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

19 Scopus citations

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	English
Title of host publication	INFOCOM 2019 - IEEE Conference on Computer Communications
Publisher	Institute of Electrical and Electronics Engineers
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
ISSN (Print)	0743-166X

Conference

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

ASJC Scopus subject areas

General Computer Science
Electrical and Electronic Engineering

Access to Document

10.1109/INFOCOM.2019.8737417

Cite this

@inproceedings{644c4e911e0a4525bb79cc0081de892c,

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 = "English",

series = "Proceedings - IEEE INFOCOM",

publisher = "Institute of Electrical and Electronics Engineers",

pages = "145--153",

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

address = "United States",

}

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, Institute of Electrical and Electronics Engineers, pp. 145-153, 2019 IEEE Conference on Computer Communications, INFOCOM 2019, Paris, France, 29/04/19. https://doi.org/10.1109/INFOCOM.2019.8737417

Distributed Self-Adjusting Tree Networks. / Peres, Bruna; Souza, Otavio A.De O.; Goussevskaia, Olga et al.
INFOCOM 2019 - IEEE Conference on Computer Communications. Institute of Electrical and Electronics Engineers, 2019. p. 145-153 8737417 (Proceedings - IEEE INFOCOM; Vol. 2019-April).

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

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.

UR - https://www.scopus.com/pages/publications/85066762487

U2 - 10.1109/INFOCOM.2019.8737417

DO - 10.1109/INFOCOM.2019.8737417

M3 - Conference contribution

AN - SCOPUS:85066762487

T3 - Proceedings - IEEE INFOCOM

SP - 145

EP - 153

BT - INFOCOM 2019 - IEEE Conference on Computer Communications

PB - Institute of Electrical and Electronics Engineers

T2 - 2019 IEEE Conference on Computer Communications, INFOCOM 2019

Y2 - 29 April 2019 through 2 May 2019

ER -

Distributed Self-Adjusting Tree Networks

Abstract

Publication series

Conference

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this