Timely-Throughput Optimal Scheduling with Prediction
For network designers and researchers, this provides theoretical insights and guidelines for using prediction to improve delay-guaranteed services in communication and computing systems.
This paper studies predictive scheduling in a stochastic single-server multi-user system to improve timely-throughput (rate of on-time packet delivery). It derives an optimal scheduling policy and quantifies the improvement as a function of prediction accuracy, deadline, and prediction window, showing a Θ(...) scaling.
Motivated by the increasing importance of providing delay-guaranteed services in general computing and communication systems, and the recent wide adoption of learning and prediction in network control, in this work, we consider a general stochastic single-server multi-user system and investigate the fundamental benefit of predictive scheduling in improving timely-throughput, being the rate of packets that are delivered to destinations before their deadlines. By adopting an error rate-based prediction model, we first derive a Markov decision process (MDP) solution to optimize the timely-throughput objective subject to an average resource consumption constraint. Based on a packet-level decomposition of the MDP, we explicitly characterize the optimal scheduling policy and rigorously quantify the timely-throughput improvement due to predictive-service, which scales as $Θ(p\left[C_{1}\frac{(a-a_{\max}q)}{p-q}ρ^τ+C_{2}(1-\frac{1}{p})\right](1-ρ^{D}))$, where $a, a_{\max}, ρ\in(0, 1), C_1>0, C_2\ge0$ are constants, $p$ is the true-positive rate in prediction, $q$ is the false-negative rate, $τ$ is the packet deadline and $D$ is the prediction window size. We also conduct extensive simulations to validate our theoretical findings. Our results provide novel insights into how prediction and system parameters impact performance and provide useful guidelines for designing predictive low-latency control algorithms.