P. Brundell, Andy Crabtree, R. Mortier, T. Rodden, P. Tennent, P. Tolmie
Recently, the HCI community has taken a strong interest in problems associated with networking. Many of those problems have also been the focus of much recent networking research, e.g., traffic identification, network management, access control. In this paper we consider these two quite different viewpoints of the problems specifically associated with home networking. Focusing on traffic identification as a core capability required by much recent HCI work, we explore the mismatch between the approaches the two communities have taken, and suggest some resulting challenges and directions for future work.
{"title":"The network from above and below","authors":"P. Brundell, Andy Crabtree, R. Mortier, T. Rodden, P. Tennent, P. Tolmie","doi":"10.1145/2018602.2018604","DOIUrl":"https://doi.org/10.1145/2018602.2018604","url":null,"abstract":"Recently, the HCI community has taken a strong interest in problems associated with networking. Many of those problems have also been the focus of much recent networking research, e.g., traffic identification, network management, access control. In this paper we consider these two quite different viewpoints of the problems specifically associated with home networking. Focusing on traffic identification as a core capability required by much recent HCI work, we explore the mismatch between the approaches the two communities have taken, and suggest some resulting challenges and directions for future work.","PeriodicalId":387856,"journal":{"name":"W-MUST '11","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130254021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ricky K. P. Mok, Edmond W. W. Chan, Xiapu Luo, R. Chang
HTTP video streaming, employed by most of the video-sharing websites, allows users to control the video playback using, for example, pausing and switching the bit rate. These user-viewing activities can be used to mitigate the temporal structure impairments of the video quality. On the other hand, other activities, such as mouse movement, do not help reduce the impairment level. In this paper, we have performed subjective experiments to analyze user-viewing activities and correlate them with network path performance and user quality of experience. The results show that network measurement alone may miss important information about user dissatisfaction with the video quality. Moreover, video impairments can trigger user-viewing activities, notably pausing and reducing the screen size. By including the pause events into the prediction model, we can increase its explanatory power.
{"title":"Inferring the QoE of HTTP video streaming from user-viewing activities","authors":"Ricky K. P. Mok, Edmond W. W. Chan, Xiapu Luo, R. Chang","doi":"10.1145/2018602.2018611","DOIUrl":"https://doi.org/10.1145/2018602.2018611","url":null,"abstract":"HTTP video streaming, employed by most of the video-sharing websites, allows users to control the video playback using, for example, pausing and switching the bit rate. These user-viewing activities can be used to mitigate the temporal structure impairments of the video quality. On the other hand, other activities, such as mouse movement, do not help reduce the impairment level. In this paper, we have performed subjective experiments to analyze user-viewing activities and correlate them with network path performance and user quality of experience. The results show that network measurement alone may miss important information about user dissatisfaction with the video quality. Moreover, video impairments can trigger user-viewing activities, notably pausing and reducing the screen size. By including the pause events into the prediction model, we can increase its explanatory power.","PeriodicalId":387856,"journal":{"name":"W-MUST '11","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133732934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. S. Miller, Amit Mondal, Rahul Potharaju, P. Dinda, A. Kuzmanovic
It is widely assumed that certain network characteristics cause end-user irritation with network performance. These assumptions then drive the selection of quality of service parameters or the goals of adaptive systems. We have developed a methodology and toolchain, SoylentLogger, that employs user studies to empirically investigate such assumptions. SoylentLogger collects client-centric network measurement data that is labeled by the end-user as being associated with irritation at perceived network performance (or not). The data collection and labeling occurs in real-time as the user normally uses the network. We conducted a study that tracked 32 ordinary users over a period of 3 weeks and then used that data to test common assumptions about network sources of user irritation.
