Target tracking based network active queue management

Shane F. Cotter, Manohar Murthi

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

Abstract

Active Queue Management (AQM) methods attempt to predict and control network router queue levels and provide feedback regarding network congestion to data sources through packet marking/dropping. AQM methods have not employed statistical signal processing principles largely due to the requirement of low complexity. In this paper, we apply optimal filtering and target tracking methods to the design of AQM. In particular, we develop Kalman Filter based AQM which results in router queues with reduced queue level variance. To account for networks with more bursty traffic, we use Interacting Multiple Models (IMM) which similarly result in reduced queue variance in simulations with both long-term and bursty short-term traffic. In comparisons with other AQM methods, these low complexity target tracking-based AQM methods give a more constant queue length without any loss in source throughput.

Original languageEnglish
Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Pages2757-2760
Number of pages4
DOIs
StatePublished - Sep 23 2009
Event2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009 - Taipei, Taiwan, Province of China
Duration: Apr 19 2009Apr 24 2009

Other

Other2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009
CountryTaiwan, Province of China
CityTaipei
Period4/19/094/24/09

Fingerprint

Active networks
Target tracking
Routers
Kalman filters
Signal processing
Throughput
Feedback

Keywords

  • Active queue management
  • Kalman Filter
  • Networking

ASJC Scopus subject areas

  • Signal Processing
  • Software
  • Electrical and Electronic Engineering

Cite this

Cotter, S. F., & Murthi, M. (2009). Target tracking based network active queue management. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (pp. 2757-2760). [4960194] https://doi.org/10.1109/ICASSP.2009.4960194

Target tracking based network active queue management. / Cotter, Shane F.; Murthi, Manohar.

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. 2009. p. 2757-2760 4960194.

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

Cotter, SF & Murthi, M 2009, Target tracking based network active queue management. in ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings., 4960194, pp. 2757-2760, 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009, Taipei, Taiwan, Province of China, 4/19/09. https://doi.org/10.1109/ICASSP.2009.4960194
Cotter SF, Murthi M. Target tracking based network active queue management. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. 2009. p. 2757-2760. 4960194 https://doi.org/10.1109/ICASSP.2009.4960194
Cotter, Shane F. ; Murthi, Manohar. / Target tracking based network active queue management. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. 2009. pp. 2757-2760
@inproceedings{d51839bdb97a4951bdb3a33a094c7f21,
title = "Target tracking based network active queue management",
abstract = "Active Queue Management (AQM) methods attempt to predict and control network router queue levels and provide feedback regarding network congestion to data sources through packet marking/dropping. AQM methods have not employed statistical signal processing principles largely due to the requirement of low complexity. In this paper, we apply optimal filtering and target tracking methods to the design of AQM. In particular, we develop Kalman Filter based AQM which results in router queues with reduced queue level variance. To account for networks with more bursty traffic, we use Interacting Multiple Models (IMM) which similarly result in reduced queue variance in simulations with both long-term and bursty short-term traffic. In comparisons with other AQM methods, these low complexity target tracking-based AQM methods give a more constant queue length without any loss in source throughput.",
keywords = "Active queue management, Kalman Filter, Networking",
author = "Cotter, {Shane F.} and Manohar Murthi",
year = "2009",
month = "9",
day = "23",
doi = "10.1109/ICASSP.2009.4960194",
language = "English",
isbn = "9781424423545",
pages = "2757--2760",
booktitle = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

}

TY - GEN

T1 - Target tracking based network active queue management

AU - Cotter, Shane F.

AU - Murthi, Manohar

PY - 2009/9/23

Y1 - 2009/9/23

N2 - Active Queue Management (AQM) methods attempt to predict and control network router queue levels and provide feedback regarding network congestion to data sources through packet marking/dropping. AQM methods have not employed statistical signal processing principles largely due to the requirement of low complexity. In this paper, we apply optimal filtering and target tracking methods to the design of AQM. In particular, we develop Kalman Filter based AQM which results in router queues with reduced queue level variance. To account for networks with more bursty traffic, we use Interacting Multiple Models (IMM) which similarly result in reduced queue variance in simulations with both long-term and bursty short-term traffic. In comparisons with other AQM methods, these low complexity target tracking-based AQM methods give a more constant queue length without any loss in source throughput.

AB - Active Queue Management (AQM) methods attempt to predict and control network router queue levels and provide feedback regarding network congestion to data sources through packet marking/dropping. AQM methods have not employed statistical signal processing principles largely due to the requirement of low complexity. In this paper, we apply optimal filtering and target tracking methods to the design of AQM. In particular, we develop Kalman Filter based AQM which results in router queues with reduced queue level variance. To account for networks with more bursty traffic, we use Interacting Multiple Models (IMM) which similarly result in reduced queue variance in simulations with both long-term and bursty short-term traffic. In comparisons with other AQM methods, these low complexity target tracking-based AQM methods give a more constant queue length without any loss in source throughput.

KW - Active queue management

KW - Kalman Filter

KW - Networking

UR - http://www.scopus.com/inward/record.url?scp=70349195922&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=70349195922&partnerID=8YFLogxK

U2 - 10.1109/ICASSP.2009.4960194

DO - 10.1109/ICASSP.2009.4960194

M3 - Conference contribution

AN - SCOPUS:70349195922

SN - 9781424423545

SP - 2757

EP - 2760

BT - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

ER -