Optimal Scheduling Control in Fluid Models of General $n\times n$ Input-Queued Switches

TL;DR

This paper develops an optimal scheduling control policy for fluid models of $n imes n$ input-queued switches, extending beyond max-weight policies to minimize fluid-flow costs under general objectives.

Contribution

It introduces a novel optimal control policy derived from a fluid model, incorporating flow maximization and Lagrangian multipliers, advancing delay optimization in input-queued switches.

Findings

Optimal policy aligns with $c\mu$-rule in certain cases.

Flow maximization characterizes the general optimal policy.

Computational experiments show improved performance over max-weight variants.

Abstract

Most of the early input-queued switch research focused on establishing throughput optimality of the max-weight scheduling policy, with some recent research showing that max-weight scheduling is optimal with respect to total expected delay asymptotically in the heavy-traffic regime. However, the question of delay-optimal scheduling in input-queued switches remains open in general, as does the question of delay-optimal scheduling under more general objective functions. To gain fundamental insights into these very difficult problems, we consider a fluid model of $n \times n$ input-queued switches with associated fluid-flow costs, and we derive an optimal scheduling control policy to an infinite horizon discounted control problem with a general linear objective function of fluid cost. Our optimal policy coincides with the $c\mu$-rule in certain parameter domains. More generally, due to the input-queued switch constraints, the optimal policy takes the form of the solution to a flow maximization problem, after we identify the Lagrangian multipliers of some key constraints through carefully designed algorithms. Computational experiments demonstrate the benefits of our optimal scheduling policy over variants of max-weight scheduling within fluid models of input-queued switches.