Modelling large timescale and small timescale service variability

Marco Gribaudo, Illés Horváth, Daniele Manini, M. Telek

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The performance of service units might depend on various randomly changing environmental effects. It is quite often the case that these effects varies on different time scales. In this paper we consider short and long scale service variability, where the short scale variability affects the instantaneous service speed of the service unit and the large scale effect is defined by a modulating background Markov chain. The main modelling challenge is that the considered short and long range variation results randomness along different axes, the short scale variability along the time axis and the long scale variability along the work axis. The work presents mostly simulation results; the mathematical setup for analytical results is provided, but the actual analysis is subject to future research.

Original languageEnglish
Title of host publicationQueueing Theory and Network Applications - 13th International Conference, QTNA 2018, Proceedings
PublisherSpringer Verlag
Pages103-111
Number of pages9
ISBN (Print)9783319937359
DOIs
Publication statusPublished - Jan 1 2018
Event13th International Conference on Queueing Theory and Network Applications, QTNA 2018 - Tsukuba, Japan
Duration: Jul 25 2018Jul 27 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10932 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other13th International Conference on Queueing Theory and Network Applications, QTNA 2018
CountryJapan
CityTsukuba
Period7/25/187/27/18

Fingerprint

Markov processes
Environmental impact
Time Scales
Modeling
Scale Effect
Unit
Randomness
Instantaneous
Markov chain
Vary
Range of data
Simulation

Keywords

  • Brownian motion
  • Markov modulation
  • Performance analysis
  • Short and long term service variability

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Gribaudo, M., Horváth, I., Manini, D., & Telek, M. (2018). Modelling large timescale and small timescale service variability. In Queueing Theory and Network Applications - 13th International Conference, QTNA 2018, Proceedings (pp. 103-111). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10932 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-93736-6_7

Modelling large timescale and small timescale service variability. / Gribaudo, Marco; Horváth, Illés; Manini, Daniele; Telek, M.

Queueing Theory and Network Applications - 13th International Conference, QTNA 2018, Proceedings. Springer Verlag, 2018. p. 103-111 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10932 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Gribaudo, M, Horváth, I, Manini, D & Telek, M 2018, Modelling large timescale and small timescale service variability. in Queueing Theory and Network Applications - 13th International Conference, QTNA 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10932 LNCS, Springer Verlag, pp. 103-111, 13th International Conference on Queueing Theory and Network Applications, QTNA 2018, Tsukuba, Japan, 7/25/18. https://doi.org/10.1007/978-3-319-93736-6_7
Gribaudo M, Horváth I, Manini D, Telek M. Modelling large timescale and small timescale service variability. In Queueing Theory and Network Applications - 13th International Conference, QTNA 2018, Proceedings. Springer Verlag. 2018. p. 103-111. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-93736-6_7
Gribaudo, Marco ; Horváth, Illés ; Manini, Daniele ; Telek, M. / Modelling large timescale and small timescale service variability. Queueing Theory and Network Applications - 13th International Conference, QTNA 2018, Proceedings. Springer Verlag, 2018. pp. 103-111 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{9bd9a67ad1734d1689db07e62abc72be,
title = "Modelling large timescale and small timescale service variability",
abstract = "The performance of service units might depend on various randomly changing environmental effects. It is quite often the case that these effects varies on different time scales. In this paper we consider short and long scale service variability, where the short scale variability affects the instantaneous service speed of the service unit and the large scale effect is defined by a modulating background Markov chain. The main modelling challenge is that the considered short and long range variation results randomness along different axes, the short scale variability along the time axis and the long scale variability along the work axis. The work presents mostly simulation results; the mathematical setup for analytical results is provided, but the actual analysis is subject to future research.",
keywords = "Brownian motion, Markov modulation, Performance analysis, Short and long term service variability",
author = "Marco Gribaudo and Ill{\'e}s Horv{\'a}th and Daniele Manini and M. Telek",
year = "2018",
month = "1",
day = "1",
doi = "10.1007/978-3-319-93736-6_7",
language = "English",
isbn = "9783319937359",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "103--111",
booktitle = "Queueing Theory and Network Applications - 13th International Conference, QTNA 2018, Proceedings",

}

TY - GEN

T1 - Modelling large timescale and small timescale service variability

AU - Gribaudo, Marco

AU - Horváth, Illés

AU - Manini, Daniele

AU - Telek, M.

PY - 2018/1/1

Y1 - 2018/1/1

N2 - The performance of service units might depend on various randomly changing environmental effects. It is quite often the case that these effects varies on different time scales. In this paper we consider short and long scale service variability, where the short scale variability affects the instantaneous service speed of the service unit and the large scale effect is defined by a modulating background Markov chain. The main modelling challenge is that the considered short and long range variation results randomness along different axes, the short scale variability along the time axis and the long scale variability along the work axis. The work presents mostly simulation results; the mathematical setup for analytical results is provided, but the actual analysis is subject to future research.

AB - The performance of service units might depend on various randomly changing environmental effects. It is quite often the case that these effects varies on different time scales. In this paper we consider short and long scale service variability, where the short scale variability affects the instantaneous service speed of the service unit and the large scale effect is defined by a modulating background Markov chain. The main modelling challenge is that the considered short and long range variation results randomness along different axes, the short scale variability along the time axis and the long scale variability along the work axis. The work presents mostly simulation results; the mathematical setup for analytical results is provided, but the actual analysis is subject to future research.

KW - Brownian motion

KW - Markov modulation

KW - Performance analysis

KW - Short and long term service variability

UR - http://www.scopus.com/inward/record.url?scp=85050470430&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85050470430&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-93736-6_7

DO - 10.1007/978-3-319-93736-6_7

M3 - Conference contribution

SN - 9783319937359

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 103

EP - 111

BT - Queueing Theory and Network Applications - 13th International Conference, QTNA 2018, Proceedings

PB - Springer Verlag

ER -