视频流应用的在线流分类

Ronit Nossenson, Shuval Polacheck
{"title":"视频流应用的在线流分类","authors":"Ronit Nossenson, Shuval Polacheck","doi":"10.1109/NCA.2015.51","DOIUrl":null,"url":null,"abstract":"Delivering video streaming services over a best-effort packet network, such as the Internet, is complicated by a number of factors including time-varying bandwidth, delay, and losses. Different solutions have been proposed to optimize Live video streaming (e.g., Multicast) and to optimize VOD streaming (e.g., Cache). Implementation of these solutions at an Internet service provider (ISP) network or at Content Data Network (CDN) can use on-line classification capability of video streaming flows (or sources) into Live streaming type and VOD streaming type to allow proper optimization. Since all streaming applications use the same streaming protocols, Deep Packet Inspection (DPI) technologies are practically useless in classifying such applications. In this paper we formulate the problem of on-line video-streaming flow classification. Then, we propose and evaluate two statistical on-line streaming flow classifiers. These classifiers are based on the statistical characterization of the flow packets length. Afterward, we study a slightly different video streaming classification problem, in which we can assume that there are at least X concurrent flows from the same video source, X>1. We propose and evaluate an on-line classifier that decides whether these X flows are Live or VOD. This classifier is based on a simple observation that live streaming flows from the same source transfer the same information almost at the same time, while VOD flows from the same source have larger information offset. The classifiers performance evaluation is based on real traffic dataset. Our single flow best classifier tags 96% of the streams correctly, while our multi-flow classifier successfully tags 96.53% of streams for X=2. We also demonstrate a more complex multi-streaming comparing function that improves the success rate of our algorithm to 97.53% for X>2, but it clearly decreases the algorithm scalability. Finally, additional contribution of this paper includes statistical characterization of live video streaming traffic vs. VOD streaming traffic.","PeriodicalId":222162,"journal":{"name":"2015 IEEE 14th International Symposium on Network Computing and Applications","volume":"123 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"On-Line Flows Classification of Video Streaming Applications\",\"authors\":\"Ronit Nossenson, Shuval Polacheck\",\"doi\":\"10.1109/NCA.2015.51\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Delivering video streaming services over a best-effort packet network, such as the Internet, is complicated by a number of factors including time-varying bandwidth, delay, and losses. Different solutions have been proposed to optimize Live video streaming (e.g., Multicast) and to optimize VOD streaming (e.g., Cache). Implementation of these solutions at an Internet service provider (ISP) network or at Content Data Network (CDN) can use on-line classification capability of video streaming flows (or sources) into Live streaming type and VOD streaming type to allow proper optimization. Since all streaming applications use the same streaming protocols, Deep Packet Inspection (DPI) technologies are practically useless in classifying such applications. In this paper we formulate the problem of on-line video-streaming flow classification. Then, we propose and evaluate two statistical on-line streaming flow classifiers. These classifiers are based on the statistical characterization of the flow packets length. Afterward, we study a slightly different video streaming classification problem, in which we can assume that there are at least X concurrent flows from the same video source, X>1. We propose and evaluate an on-line classifier that decides whether these X flows are Live or VOD. This classifier is based on a simple observation that live streaming flows from the same source transfer the same information almost at the same time, while VOD flows from the same source have larger information offset. The classifiers performance evaluation is based on real traffic dataset. Our single flow best classifier tags 96% of the streams correctly, while our multi-flow classifier successfully tags 96.53% of streams for X=2. We also demonstrate a more complex multi-streaming comparing function that improves the success rate of our algorithm to 97.53% for X>2, but it clearly decreases the algorithm scalability. Finally, additional contribution of this paper includes statistical characterization of live video streaming traffic vs. VOD streaming traffic.\",\"PeriodicalId\":222162,\"journal\":{\"name\":\"2015 IEEE 14th International Symposium on Network Computing and Applications\",\"volume\":\"123 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-09-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE 14th International Symposium on Network Computing and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NCA.2015.51\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE 14th International Symposium on Network Computing and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NCA.2015.51","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9

摘要

在尽力而为的分组网络(如Internet)上提供视频流服务,由于包括时变带宽、延迟和丢失在内的许多因素而变得复杂。已经提出了不同的解决方案来优化实时视频流(例如,多播)和优化VOD流(例如,缓存)。在互联网服务提供商(ISP)网络或内容数据网络(CDN)上实现这些解决方案,可以使用视频流流(或源)的在线分类功能,分为直播流媒体类型和VOD流媒体类型,以便进行适当的优化。由于所有流应用都使用相同的流协议,深度包检测(DPI)技术在对此类应用进行分类时实际上是无用的。本文提出了在线视频流流分类问题。然后,我们提出并评价了两种统计在线流分类器。这些分类器基于流数据包长度的统计特征。之后,我们研究了一个稍微不同的视频流分类问题,其中我们可以假设至少有X个来自同一视频源的并发流,X>1。我们提出并评估了一个在线分类器,该分类器决定这些X流是Live还是VOD。该分类器基于一个简单的观察,即来自同一来源的直播流几乎同时传输相同的信息,而来自同一来源的VOD流具有更大的信息偏移。分类器的性能评价是基于真实交通数据集的。我们的单流最佳分类器正确标记了96%的流,而我们的多流分类器成功标记了X=2的96.53%的流。我们还演示了一个更复杂的多流比较函数,当X>2时,该函数将算法的成功率提高到97.53%,但它明显降低了算法的可扩展性。最后,本文的其他贡献包括直播视频流流量与点播流流量的统计特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On-Line Flows Classification of Video Streaming Applications
Delivering video streaming services over a best-effort packet network, such as the Internet, is complicated by a number of factors including time-varying bandwidth, delay, and losses. Different solutions have been proposed to optimize Live video streaming (e.g., Multicast) and to optimize VOD streaming (e.g., Cache). Implementation of these solutions at an Internet service provider (ISP) network or at Content Data Network (CDN) can use on-line classification capability of video streaming flows (or sources) into Live streaming type and VOD streaming type to allow proper optimization. Since all streaming applications use the same streaming protocols, Deep Packet Inspection (DPI) technologies are practically useless in classifying such applications. In this paper we formulate the problem of on-line video-streaming flow classification. Then, we propose and evaluate two statistical on-line streaming flow classifiers. These classifiers are based on the statistical characterization of the flow packets length. Afterward, we study a slightly different video streaming classification problem, in which we can assume that there are at least X concurrent flows from the same video source, X>1. We propose and evaluate an on-line classifier that decides whether these X flows are Live or VOD. This classifier is based on a simple observation that live streaming flows from the same source transfer the same information almost at the same time, while VOD flows from the same source have larger information offset. The classifiers performance evaluation is based on real traffic dataset. Our single flow best classifier tags 96% of the streams correctly, while our multi-flow classifier successfully tags 96.53% of streams for X=2. We also demonstrate a more complex multi-streaming comparing function that improves the success rate of our algorithm to 97.53% for X>2, but it clearly decreases the algorithm scalability. Finally, additional contribution of this paper includes statistical characterization of live video streaming traffic vs. VOD streaming traffic.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信