arrow
Return

Storage-Efficient Edge Caching With Asynchronous User Requests

delete2020-03-01
delete7
PRE
AI
Z
Zhanyuan Xie
W
Wei Chen *
DOI:10.1109/TCCN.2019.2954391delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
Edge caching has attracted great attention recently due to its potential for reducing service delays. One of the key performance metrics in caching is storage efficiency. To achieve high storage efficiency, we present an edge caching strategy with time-domain buffer sharing in this paper. More particularly, our scheme can determine not only which content items deserve pushing by the core network, but also how long the content items deserve caching in the buffer of the base station. To this end, we formulate a queueing model, in which the storage cost and the maximum caching time are bridged via Little's Law. Based on this model, we present a probabilistic edge caching strategy with random maximum caching time to strike the optimal tradeoff between the storage cost and the overall hit ratio of content items. For different content items having different users' demand preferences, we further formulate a nonconvex optimization problem to jointly allocate the transmission and storage resources. An efficient two-layer searching algorithm is presented to achieve an optimal solution. Moreover, we also present the analytical solution to the joint transmission and storage allocation problem in the special scenario where all content items have been cached in the core network.
Keywords:
Edge caching
storage cost
maximum caching time
hit ratio
storage-communication tradeoff
queueing theory
cognitive radio
memory allocation
spectrum sharing
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

I
IEEE Transactions on Cognitive Communications and Networking
IF:
7
Papers:
1.5K
Citations:
5.5K

Organization

T
tsinghua university
Scholars:
11.8W
Papers: 10.0W
Citations: 137