Persival: Using Delayed Remote Updates in a Distributed Mobile Simulation

Executing complex simulations on mobile devices such as augmented reality (AR) glasses or smartphones enables many novel pervasive applications. For instance, a physiotherapist can display muscles and bones in real-time as a visual overlay on the patient's body. The major challenge of such pervasive simulations is the complexity of the simulation, which typically exceeds the resources of the mobile device by far. Offloading computationally intensive simulations to a remote server is a promising method to enable real-time simulations on resource-constrained mobile devices without compromising the quality of the simulation results. However, the results of offloaded computations may arrive with an inevitable communication delay, which is critical for real-time simulations and also induces communication overhead. In this work, we tackle these challenges by proposing a novel approach for pervasive simulations on mobile devices. We combine a low-quality local Neural Network (NN) model on the mobile device with a high-quality NN model on a remote server, particularly taking care to integrate delayed updates from the server with the local simulation results. This distributed approach has several advantages over purely local or remote execution models: We benefit from high-quality remote results, while being robust to dynamic delays, server and network failures, and we reduce the communication overhead.