LIFO-Backpressure Achieves Near Optimal Utility-Delay Tradeoff

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.

References in corpus (4)

• Stability and Capacity Regions or Discrete Time Queueing Networks
• Max-Weight Achieves the Exact $[O(1/V),O(V)]$ Utility-Delay Tradeoff Under Markov Dynamics
• Novel Architectures and Algorithms for Delay Reduction in Back-pressure Scheduling and Routing
• Backpressure-based Packet-by-Packet Adaptive Routing in Communication Networks