Service delays in strongly linked network communities

M. I. Bogachev; N. S. Pyko; S. A. Pyko; A. N. Vasenev; A. N. Vasenev

doi:10.1088/1742-6596/1352/1/012006

Service delays in strongly linked network communities

M. I. Bogachev^*
, N. S. Pyko
, S. A. Pyko
, A. N. Vasenev
, A. N. Vasenev

^*Corresponding author for this work

Research output: Contribution to journal › Conference article › Academic › peer-review

2 Citations (Scopus)

58 Downloads (Pure)

Abstract

We analyze aggregated traffic dynamics obtained from strongly linked network communities. Our results based on two empirical data traces from university campus networks indicate that neglecting the statistical links between traffic patterns generated by individual network nodes leads to the drastic underestimation of both waiting and sojourn times. We also show that similar effects can be observed in simulated traffic patterns obtained by agent based modeling. Moreover, we suggest several indices that could be used to quantify the links between nodes and show their relation with the queuing system performance indicators.

Original language	English
Article number	012006
Journal	Journal of physics: Conference series
Volume	1352
Issue number	1
DOIs	https://doi.org/10.1088/1742-6596/1352/1/012006
Publication status	Published - 4 Nov 2019
Event	International Scientific and Practical Conference on Mathematical Modeling, Programming and Applied Mathematics 2019, MMPAM 2019 - Veliky Novgorod, Russian Federation Duration: 27 Jun 2019 → 28 Jun 2019

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10.1088/1742-6596/1352/1/012006Licence: CC BY

10.1088_1742-6596_1352Final published version, 1.49 MBLicence: CC BY

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title = "Service delays in strongly linked network communities",

abstract = "We analyze aggregated traffic dynamics obtained from strongly linked network communities. Our results based on two empirical data traces from university campus networks indicate that neglecting the statistical links between traffic patterns generated by individual network nodes leads to the drastic underestimation of both waiting and sojourn times. We also show that similar effects can be observed in simulated traffic patterns obtained by agent based modeling. Moreover, we suggest several indices that could be used to quantify the links between nodes and show their relation with the queuing system performance indicators.",

author = "Bogachev, \{M. I.\} and Pyko, \{N. S.\} and Pyko, \{S. A.\} and Vasenev, \{A. N.\} and Vasenev, \{A. N.\}",

note = "Funding Information: We would like to acknowledge the financial support of this work by the Ministry of Science and Higher Education of the Russian Federation in the framework of the basic state assignment to St. Petersburg Electrotechnical University (research project no. 2.5475.2017/6.7 to MIB). Publisher Copyright: {\textcopyright} Published under licence by IOP Publishing Ltd.; International Scientific and Practical Conference on Mathematical Modeling, Programming and Applied Mathematics 2019, MMPAM 2019, MMPAM 2019 ; Conference date: 27-06-2019 Through 28-06-2019",

year = "2019",

month = nov,

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doi = "10.1088/1742-6596/1352/1/012006",

language = "English",

volume = "1352",

journal = "Journal of physics: Conference series",

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TY - JOUR

T1 - Service delays in strongly linked network communities

AU - Bogachev, M. I.

AU - Pyko, N. S.

AU - Pyko, S. A.

AU - Vasenev, A. N.

N1 - Funding Information: We would like to acknowledge the financial support of this work by the Ministry of Science and Higher Education of the Russian Federation in the framework of the basic state assignment to St. Petersburg Electrotechnical University (research project no. 2.5475.2017/6.7 to MIB). Publisher Copyright: © Published under licence by IOP Publishing Ltd.

PY - 2019/11/4

Y1 - 2019/11/4

N2 - We analyze aggregated traffic dynamics obtained from strongly linked network communities. Our results based on two empirical data traces from university campus networks indicate that neglecting the statistical links between traffic patterns generated by individual network nodes leads to the drastic underestimation of both waiting and sojourn times. We also show that similar effects can be observed in simulated traffic patterns obtained by agent based modeling. Moreover, we suggest several indices that could be used to quantify the links between nodes and show their relation with the queuing system performance indicators.

AB - We analyze aggregated traffic dynamics obtained from strongly linked network communities. Our results based on two empirical data traces from university campus networks indicate that neglecting the statistical links between traffic patterns generated by individual network nodes leads to the drastic underestimation of both waiting and sojourn times. We also show that similar effects can be observed in simulated traffic patterns obtained by agent based modeling. Moreover, we suggest several indices that could be used to quantify the links between nodes and show their relation with the queuing system performance indicators.

UR - https://www.scopus.com/pages/publications/85076136066

U2 - 10.1088/1742-6596/1352/1/012006

DO - 10.1088/1742-6596/1352/1/012006

M3 - Conference article

AN - SCOPUS:85076136066

SN - 1742-6588

VL - 1352

JO - Journal of physics: Conference series

JF - Journal of physics: Conference series

IS - 1

M1 - 012006

T2 - International Scientific and Practical Conference on Mathematical Modeling, Programming and Applied Mathematics 2019, MMPAM 2019

Y2 - 27 June 2019 through 28 June 2019

ER -

Service delays in strongly linked network communities

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