2018 Network Traffic Measurement and Analysis Conference (TMA)最新文献_第2页

Distributed Internet Paths Performance Analysis Through Machine Learning 基于机器学习的分布式互联网路径性能分析

2018 Network Traffic Measurement and Analysis Conference (TMA) Pub Date : 2018-06-01 DOI: 10.23919/TMA.2018.8506572

Sarah Wassermann, P. Casas

引用次数: 0

A First Look at Data Center Network Condition Through The Eyes of PTPmesh 从PTPmesh的角度看数据中心网络状况

2018 Network Traffic Measurement and Analysis Conference (TMA) Pub Date : 2018-06-01 DOI: 10.23919/TMA.2018.8506493

Diana Andreea Popescu, A. Moore

引用次数: 10

A Wrapper for Automatic Measurements with YouTube's Native Android App YouTube原生Android应用程序自动测量的包装器

2018 Network Traffic Measurement and Analysis Conference (TMA) Pub Date : 2018-06-01 DOI: 10.23919/TMA.2018.8506488

Michael Seufert, Bernd Zeidler, Florian Wamser, Theodoros Karagkioules, D. Tsilimantos, Frank Loh, P. Tran-Gia, S. Valentin

{"title":"A Wrapper for Automatic Measurements with YouTube's Native Android App","authors":"Michael Seufert, Bernd Zeidler, Florian Wamser, Theodoros Karagkioules, D. Tsilimantos, Frank Loh, P. Tran-Gia, S. Valentin","doi":"10.23919/TMA.2018.8506488","DOIUrl":"https://doi.org/10.23919/TMA.2018.8506488","url":null,"abstract":"YouTube is one of the most popular and demanding services in the Internet today. Thereby, a large portion of this traffic is generated by YouTube's mobile app. While past studies have shown how to monitor browser-based streaming on desktop PCs (e.g., YoMo) or mobile devices (e.g., YoMoApp), streaming in the native app has not been monitored yet. This paper presents an automated framework for monitoring the streaming in YouTube's native app for Android. The concept is based on a wrapper application and the Android Debug Bridge (adb), and can be also extended to automatic measurements with other apps. For YouTube, it allows to collect application-layer streaming data, such as current playtime, buffered playtime, video encoding, and quality switches. These data can be complemented with network measurements on the mobile access link to obtain a holistic view on mobile YouTube streaming on Android devices. In addition to describing the software design and testbed setup, this paper discusses an experimental measurement. This study analyzes the streaming in the native YouTube app and compares it to the streaming from the mobile YouTube website via YoMoApp.","PeriodicalId":6607,"journal":{"name":"2018 Network Traffic Measurement and Analysis Conference (TMA)","volume":"39 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81296540","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: 13

Tracing Internet Path Transparency 追踪互联网路径透明度

2018 Network Traffic Measurement and Analysis Conference (TMA) Pub Date : 2018-06-01 DOI: 10.23919/TMA.2018.8506532

M. Kühlewind, Michael Walter, Iain R. Learmonth, B. Trammell

{"title":"Tracing Internet Path Transparency","authors":"M. Kühlewind, Michael Walter, Iain R. Learmonth, B. Trammell","doi":"10.23919/TMA.2018.8506532","DOIUrl":"https://doi.org/10.23919/TMA.2018.8506532","url":null,"abstract":"Investigating Internet Path Transparency means measuring if a network path between two endhosts is impaired by in-network functions on the path. A path is considered transparent if it provides connectivity and the same performance independent of the protocol or protocol stack that is used for the transmission. Unfortunately this is not always the case. Simple firewalls that block e.g. UDP, are an example. Of course such in-network functions are often valuable, like firewalls. However, these middleboxes also, sometimes unintentionally, make assumptions about the traffic passing through them that restricts innovation in the Internet on the higher layers, e.g. the deployment of new UDP-based protocols such as QUIC, to stick with the previous example. PATHspider is an active measurement tool to test for Path Transparency. In this paper we present a new feature of PATH-spider that integrates tracebox-based functionality and analysis to not only detect in-transparency but also further locate the origin of the impairment observed. As an example study we show updated and extended measurements on ECN support and connectivity. By using our enhanced ECN PATHspider plugin to test network support of the ECN IP codepoint and additional path tracing that is correlated with DSCP testing, we show that most in-network ECN IP codepoint zeroing is due to use of the deprecated definition of the IP ToS field for domain-internal service differentiation, while pure resetting of the ECN IP field is more likely an active inference in border networks.","PeriodicalId":6607,"journal":{"name":"2018 Network Traffic Measurement and Analysis Conference (TMA)","volume":"30 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83577332","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: 6

