Users' reaction to network quality during web browsing on smartphones

Koto, Hideyuki; Fukumoto, N.; Niida, Sumaru; Yokota, Hidetoshi; Arakawa, Shin’ichi; Murata, Masayuki

doi:10.1109/itc.2014.6932954

Cited by 8 publications

(4 citation statements)

References 17 publications

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“…To do this, we need do have traffic data that can be separated for each application such as Deep Packet Inspection (DPI) data. In addition, an effort should be made to estimate γ from user engagement models that describe how much user communication behavior will change depending on the degree of QoS [30]- [32].…”

Section: Resultsmentioning

confidence: 99%

“…Dobrian et al evaluated the relationship between user engagement with a video streaming service (video play time) and application-level performance such as stalling length of video replay [31]. Along the same lines, Koto et al investigated the relationship between web-browsing behavior and network performance (download throughput) [32].…”

Section: Upper-layer Qos Degradation Reactionmentioning

confidence: 99%

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Modeling Upper Layer Reaction to QoS Degradation as a Congestion Avoidance Mechanism

Harada

Ishibashi

Kawahara

2020

IEICE Trans. Commun.

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On the Internet, end hosts and network nodes interdependently work to smoothly transfer traffic. Observed traffic dynamics are the result of those interactions among those entities. To manage Internet traffic to provide satisfactory quality services, such dynamics need to be well understood to predict traffic patterns. In particular, some nodes have a function that sends back-pressure signals to backward nodes to reduce their sending rate and mitigate congestion. Transmission Control Protocol (TCP) congestion control in end-hosts also mitigates traffic deviation to eliminate temporary congestion by reducing the TCP sending rate. How these congestion controls mitigate congestion has been extensively investigated. However, these controls only throttle their sending rate but do not reduce traffic volume. Such congestion control fails if congestion is persistent, e.g., for hours, because unsent traffic demand will infinitely accumulate. However, on the actual Internet, even with persistent congestion, such accumulation does not seem to occur. During congestion, users and/or applications tend to reduce their traffic demand in reaction to quality of service (QoS) degradation to avoid negative service experience. We previously estimated that 2% packet loss results in 23% traffic reduction because of this upper-layer reaction [1]. We view this reduction as an upper-layer congestion-avoidance mechanism and construct a closed-loop model of this mechanism, which we call the Upper-Layer Closed-Loop (ULCL) model. We also show that by using ULCL, we can predict the degree of QoS degradation and traffic reduction as an equilibrium of the feedback loop. We applied our model to traffic and packet-loss ratio time series data gathered in an actual network and demonstrate that it effectively estimates actual traffic and packet-loss ratio.

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Section: Resultsmentioning

confidence: 99%

Section: Upper-layer Qos Degradation Reactionmentioning

confidence: 99%

Modeling Upper Layer Reaction to QoS Degradation as a Congestion Avoidance Mechanism

Harada

Ishibashi

Kawahara

2020

IEICE Trans. Commun.

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“…Specifically, Dobrian et al [5] evaluated the relationship between user's engagement on a video streaming service (video play time) and application-level performance such as stalling length of video replay. In the same line, Koto et al investigated the relationship between web browsing behavior and network performance (download throughput) [7]. From the view point of network operators, those effects should be translated into the relationship between user's aggregated traffic change (reduction) and network QoS.…”

Section: Related Workmentioning

confidence: 99%

“…The latter reason has been investigated in terms of user engagement. That is, when QoS degrades, users tend to stop or shorten the usage of Internet services such as video streaming [5], [6] or web services [7]. These behaviors are described as the effects of QoS degradation.…”

Section: Introductionmentioning

confidence: 99%

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

Ishibashi

Harada

Kawahara

2019

IEICE Trans. Commun.

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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%.

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Log analysis in a HTTP proxy server for accurately estimating web QoE

Sawabe

Yoshida

Nogami

2018

2018 15th IEEE Annual Consumer Communications &Amp; Networking Conference (CCNC)

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