numerical investigations reveal that the performance can increase by more than 400 compared to powerful randomized greedy algorithms. To this end, the proposed technique seems as a promising way forward to the holy grail aspect in resource orchestration which is providing high quality decision making in real time.

KW - Convolutional Neural Networks

KW - Deep Learning

KW - Grayscale Image

KW - Mixed Integer Linear Programming

KW - Proactive Caching

UR - http://www.scopus.com/inward/record.url?scp=85089423330&partnerID=8YFLogxK

U2 - 10.1109/ICC40277.2020.9148854

DO - 10.1109/ICC40277.2020.9148854

M3 - Conference paper

AN - SCOPUS:85089423330

T3 - IEEE International Conference on Communications

BT - 2020 IEEE International Conference on Communications, ICC 2020 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 IEEE International Conference on Communications, ICC 2020

Y2 - 7 June 2020 through 11 June 2020

ER -