{"title":"Understanding end-user perception of network problems","authors":"J. S. Miller, Amit Mondal, Rahul Potharaju, P. Dinda, A. Kuzmanovic","doi":"10.1145/2018602.2018613","DOIUrl":"https://doi.org/10.1145/2018602.2018613","url":null,"abstract":"It is widely assumed that certain network characteristics cause end-user irritation with network performance. These assumptions then drive the selection of quality of service parameters or the goals of adaptive systems. We have developed a methodology and toolchain, SoylentLogger, that employs user studies to empirically investigate such assumptions. SoylentLogger collects client-centric network measurement data that is labeled by the end-user as being associated with irritation at perceived network performance (or not). The data collection and labeling occurs in real-time as the user normally uses the network. We conducted a study that tracked 32 ordinary users over a period of 3 weeks and then used that data to test common assumptions about network sources of user irritation.","PeriodicalId":387856,"journal":{"name":"W-MUST '11","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123907957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zachary S. Bischof, J. Otto, Mario A. Sánchez, John P. Rula, D. Choffnes, F. Bustamante
Evaluating and characterizing Internet Service Providers (ISPs) is critical to subscribers shopping for alternative ISPs, companies providing reliable Internet services, and governments surveying the coverage of broadband services to its citizens. Ideally, ISP characterization should be done at scale, continuously, and from end users. While there has been significant progress toward this end, current approaches exhibit apparently unavoidable tradeoffs between coverage, continuous monitoring and capturing user-perceived performance. In this paper, we argue that network-intensive applications running on end systems avoid these tradeoffs, thereby offering an ideal platform for ISP characterization. Based on data collected from 500,000 peer-to-peer BitTorrent users across 3,150 networks, together with the reported results from the U.K. Ofcom/SamKnows studies, we show the feasibility of this approach to characterize the service that subscribers can expect from a particular ISP. We discuss remaining research challenges and design requirements for a solution that enables efficient and accurate ISP characterization at an Internet scale.
{"title":"Crowdsourcing ISP characterization to the network edge","authors":"Zachary S. Bischof, J. Otto, Mario A. Sánchez, John P. Rula, D. Choffnes, F. Bustamante","doi":"10.1145/2018602.2018617","DOIUrl":"https://doi.org/10.1145/2018602.2018617","url":null,"abstract":"Evaluating and characterizing Internet Service Providers (ISPs) is critical to subscribers shopping for alternative ISPs, companies providing reliable Internet services, and governments surveying the coverage of broadband services to its citizens. Ideally, ISP characterization should be done at scale, continuously, and from end users. While there has been significant progress toward this end, current approaches exhibit apparently unavoidable tradeoffs between coverage, continuous monitoring and capturing user-perceived performance.\u0000 In this paper, we argue that network-intensive applications running on end systems avoid these tradeoffs, thereby offering an ideal platform for ISP characterization. Based on data collected from 500,000 peer-to-peer BitTorrent users across 3,150 networks, together with the reported results from the U.K. Ofcom/SamKnows studies, we show the feasibility of this approach to characterize the service that subscribers can expect from a particular ISP. We discuss remaining research challenges and design requirements for a solution that enables efficient and accurate ISP characterization at an Internet scale.","PeriodicalId":387856,"journal":{"name":"W-MUST '11","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122756626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yu Jin, N. Duffield, Alexandre Gerber, P. Haffner, Wen-Ling Hsu, G. Jacobson, S. Sen, Shobha Venkataraman, Zhi-Li Zhang
In this paper, we study the Location-based Reporting Tool (LRT), a smartphone application for collecting large-scale feedback from mobile customers. Using one-year data collected from one of the largest cellular networks in the US, we compare LRT feedback to the traditional customer feedback channel -- customer care tickets. Our analysis shows that, due to the light-weight design, LRT encourages customers to report more problems from anywhere and at any time. In addition, we find LRT users access network services more intensively than other mobile users, and hence are more likely to experience and are more sensitive to network problems. All these render LRT feedback a valuable information source for early detection of emerging network problems.
