A New Dependence Model for Heterogeneous Markov Modulated Poisson Processes

Fang Dong; Kui Wu; Venkatesh Srinivasan

A New Dependence Model for Heterogeneous Markov Modulated Poisson Processes

Fang Dong, Kui Wu, Venkatesh Srinivasan

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

Markov Modulated Poisson Process (MMPP) has been extensively studied in random process theory and widely applied in various applications involving Poisson arrivals whose rate varies following a Markov process. The most general form of aggregated MMPP is the superposition of heterogeneous MMPPs (HeMMPP), in which each constituent MMPP has different parameters. Due to the generality of HeMMPP, studying its temporal dependence will benefit network traffic monitoring and traffic prediction. Modeling the temporal dependence of HeMMPP, however, is extremely hard because the total number of states in a HeMMPP increases exponentially with the number of states in constituent MMPPs. This paper tackles the above challenge with copula analysis. It not only presents a novel framework to capture the functional dependence structure of HeMMPP, but also provides a recursive algorithm to effectively calculate HeMMPP copula values. The theoretical analysis and the algorithms together offer a complete solution for modeling the temporal dependence of HeMMPP. Another contribution of the paper is the application of HeMMPP copula for traffic prediction. © 2018 IFIP

Original language	English
Title of host publication	17th International IFIP TC6 Networking Conference, Networking 2018, Zurich, Switzerland, May 14-16, 2018
Publisher	IFIP
Pages	397-405
ISBN (Electronic)	9783903176089
Publication status	Published - May 2018
Externally published	Yes
Event	17th International IFIP TC6 Networking Conference (NETWORKING 2018) - Swissôtel Zürich, Zurich, Switzerland Duration: 14 May 2018 → 16 May 2018 https://networking.ifip.org/2018/

Publication series

Name	International IFIP Networking Conference, Networking

Conference

Conference	17th International IFIP TC6 Networking Conference (NETWORKING 2018)
Place	Switzerland
City	Zurich
Period	14/05/18 → 16/05/18
Internet address	https://networking.ifip.org/2018/

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Access Output

https://dl.ifip.org/db/conf/networking/networking2018/index.html

Other Access Options

Check CityUHK Library

Permanent Link

Right click to copy link

Cite this

@inproceedings{a7fc07a9ffd445a1b63fabd692c0d467,

title = "A New Dependence Model for Heterogeneous Markov Modulated Poisson Processes",

abstract = "Markov Modulated Poisson Process (MMPP) has been extensively studied in random process theory and widely applied in various applications involving Poisson arrivals whose rate varies following a Markov process. The most general form of aggregated MMPP is the superposition of heterogeneous MMPPs (HeMMPP), in which each constituent MMPP has different parameters. Due to the generality of HeMMPP, studying its temporal dependence will benefit network traffic monitoring and traffic prediction. Modeling the temporal dependence of HeMMPP, however, is extremely hard because the total number of states in a HeMMPP increases exponentially with the number of states in constituent MMPPs. This paper tackles the above challenge with copula analysis. It not only presents a novel framework to capture the functional dependence structure of HeMMPP, but also provides a recursive algorithm to effectively calculate HeMMPP copula values. The theoretical analysis and the algorithms together offer a complete solution for modeling the temporal dependence of HeMMPP. Another contribution of the paper is the application of HeMMPP copula for traffic prediction. {\textcopyright} 2018 IFIP",

author = "Fang Dong and Kui Wu and Venkatesh Srinivasan",

note = "Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].; 17th International IFIP TC6 Networking Conference (NETWORKING 2018) ; Conference date: 14-05-2018 Through 16-05-2018",

year = "2018",

month = may,

language = "English",

series = "International IFIP Networking Conference, Networking",

publisher = "IFIP",

pages = "397--405",

booktitle = "17th International IFIP TC6 Networking Conference, Networking 2018, Zurich, Switzerland, May 14-16, 2018",

url = "https://networking.ifip.org/2018/",

}

Dong, F, Wu, K & Srinivasan, V 2018, A New Dependence Model for Heterogeneous Markov Modulated Poisson Processes. in 17th International IFIP TC6 Networking Conference, Networking 2018, Zurich, Switzerland, May 14-16, 2018. International IFIP Networking Conference, Networking, IFIP, pp. 397-405, 17th International IFIP TC6 Networking Conference (NETWORKING 2018), Zurich, Switzerland, 14/05/18. <https://dl.ifip.org/db/conf/networking/networking2018/index.html>

