Efficient Cache Placement Strategy in Two-Tier Wireless Content Delivery Network

Sung, Jihoon; Kim, Minseok; Lim, Kyongchun; Rhee, June-Koo Kevin

doi:10.1109/tmm.2016.2543658

Cited by 46 publications

(19 citation statements)

References 39 publications

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“…The authors in [14] allow jointly exploiting the wireless and social context of wireless users for optimizing the overall resources allocation and improving the traffic offload in small cell networks with device-to-device communication. In [15], the authors propose an efficient cache placement strategy which uses separate channels for content dissemination and content service. The authors in [16] propose a low-complexity search algorithm to minimize the average caching failure rate.…”

Section: Introductionmentioning

confidence: 99%

Echo State Networks for Proactive Caching in Cloud-Based Radio Access Networks With Mobile Users

Chen

Saad

Yin

et al. 2017

IEEE Trans. Wireless Commun.

166

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In this paper, the problem of proactive caching is studied for cloud radio access networks (CRANs). In the studied model, the baseband units (BBUs) can predict the content request distribution and mobility pattern of each user, determine which content to cache at remote radio heads and BBUs. This problem is formulated as an optimization problem which jointly incorporates backhaul and fronthaul loads and content caching. To solve this problem, an algorithm that combines the machine learning framework of echo state networks with sublinear algorithms is proposed. Using echo state networks (ESNs), the BBUs can predict each user's content request distribution and mobility pattern while having only limited information on the network's and user's state. In order to predict each user's periodic mobility pattern with minimal complexity, the memory capacity of the corresponding ESN is derived for a periodic input. This memory capacity is shown to capture the maximum amount of user information needed for the proposed ESN model. Then, a sublinear algorithm is proposed to determine which content to cache while using limited content request distribution samples. Simulation results using real data from Youku and the Beijing University of Posts and Telecommunications show that the proposed approach yields significant gains, in terms of sum effective capacity, that reach up to 27.8% and 30.7%, respectively, compared to random caching with clustering and random caching without clustering algorithm.

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Section: Introductionmentioning

confidence: 99%

Echo State Networks for Proactive Caching in Cloud-Based Radio Access Networks With Mobile Users

Chen

Saad

Yin

et al. 2017

IEEE Trans. Wireless Commun.

166

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“…The QoE-driven mobile edge caching placement problem for adaptive streaming can be summarized as follows: given the representation set of source video files, the file popularity distribution, the edge server storage capacity and the network topology, how to place the representations of the video files in the distributed edge servers such that the total system utility (which is defined by Eqs. (7) and (8a) in the next subsection) is maximized subject to the caching capacity constraint of each edge server and the downloading delay requirement of each user.…”

Section: A Problem Description and Challengesmentioning

confidence: 99%

“…For mobile video delivery, caching at distributed edge servers is demonstrated to be capable of greatly reducing the service load of base station, and replacing the usually weak backhaul connections from the base station with high-speed local links from the edge servers to guarantee the low delay requirement of users [6] 1 . An efficient caching placement strategy is designed for twotier wireless content delivery networks to reduce the system design complexity by using separate channels for content dissemination and service [7]. For adaptive streaming, the work in [8] derives a logarithmic QoE model based on empirical results and formulates the cache management problem as a convex optimization problem.…”

Section: Introductionmentioning

confidence: 99%

QoE-Driven Mobile Edge Caching Placement for Adaptive Video Streaming

Toni

Zou

et al. 2018

IEEE Trans. Multimedia

137

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Caching at mobile edge servers can smooth temporal traffic variability and reduce service load of base stations in mobile video delivery. However, the assignment of multiple video representations to distributed servers is still a challenging question in the context of adaptive streaming, since any two representations from different videos or even from the same video will compete for the limited caching storage. It is therefore important, yet challenging, to optimally select the cached representations for each edge server in order to effectively reduce the service load of base station while maintaining a high quality of experience (QoE) for users. To address this, we study on a QoE-driven mobile edge caching placement optimization problem for dynamic adaptive video streaming that properly takes into account the different ratedistortion (R-D) characteristics of videos and the coordination among distributed edge servers. Then, by the optimal caching placement of representations for multiple videos, we maximize the aggregate average video distortion reduction of all users while minimizing the additional cost of representation downloading from the base station, subject not only to the storage capacity constraints at the edge servers, but also to the transmission and initial startup delay constraints at the users. We formulate the proposed optimization problem as an integer linear program (ILP) to provide the performance upper bound, and as a submodular maximization problem with a set of knapsack constraints to develop a practically feasible cost benefit greedy algorithm. The proposed algorithm has polynomial computational complexity and a theoretical lower bound on its performance. Simulation results further show that the proposed algorithm is able to achieve a near-optimal performance with very low time complexity. Therefore, the proposed optimization framework reveals the caching performance upper bound for general adaptive video streaming systems, while the proposed algorithm provides some design guidelines for the edge servers to select the cached representations in practice based on both the video popularity and content information.

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“…Lu et al [24] proposed a session-based cloud video delivery network framework for the dynamic characteristics of mobile users in cloud video delivery networks which effectively reduced the cost and improved the performance of the algorithm. Documents [25] and [26] propose efficient cache placement strategy in wireless content delivery networks. Zheng and Zheng [27] propose an improved heuristic genetic algorithm for static content delivery in cloud storage and obtain optimal content delivery program.…”

Section: Related Workmentioning

confidence: 99%

Q-Learning Based Content Placement Method for Dynamic Cloud Content Delivery Networks

Liu¹,

Lu²,

Zhang³

et al. 2019

IEEE Access

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How to reduce the content placement cost of cloud content delivery networks (CCDNs) is a hot topic in recent years. Traditional content placement methods mainly reduce the cost of content placement by constructing delivery trees, but they cannot adapt to the dynamic deployment of cloud proxy servers in the CCDNs. In addition, the traditional content placement method only provides delivery paths according to local decision-making without considering global dynamics of the congestion in the CCDNs, which is also one of the main factors causing high cost of content placement. To solve these problems, we propose a content placement model based on Q-learning for the dynamic CCDNs, called Q-content placement model (Q-CPM). This Q-learning approach can lead to better routing decisions due to up-to-date and more reliable congestion values. Then, based on the Q-CPM model, an algorithm is proposed to construct the Q-adaptive delivery tree (Q-ADT). In this algorithm, local and nonlocal congestion information is propagated over network learning packets. Through this algorithm, the paths with low congestion cost will be selected and can adapt to the dynamic cloud delivery environment. The experimental results show that the method can adapt to the dynamic changes of the CCDNs flexibly and reduce the overall congestion cost of content placement effectively.

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Efficient Cache Placement Strategy in Two-Tier Wireless Content Delivery Network

Cited by 46 publications

References 39 publications

Echo State Networks for Proactive Caching in Cloud-Based Radio Access Networks With Mobile Users

Echo State Networks for Proactive Caching in Cloud-Based Radio Access Networks With Mobile Users

QoE-Driven Mobile Edge Caching Placement for Adaptive Video Streaming

Q-Learning Based Content Placement Method for Dynamic Cloud Content Delivery Networks

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