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dc.contributor.authorTsitsiklis, John N.
dc.contributor.authorXu, Kuang
dc.date.accessioned2013-09-26T14:17:12Z
dc.date.available2013-09-26T14:17:12Z
dc.date.issued2011-06
dc.identifier.isbn9781450308144
dc.identifier.urihttp://hdl.handle.net/1721.1/81190
dc.description.abstractWe propose and analyze a multi-server model that captures a performance trade-off between centralized and distributed processing. In our model, a fraction p of an available resource is deployed in a centralized manner (e.g., to serve a most loaded station) while the remaining fraction 1-p is allocated to local servers that can only serve requests addressed specifically to their respective stations. Using a fluid model approach, we demonstrate a surprising phase transition in steady-state delay, as p changes: in the limit of a large number of stations, and when any amount of centralization is available (p>0), the average queue length in steady state scales as log [subscript 1/1-p] 1/1-λ when the traffic intensity λ goes to 1. This is exponentially smaller than the usual M/M/1-queue delay scaling of 1/1-λ, obtained when all resources are fully allocated to local stations (p=0). This indicates a strong qualitative impact of even a small degree of centralization. We prove convergence to a fluid limit, and characterize both the transient and steady-state behavior of the finite system, in the limit as the number of stations N goes to infinity. We show that the queue-length process converges to a unique fluid trajectory (over any finite time interval, as N → ∞), and that this fluid trajectory converges to a unique invariant state v[superscript I], for which a simple closed-form expression is obtained. We also show that the steady-state distribution of the N-server system concentrates on v[superscript I] as N goes to infinity.en_US
dc.description.sponsorshipIrwin Mark Jacobs and Joan Klein Jacobs Presidential Fellowshipen_US
dc.description.sponsorshipXerox Fellowship Programen_US
dc.description.sponsorshipThomas and Stacey Siebel Foundation (Scholarship)en_US
dc.description.sponsorshipNational Science Foundation (U.S.) (Grant CCF-0728554)en_US
dc.language.isoen_US
dc.publisherAssociation for Computing Machinery (ACM)en_US
dc.relation.isversionofhttp//dx.doi.org/10.1145/1993744.1993759en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alike 3.0en_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/en_US
dc.sourceMIT web domainen_US
dc.titleOn the Power of (even a little) Centralization in Distributed Processingen_US
dc.typeArticleen_US
dc.identifier.citationJohn N. Tsitsiklis and Kuang Xu. 2011. On the power of (even a little) centralization in distributed processing. In Proceedings of the ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems (SIGMETRICS '11). ACM, New York, NY, USA, 161-172.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.approverTsitsiklis, John N.en_US
dc.contributor.mitauthorTsitsiklis, John N.en_US
dc.contributor.mitauthorXu, Kuangen_US
dc.relation.journalProceedings of the ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems (SIGMETRICS '11)en_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
dspace.orderedauthorsTsitsiklis, John N.; Xu, Kuangen_US
dc.identifier.orcidhttps://orcid.org/0000-0003-2658-8239
mit.licenseOPEN_ACCESS_POLICYen_US


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