Scalable Cyclic Schedulers for Age of Information Optimization in Large-Scale Status Update Systems

We study open-loop cyclic scheduling for generateat-will (GAW) multi-source status update systems with heterogeneous service times and packet drop probabilities, with the goal of minimizing the weighted sum age of information (AoI), called system AoI, or the weighted sum peak AoI (PAoI), called system PAoI. In particular, we propose an offline method to obtain a well-performing cyclic schedule, which can scale to very large number of information sources. Moreover, the proposed schedule has a very low online implementation complexity. The proposed schedules are comparatively studied against existing age-agnostic scheduling algorithms in terms of computational load and system AoI/PAoI performance, to validate their effectiveness.