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

2022/03/15 by Yurong Qian, Qian, Yurong, Jindan Xu +9
Computer Science · #Age of Information Optimization #FOS: Computer and information sciences #FOS: Electrical engineering #IoT and Edge/Fog Computing #Machine Learning (cs.LG) #Signal Processing (eess.SP) #Stochastic Gradient Optimization Techniques #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2203.09954

openalex publication_date 2022/03/15 · openalex created_date 2022/04/03 · openalex updated_date 2026/07/28

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%.

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