Large Deviations of Queues Sharing a Randomly Time-Varying Server
Queueing Systems: Theory and Applications
On wireless scheduling algorithms for minimizing the queue-overflow probability
IEEE/ACM Transactions on Networking (TON)
A Large Deviations Analysis of Scheduling in Wireless Networks
IEEE Transactions on Information Theory
Qualitative properties of α-weighted scheduling policies
Proceedings of the ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Large deviations sum-queue optimality of a radial sum-rate monotone opportunistic scheduler
IEEE Transactions on Information Theory
Delay-optimal opportunistic scheduling and approximations: the log rule
IEEE/ACM Transactions on Networking (TON)
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We show that for a large class of scheduling algorithms, when the algorithm minimizes the drift of a Lyapunov function, the algorithm is optimal in maximizing the asymptotic decay-rate of the probability that the Lyapunov function value exceeds a large threshold. The result in this paper extends our prior results to the important and practically-useful case when the Lyapunov function is not linear in scale, in which case the evolution of the fluid-sample-paths becomes more difficult to track. We use the notion of generalized fluid-sample-paths to address this difficulty, and provide easy-to-verify conditions for checking the large-deviations optimality of scheduling algorithms. As an immediate application of the result, we show that the log-rule is optimal in maximizing the asymptotic decay-rate of the probability that the sum queue exceeds a threshold B.