A Multi-objective Optimization Approach to Resource Allocation for Edge-Based Digital Twin

Digital twin technology can be combined with wire-less virtual reality (VR) to provide workers with an immersive virtual industrial manufacturing environment. In this paper, we consider a scenario of utilizing computing resources at the edge servers to render the virtual representation of physical industrial scenes for workers. In order to provide an immersive experience, the rendered virtual representation in the form of high-quality video needs to be delivered with low latency. The quality of delivered contents is associated with rendering resources and content bitrate, and high-quality contents result in large rendering and transmission delay. The joint resource allocation problem has been formulated, allocating limited bandwidth and edge computing resources, and determining the bitrate of content to achieve low latency and high content quality, in which two objectives are conflicting. To solve this problem, we employ a deep reinforcement learning (DRL) based multi-objective optimization approach which decomposes the original problem into a series of subproblems. Instead of training the DRL model for each subproblem with equal number of episodes, an adaptive subproblem model training scheme has been proposed which terminates the training of subproblem model early based on the model convergence to improve the overall training efficiency. Simulation results verify the effectiveness of the proposed resource allocation strategy in terms of content quality and delay.