Towards Provable Network Traffic Measurement and Analysis via Semi-Labeled Trace Datasets 基于半标记跟踪数据集的可证明网络流量测量与分析

2018 Network Traffic Measurement and Analysis Conference (TMA) Pub Date : 2018-06-01 DOI: 10.23919/TMA.2018.8506498

Milan Cermák, Tomás Jirsík, P. Velan, Jana Komárková, Stanislav Špaček, Martin Drasar, Tomáš Plesník

{"title":"Towards Provable Network Traffic Measurement and Analysis via Semi-Labeled Trace Datasets","authors":"Milan Cermák, Tomás Jirsík, P. Velan, Jana Komárková, Stanislav Špaček, Martin Drasar, Tomáš Plesník","doi":"10.23919/TMA.2018.8506498","DOIUrl":"https://doi.org/10.23919/TMA.2018.8506498","url":null,"abstract":"Research in network traffic measurement and analysis is a long-lasting field with growing interest from both scientists and the industry. However, even after so many years, results replication, criticism, and review are still rare. We face not only a lack of research standards, but also inaccessibility of appropriate datasets that can be used for methods development and evaluation. Therefore, a lot of potentially high-quality research cannot be verified and is not adopted by the industry or the community. The aim of this paper is to overcome this controversy with a unique solution based on a combination of distinct approaches proposed by other research works. Unlike these studies, we focus on the whole issue covering all areas of data anonymization, authenticity, recency, publicity, and their usage for research provability. We believe that these challenges can be solved by utilization of semi-labeled datasets composed of real-world network traffic and annotated units with interest-related packet traces only. In this paper, we outline the basic ideas of the methodology from unit trace collection and semi-labeled dataset creation to its usage for research evaluation. We strive for this proposal to start a discussion of the approach and help to overcome some of the challenges the research faces today.","PeriodicalId":6607,"journal":{"name":"2018 Network Traffic Measurement and Analysis Conference (TMA)","volume":"48 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80252954","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: 11

Non-Parametric Bootstrap Detection of Availability Service Level Objective Violations in Cloud Storage 云存储中可用性服务水平目标违规的非参数自举检测

2018 Network Traffic Measurement and Analysis Conference (TMA) Pub Date : 2018-06-01 DOI: 10.23919/TMA.2018.8506563

M. Naldi

引用次数: 0

Demystifying TCP Initial Window Configurations of Content Distribution Networks 揭秘内容分发网络的TCP初始窗口配置

2018 Network Traffic Measurement and Analysis Conference (TMA) Pub Date : 2018-06-01 DOI: 10.23919/TMA.2018.8506549

Jan Rüth, O. Hohlfeld

{"title":"Demystifying TCP Initial Window Configurations of Content Distribution Networks","authors":"Jan Rüth, O. Hohlfeld","doi":"10.23919/TMA.2018.8506549","DOIUrl":"https://doi.org/10.23919/TMA.2018.8506549","url":null,"abstract":"Driven by their quest to improve web performance, Content Delivery Networks (CDNs) are known adaptors of performance optimizations. In this regard, TCP congestion control and particularly its initial congestion window (IW) size is one long-debated topic that can influence CDN performance. Its size is, however, assumed to be static by IETF recommendations-despite being network- and application-dependent-and only infrequently changed in its history. To understand if the standardization and research perspective still meets Internet reality, we study the IW configurations of major CDNs. Our study uses a globally distributed infrastructure of VPNs giving access to residential access links that enable to shed light on network-dependent configurations. We observe that most CDNs are well aware of the IW's impact and find a high amount of customization that is beyond current Internet standards. Further, we find CDNs that utilize different IWs for different customers and content while others resort to fixed values. We find various initial window configurations, most below 50 segments yet with exceptions of up to 100 segments—the tenfold of current standards. Our study highlights that Internet reality drifted away from recommended and standardized practices.","PeriodicalId":6607,"journal":{"name":"2018 Network Traffic Measurement and Analysis Conference (TMA)","volume":"7 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88947552","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: 17

