Timely-Throughput Optimal Scheduling With Prediction

• Published in: IEEE/ACM transactions on networking Vol. 26; no. 6; pp. 2457 - 2470
• Main Authors: Chen, Kun, Huang, Longbo
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Computer simulation; Control algorithms; delay-guaranteed service; Delays; IEEE transactions; Markov chains; Markov processes; Network control; Optimal scheduling; Optimization; Prediction algorithms; Predictive control; Predictive models; predictive scheduling; Scheduling; timely-throughput
• ISSN: 1063-6692; 1558-2566
• DOI: 10.1109/TNET.2018.2869583

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 paper, 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 <inline-formula> <tex-math notation="LaTeX">\Theta (p[C_{1}{(a-a_{\max }q)}\rho ^{\tau }/({p-q})+C_{2}(1-({1}/{p})](1-\rho ^{D})) </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">a, a_{\max }, \rho \in (0, 1), C_{1}>0, C_{2}\ge 0 </tex-math></inline-formula> are constants, <inline-formula> <tex-math notation="LaTeX">p </tex-math></inline-formula> is the true-positive rate in prediction, <inline-formula> <tex-math notation="LaTeX">q </tex-math></inline-formula> is the false-negative rate, <inline-formula> <tex-math notation="LaTeX">\tau </tex-math></inline-formula> is the packet deadline, and <inline-formula> <tex-math notation="LaTeX">D </tex-math></inline-formula> 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.