Intelligent and efficient task caching for mobile edge computing

Abstract

Given the problems with a centralized cloud and the emergence of ultra-low latency applications, and the needs of the Internet of Things (IoT), it has been found that novel methods are needed to support centralized cloud technology. Mobile edge computing is one of the solutions to mitigate these challenges. In this paper, we study task caching at Device to Device (D2D)-assisted network edge. In the proposed scheme, we predict the possibility of re-requesting tasks in the future using convolutional neural networks (CNN). Based on this predicted possibility, the number of last requests, and the number of cached versions of this task type in the neighbors, in addition to the characteristics of a task itself, including the required cache volume and processing resources, we rank the tasks using the proposed Multi-Criteria Task Ranking using Predicted requests (MCTRP) scheme and select the best replacement option in the cache of each Mobile User Equipment (MUE). The proposed scheme has proved to be of considerable benefit in terms of reducing delay and energy consumption and improving the utility of MUEs.