{"title":"Large-scale app-based reporting of customer problems in cellular networks: potential and limitations","authors":"Yu Jin, N. Duffield, Alexandre Gerber, P. Haffner, Wen-Ling Hsu, G. Jacobson, S. Sen, Shobha Venkataraman, Zhi-Li Zhang","doi":"10.1145/2018602.2018607","DOIUrl":"https://doi.org/10.1145/2018602.2018607","url":null,"abstract":"In this paper, we study the Location-based Reporting Tool (LRT), a smartphone application for collecting large-scale feedback from mobile customers. Using one-year data collected from one of the largest cellular networks in the US, we compare LRT feedback to the traditional customer feedback channel -- customer care tickets. Our analysis shows that, due to the light-weight design, LRT encourages customers to report more problems from anywhere and at any time. In addition, we find LRT users access network services more intensively than other mobile users, and hence are more likely to experience and are more sensitive to network problems. All these render LRT feedback a valuable information source for early detection of emerging network problems.","PeriodicalId":387856,"journal":{"name":"W-MUST '11","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125456749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. Kreibich, N. Weaver, Gregor Maier, B. Nechaev, V. Paxson
Netalyzr is a widely used network diagnostic and debugging tool that has collected 259,000 measurement sessions to date. To use Netalyzr, users visit its website, download an applet that proceeds to conduct a suite of tests and measurements, and obtain a summary report detailing the findings. Along with the measurement data, for each session, we record the HTTP referrer that brought the user to the Netalyzr page, the level of trust the user bestowed upon the applet, and any feedback that the user voluntarily left via a form that we include at the bottom of the report page. These data sources illuminate how Netalyzr's users employ the tool, and can provide insights as to how other measurement tools or user surveys involving end-host measurement could effectively involve users. We find that even with little prompting, users leave explicit comments 3% of the time and answer one or more survey questions in 17% of the sessions, reaching up to 44% og sessions during bursts of activity. We also find that significant usage of the tool comes from four types of need: (i) to aid in troubleshooting performance for an on-line game, often via measurement sessions conducted when requested by more sophisticated users in a help forum; (ii) curiosity, often exacerbated by blog postings and other mentions on high-profile websites; (iii) repeat visitors who arrive via a search engine that they used to locate Netalyzr's website; and (iv) IPv6 deployment tests conducted or organized by specialists.
{"title":"Experiences from Netalyzr with engaging users in end-system measurement","authors":"C. Kreibich, N. Weaver, Gregor Maier, B. Nechaev, V. Paxson","doi":"10.1145/2018602.2018609","DOIUrl":"https://doi.org/10.1145/2018602.2018609","url":null,"abstract":"Netalyzr is a widely used network diagnostic and debugging tool that has collected 259,000 measurement sessions to date. To use Netalyzr, users visit its website, download an applet that proceeds to conduct a suite of tests and measurements, and obtain a summary report detailing the findings. Along with the measurement data, for each session, we record the HTTP referrer that brought the user to the Netalyzr page, the level of trust the user bestowed upon the applet, and any feedback that the user voluntarily left via a form that we include at the bottom of the report page. These data sources illuminate how Netalyzr's users employ the tool, and can provide insights as to how other measurement tools or user surveys involving end-host measurement could effectively involve users. We find that even with little prompting, users leave explicit comments 3% of the time and answer one or more survey questions in 17% of the sessions, reaching up to 44% og sessions during bursts of activity. We also find that significant usage of the tool comes from four types of need: (i) to aid in troubleshooting performance for an on-line game, often via measurement sessions conducted when requested by more sophisticated users in a help forum; (ii) curiosity, often exacerbated by blog postings and other mentions on high-profile websites; (iii) repeat visitors who arrive via a search engine that they used to locate Netalyzr's website; and (iv) IPv6 deployment tests conducted or organized by specialists.","PeriodicalId":387856,"journal":{"name":"W-MUST '11","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125254356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiao Sophia Wang, D. Choffnes, Patrick Gage Kelley, Ben Greenstein, D. Wetherall
Users increasingly entrust websites with their personal and sensitive information. Sites commonly protect this information using user-supplied credentials (i.e., logins). We conducted a measurement study of top websites and surprisingly found that they transmit these credentials in the clear, thus leaving them vulnerable to eavesdropping. To make matters worse, users are often unaware of this threat because sites and browsers reflect little information about how logins are handled. As a first step towards solving this problem, we develop techniques for measuring logins on browsers to predict how logins would be handled before they are submitted. We demonstrate that achieving this goal requires instrumentation at the application layer and inside browsers. Specifically, network traces are not sufficient for determining login safety in general due to application-layer encryption; similarly, application-layer traces are insufficient because login submission logic may be generated in the browser at runtime. Based on a measurement study using login pages gathered from popular sites in addition to those visited by users through normal Web browsing, we found such predictions to be quite challenging due to a lack of any standard formats for Web logins. However, by applying a carefully chosen set of rules when measuring logins, we almost always correctly predict how logins will be handled.
