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dc.contributor.authorLiang, Qingkai
dc.contributor.authorModiano, Eytan H
dc.date.accessioned2020-07-22T12:06:06Z
dc.date.available2020-07-22T12:06:06Z
dc.date.issued2019-04
dc.identifier.isbn9781728105154
dc.identifier.urihttps://hdl.handle.net/1721.1/126298
dc.description.abstractThe effectiveness of many optimal network control algorithms (e.g., BackPressure) relies on the premise that all of the nodes are fully controllable. However, these algorithms may yield poor performance in a partially-controllable network where a subset of nodes are uncontrollable and use some unknown policy. Such a partially-controllable model is of increasing importance in real-world networked systems such as overlay-underlay networks. In this paper, we design optimal network control algorithms that can stabilize a partially-controllable network. We first study the scenario where uncontrollable nodes use a queue-agnostic policy, and propose a low-complexity throughput-optimal algorithm, called Tracking-MaxWeight (TMW), which enhances the original MaxWeight algorithm with an explicit learning of the policy used by uncontrollable nodes. Next, we investigate the scenario where uncontrollable nodes use a queue-dependent policy and the problem is formulated as an MDP with unknown queueing dynamics. We propose a new reinforcement learning algorithm, called Truncated Upper Confidence Reinforcement Learning (TUCRL), and prove that TUCRL achieves tunable three-way tradeoffs between throughput, delay and convergence rate.en_US
dc.description.sponsorshipNational Science Foundation (U.S.) (Grant CNS-1524317)en_US
dc.description.sponsorshipUnited States. Defense Advanced Research Projects Agency (Contract HROO l l-l 5-C-0097)en_US
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.isversionof10.1109/INFOCOM.2019.8737528en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceMIT web domainen_US
dc.titleOptimal Network Control in Partially-Controllable Networksen_US
dc.typeArticleen_US
dc.identifier.citationLiang, Qingkai and Eytan Modiano. “Optimal Network Control in Partially-Controllable Networks.” Paper presented at IEEE INFOCOM 2019, Paris, France, April 29-May 2, 2019 © 2019 The Author(s)en_US
dc.contributor.departmentMassachusetts Institute of Technology. Laboratory for Information and Decision Systemsen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Aeronautics and Astronauticsen_US
dc.relation.journalIEEE INFOCOM 2019en_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2019-10-30T16:26:33Z
dspace.date.submission2019-10-30T16:26:36Z
mit.journal.volume2019en_US


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