LIFO-Backpressure Achieves Near Optimal Utility-Delay Tradeoff

TL;DR

This paper introduces LIFO-Backpressure, a queueing discipline combined with Backpressure algorithms, achieving near-optimal utility and low delay in stochastic network optimization without modifying the core algorithm.

Contribution

It demonstrates that simply changing to a LIFO queueing discipline enables Backpressure to achieve near-optimal utility-delay tradeoff in general stochastic networks.

Findings

LIFO-Backpressure achieves utility within O(1/V) of optimal.

Average delay is O((log V)^2) for most traffic.

Empirical results match theoretical predictions.

Abstract

There has been considerable recent work developing a new stochastic network utility maximization framework using Backpressure algorithms, also known as MaxWeight. A key open problem has been the development of utility-optimal algorithms that are also delay efficient. In this paper, we show that the Backpressure algorithm, when combined with the LIFO queueing discipline (called LIFO-Backpressure), is able to achieve a utility that is within $O(1/V)$ of the optimal value, while maintaining an average delay of $O([\log(V)]^2)$ for all but a tiny fraction of the network traffic. This result holds for general stochastic network optimization problems and general Markovian dynamics. Remarkably, the performance of LIFO-Backpressure can be achieved by simply changing the queueing discipline; it requires no other modifications of the original Backpressure algorithm. We validate the results through empirical measurements from a sensor network testbed, which show good match between theory and practice.