Jitter-based delay-boundary prediction of wide-area networks

The delay-boundary prediction algorithms currently implemented by transport protocols are lowpass filters based on autoregressive and moving average (ARMA) models. However, previous studies have revealed a fractal-like structure of delay sequences, which may not be well suited to ARMA models. We pro...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE/ACM transactions on networking Jg. 9; H. 5; S. 578 - 590
Hauptverfasser: Qiong Li, Mills, D.L.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.10.2001
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Delay
Deviation
Error analysis
Feasibility
Filters
Fractals
Jacobian matrices
Jitter
Joints
Mathematical models
Prediction algorithms
Predictive models
Robustness
Transport
Transport protocols
ISSN:1063-6692, 1558-2566
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:The delay-boundary prediction algorithms currently implemented by transport protocols are lowpass filters based on autoregressive and moving average (ARMA) models. However, previous studies have revealed a fractal-like structure of delay sequences, which may not be well suited to ARMA models. We propose a novel delay-boundary prediction algorithm based on a deviation-lag function (DLF) to characterize the end-to-end delay variations. Compared to conventional algorithms derived from ARMA models, the new algorithm can adapt to delay variations more rapidly and share the delay's robust high-order statistical information (jitter deviation) among competing connections along a common network path. Preliminary experiments show that it outperforms Jacobson's (1988) algorithm, which is based on an ARMA model, by significantly reducing the prediction error rate. To show the practical feasibility of the DLF algorithm, we also propose a skeleton implementation model.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ObjectType-Article-2
ObjectType-Feature-1
ISSN:1063-6692
1558-2566
DOI:10.1109/90.958327