2019 Network Traffic Measurement and Analysis Conference (TMA)最新文献

Measuring mobile performance in the Tor network with OnionPerf 使用OnionPerf测量Tor网络中的移动性能

2019 Network Traffic Measurement and Analysis Conference (TMA) Pub Date : 2019-08-05 DOI: 10.23919/TMA.2019.8784601

A. Custura, Iain R. Learmonth, G. Fairhurst

引用次数: 0

Kraaler: A User-Perspective Web Crawler Kraaler:用户视角的网络爬虫

2019 Network Traffic Measurement and Analysis Conference (TMA) Pub Date : 2019-08-05 DOI: 10.23919/TMA.2019.8784660

Thomas Kobber Panum, René Rydhof Hansen, J. Pedersen

引用次数: 5

Revisiting Subnet Inference WISE-ly 明智地重新审视子网推理

2019 Network Traffic Measurement and Analysis Conference (TMA) Pub Date : 2019-06-19 DOI: 10.23919/TMA.2019.8784582

J. Grailet, B. Donnet

{"title":"Revisiting Subnet Inference WISE-ly","authors":"J. Grailet, B. Donnet","doi":"10.23919/TMA.2019.8784582","DOIUrl":"https://doi.org/10.23919/TMA.2019.8784582","url":null,"abstract":"Since the late 90’s, the Internet topology discovery has been an attractive and important research topic, leading, among others, to multiple probing and data analysis tools developed by the research community. This paper looks at the particular problem of discovering subnets (i.e., a set of devices that are located on the same connection medium and that can communicate directly with each other at the link layer).In this paper, we first show that the use of traffic engineering policies may increase the difficulty of subnet inference. We carefully characterize those difficulties and quantify their prevalence in the wild. Next, we introduce WISE (Wide and lInear Subnet inferencE), a novel tool for subnet inference designed to deal with those issues and able to discover subnets on wide ranges of IP addresses in a linear time. Using two groundtruth networks, we demonstrate that WISE performs better than state-of-the-art tools while being competitive in terms of subnet accuracy. We also show, through large-scale measurements, that the selection of vantage point with WISE does not matter in terms of subnet accuracy. Finally, all our code (WISE, data processing, results plotting) and collected data are freely available.","PeriodicalId":241672,"journal":{"name":"2019 Network Traffic Measurement and Analysis Conference (TMA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121688762","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: 4

A Cloud Provider’s View of EDNS Client-Subnet Adoption 云提供商对EDNS客户端-子网采用的看法

2019 Network Traffic Measurement and Analysis Conference (TMA) Pub Date : 2019-06-19 DOI: 10.23919/TMA.2019.8784530

Matt Calder, Xun Fan, Liang Zhu

{"title":"A Cloud Provider’s View of EDNS Client-Subnet Adoption","authors":"Matt Calder, Xun Fan, Liang Zhu","doi":"10.23919/TMA.2019.8784530","DOIUrl":"https://doi.org/10.23919/TMA.2019.8784530","url":null,"abstract":"Directing users to a nearby, high-performing front-end is core to the business of content delivery networks (CDNs). CDNs which use DNS to direct users to servers face the challenge of making decisions based at the LDNS-level, not based on the client’s IP address, and, in many cases, an LDNS is not representative of the clients it serves. The EDNS Client Subnet specification provides a solution by embedding a portion of the client’s IP address in the DNS query to help CDNs make better redirection decisions, but both the LDNS and authoritative resolver (CDN side) must support the standard. While there has been well-publicized adoption of Client Subnet by authoritative CDN resolvers, adoption rates across LDNSes are unknown.In this work, we examine Client Subnet adoption in LDNSes. We analyze DNS queries captured over one month from Mi-crosoft’s Azure Cloud platform. We find that adoption on the Internet is very low across ISPs but query volume is relatively high due to the popularity of public DNS services. We discover high network adoption rates in China and reveal that Chinese public DNS services deploy LDNSes deep into end-user networks.","PeriodicalId":241672,"journal":{"name":"2019 Network Traffic Measurement and Analysis Conference (TMA)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129054418","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: 14

Reducing Consumed Data Volume in Bandwidth Measurements via a Machine Learning Approach 通过机器学习方法减少带宽测量中消耗的数据量

2019 Network Traffic Measurement and Analysis Conference (TMA) Pub Date : 2019-06-19 DOI: 10.23919/TMA.2019.8784575

Christian Maier, P. Dorfinger, J. Du, Sven Gschweitl, Johannes Lusak

引用次数: 3

Know your Big Data Trade-offs when Classifying Encrypted Mobile Traffic with Deep Learning 在使用深度学习对加密移动流量进行分类时，了解您的大数据权衡

