232 research outputs found
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
A review on green caching strategies for next generation communication networks
© 2020 IEEE. In recent years, the ever-increasing demand for networking resources and energy, fueled by the unprecedented upsurge in Internet traffic, has been a cause for concern for many service providers. Content caching, which serves user requests locally, is deemed to be an enabling technology in addressing the challenges offered by the phenomenal growth in Internet traffic. Conventionally, content caching is considered as a viable solution to alleviate the backhaul pressure. However, recently, many studies have reported energy cost reductions contributed by content caching in cache-equipped networks. The hypothesis is that caching shortens content delivery distance and eventually achieves significant reduction in transmission energy consumption. This has motivated us to conduct this study and in this article, a comprehensive survey of the state-of-the-art green caching techniques is provided. This review paper extensively discusses contributions of the existing studies on green caching. In addition, the study explores different cache-equipped network types, solution methods, and application scenarios. We categorically present that the optimal selection of the caching nodes, smart resource management, popular content selection, and renewable energy integration can substantially improve energy efficiency of the cache-equipped systems. In addition, based on the comprehensive analysis, we also highlight some potential research ideas relevant to green content caching
Caching with Unknown Popularity Profiles in Small Cell Networks
A heterogenous network is considered where the base stations (BSs), small
base stations (SBSs) and users are distributed according to independent Poisson
point processes (PPPs). We let the SBS nodes to posses high storage capacity
and are assumed to form a distributed caching network. Popular data files are
stored in the local cache of SBS, so that users can download the desired files
from one of the SBS in the vicinity subject to availability. The
offloading-loss is captured via a cost function that depends on a random
caching strategy proposed in this paper. The cost function depends on the
popularity profile, which is, in general, unknown. In this work, the popularity
profile is estimated at the BS using the available instantaneous demands from
the users in a time interval . This is then used to find an estimate
of the cost function from which the optimal random caching strategy is devised.
The main results of this work are the following: First it is shown that the
waiting time to achieve an difference between the achieved
and optimal costs is finite, provided the user density is greater than a
predefined threshold. In this case, is shown to scale as , where
is the support of the popularity profile. Secondly, a transfer
learning-based approach is proposed to obtain an estimate of the popularity
profile used to compute the empirical cost function. A condition is derived
under which the proposed transfer learning-based approach performs better than
the random caching strategy.Comment: 6 pages, Proceedings of IEEE Global Communications Conference, 201
- …