{"title":"MIMIC: Using passive network measurements to estimate HTTP-based adaptive video QoE metrics","authors":"Tarun Mangla, Emir Halepovic, M. Ammar, E. Zegura","doi":"10.23919/TMA.2017.8002920","DOIUrl":"https://doi.org/10.23919/TMA.2017.8002920","url":null,"abstract":"HTTP-based Adaptive Streaming (HAS) has seen a major growth in the cellular networks. As a key application and network demand driver, user-perceived Quality of Experience (QoE) of video streaming contributes to the overall user satisfaction. Therefore, it becomes critical for the cellular network operators to understand the QoE of video streams. It can help with long-term network planning and provisioning and QoE-aware traffic management. However, tracking QoE is challenging as network operators do not have direct access to the video streaming apps, user devices or servers. In this paper, we provide a methodology that uses passive network measurements of unencrypted HAS video streams to estimate three key video QoE metrics — average bitrate, re-buffering ratio and bitrate switches. Our approach relies on the semantics of HAS to model a video session on the client. We first develop and validate our methodology through controlled experiments in the lab. Then, we conduct a large-scale validation of our approach using network data from a major cellular operator and ground truth QoE metrics from a large video service. We accurately predict the value of average bitrate within a relative error of 10% for 70%–90% of video sessions and re-buffering ratio within 1 percentage point for 65–90% of sessions. We further quantify the network overhead due to video chunk replacement and observe that a significant number of sessions have a high overhead of 20% or more. Finally, we highlight several challenges with video QoE metrics estimation in a large-scale monitoring system.","PeriodicalId":118082,"journal":{"name":"2017 Network Traffic Measurement and Analysis Conference (TMA)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117081492","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}
{"title":"Enabling packet fan-out in the libpcap library for parallel traffic processing","authors":"Nicola Bonelli, S. Giordano, G. Procissi","doi":"10.23919/TMA.2017.8002904","DOIUrl":"https://doi.org/10.23919/TMA.2017.8002904","url":null,"abstract":"The large availability of multi-gigabit network cards for commodity PCs requires network applications to potentially cope with high volumes of traffic. However, computation intensive operations may not catch up with high traffic rates and need to be run in parallel over multiple processing cores. As of today, the vast majority of network applications are still based on the use of the pcap library interface which, unfortunately, does not provide a native multi-core support, even though the underlying capture technologies do. This paper introduces a novel version of the pcap library for the Linux operating-system that allows application level parallelism. The new library natively supports fanout operations for both multi-threaded and multi-process applications, by means of extended API as well as by a declarative grammar configuration suitable for legacy applications. In addition, the library can transparently run on top of the standard Linux socket and other accelerated capture engines. Performance evaluation has been carried out on a multi-core architecture in pure capture tests and in more realistic use cases involving monitoring applications such as Tstat and Bro, with standard Linux socket and the PFQ accelerated engine.","PeriodicalId":118082,"journal":{"name":"2017 Network Traffic Measurement and Analysis Conference (TMA)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127596820","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}
Anika Schwind, Michael Seufert, Özgü Alay, P. Casas, P. Tran-Gia, Florian Wamser
{"title":"Concept and implementation of video QoE measurements in a mobile broadband testbed","authors":"Anika Schwind, Michael Seufert, Özgü Alay, P. Casas, P. Tran-Gia, Florian Wamser","doi":"10.23919/TMA.2017.8002921","DOIUrl":"https://doi.org/10.23919/TMA.2017.8002921","url":null,"abstract":"The MONROE testbed enables the objective performance assessment of Mobile Broadband (MBB) networks from the end-user perspective, using highly distributed measurements from fixed and mobile nodes. To quantify the performance of MBB networks for popular Internet services from a user-centric perspective, dedicated tools are needed. In this paper we extend the MONROE testbed to the Quality of Experience (QoE) domain, presenting the design and implementation of a QoE-capable measurement tool for YouTube video streaming. The measurement concept is based on emulating a virtual end-user device requesting video streams, which are then monitored at the network and application layers, on the basis of QoE-relevant features. The initial measurements conducted in the MONROE testbed and reported in this paper demonstrate the applicability of the implemented measurement concept.","PeriodicalId":118082,"journal":{"name":"2017 Network Traffic Measurement and Analysis Conference (TMA)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129629761","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}
{"title":"Hic sunt NATs: Uncovering address translation with a smart traceroute","authors":"R. Zullo, A. Pescapé, Korian Edeline, B. Donnet","doi":"10.23919/TMA.2017.8002924","DOIUrl":"https://doi.org/10.23919/TMA.2017.8002924","url":null,"abstract":"Middleboxes are pervasive in today's Internet as they are deployed for an increasing number of reasons. An example is the network address translation (NAT), one of the first task to be performed to cope with the lack of IPv4 addresses. Recently the landscape for NATs has become even more crowded, especially in mobile networks, mainly due to the impossibility of IPv6 to be a large-scale solution to addressing issues. In this paper, we present a novel methodology for detecting NATs embodied in Mobile Tracebox, a measurement tool for Android smart devices that detects a wide range of middle-boxes. It analyzes ICMP time-exceeded messages received during traceroute and points at IP and transport checksum inconsistencies in the embedded packets to uncover address translation along a path. We deployed Mobile Tracebox through a crowdsourcing approach and used the collected dataset to validate our methodology. Results showed that, in absence of middleboxes breaking traceroute, it can help to detect and locate NATs in the majority of the cases.","PeriodicalId":118082,"journal":{"name":"2017 Network Traffic Measurement and Analysis Conference (TMA)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116201183","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}
