Optimal Latency-Oriented Scheduling in Parallel Queuing Systems

TL;DR

This paper develops optimal scheduling strategies for low-latency data delivery over parallel links in 5G networks, using Markov Decision Processes to maximize timely delivery of data blocks.

Contribution

It introduces a novel MDP-based framework for joint coding and scheduling in parallel queuing systems with strict latency constraints, providing optimal policies.

Findings

Optimal policies significantly improve deadline adherence.

Heuristic solutions are less effective than the proposed optimal approach.

Simulation results demonstrate the advantages of the MDP-based scheduling.

Abstract

The evolution of 5G and Beyond networks has enabled new applications with stringent end-to-end latency requirements, but providing reliable low-latency service with high throughput over public wireless networks is still a significant challenge. One of the possible ways to solve this is to exploit path diversity, encoding the information flow over multiple streams across parallel links. The challenge presented by this approach is the design of joint coding and scheduling algorithms that adapt to the state of links to take full advantage of path diversity. In this paper, we address this problem for a synchronous traffic source that generates data blocks at regular time intervals (e.g., a video with constant frame rate) and needs to deliver each block within a predetermined deadline. We first develop a closed-form performance analysis in the simple case of two parallel servers without any buffering and single-packet blocks, and propose a model for the general problem based on a Markov Decision Process (MDP). We apply policy iteration to obtain the coding and scheduling policy that maximizes the fraction of source blocks delivered within the deadline: our simulations show the drawbacks of different commonly applied heuristic solutions, drawing general design insights on the optimal policy.