CABaRet: Leveraging Recommendation Systems for Mobile Edge Caching
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The results show that significant caching gains can be achieved in practice; 8 to 10 times increase in the cache hit ratio from cache-aware recommendations, and an extra 2 times increase from caching optimization.Abstract:
Joint caching and recommendation has been recently proposed for increasing the efficiency of mobile edge caching. While previous works assume collaboration between mobile network operators and content providers (who control the recommendation systems), this might be challenging in today's economic ecosystem, with existing protocols and architectures. In this paper, we propose an approach that enables cache-aware recommendations without requiring a network and content provider collaboration. We leverage information provided publicly by the recommendation system, and build a system that provides cache-friendly and high-quality recommendations. We apply our approach to the YouTube service, and conduct measurements on YouTube video recommendations and experiments with video requests, to evaluate the potential gains in the cache hit ratio. Finally, we analytically study the problem of caching optimization under our approach. Our results show that significant caching gains can be achieved in practice; 8 to 10 times increase in the cache hit ratio from cache-aware recommendations, and an extra 2 times increase from caching optimization.read more
Citations
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Journal ArticleDOI
Caching Efficiency Maximization for Device-to-Device Communication Networks: A Recommend to Cache Approach
TL;DR: This work quantitatively investigates how recommendation can be applied to enhance the caching efficiency of D2D enabled wireless content caching networks and shows that the optimal recommendation and caching policies which jointly maximize the cache efficiency is NP-hard to compute.
Journal ArticleDOI
Joint User Association, Content Caching and Recommendations in Wireless Edge Networks
TL;DR: This paper establishes a framework for the joint user association, content caching and recommendations problem, and proposes a heuristic that tackles the joint problem when the objective is to maximize the total hit ratio over all caches.
Journal ArticleDOI
Content Pushing Over Multiuser MISO Downlinks With Multicast Beamforming and Recommendation: A Cross-Layer Approach
Zhiyuan Lin,Wei Chen +1 more
TL;DR: JPR schemes are presented for multiuser multiple-input single-output (MISO) systems, in which content items are pushed over MISO downlinks with multicast beamforming with results that show that presented JPR policies are capable of attaining significant effective throughput gains.
Journal ArticleDOI
EICache: A learning-based intelligent caching strategy in mobile edge computing
Bing Tang,Linyao Kang +1 more
TL;DR: In this paper, an intelligent caching strategy for MEC based on machine learning has been proposed, which considers the user's mobility and interest preferences, and it could predict user mobility using historical trajectory based on LSTM algorithm, and predict interest using Gradient Boosting Decision Tree (GBDT) method, to obtain the content of interest in advance, and then cache the content in advance on the neighboring edge node where the user is likely to go.
Proceedings ArticleDOI
SOBA: Session optimal MDP-based network friendly recommendations
TL;DR: In this paper, the authors propose a Markov Decision Process (MDP) formulation to model a user with random session length and provide flexibility to model users who are reactive to the quality of the received recommendations.
References
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