论文标题
学会优化移动边缘计算中任务卸载的资源分配
Learning to Optimize Resource Assignment for Task Offloading in Mobile Edge Computing
论文作者
论文摘要
在本文中，我们考虑了多用户移动边缘计算（MEC）系统，其中使用混合卸载策略来帮助资源分配以进行任务卸载。尽管可以应用常规的分支和绑定方法（BNB）方法来解决此问题，但出现了巨大的计算复杂性负担，这限制了BNB的应用。为了解决这个问题，我们提出了一种智能BNB（IBNB）方法，该方法采用深度学习（DL）来学习BNB方法的修剪策略。通过使用此学习方案，BNB方法的结构确保了近乎最佳的性能，同时基于DL的修剪策略会大大降低复杂性。数值结果验证了提出的IBNB方法可实现最佳性能，复杂性降低了80％以上。
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%.
