Max Weight Learning Algorithms with Application to Scheduling in Unknown Environments
| dc.creator | Neely, Michael J. | |
| dc.date | 2009-02-04 | |
| dc.date.accessioned | 2026-07-07T12:37:33Z | |
| dc.date.available | 2026-07-07T12:37:33Z | |
| dc.description | We consider a discrete time stochastic queueing system where a controller makes a 2-stage decision every slot. The decision at the first stage reveals a hidden source of randomness with a control-dependent (but unknown) probability distribution. The decision at the second stage incurs a penalty vector that depends on this revealed randomness. The goal is to stabilize all queues and minimize a convex function of the time average penalty vector subject to an additional set of time average penalty constraints. This setting fits a wide class of stochastic optimization problems. This includes problems of opportunistic scheduling in wireless networks, where a 2-stage decision about channel measurement and packet transmission must be made every slot without knowledge of the underlying transmission success probabilities. We develop a simple max-weight algorithm that learns efficient behavior by averaging functionals of previous outcomes. The algorithm yields performance that can be pushed arbitrarily close to optimal, with a tradeoff in convergence time and delay. | |
| dc.description | 30 pages, to be presented at the ITA workshop, University of California at San Diego, Feb. 2009 | |
| dc.identifier | https://arxiv.org/abs/0902.0630 | |
| dc.identifier | http://arxiv.org/abs/0902.0630 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/218478 | |
| dc.subject | Optimization and Control | |
| dc.title | Max Weight Learning Algorithms with Application to Scheduling in Unknown Environments | |
| dc.type | text |