Profile-driven regression for modeling and runtime optimization of mobile networks

Daniel W. Mcclary, Violet Syrotiuk, Murat Kulahci

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Computer networks often display nonlinear behavior when examined over a wide range of operating conditions. There are few strategies available for modeling such behavior and optimizing such systems as they run. Profile-driven regression is developed and applied to modeling and runtime optimization of throughput in a mobile ad hoc network, a self-organizing collection of mobile wireless nodes without any fixed infrastructure. The intermediate models generated in profile-driven regression are used to fit an overall model of throughput, and are also used to optimize controllable factors at runtime. Unlike others, the throughput model accounts for node speed. The resulting optimization is very effective; locally optimizing the network factors at runtime results in throughput as much as six times higher than that achieved with the factors at their default levels.

Original languageEnglish (US)
Article number17
JournalACM Transactions on Modeling and Computer Simulation
Volume20
Issue number3
DOIs
StatePublished - Sep 2010

Fingerprint

Mobile Networks
Wireless networks
Throughput
Regression
Optimization
Computer simulation
Modeling
Behavior Modeling
Computer Networks
Self-organizing
Mobile ad hoc networks
Vertex of a graph
Mobile Ad Hoc Networks
Computer networks
Infrastructure
Optimise
Model
Profile
Range of data

Keywords

  • Mobile ad hoc networks
  • Regression modeling
  • Runtime optimization

ASJC Scopus subject areas

  • Computer Science Applications
  • Modeling and Simulation

Cite this

Profile-driven regression for modeling and runtime optimization of mobile networks. / Mcclary, Daniel W.; Syrotiuk, Violet; Kulahci, Murat.

In: ACM Transactions on Modeling and Computer Simulation, Vol. 20, No. 3, 17, 09.2010.

Research output: Contribution to journalArticle

@article{d6d80f16373f43b898cbbd27a345dc49,
title = "Profile-driven regression for modeling and runtime optimization of mobile networks",
abstract = "Computer networks often display nonlinear behavior when examined over a wide range of operating conditions. There are few strategies available for modeling such behavior and optimizing such systems as they run. Profile-driven regression is developed and applied to modeling and runtime optimization of throughput in a mobile ad hoc network, a self-organizing collection of mobile wireless nodes without any fixed infrastructure. The intermediate models generated in profile-driven regression are used to fit an overall model of throughput, and are also used to optimize controllable factors at runtime. Unlike others, the throughput model accounts for node speed. The resulting optimization is very effective; locally optimizing the network factors at runtime results in throughput as much as six times higher than that achieved with the factors at their default levels.",
keywords = "Mobile ad hoc networks, Regression modeling, Runtime optimization",
author = "Mcclary, {Daniel W.} and Violet Syrotiuk and Murat Kulahci",
year = "2010",
month = "9",
doi = "10.1145/1842713.1842720",
language = "English (US)",
volume = "20",
journal = "ACM Transactions on Modeling and Computer Simulation",
issn = "1049-3301",
publisher = "Association for Computing Machinery (ACM)",
number = "3",

}

TY - JOUR

T1 - Profile-driven regression for modeling and runtime optimization of mobile networks

AU - Mcclary, Daniel W.

AU - Syrotiuk, Violet

AU - Kulahci, Murat

PY - 2010/9

Y1 - 2010/9

N2 - Computer networks often display nonlinear behavior when examined over a wide range of operating conditions. There are few strategies available for modeling such behavior and optimizing such systems as they run. Profile-driven regression is developed and applied to modeling and runtime optimization of throughput in a mobile ad hoc network, a self-organizing collection of mobile wireless nodes without any fixed infrastructure. The intermediate models generated in profile-driven regression are used to fit an overall model of throughput, and are also used to optimize controllable factors at runtime. Unlike others, the throughput model accounts for node speed. The resulting optimization is very effective; locally optimizing the network factors at runtime results in throughput as much as six times higher than that achieved with the factors at their default levels.

AB - Computer networks often display nonlinear behavior when examined over a wide range of operating conditions. There are few strategies available for modeling such behavior and optimizing such systems as they run. Profile-driven regression is developed and applied to modeling and runtime optimization of throughput in a mobile ad hoc network, a self-organizing collection of mobile wireless nodes without any fixed infrastructure. The intermediate models generated in profile-driven regression are used to fit an overall model of throughput, and are also used to optimize controllable factors at runtime. Unlike others, the throughput model accounts for node speed. The resulting optimization is very effective; locally optimizing the network factors at runtime results in throughput as much as six times higher than that achieved with the factors at their default levels.

KW - Mobile ad hoc networks

KW - Regression modeling

KW - Runtime optimization

UR - http://www.scopus.com/inward/record.url?scp=77958076546&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77958076546&partnerID=8YFLogxK

U2 - 10.1145/1842713.1842720

DO - 10.1145/1842713.1842720

M3 - Article

AN - SCOPUS:77958076546

VL - 20

JO - ACM Transactions on Modeling and Computer Simulation

JF - ACM Transactions on Modeling and Computer Simulation

SN - 1049-3301

IS - 3

M1 - 17

ER -