W-MUST '11最新文献

The network from above and below 从上到下的网络

W-MUST '11 Pub Date : 2011-08-19 DOI: 10.1145/2018602.2018604

P. Brundell, Andy Crabtree, R. Mortier, T. Rodden, P. Tennent, P. Tolmie

引用次数: 12

Inferring the QoE of HTTP video streaming from user-viewing activities 从用户观看活动推断HTTP视频流的QoE

W-MUST '11 Pub Date : 2011-08-19 DOI: 10.1145/2018602.2018611

Ricky K. P. Mok, Edmond W. W. Chan, Xiapu Luo, R. Chang

引用次数: 156

Understanding end-user perception of network problems 了解终端用户对网络问题的看法

W-MUST '11 Pub Date : 2011-08-19 DOI: 10.1145/2018602.2018613

J. S. Miller, Amit Mondal, Rahul Potharaju, P. Dinda, A. Kuzmanovic

引用次数: 15

Crowdsourcing ISP characterization to the network edge 众包ISP表征到网络边缘

W-MUST '11 Pub Date : 2011-08-19 DOI: 10.1145/2018602.2018617

Zachary S. Bischof, J. Otto, Mario A. Sánchez, John P. Rula, D. Choffnes, F. Bustamante

引用次数: 58

Large-scale app-based reporting of customer problems in cellular networks: potential and limitations 大规模基于应用程序的蜂窝网络客户问题报告:潜力和局限性

W-MUST '11 Pub Date : 2011-08-19 DOI: 10.1145/2018602.2018607

Yu Jin, N. Duffield, Alexandre Gerber, P. Haffner, Wen-Ling Hsu, G. Jacobson, S. Sen, Shobha Venkataraman, Zhi-Li Zhang

引用次数: 6

Experiences from Netalyzr with engaging users in end-system measurement Netalyzr在终端系统测量中吸引用户的经验

W-MUST '11 Pub Date : 2011-08-19 DOI: 10.1145/2018602.2018609

C. Kreibich, N. Weaver, Gregor Maier, B. Nechaev, V. Paxson

{"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}

引用次数: 10

Measuring and predicting web login safety 测量和预测web登录安全性

W-MUST '11 Pub Date : 2011-08-19 DOI: 10.1145/2018602.2018616

Xiao Sophia Wang, D. Choffnes, Patrick Gage Kelley, Ben Greenstein, D. Wetherall

{"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}

引用次数: 5

Performance of networked applications: the challenges in capturing the user's perception 网络应用程序的性能:捕捉用户感知的挑战

W-MUST '11 Pub Date : 2011-08-19 DOI: 10.1145/2018602.2018612

D. Joumblatt, R. Teixeira, J. Chandrashekar, N. Taft

引用次数: 12

Studying the experience of mobile applications used in different contexts of daily life 研究在日常生活的不同环境中使用移动应用程序的体验

W-MUST '11 Pub Date : 2011-08-19 DOI: 10.1145/2018602.2018605

K. Wac, Selim Ickin, Jin-Hyuk Hong, L. Janowski, M. Fiedler, A. Dey

{"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}

引用次数: 54

Identifying performance bottlenecks in CDNs through TCP-level monitoring 通过tcp级监控识别cdn中的性能瓶颈

W-MUST '11 Pub Date : 2011-08-19 DOI: 10.1145/2018602.2018615

Peng Sun, Minlan Yu, M. Freedman, J. Rexford

{"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}

引用次数: 39