A New Dependence Model for Heterogeneous Markov Modulated Poisson Processes. / Dong, Fang; Wu, Kui; Srinivasan, Venkatesh.
17th International IFIP TC6 Networking Conference, Networking 2018, Zurich, Switzerland, May 14-16, 2018. IFIP, 2018. p. 397-405 (International IFIP Networking Conference, Networking).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - A New Dependence Model for Heterogeneous Markov Modulated Poisson Processes

AU - Dong, Fang

AU - Wu, Kui

AU - Srinivasan, Venkatesh

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

PY - 2018/5

Y1 - 2018/5

N2 - Markov Modulated Poisson Process (MMPP) has been extensively studied in random process theory and widely applied in various applications involving Poisson arrivals whose rate varies following a Markov process. The most general form of aggregated MMPP is the superposition of heterogeneous MMPPs (HeMMPP), in which each constituent MMPP has different parameters. Due to the generality of HeMMPP, studying its temporal dependence will benefit network traffic monitoring and traffic prediction. Modeling the temporal dependence of HeMMPP, however, is extremely hard because the total number of states in a HeMMPP increases exponentially with the number of states in constituent MMPPs. This paper tackles the above challenge with copula analysis. It not only presents a novel framework to capture the functional dependence structure of HeMMPP, but also provides a recursive algorithm to effectively calculate HeMMPP copula values. The theoretical analysis and the algorithms together offer a complete solution for modeling the temporal dependence of HeMMPP. Another contribution of the paper is the application of HeMMPP copula for traffic prediction. © 2018 IFIP

AB - Markov Modulated Poisson Process (MMPP) has been extensively studied in random process theory and widely applied in various applications involving Poisson arrivals whose rate varies following a Markov process. The most general form of aggregated MMPP is the superposition of heterogeneous MMPPs (HeMMPP), in which each constituent MMPP has different parameters. Due to the generality of HeMMPP, studying its temporal dependence will benefit network traffic monitoring and traffic prediction. Modeling the temporal dependence of HeMMPP, however, is extremely hard because the total number of states in a HeMMPP increases exponentially with the number of states in constituent MMPPs. This paper tackles the above challenge with copula analysis. It not only presents a novel framework to capture the functional dependence structure of HeMMPP, but also provides a recursive algorithm to effectively calculate HeMMPP copula values. The theoretical analysis and the algorithms together offer a complete solution for modeling the temporal dependence of HeMMPP. Another contribution of the paper is the application of HeMMPP copula for traffic prediction. © 2018 IFIP

UR - http://www.scopus.com/inward/record.url?scp=85127986975&partnerID=8YFLogxK

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85127986975&origin=recordpage

M3 - RGC 32 - Refereed conference paper (with host publication)

T3 - International IFIP Networking Conference, Networking

SP - 397

EP - 405

BT - 17th International IFIP TC6 Networking Conference, Networking 2018, Zurich, Switzerland, May 14-16, 2018

PB - IFIP

T2 - 17th International IFIP TC6 Networking Conference (NETWORKING 2018)

Y2 - 14 May 2018 through 16 May 2018

ER -

A New Dependence Model for Heterogeneous Markov Modulated Poisson Processes

Abstract

Publication series

Conference

Bibliographical note

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

A New Dependence Model for Heterogeneous Markov Modulated Poisson Processes

Cite this