J. S. Miller, Amit Mondal, Rahul Potharaju, P. Dinda, A. Kuzmanovic
{"title":"了解终端用户对网络问题的看法","authors":"J. S. Miller, Amit Mondal, Rahul Potharaju, P. Dinda, A. Kuzmanovic","doi":"10.1145/2018602.2018613","DOIUrl":null,"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":0.0000,"publicationDate":"2011-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":"{\"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\":null,\"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\":0.0000,\"publicationDate\":\"2011-08-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"15\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"W-MUST '11\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2018602.2018613\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"W-MUST '11","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2018602.2018613","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Understanding end-user perception of network problems
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.