4 research outputs found
Network Orchestration in Mobile Networks via a Synergy of Model-driven and AI-based Techniques
As data traffic volume continues to increase, caching of popular content at
strategic network locations closer to the end user can enhance not only user
experience but ease the utilization of highly congested links in the network. A
key challenge in the area of proactive caching is finding the optimal locations
to host the popular content items under various optimization criteria. These
problems are combinatorial in nature and therefore finding optimal and/or near
optimal decisions is computationally expensive. In this paper a framework is
proposed to reduce the computational complexity of the underlying integer
mathematical program by first predicting decision variables related to optimal
locations using a deep convolutional neural network (CNN). The CNN is trained
in an offline manner with optimal solutions and is then used to feed a much
smaller optimization problems which is amenable for real-time decision making.
Numerical investigations reveal that the proposed approach can provide in an
online manner high quality decision making; a feature which is crucially
important for real-world implementations.Comment: 6 pages, 3 figures, the conference accepted versio
Caching as an Image Characterization Problem using Deep Convolutional Neural Networks
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.Comment: 7 pages, 5 figure
A Survey of Deep Learning for Data Caching in Edge Network
The concept of edge caching provision in emerging 5G and beyond mobile
networks is a promising method to deal both with the traffic congestion problem
in the core network as well as reducing latency to access popular content. In
that respect end user demand for popular content can be satisfied by
proactively caching it at the network edge, i.e, at close proximity to the
users. In addition to model based caching schemes learning-based edge caching
optimizations has recently attracted significant attention and the aim
hereafter is to capture these recent advances for both model based and data
driven techniques in the area of proactive caching. This paper summarizes the
utilization of deep learning for data caching in edge network. We first outline
the typical research topics in content caching and formulate a taxonomy based
on network hierarchical structure. Then, a number of key types of deep learning
algorithms are presented, ranging from supervised learning to unsupervised
learning as well as reinforcement learning. Furthermore, a comparison of
state-of-the-art literature is provided from the aspects of caching topics and
deep learning methods. Finally, we discuss research challenges and future
directions of applying deep learning for cachin