On Stability Region and Delay Performance of Linear-Memory Randomized Scheduling for Time-Varying Networks

TL;DR

This paper analyzes a linear-memory randomized scheduling policy for time-varying networks, demonstrating its ability to stabilize a significant portion of the capacity region and providing explicit delay performance metrics.

Contribution

It introduces and analyzes LM-RSP, a low-memory randomized scheduling policy, showing its effectiveness in stabilizing network capacity and characterizing delay performance.

Findings

LM-RSP stabilizes a fraction of the capacity region.

Explicit delay bounds are derived for Markovian channels.

Performance depends on channel variation and scheduling efficiency.

Abstract

Throughput optimal scheduling policies in general require the solution of a complex and often NP-hard optimization problem. Related literature has shown that in the context of time-varying channels, randomized scheduling policies can be employed to reduce the complexity of the optimization problem but at the expense of a memory requirement that is exponential in the number of data flows. In this paper, we consider a Linear-Memory Randomized Scheduling Policy (LM-RSP) that is based on a pick-and-compare principle in a time-varying network with $N$ one-hop data flows. For general ergodic channel processes, we study the performance of LM-RSP in terms of its stability region and average delay. Specifically, we show that LM-RSP can stabilize a fraction of the capacity region. Our analysis characterizes this fraction as well as the average delay as a function of channel variations and the efficiency of LM-RSP in choosing an appropriate schedule vector. Applying these results to a class of Markovian channels, we provide explicit results on the stability region and delay performance of LM-RSP.