385 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
A Learning-Based Approach to Caching in Heterogenous Small Cell Networks
A heterogenous network with base stations (BSs), small base stations (SBSs)
and users distributed according to independent Poisson point processes is
considered. SBS nodes are assumed to possess high storage capacity and to form
a distributed caching network. Popular files are stored in local caches of
SBSs, so that a user can download the desired files from one of the SBSs in its
vicinity. The offloading-loss is captured via a cost function that depends on
the random caching strategy proposed here. The popularity profile of cached
content is unknown and estimated using instantaneous demands from users within
a specified time interval. An estimate of the cost function is obtained from
which an optimal random caching strategy is devised. The training time to
achieve an difference between the achieved and optimal costs is
finite provided the user density is greater than a predefined threshold, and
scales as , where is the support of the popularity profile. A transfer
learning-based approach to improve this estimate is proposed. The training time
is reduced when the popularity profile is modeled using a parametric family of
distributions; the delay is independent of and scales linearly with the
dimension of the distribution parameter.Comment: 12 pages, 5 figures, published in IEEE Transactions on
Communications, 2016. arXiv admin note: text overlap with arXiv:1504.0363
A Survey on Caching in Distributed Small Cell Networks
The exponential growth of mobile devices such as smartphones and tablets, coupled with proliferation of online social networks has considerably increased the traffic in cellular networks. In contrast to classical cellular traffic that was only based on voice and audio communications, the recent technologies have resulted in bandwidth-intensive services such as video streaming, and video conferencing increases the traffic among users. This traffic surge affects the capacity of existing wireless networks which makes it difficult to ensure the high quality-of-service (QoS) required by the cellular services. In order to handle with the limited capacity of existing cellular networks and keep up with the strict QoS requirements, in terms of data rate and delay tolerable application-specific delays, a new generation of wireless networks has emerged. To achieve the requirements of this new generation and provide efficient infrastructure support for this data deluge, several research challenges must be addressed and solved. In this paper a survey on literature about small cell networks in distributed environment is presented which focus on caching aspect to improve the performance. The related work for caching in distributed small cell networks is also presented
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