Distributed Self-Adjusting Tree Networks

Bruna Peres; Otavio A.De O. Souza; Olga Goussevskaia; Chen Avin; Stefan Schmid

doi:10.1109/INFOCOM.2019.8737417

Distributed Self-Adjusting Tree Networks

Bruna Peres, Otavio A.De O. Souza, Olga Goussevskaia, Chen Avin, Stefan Schmid

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

We consider the problem of designing dynamic network topologies that self-adjust to the (possibly changing) traffic pattern they serve. Such demand-aware networks currently receive much attention, especially in the context of datacenters, due to emerging technologies supporting the fast reconfiguration of the physical topology. We present the first fully distributed, provably efficient self-adjusting network. Our network called DiSptayNet relies on algorithms that perform decentralized and concurrent topological adjustments to account for changes in the demand. We present a rigorous formal analysis of the correctness and performance of DiSptayNet, which can be seen as an interesting generalization of analyses known from sequential self-adjusting datastructures. We also report on results from extensive trace-driven simulations.

Original language	American English
Title of host publication	INFOCOM 2019 - IEEE Conference on Computer Communications
Pages	145-153
Number of pages	9
ISBN (Electronic)	9781728105154
DOIs	https://doi.org/10.1109/INFOCOM.2019.8737417
State	Published - 1 Apr 2019
Event	2019 IEEE Conference on Computer Communications, INFOCOM 2019 - Paris, France Duration: 29 Apr 2019 → 2 May 2019

Publication series

Name	Proceedings - IEEE INFOCOM
Volume	2019-April

Conference

Conference	2019 IEEE Conference on Computer Communications, INFOCOM 2019
Country/Territory	France
City	Paris
Period	29/04/19 → 2/05/19

All Science Journal Classification (ASJC) codes

General Computer Science
Electrical and Electronic Engineering

Access to Document

10.1109/INFOCOM.2019.8737417

Cite this

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title = "Distributed Self-Adjusting Tree Networks",

abstract = "We consider the problem of designing dynamic network topologies that self-adjust to the (possibly changing) traffic pattern they serve. Such demand-aware networks currently receive much attention, especially in the context of datacenters, due to emerging technologies supporting the fast reconfiguration of the physical topology. We present the first fully distributed, provably efficient self-adjusting network. Our network called DiSptayNet relies on algorithms that perform decentralized and concurrent topological adjustments to account for changes in the demand. We present a rigorous formal analysis of the correctness and performance of DiSptayNet, which can be seen as an interesting generalization of analyses known from sequential self-adjusting datastructures. We also report on results from extensive trace-driven simulations.",

author = "Bruna Peres and Souza, \{Otavio A.De O.\} and Olga Goussevskaia and Chen Avin and Stefan Schmid",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Conference on Computer Communications, INFOCOM 2019 ; Conference date: 29-04-2019 Through 02-05-2019",

year = "2019",

month = apr,

day = "1",

doi = "10.1109/INFOCOM.2019.8737417",

language = "American English",

series = "Proceedings - IEEE INFOCOM",

pages = "145--153",

booktitle = "INFOCOM 2019 - IEEE Conference on Computer Communications",

}

Peres, B, Souza, OADO, Goussevskaia, O, Avin, C & Schmid, S 2019, Distributed Self-Adjusting Tree Networks. in INFOCOM 2019 - IEEE Conference on Computer Communications., 8737417, Proceedings - IEEE INFOCOM, vol. 2019-April, pp. 145-153, 2019 IEEE Conference on Computer Communications, INFOCOM 2019, Paris, France, 29/04/19. https://doi.org/10.1109/INFOCOM.2019.8737417

TY - GEN

T1 - Distributed Self-Adjusting Tree Networks

AU - Peres, Bruna

AU - Souza, Otavio A.De O.

AU - Goussevskaia, Olga

AU - Avin, Chen

AU - Schmid, Stefan

PY - 2019/4/1

Y1 - 2019/4/1

N2 - We consider the problem of designing dynamic network topologies that self-adjust to the (possibly changing) traffic pattern they serve. Such demand-aware networks currently receive much attention, especially in the context of datacenters, due to emerging technologies supporting the fast reconfiguration of the physical topology. We present the first fully distributed, provably efficient self-adjusting network. Our network called DiSptayNet relies on algorithms that perform decentralized and concurrent topological adjustments to account for changes in the demand. We present a rigorous formal analysis of the correctness and performance of DiSptayNet, which can be seen as an interesting generalization of analyses known from sequential self-adjusting datastructures. We also report on results from extensive trace-driven simulations.

AB - We consider the problem of designing dynamic network topologies that self-adjust to the (possibly changing) traffic pattern they serve. Such demand-aware networks currently receive much attention, especially in the context of datacenters, due to emerging technologies supporting the fast reconfiguration of the physical topology. We present the first fully distributed, provably efficient self-adjusting network. Our network called DiSptayNet relies on algorithms that perform decentralized and concurrent topological adjustments to account for changes in the demand. We present a rigorous formal analysis of the correctness and performance of DiSptayNet, which can be seen as an interesting generalization of analyses known from sequential self-adjusting datastructures. We also report on results from extensive trace-driven simulations.

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U2 - 10.1109/INFOCOM.2019.8737417

DO - 10.1109/INFOCOM.2019.8737417

M3 - Conference contribution

T3 - Proceedings - IEEE INFOCOM

SP - 145

EP - 153

BT - INFOCOM 2019 - IEEE Conference on Computer Communications

T2 - 2019 IEEE Conference on Computer Communications, INFOCOM 2019

Y2 - 29 April 2019 through 2 May 2019

ER -

Distributed Self-Adjusting Tree Networks

Abstract

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Conference

All Science Journal Classification (ASJC) codes

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