2019 Network Traffic Measurement and Analysis Conference (TMA) Pub Date : 2019-06-19 DOI: 10.23919/TMA.2019.8784565

Giuseppe Aceto, D. Ciuonzo, Antonio Montieri, V. Persico, A. Pescapé

{"title":"Know your Big Data Trade-offs when Classifying Encrypted Mobile Traffic with Deep Learning","authors":"Giuseppe Aceto, D. Ciuonzo, Antonio Montieri, V. Persico, A. Pescapé","doi":"10.23919/TMA.2019.8784565","DOIUrl":"https://doi.org/10.23919/TMA.2019.8784565","url":null,"abstract":"The spread of handheld devices has led to the unprecedented growth of traffic volumes traversing both local networks and the Internet, appointing mobile traffic classification as a key tool for gathering highly-valuable profiling information, other than traffic engineering and service management. However, the nature of mobile traffic severely challenges state-of-art Machine-Learning (ML) approaches, since the quickly evolving and expanding set of apps generating traffic hinders ML-based approaches, that require domain-expert design. Deep Learning (DL) represents a promising solution to this issue, but results in higher completion times, in turn suggesting the application of the Big-Data (BD) paradigm. In this paper, we investigate for the first time BD-enabled classification of encrypted mobile traffic using DL from a general standpoint, (a) defining general design guidelines, (b) leveraging a public-cloud platform, and (c) resorting to a realistic experimental setup. We found that, while BD represents a transparent accelerator for some tasks, this is not the case for the training phase of DL architectures for traffic classification, requiring a specific BD-informed design. The experimental setup is built upon a three-dimensional investigation path in the BD adoption, namely: (i) completion time, (ii) deployment costs, and (iii) classification performance, highlighting relevant non-trivial trade-offs.","PeriodicalId":241672,"journal":{"name":"2019 Network Traffic Measurement and Analysis Conference (TMA)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114699970","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: 31

Autonomous IoT Device Identification Prototype 自主物联网设备识别原型

2019 Network Traffic Measurement and Analysis Conference (TMA) Pub Date : 2019-06-19 DOI: 10.23919/TMA.2019.8784517

Nesrine Ammar, L. Noirie, S. Tixeuil

引用次数: 6

Let me Decrypt your Beauty: Real-time Prediction of Video Resolution and Bitrate for Encrypted Video Streaming 让我解密你的美:实时预测视频分辨率和比特率的加密视频流

2019 Network Traffic Measurement and Analysis Conference (TMA) Pub Date : 2019-06-17 DOI: 10.23919/TMA.2019.8784589

Sarah Wassermann, Michael Seufert, P. Casas, Li Gang, Kuang Li

引用次数: 15

Tackling Mobile Traffic Critical Path Analysis With Passive and Active Measurements 利用被动和主动测量解决移动流量关键路径分析

2019 Network Traffic Measurement and Analysis Conference (TMA) Pub Date : 2019-06-01 DOI: 10.23919/TMA.2019.8784636

Gioacchino Tangari, Diego Perino, A. Finamore, M. Charalambides, G. Pavlou

{"title":"Tackling Mobile Traffic Critical Path Analysis With Passive and Active Measurements","authors":"Gioacchino Tangari, Diego Perino, A. Finamore, M. Charalambides, G. Pavlou","doi":"10.23919/TMA.2019.8784636","DOIUrl":"https://doi.org/10.23919/TMA.2019.8784636","url":null,"abstract":"Critical Path Analysis (CPA) studies the delivery of webpages to identify page resources, their interrelations, as well as their impact on the page loading latency. Despite CPA being a generic methodology, its mechanisms have been applied only to browsers and web traffic, but those do not directly apply to study generic mobile apps. Likewise, web browsing represents only a small fraction of the overall mobile traffic. In this paper, we take a first step towards filling this gap by exploring how CPA can be performed for generic mobile applications. We propose Mobile Critical Path Analysis (MCPA), a methodology based on passive and active network measurements that is applicable to a broad set of apps to expose a fine-grained view of their traffic dynamics. We validate MCPA on popular apps across different categories and usage scenarios. We show that MCPA can identify user interactions with mobile apps only based on traffic monitoring, and the relevant network activities that are bottlenecks. Overall, we observe that apps spend 60% of time and 84% of bytes on critical traffic on average, corresponding to +22% time and +13% bytes than what observed for browsing.","PeriodicalId":241672,"journal":{"name":"2019 Network Traffic Measurement and Analysis Conference (TMA)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125266800","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: 3

Inferring the Deployment of Top Domains over Public Clouds using DNS Data 利用DNS数据推断公有云顶级域部署

2019 Network Traffic Measurement and Analysis Conference (TMA) Pub Date : 2019-06-01 DOI: 10.23919/TMA.2019.8784472

Quentin Jacquemart, Clément Pigout, G. Urvoy-Keller

{"title":"Inferring the Deployment of Top Domains over Public Clouds using DNS Data","authors":"Quentin Jacquemart, Clément Pigout, G. Urvoy-Keller","doi":"10.23919/TMA.2019.8784472","DOIUrl":"https://doi.org/10.23919/TMA.2019.8784472","url":null,"abstract":"Cloud technologies are becoming pervasive and available for private companies or public institutions in different flavors, mostly public cloud or private clouds. Our focus in this work is on the usage of public cloud technologies by the most popular sites in the Internet. While some studies have described the nascent landscape of public cloud computing 5 years ago, surprising little effort has been put to study the recent evolution of this domain.Using DNS data that enables us to map domains (e.g., netflix.com) and their subdomains (e.g., api.netflix.com) with the cloud providers actually used, we refresh our understanding of this ecosystem. We focus on the dominant four cloud providers, namely Amazon Web Services, Microsoft Azure, Google Cloud Computing and IBM Bluemix. We demonstrate that cloud penetration has clearly increased since 2013, reaching almost 50% of the top 1000 domains, from the Alexa list. Furthermore, a significant fraction of domains use multiple cloud providers simultaneously. Still, domain owenrs remain cautious when it comes to choose which subdomain is actually hosted in the cloud and only 17.8% on average of the subdomains are actually hosted in the cloud. In terms of performance, preliminary results indicate that hosting a subdomain in the cloud pays off as compared to private hosting with a decrease of application level latency of 28%.","PeriodicalId":241672,"journal":{"name":"2019 Network Traffic Measurement and Analysis Conference (TMA)","volume":"244 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115097149","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