Intelligent Task Offloading and Resource Allocation in Digital Twin Based Aerial Computing Networks
Hongzhi Guo, Xiaoyi Zhou, Jiadai Wang, Jiajia Liu, Abderrahim Benslimane
Abstract
To meet the future demands for ubiquitous communication coverage and temporary / unexpected computing resources, aerial computing networks have been envisioned as a new paradigm. Nevertheless, dynamic changes on the network make it particularly challenging to achieve global optimal resource allocation. As an emerging technology, digital twin (DT) can represent real objects in physical network by creating virtual models. With the help of DT, we can easily obtain comprehensive real-world high-fidelity state information for model training, so as to achieve intelligent efficient decision-making. Accordingly, DT-based aerial computing networks have emerged as a potential solution. Note that available researches mostly assumed simple ground user distribution like uniform distribution, and adopted binary / partial offloading in task processing, neglecting the task separability and data inter-dependency among subtasks. Toward this end, we introduce DT into aerial computing networks, and study the problem of intelligent UAV deployment and resource allocation. Specifically, we firstly propose a DT-assisted UAV deployment strategy and model the data inter-dependency among subtasks. After that, two DT-assisted hybrid (binary and partial) task offloading schemes are presented, i.e., heuristic greedy and DQN-based schemes. Extensive analysis and numerical results confirm the effectiveness of our proposed DT-assisted UAV deployment and hybrid task offloading strategies.