On the Analysis of Network Measurements Through Machine Learning: The Power of the Crowd 通过机器学习分析网络测量:人群的力量

2018 Network Traffic Measurement and Analysis Conference (TMA) Pub Date : 2018-06-01 DOI: 10.23919/TMA.2018.8506486

P. Casas

{"title":"On the Analysis of Network Measurements Through Machine Learning: The Power of the Crowd","authors":"P. Casas","doi":"10.23919/TMA.2018.8506486","DOIUrl":"https://doi.org/10.23919/TMA.2018.8506486","url":null,"abstract":"The application of Machine Learning (ML) models to the analysis of network measurement problems has largely increased in the last decade; however, there is still no clear best-practice or silver bullet approach to address these problems in a general context, and only adhoc and very tailored approaches have been evaluated so far. While deep-learning models have provided a major breakthrough in highly-dimensional problems such as image processing, it is difficult to say today which is the best model or most fitted category of models to address the analysis of large volumes of highly-dimensional data collected in operational networks. In this paper we evaluate and benchmark different ML models applied to the analysis of three different and assorted network measurement problems, including detection of network attacks, detection of smartphone-apps anomalies and QoE prediction in cellular networks. We consider an extensive battery of ML models, including both supervised and semi-supervised techniques, as well as ML ensembles such as bagging, boosting and stacking. Proposed models are evaluated using real network measurements coming from operational networks. Results suggest that both neural networks and decision-tree-based models provide in general better results in terms of accuracy and prediction, with a much smaller computation overhead for decision trees as compared to models based on neural networks or support vector machines. In addition, collaborative models taking advantage of multiple machine learning algorithms, and in particular stacking models, are more robust and perform better than single ML models, pointing out the benefits of a crowd as compared to individual models.","PeriodicalId":6607,"journal":{"name":"2018 Network Traffic Measurement and Analysis Conference (TMA)","volume":"24 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73480614","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

An Intelligent Data Visualization Service Platform for Mobile Network Operators 面向移动网络运营商的智能数据可视化服务平台

2018 Network Traffic Measurement and Analysis Conference (TMA) Pub Date : 2018-06-01 DOI: 10.23919/TMA.2018.8506552

Naz Albayrak, E. Zeydan

引用次数: 1

FlowMon-DPDK: Parsimonious Per-Flow Software Monitoring at Line Rate FlowMon-DPDK:精简的按流量软件监控

2018 Network Traffic Measurement and Analysis Conference (TMA) Pub Date : 2018-06-01 DOI: 10.23919/TMA.2018.8506565

Tianzhu Zhang, Leonardo Linguaglossa, Massimo Gallo, P. Giaccone, D. Rossi

{"title":"FlowMon-DPDK: Parsimonious Per-Flow Software Monitoring at Line Rate","authors":"Tianzhu Zhang, Leonardo Linguaglossa, Massimo Gallo, P. Giaccone, D. Rossi","doi":"10.23919/TMA.2018.8506565","DOIUrl":"https://doi.org/10.23919/TMA.2018.8506565","url":null,"abstract":"Testing experimental network devices requires deep performance analysis, which is usually performed with expensive, not flexible, hardware equipment. With the advent of highspeed packet I/O frameworks, general purpose equipments have narrowed the performance gap in respect of dedicated hardware and a variety of software-based solutions have emerged for handling traffic at very high speed. While the literature abounds with software traffic generators, existing monitoring solutions do not target worst-case scenarios (i.e., 64B packets at line rate) that are particularly relevant for stress-testing high-speed network functions, or occupy too many resources. In this paper we first analyse the design space for high-speed traffic monitoring that leads us to specific choices characterizing FlowMon-DPDK, a DPDK-based software traffic monitor that we release as an open source project. In a nutshell, FlowMon-DPDK provides tunable fine-grained statistics at both packet and flow levels. Experimental results demonstrate that our traffic monitor is able to provide per-flow statistics with 5-nines precision at high-speed (14.88 Mpps) using an exiguous amount of resources. Finally, we showcase FlowMon-DPDK usage by testing two open source prototypes for stateful flow-level end-host and in-network packet processing.","PeriodicalId":6607,"journal":{"name":"2018 Network Traffic Measurement and Analysis Conference (TMA)","volume":"159 s1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91422758","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: 16