Inferring Latent Traffic Demand Offered to an Overloaded Link with Modeling QoS-Degradation Effect

Keisuke ISHIBASHI; Shigeaki HARADA; Ryoichi KAWAHARA

doi:10.1587/transcom.2018EBP3093

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Inferring Latent Traffic Demand Offered to an Overloaded Link with Modeling QoS-Degradation Effect

Keisuke ISHIBASHI, Shigeaki HARADA, Ryoichi KAWAHARA

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Summary :

In this paper, we propose a CTRIL (Common Trend and Regression with Independent Loss) model to infer latent traffic demand in overloaded links as well as how much it is reduced due to QoS (Quality of Service) degradation. To appropriately provision link bandwidth for such overloaded links, we need to infer how much traffic will increase without QoS degradation. Because original latent traffic demand cannot be observed, we propose a method that compares the other traffic time series of an underloaded link, and by assuming that the latent traffic demands in both overloaded and underloaded are common, and actualized traffic demand in the overloaded link is decreased from common pattern due to the effect of QoS degradation. To realize the method, we developed a CTRIL model on the basis of a state-space model where observed traffic is generated from a latent trend but is decreased by the QoS degradation. By applying the CTRIL model to actual HTTP (Hypertext transfer protocol) traffic and QoS time series data, we reveal that 1% packet loss decreases traffic demand by 12.3%, and the estimated latent traffic demand is larger than the observed one by 23.0%.

Publication: IEICE TRANSACTIONS on Communications Vol.E102-B No.4 pp.790-798

Publication Date: 2019/04/01

Publicized: 2018/10/10

Online ISSN: 1745-1345

DOI: 10.1587/transcom.2018EBP3093

Type of Manuscript: PAPER

Category: Fundamental Theories for Communications

Authors

Keisuke ISHIBASHI
  International Christian University
Shigeaki HARADA
  NTT Corporation
Ryoichi KAWAHARA
  Toyo University

Keyword

latent traffic demand, engagement, state space model

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Keisuke ISHIBASHI, Shigeaki HARADA, Ryoichi KAWAHARA, "Inferring Latent Traffic Demand Offered to an Overloaded Link with Modeling QoS-Degradation Effect" in IEICE TRANSACTIONS on Communications, vol. E102-B, no. 4, pp. 790-798, April 2019, doi: 10.1587/transcom.2018EBP3093.
Abstract: In this paper, we propose a CTRIL (Common Trend and Regression with Independent Loss) model to infer latent traffic demand in overloaded links as well as how much it is reduced due to QoS (Quality of Service) degradation. To appropriately provision link bandwidth for such overloaded links, we need to infer how much traffic will increase without QoS degradation. Because original latent traffic demand cannot be observed, we propose a method that compares the other traffic time series of an underloaded link, and by assuming that the latent traffic demands in both overloaded and underloaded are common, and actualized traffic demand in the overloaded link is decreased from common pattern due to the effect of QoS degradation. To realize the method, we developed a CTRIL model on the basis of a state-space model where observed traffic is generated from a latent trend but is decreased by the QoS degradation. By applying the CTRIL model to actual HTTP (Hypertext transfer protocol) traffic and QoS time series data, we reveal that 1% packet loss decreases traffic demand by 12.3%, and the estimated latent traffic demand is larger than the observed one by 23.0%.
URL: https://globals.ieice.org/en_transactions/communications/10.1587/transcom.2018EBP3093/_p

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@ARTICLE{e102-b_4_790,
author={Keisuke ISHIBASHI, Shigeaki HARADA, Ryoichi KAWAHARA, },
journal={IEICE TRANSACTIONS on Communications},
title={Inferring Latent Traffic Demand Offered to an Overloaded Link with Modeling QoS-Degradation Effect},
year={2019},
volume={E102-B},
number={4},
pages={790-798},
abstract={In this paper, we propose a CTRIL (Common Trend and Regression with Independent Loss) model to infer latent traffic demand in overloaded links as well as how much it is reduced due to QoS (Quality of Service) degradation. To appropriately provision link bandwidth for such overloaded links, we need to infer how much traffic will increase without QoS degradation. Because original latent traffic demand cannot be observed, we propose a method that compares the other traffic time series of an underloaded link, and by assuming that the latent traffic demands in both overloaded and underloaded are common, and actualized traffic demand in the overloaded link is decreased from common pattern due to the effect of QoS degradation. To realize the method, we developed a CTRIL model on the basis of a state-space model where observed traffic is generated from a latent trend but is decreased by the QoS degradation. By applying the CTRIL model to actual HTTP (Hypertext transfer protocol) traffic and QoS time series data, we reveal that 1% packet loss decreases traffic demand by 12.3%, and the estimated latent traffic demand is larger than the observed one by 23.0%.},
keywords={},
doi={10.1587/transcom.2018EBP3093},
ISSN={1745-1345},
month={April},}

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TY - JOUR
TI - Inferring Latent Traffic Demand Offered to an Overloaded Link with Modeling QoS-Degradation Effect
T2 - IEICE TRANSACTIONS on Communications
SP - 790
EP - 798
AU - Keisuke ISHIBASHI
AU - Shigeaki HARADA
AU - Ryoichi KAWAHARA
PY - 2019
DO - 10.1587/transcom.2018EBP3093
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E102-B
IS - 4
JA - IEICE TRANSACTIONS on Communications
Y1 - April 2019
AB - In this paper, we propose a CTRIL (Common Trend and Regression with Independent Loss) model to infer latent traffic demand in overloaded links as well as how much it is reduced due to QoS (Quality of Service) degradation. To appropriately provision link bandwidth for such overloaded links, we need to infer how much traffic will increase without QoS degradation. Because original latent traffic demand cannot be observed, we propose a method that compares the other traffic time series of an underloaded link, and by assuming that the latent traffic demands in both overloaded and underloaded are common, and actualized traffic demand in the overloaded link is decreased from common pattern due to the effect of QoS degradation. To realize the method, we developed a CTRIL model on the basis of a state-space model where observed traffic is generated from a latent trend but is decreased by the QoS degradation. By applying the CTRIL model to actual HTTP (Hypertext transfer protocol) traffic and QoS time series data, we reveal that 1% packet loss decreases traffic demand by 12.3%, and the estimated latent traffic demand is larger than the observed one by 23.0%.
ER -