A Fluid Model for Mobile Data Offloading Based on Device-to-Device Communications with Time Constraints

Device-to-Device (D2D) communications play a pivotal role in 5G systems by enabling new ser- vices, reducing latency, and alleviating network congestion. For instance, proximity-based content sharing between nearby devices – without routing through the base station – has been proposed to offload traffic from the core cellular network. Existing D2D-based offloading strategies often assume that users requesting content can tolerate some delay before receiving it. Several analytical mod- els have been developed to derive theoretical performance bounds based on user mobility patterns and the routing algorithms used for content dissemination. In this study, we propose a novel fluid model based on Ordinary Differential Equations (ODEs) for the performance analysis of a general D2D-based mobile data offloading scheme, called OORS, which considers both content delivery guar- antees and time limitations for storing content copies in local device caches. Unlike similar existing models, our approach allows for the analysis of time-limited caching and forwarding policies with both constant and asynchronous timeouts, making it more practical for real-world applications. We also formulate an optimisation problem to maximise the utility of the content dissemination process through a simplified analysis of the stationary regime of the ODE model. Simulation results validate the accuracy of our model predictions, in terms of both aggregate statistics and the temporal evo- lution of the system state, using both synthetic and real-life mobility datasets. Finally, we compare OORS – optimally tuned with respect to protocol parameters – to Push-and-track, a state-of-the-art content data offloading scheme. Our results show that OORS achieves similar offloading efficiency as Push-and-track, while reducing the number of content copies by at least 50%.