{"title":"Measuring and predicting web login safety","authors":"Xiao Sophia Wang, D. Choffnes, Patrick Gage Kelley, Ben Greenstein, D. Wetherall","doi":"10.1145/2018602.2018616","DOIUrl":"https://doi.org/10.1145/2018602.2018616","url":null,"abstract":"Users increasingly entrust websites with their personal and sensitive information. Sites commonly protect this information using user-supplied credentials (i.e., logins). We conducted a measurement study of top websites and surprisingly found that they transmit these credentials in the clear, thus leaving them vulnerable to eavesdropping. To make matters worse, users are often unaware of this threat because sites and browsers reflect little information about how logins are handled.\u0000 As a first step towards solving this problem, we develop techniques for measuring logins on browsers to predict how logins would be handled before they are submitted. We demonstrate that achieving this goal requires instrumentation at the application layer and inside browsers. Specifically, network traces are not sufficient for determining login safety in general due to application-layer encryption; similarly, application-layer traces are insufficient because login submission logic may be generated in the browser at runtime. Based on a measurement study using login pages gathered from popular sites in addition to those visited by users through normal Web browsing, we found such predictions to be quite challenging due to a lack of any standard formats for Web logins. However, by applying a carefully chosen set of rules when measuring logins, we almost always correctly predict how logins will be handled.","PeriodicalId":387856,"journal":{"name":"W-MUST '11","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130912102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
D. Joumblatt, R. Teixeira, J. Chandrashekar, N. Taft
There is much interest recently in doing automated performance diagnosis on user laptops or desktops. One interesting aspect of performance diagnosis that has received little attention is the user perspective on performance. To conduct research on both end-host performance diagnosis and user perception of network and application performance, we designed an end-host data collection tool, called HostView. HostView not only collects network, application and machine level data, but also gathers feedback directly from users. User feedback is obtained via two mechanisms, a system-triggered questionnaire and a user-triggered feedback form, that for example asks users to rate the performance of their network and applications. In this paper, we describe our experience with the first deployment of HostView. Using data from 40 users, we illustrate the diversity of our users, articulate the challenges in this line of research, and report on initial findings in correlating user data to system-level data.
{"title":"Performance of networked applications: the challenges in capturing the user's perception","authors":"D. Joumblatt, R. Teixeira, J. Chandrashekar, N. Taft","doi":"10.1145/2018602.2018612","DOIUrl":"https://doi.org/10.1145/2018602.2018612","url":null,"abstract":"There is much interest recently in doing automated performance diagnosis on user laptops or desktops. One interesting aspect of performance diagnosis that has received little attention is the user perspective on performance. To conduct research on both end-host performance diagnosis and user perception of network and application performance, we designed an end-host data collection tool, called HostView. HostView not only collects network, application and machine level data, but also gathers feedback directly from users. User feedback is obtained via two mechanisms, a system-triggered questionnaire and a user-triggered feedback form, that for example asks users to rate the performance of their network and applications. In this paper, we describe our experience with the first deployment of HostView. Using data from 40 users, we illustrate the diversity of our users, articulate the challenges in this line of research, and report on initial findings in correlating user data to system-level data.","PeriodicalId":387856,"journal":{"name":"W-MUST '11","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130688267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
K. Wac, Selim Ickin, Jin-Hyuk Hong, L. Janowski, M. Fiedler, A. Dey
Mobile applications and services increasingly assist us in our daily life situations, fulfilling our needs for information, communication, entertainment or leisure. However, user acceptance of a mobile application depends on at least two conditions; the application's perceived Quality of Experience (QoE) and the appropriateness of the application to the user's situation and context. Yet, there is generally a weak understanding of a mobile user's QoE and the factors influencing it. The mobile user's experience is related to the Quality of Service (QoS) provided by the underlying service and network infrastructures, which provides a starting point for our work. We present "work-in-progress" results from an ongoing study of Android phone users. In this study, we aim to derive and improve understanding of their QoE in different situations and daily life environments. In particular, we evaluate the user's qualitative QoE for a set of widely used mobile applications in the users' natural environments and different contexts, and we analyze this experience and its relation to the underlying quantitative QoS. In our approach we collect both QoE and QoS measures through a combination of user, application and network input from mobile phones. We present initial data acquired in the study and derived from that, a set of preliminary implications for mobile applications design.
