Learning to Optimize Resource Assignment for Task Offloading in Mobile Edge Computing
This addresses the computational burden in mobile edge computing systems, offering a more efficient solution for task offloading, though it is incremental as it builds on existing BnB methods.
The paper tackles the high computational complexity of branch and bound (BnB) for resource assignment in mobile edge computing by proposing an intelligent BnB (IBnB) approach that uses deep learning to learn pruning strategies, achieving near-optimal performance with complexity reduced by over 80%.
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%.