Max-Weight Achieves the Exact $[O(1/V), O(V)]$ Utility-Delay Tradeoff Under Markov Dynamics

TL;DR

This paper proves that the MaxWeight (QLA) algorithm achieves an exact utility-delay tradeoff of [O(1/V), O(V)] in stochastic networks with Markovian dynamics, extending prior results beyond i.i.d. assumptions.

Contribution

The paper provides a formal proof that QLA attains the exact utility-delay tradeoff under Markov network dynamics using a novel variable multi-slot Lyapunov drift analysis.

Findings

QLA achieves the [O(1/V), O(V)] utility-delay tradeoff under Markov dynamics.

The analysis introduces a new variable multi-slot Lyapunov drift technique.

Performance bounds are expressed in terms of network dynamic parameters.

Abstract

In this paper, we show that the Quadratic Lyapunov function based Algorithm (QLA, also known as MaxWeight or Backpressure) achieves an exact $[O(1/V), O(V)]$ utility-delay tradeoff in stochastic network optimization problems with Markovian network dynamics. Note that though the QLA algorithm has been extensively studied, most of the performance results are obtained under i.i.d. network radnomness, and it has not been formally proven that QLA achieves the exact $[O(1/V), O(V)]$ utility-delay tradeoff under Markov dynamics. Our analysis uses a combination of duality theory and a variable multi-slot Lyapunov drift argument. The variable multi-slot Lapunov drift argument here is different from previous multi-slot drift analysis, in that the slot number is a random variable corresponding to the renewal time of the network randomness. This variable multi-slot drift argument not only allows us to obtain an exact $[O(1/V), O(V)]$ tradeoff, but also allows us to state the performance of QLA in terms of explicit parameters of the network dynamic process.