ﻻ يوجد ملخص باللغة العربية
We consider the problem of video caching across a set of 5G small-cell base stations (SBS) connected to each other over a high-capacity short-delay back-haul link, and linked to a remote server over a long-delay connection. Even though the problem of minimizing the overall video delivery delay is NP-hard, the Collaborative Caching Algorithm (CCA) that we present can efficiently compute a solution close to the optimal, where the degree of sub-optimality depends on the worst case video-to-cache size ratio. The algorithm is naturally amenable to distributed implementation that requires zero explicit coordination between the SBSs, and runs in $O(N + K log K)$ time, where $N$ is the number of SBSs (caches) and $K$ the maximum number of videos. We extend CCA to an online setting where the video popularities are not known a priori but are estimated over time through a limited amount of periodic information sharing between SBSs. We demonstrate that our algorithm closely approaches the optimal integral caching solution as the cache size increases. Moreover, via simulations carried out on real video access traces, we show that our algorithm effectively uses the SBS caches to reduce the video delivery delay and conserve the remote servers bandwidth, and that it outperforms two other reference caching methods adapted to our system setting.
This paper investigates learning-based caching in small-cell networks (SCNs) when user preference is unknown. The goal is to optimize the cache placement in each small base station (SBS) for minimizing the system long-term transmission delay. We mode
Due to explosive growth of online video content in mobile wireless networks, in-network caching is becoming increasingly important to improve the end-user experience and reduce the Internet access cost for mobile network operators. However, caching i
Recently, Mobile-Edge Computing (MEC) has arisen as an emerging paradigm that extends cloud-computing capabilities to the edge of the Radio Access Network (RAN) by deploying MEC servers right at the Base Stations (BSs). In this paper, we envision a c
Mobile Edge Computing (MEC) pushes computing functionalities away from the centralized cloud to the proximity of data sources, thereby reducing service provision latency and saving backhaul network bandwidth. Although computation offloading has been
We state and solve a problem of the optimal geographic caching of content in cellular networks, where linear combinations of contents are stored in the caches of base stations. We consider a general content popularity distribution and a general distr