Learning to Optimize Resource Assignment for Task Offloading in Mobile Edge Computing

TL;DR

This paper introduces an intelligent branch and bound method enhanced with deep learning to optimize resource assignment in mobile edge computing, significantly reducing computational complexity while maintaining near-optimal solutions.

Contribution

The paper proposes a novel deep learning-based pruning strategy for branch and bound in MEC task offloading, improving efficiency over traditional methods.

Findings

Achieves over 80% reduction in computational complexity.

Maintains near-optimal performance with the proposed IBnB approach.

Demonstrates effectiveness through numerical simulations.

Abstract

In this paper, we consider a multiuser mobile edge computing (MEC) system, where a mixed-integer offloading strategy is used to assist the resource assignment for task offloading. Although the conventional branch and bound (BnB) approach can be applied to solve this problem, a huge burden of computational complexity arises which limits the application of BnB. To address this issue, we propose an intelligent BnB (IBnB) approach which applies deep learning (DL) to learn the pruning strategy of the BnB approach. By using this learning scheme, the structure of the BnB approach ensures near-optimal performance and meanwhile DL-based pruning strategy significantly reduces the complexity. Numerical results verify that the proposed IBnB approach achieves optimal performance with complexity reduced by over 80%.