{"title":"非持久TCP流吞吐量的随机模型","authors":"F. Baccelli, D. McDonald","doi":"10.1145/1190095.1190169","DOIUrl":null,"url":null,"abstract":"The general aim of this paper is to analyze the throughput of a HTTP flow. For this, we introduce a simplified model of such a flow which consists of a succession of idle and download periods. The file downloads are subject to a fixed packet loss probability. The same TCP connection is possibly used for the download of a random number of files, for which the effect of the slow start is taken into account. For this stochastic model, we derive a closed form formula for the stationary throughput obtained by a flow. We also derive closed form expressions for the mean time to transfer a file and for the distribution of the throughput. Several laws of file sizes and idle times are considered including heavy tailed distributions. We also briefly discuss how the formulas can be applied to predict bandwidth sharing among competing HTTP flows.","PeriodicalId":19766,"journal":{"name":"Perform. Evaluation","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2006-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"19","resultStr":"{\"title\":\"A stochastic model for the throughput of non-persistent TCP flows\",\"authors\":\"F. Baccelli, D. McDonald\",\"doi\":\"10.1145/1190095.1190169\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The general aim of this paper is to analyze the throughput of a HTTP flow. For this, we introduce a simplified model of such a flow which consists of a succession of idle and download periods. The file downloads are subject to a fixed packet loss probability. The same TCP connection is possibly used for the download of a random number of files, for which the effect of the slow start is taken into account. For this stochastic model, we derive a closed form formula for the stationary throughput obtained by a flow. We also derive closed form expressions for the mean time to transfer a file and for the distribution of the throughput. Several laws of file sizes and idle times are considered including heavy tailed distributions. We also briefly discuss how the formulas can be applied to predict bandwidth sharing among competing HTTP flows.\",\"PeriodicalId\":19766,\"journal\":{\"name\":\"Perform. Evaluation\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-10-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"19\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Perform. Evaluation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1190095.1190169\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Perform. Evaluation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1190095.1190169","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A stochastic model for the throughput of non-persistent TCP flows
The general aim of this paper is to analyze the throughput of a HTTP flow. For this, we introduce a simplified model of such a flow which consists of a succession of idle and download periods. The file downloads are subject to a fixed packet loss probability. The same TCP connection is possibly used for the download of a random number of files, for which the effect of the slow start is taken into account. For this stochastic model, we derive a closed form formula for the stationary throughput obtained by a flow. We also derive closed form expressions for the mean time to transfer a file and for the distribution of the throughput. Several laws of file sizes and idle times are considered including heavy tailed distributions. We also briefly discuss how the formulas can be applied to predict bandwidth sharing among competing HTTP flows.