Universal Scheduling for Networks with Arbitrary Traffic, Channels, and Mobility

TL;DR

This paper develops a universal scheduling algorithm for networks experiencing arbitrary, unpredictable changes without probabilistic assumptions, achieving near-optimal throughput and utility performance.

Contribution

It introduces a non-anticipating scheduling algorithm that closely matches the performance of ideal lookahead policies in arbitrary network conditions.

Findings

Achieves throughput-utility close to T-slot lookahead policies.

Provides a methodology for optimizing time averages in arbitrary, time-varying networks.

Offers performance guarantees even under worst-case network dynamics.

Abstract

We extend stochastic network optimization theory to treat networks with arbitrary sample paths for arrivals, channels, and mobility. The network can experience unexpected link or node failures, traffic bursts, and topology changes, and there are no probabilistic assumptions describing these time varying events. Performance of our scheduling algorithm is compared against an ideal T-slot lookahead policy that can make optimal decisions based on knowledge up to T-slots into the future. We develop a simple non-anticipating algorithm that provides network throughput-utility that is arbitrarily close to (or better than) that of the T-slot lookahead policy, with a tradeoff in the worst case queue backlog kept at any queue. The same policy offers even stronger performance, closely matching that of an ideal infinite lookahead policy, when ergodic assumptions are imposed. Our analysis uses a sample path version of Lyapunov drift and provides a methodology for optimizing time averages in general time-varying optimization problems.