Active Content Popularity Learning via Query-by-Committee for Edge Caching

Edge caching has received much attention as an effective solution to face the stringent latency requirements in 5G networks due to the proliferation of handset devices as well as data-hungry applications. One of the challenges in edge caching systems is to optimally cache strategic contents to maximize the percentage of total requests served by the edge caches. To enable the optimal caching strategy, we propose an Active Learning approach (AL) to learn and design an accurate content request prediction algorithm. Specifically, we use an AL based Query-by-committee (QBC) matrix completion algorithm with a strategy of querying the most informative missing entries of the content popularity matrix. The proposed AL framework leverage's the trade-off between exploration and exploitation of the network, and learn the user's preferences by posing queries or recommendations. Later, it exploits the known information to maximize the system performance. The effectiveness of proposed AL based QBC content learning algorithm is demonstrated via numerical results

Active Content Popularity Learning and Caching Optimization with Hit Ratio Guarantees

Edge caching is an effective solution to reduce delivery latency and network congestion by bringing contents close to end-users. A deep understanding of content popularity and the principles underlying the content request sequence are required to effectively utilize the cache. Most existing works design caching policies based on global content requests with very limited consideration of individual content requests which reflect personal preferences. To enable the optimal caching strategy, in this paper, we propose an Active learning (AL) approach to learn the content popularities and design an accurate content request prediction model. We model the content requests from user terminals as a demand matrix and then employ AL-based query-by-committee (QBC) matrix completion to predict future missing requests. The main principle of QBC is to query the most informative missing entries of the demand matrix. Based on the prediction provided by the QBC, we propose an adaptive optimization caching framework to learn popularities as fast as possible while guaranteeing an operational cache hit ratio requirement. The proposed framework is model-free, thus does not require any statistical knowledge about the underlying traffic demands. We consider both the fixed and time-varying nature of content popularities. The effectiveness of the proposed learning caching policies over the existing methods is demonstrated in terms of root mean square error, cache hit ratio, and cache size on a simulated dataset

On the Exploration and Exploitation Trade-off in Cooperative Caching-enabled Networks

Edge-caching is considered as a promising solution to address network congestion and reduce delivery latency in the future by bringing the relevant contents close to users. In this context, the commonly used notion involves the storage of the most popular contents in the cache, while consequently increasing the cache hit ratio (CHR). In the majority of prior works, the content popularity is assumed to be perfectly known and often a priori. However, in reality, the content popularity has to be explored especially for uncertain contents, such as new entrants and fast varying items. In this paper, we develop a framework to analyze the joint exploration and exploitation trade-off by caching both popular and uncertain contents to enable more efficient content caching. Particularly, we formulate an optimization problem to maximize the trade-off between exploration and exploitation subject to the maximum storage capacity, guaranteed CHR, and back-haul energy budget constraints. Further, we solve the formulated mixed-integer combinatorial problem using branch-and-bound optimizer by relaxing the binary to box constraints. The superiority in performance of the proposed method over the state-of-the-art is demonstrated in terms of the CHR and back-haul energy on a realistic Movie-lens dataset

Active Popularity Learning with Cache Hit Ratio Guarantees using a Matrix Completion Committee

Edge caching is a promising technology to facethe stringent latency requirements and back-haul trafficoverloading in 5G wireless networks. However, acquiringthe contents and modeling the optimal cache strategy is achallenging task. In this work, we use an active learningapproach to learn the content popularities since it allowsthe system to leverage the trade-off between explorationand exploitation. Exploration refers to caching new fileswhereas exploitation use known files to cache, to achievea good cache hit ratio. In this paper, we mainly focus tolearn popularities as fast as possible while guaranteeing anoperational cache hit ratio constraint. The effectiveness ofproposed learning and caching policies are demonstratedvia simulation results as a function of variance, cache hitratio and used storage