Towards Maximizing Nonlinear Delay Sensitive Rewards in Queuing Systems

TL;DR

This paper introduces the shortest predicted sojourn time (SPST) discipline to maximize nonlinear delay-sensitive rewards in queuing systems, demonstrating improved performance over existing methods through simulations and analytical insights.

Contribution

The paper proposes a novel service discipline, SPST, tailored for nonlinear delay-sensitive reward maximization, with initial analytical guarantees and simulation results.

Findings

SPST outperforms traditional disciplines in simulations.

Limited analytical guarantees are provided for the proposed method.

The approach is applicable to quantum information processing and multimedia streaming.

Abstract

We consider maximizing the long-term average reward in a single server queue, where the reward obtained for a job is a non-increasing function of its sojourn time. The motivation behind this work comes from multiple applications, including quantum information processing and multimedia streaming. We introduce a new service discipline, shortest predicted sojourn time (SPST), which, in simulations, performs better than well-known disciplines. We also present some limited analytical guarantees for this highly intricate problem.