A Framework for Semantic In-network Caching and Prefetching in 5G Mobile Networks

TL;DR

This paper introduces a semantic inference-based in-network caching framework for 5G networks that predicts user requests and prefetches content to significantly reduce latency.

Contribution

It presents a novel semantic inference approach for in-network caching and prefetching in 5G, improving latency reduction over existing methods.

Findings

Significant latency reduction demonstrated through emulations

Effective prediction of user requests using semantic inference

Outperforms current state-of-the-art caching techniques

Abstract

Recent popularity of mobile devices increased the demand for mobile network services and applications that require minimal delay. 5G mobile networks are expected to provide much lesser delay than the present mobile networks. One of the conventional ways for decreasing latency is caching content closer to end users. However, currently deployed methods are not effective enough. In this paper, we propose a new astute in-network caching framework that is able to smartly predict subsequent user requests and prefetch necessary contents to remarkably decrease the end-to-end latency in 5G mobile networks. We employ semantic inference by edge computing, deduce what the end-user may request in the sequel and prefetch the content. We validate the proposed technique by emulations, compare it with the state of the art and present impressive gains.