IEICE TRANSACTIONS on Communications
Characterization of Host-Level Application Traffic with Multi-Scale Gamma Model
Summary :
In this paper, we discuss the validity of the multi-scale gamma model and characterize the differences in host-level application traffic with this model by using a real traffic trace collected on a 150-Mbps transpacific link. First, we investigate the dependency of the model (parameters α and β, and fitting accuracy ε) on time scale Δ, then find suitable time scales for the model. Second, we inspect the relations among α, β, and ε, in order to characterize the differences in the types of applications. The main findings of the paper are as follows. (1) Different types of applications show different dependencies of α, β, and ε on Δ, and display different suitable Δs for the model. The model is more accurate if the traffic consists of intermittently-sent packets than other. (2) More appropriate models are obtained with specific α and β values (e.g., 0.1 < α < 1, and β < 2 for Δ = 500 ms). Also, application-specific traffic presents specific ranges of α, β, and ε for each Δ, so that these characteristics can be used in application identification methods such as anomaly detection and other machine learning methods.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E93-B No.11 pp.3048-3057
- Publication Date
- 2010/11/01
- Publicized
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.E93.B.3048
- Type of Manuscript
- PAPER
- Category
- Internet
Authors
Keyword
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Yosuke HIMURA, Kensuke FUKUDA, Patrice ABRY, Kenjiro CHO, Hiroshi ESAKI, "Characterization of Host-Level Application Traffic with Multi-Scale Gamma Model" in IEICE TRANSACTIONS on Communications,
vol. E93-B, no. 11, pp. 3048-3057, November 2010, doi: 10.1587/transcom.E93.B.3048.
Abstract: In this paper, we discuss the validity of the multi-scale gamma model and characterize the differences in host-level application traffic with this model by using a real traffic trace collected on a 150-Mbps transpacific link. First, we investigate the dependency of the model (parameters α and β, and fitting accuracy ε) on time scale Δ, then find suitable time scales for the model. Second, we inspect the relations among α, β, and ε, in order to characterize the differences in the types of applications. The main findings of the paper are as follows. (1) Different types of applications show different dependencies of α, β, and ε on Δ, and display different suitable Δs for the model. The model is more accurate if the traffic consists of intermittently-sent packets than other. (2) More appropriate models are obtained with specific α and β values (e.g., 0.1 < α < 1, and β < 2 for Δ = 500 ms). Also, application-specific traffic presents specific ranges of α, β, and ε for each Δ, so that these characteristics can be used in application identification methods such as anomaly detection and other machine learning methods.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.E93.B.3048/_p
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@ARTICLE{e93-b_11_3048,
author={Yosuke HIMURA, Kensuke FUKUDA, Patrice ABRY, Kenjiro CHO, Hiroshi ESAKI, },
journal={IEICE TRANSACTIONS on Communications},
title={Characterization of Host-Level Application Traffic with Multi-Scale Gamma Model},
year={2010},
volume={E93-B},
number={11},
pages={3048-3057},
abstract={In this paper, we discuss the validity of the multi-scale gamma model and characterize the differences in host-level application traffic with this model by using a real traffic trace collected on a 150-Mbps transpacific link. First, we investigate the dependency of the model (parameters α and β, and fitting accuracy ε) on time scale Δ, then find suitable time scales for the model. Second, we inspect the relations among α, β, and ε, in order to characterize the differences in the types of applications. The main findings of the paper are as follows. (1) Different types of applications show different dependencies of α, β, and ε on Δ, and display different suitable Δs for the model. The model is more accurate if the traffic consists of intermittently-sent packets than other. (2) More appropriate models are obtained with specific α and β values (e.g., 0.1 < α < 1, and β < 2 for Δ = 500 ms). Also, application-specific traffic presents specific ranges of α, β, and ε for each Δ, so that these characteristics can be used in application identification methods such as anomaly detection and other machine learning methods.},
keywords={},
doi={10.1587/transcom.E93.B.3048},
ISSN={1745-1345},
month={November},}
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TY - JOUR
TI - Characterization of Host-Level Application Traffic with Multi-Scale Gamma Model
T2 - IEICE TRANSACTIONS on Communications
SP - 3048
EP - 3057
AU - Yosuke HIMURA
AU - Kensuke FUKUDA
AU - Patrice ABRY
AU - Kenjiro CHO
AU - Hiroshi ESAKI
PY - 2010
DO - 10.1587/transcom.E93.B.3048
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E93-B
IS - 11
JA - IEICE TRANSACTIONS on Communications
Y1 - November 2010
AB - In this paper, we discuss the validity of the multi-scale gamma model and characterize the differences in host-level application traffic with this model by using a real traffic trace collected on a 150-Mbps transpacific link. First, we investigate the dependency of the model (parameters α and β, and fitting accuracy ε) on time scale Δ, then find suitable time scales for the model. Second, we inspect the relations among α, β, and ε, in order to characterize the differences in the types of applications. The main findings of the paper are as follows. (1) Different types of applications show different dependencies of α, β, and ε on Δ, and display different suitable Δs for the model. The model is more accurate if the traffic consists of intermittently-sent packets than other. (2) More appropriate models are obtained with specific α and β values (e.g., 0.1 < α < 1, and β < 2 for Δ = 500 ms). Also, application-specific traffic presents specific ranges of α, β, and ε for each Δ, so that these characteristics can be used in application identification methods such as anomaly detection and other machine learning methods.
ER -
English
