Recently, a new class of mobile applications has appeared that takes into account the behavior of physical phenomenon. Prominent examples of such applications include augmented reality applications visualizing physical processes on a mobile device or mobile cyber-physical systems like autonomous vehicles or robots. Typically, these applications need to solve partial differential equations (PDE) to simulate the behavior of a physical system. There are two basic strategies to numerically solve these PDEs: (1) offload all computations to a remote server; (2) solve the PDE on the resource-constrained mobile device. However, both strategies have severe drawbacks. Offloading will fail if the mobile device is disconnected, and resource constraints require to reduce the quality of the solution.

Therefore, we propose a new approach for mobile simulations using a hybrid strategy that is robust to communication failures and can still benefit from powerful server resources. The basic idea of this approach is to dynamically decide on the placement of the PDE solver based on a prediction of the wireless link availability using Markov Chains. Our tests based on measurement in real cellular networks and real mobile devices show that this approach is able to keep deadline constraints in more than 61 % of the cases compared to a pure offloading approach, while saving up to 74 % of energy compared to a simplified approach.