{"title":"Studying the experience of mobile applications used in different contexts of daily life","authors":"K. Wac, Selim Ickin, Jin-Hyuk Hong, L. Janowski, M. Fiedler, A. Dey","doi":"10.1145/2018602.2018605","DOIUrl":"https://doi.org/10.1145/2018602.2018605","url":null,"abstract":"Mobile applications and services increasingly assist us in our daily life situations, fulfilling our needs for information, communication, entertainment or leisure. However, user acceptance of a mobile application depends on at least two conditions; the application's perceived Quality of Experience (QoE) and the appropriateness of the application to the user's situation and context. Yet, there is generally a weak understanding of a mobile user's QoE and the factors influencing it. The mobile user's experience is related to the Quality of Service (QoS) provided by the underlying service and network infrastructures, which provides a starting point for our work. We present \"work-in-progress\" results from an ongoing study of Android phone users. In this study, we aim to derive and improve understanding of their QoE in different situations and daily life environments. In particular, we evaluate the user's qualitative QoE for a set of widely used mobile applications in the users' natural environments and different contexts, and we analyze this experience and its relation to the underlying quantitative QoS. In our approach we collect both QoE and QoS measures through a combination of user, application and network input from mobile phones. We present initial data acquired in the study and derived from that, a set of preliminary implications for mobile applications design.","PeriodicalId":387856,"journal":{"name":"W-MUST '11","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130940635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Content distribution networks (CDNs) need to make decisions, such as server selection and routing, to improve performance for their clients. The performance may be limited by various factors such as packet loss in the network, a small receive buffer at the client, or constrained server CPU and disk resources. Conventional measurement techniques are not effective for distinguishing these performance problems: application-layer logs are too coarse-grained, while network-level traces are too expensive to collect all the time. We argue that passively monitoring the transport-level statistics in the server's network stack is a better approach. This paper presents a tool for monitoring and analyzing TCP statistics, and an analysis of a CoralCDN node in PlanetLab for six weeks. Our analysis shows that more than 10% of connections are server-limited at least 40% of the time, and many connections are limited by the congestion window despite no packet loss. Still, we see that clients in 377 Autonomous Systems (ASes) experience persistent packet loss. By separating network congestion from other performance problems, our analysis provides a much more accurate view of the performance of the network paths than what is possible with server logs alone.
{"title":"Identifying performance bottlenecks in CDNs through TCP-level monitoring","authors":"Peng Sun, Minlan Yu, M. Freedman, J. Rexford","doi":"10.1145/2018602.2018615","DOIUrl":"https://doi.org/10.1145/2018602.2018615","url":null,"abstract":"Content distribution networks (CDNs) need to make decisions, such as server selection and routing, to improve performance for their clients. The performance may be limited by various factors such as packet loss in the network, a small receive buffer at the client, or constrained server CPU and disk resources. Conventional measurement techniques are not effective for distinguishing these performance problems: application-layer logs are too coarse-grained, while network-level traces are too expensive to collect all the time. We argue that passively monitoring the transport-level statistics in the server's network stack is a better approach.\u0000 This paper presents a tool for monitoring and analyzing TCP statistics, and an analysis of a CoralCDN node in PlanetLab for six weeks. Our analysis shows that more than 10% of connections are server-limited at least 40% of the time, and many connections are limited by the congestion window despite no packet loss. Still, we see that clients in 377 Autonomous Systems (ASes) experience persistent packet loss. By separating network congestion from other performance problems, our analysis provides a much more accurate view of the performance of the network paths than what is possible with server logs alone.","PeriodicalId":387856,"journal":{"name":"W-MUST '11","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124201693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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