A Novel Cross Entropy Approach for Offloading Learning in Mobile Edge Computing

TL;DR

This paper introduces a new cross entropy-based method for offloading learning in mobile edge computing, balancing energy use and latency efficiently without relying on traditional optimization tools.

Contribution

It presents a novel cross entropy approach that enables parallel computation and achieves near-optimal performance in offloading decisions for mobile edge networks.

Findings

Achieves performance close to the optimal solution.

Permits parallel computing architecture.

Computationally efficient compared to existing methods.

Abstract

In this paper, we propose a novel offloading learning approach to compromise energy consumption and latency in multi-tier network with mobile edge computing. In order to solve this integer programming problem, instead of using conventional optimization tools, we apply a cross entropy approach with iterative learning of the probability of elite solution samples. Compared to existing methods, the proposed one in this network permits a parallel computing architecture and is verified to be computationally very efficient. Specifically, it achieves performance close to the optimal and performs well with different choices of the values of hyperparameters in the proposed learning approach.