{"title":"A view from the edge: A stub-AS perspective of traffic localization and its implications","authors":"B. Yeganeh, R. Rejaie, W. Willinger","doi":"10.23919/TMA.2017.8002900","DOIUrl":"https://doi.org/10.23919/TMA.2017.8002900","url":null,"abstract":"Serving user requests from near-by caches or servers has been a powerful technique for localizing Internet traffic with the intent of providing lower delay and higher throughput to end users while also lowering the cost for network operators. This basic concept has led to the deployment of different types of infrastructures of varying degrees of complexity that large CDNs, ISPs, and content providers operate to localize their user traffic. Prior measurement studies in this area have focused mainly on revealing these deployed infrastructures, reverse-engineering the techniques used by these companies to map end users to close-by caches or servers, or evaluating the performance benefits that “typical” end users experience from well-localized traffic. To our knowledge, there has been no empirical study that assesses the nature and implications of traffic localization as experienced by end users at an actual stub-AS. This paper reports on such a study for the stub-AS UOnet (AS3582), a Research & Education network operated by the University of Oregon. Based on a complete flow-level view of the delivered traffic from the Internet to UOnet, we characterize the stub-AS's traffic footprint (i.e. a detailed assessment of the locality of the delivered traffic by all major content providers), examine how effective individual content providers utilize their built-out infrastructures for localizing their delivered traffic to UOnet, and investigate the impact of traffic localization on perceived throughput by end users served by UOnet. Our empirical findings offer valuable insights into important practical aspects of content delivery to real-world stub-ASes such as UOnet.","PeriodicalId":118082,"journal":{"name":"2017 Network Traffic Measurement and Analysis Conference (TMA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130134098","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}
I. Alepuz, Jorge Cabrejas-Peñuelas, J. Monserrat, Alvaro G. Perez, G. Pajares, Roberto Gimenez
{"title":"Use of mobile network analytics for application performance design","authors":"I. Alepuz, Jorge Cabrejas-Peñuelas, J. Monserrat, Alvaro G. Perez, G. Pajares, Roberto Gimenez","doi":"10.23919/TMA.2017.8002919","DOIUrl":"https://doi.org/10.23919/TMA.2017.8002919","url":null,"abstract":"With the 5G technology, data traffic is going to grow by a factor of 1000, while the number of connected devices is likely going to be two orders of magnitude higher. With smartphones being cornerstone in our daily lives, understanding mobile network performance is critical for providing a superior user experience and, consequently, determining the success of an application. This paper presents a solution that uses the radio parameters measured by a mobile terminal to determine the best Application Protocol (APPP) for a service, so as it could adapt to the varying network conditions. From the training of an inference system with actual Mean Opinion Score (MOS) data, it will be possible to discern which radio Key Performance Indicators (KPIs) are best suited to characterize the state of the network and make the best possible decision. Results show how the decision system based on only three radio KPI is able to determine the user application experience with a success of up to 83%. Thanks to the use of this approach, application developers may fill the gap of knowledge between network KPIs and user experience.","PeriodicalId":118082,"journal":{"name":"2017 Network Traffic Measurement and Analysis Conference (TMA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121547076","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}
Pedro M. B. Torres, P. Marques, Hugo Marques, Rogerio Dionisio, Tiago Alves, Luis Pereira, J. Ribeiro
{"title":"Data analytics for forecasting cell congestion on LTE networks","authors":"Pedro M. B. Torres, P. Marques, Hugo Marques, Rogerio Dionisio, Tiago Alves, Luis Pereira, J. Ribeiro","doi":"10.23919/TMA.2017.8002917","DOIUrl":"https://doi.org/10.23919/TMA.2017.8002917","url":null,"abstract":"This paper presents a methodology for forecasting the average downlink throughput for an LTE cell by using real measurement data collected by multiple LTE probes. The approach uses data analytics techniques, namely forecasting algorithms to anticipate cell congestion events which can then be used by Self-Organizing Network (SON) strategies for triggering network re-configurations, such as shifting coverage and capacity to areas where they are most needed, before subscribers have been impacted by dropped calls or reduced data speeds. The presented implementation results show the prediction of network behaviour is possible with a high level of accuracy, effectively allowing SON strategies to be enforced in time.","PeriodicalId":118082,"journal":{"name":"2017 Network Traffic Measurement and Analysis Conference (TMA)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122726192","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}
{"title":"Veiled in clouds? Assessing the prevalence of cloud computing in the email landscape","authors":"Martin Henze, Mary Sanford, O. Hohlfeld","doi":"10.23919/TMA.2017.8002910","DOIUrl":"https://doi.org/10.23919/TMA.2017.8002910","url":null,"abstract":"The ongoing adoption of cloud-based email services — mainly run by few operators — transforms the largely decentralized email infrastructure into a more centralized one. Yet, little empirical knowledge on this transition and its implications exists. To address this gap, we assess the prevalence and exposure of Internet users to cloud-based email in a measurement study. In a first step, we study the email infrastructure and detect SMTP servers running in the cloud by analyzing all 154 M .com/.net/.org domains for cloud usage. Informed by this infrastructure assessment, we then study the prevalence of cloud-based SMTP services among actual email exchanges. Here, we analyze 31M exchanged emails, ranging from public email archives to the personal emails of 20 users. Our results show that as of today, 13% to 25% of received emails utilize cloud services and 30% to 70% of this cloud usage is invisible for users.","PeriodicalId":118082,"journal":{"name":"2017 Network Traffic Measurement and Analysis Conference (TMA)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124086583","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}
S. Traverso, Martino Trevisan, Leonardo Giannantoni, M. Mellia, H. Metwalley
{"title":"Benchmark and comparison of tracker-blockers: Should you trust them?","authors":"S. Traverso, Martino Trevisan, Leonardo Giannantoni, M. Mellia, H. Metwalley","doi":"10.23919/TMA.2017.8002898","DOIUrl":"https://doi.org/10.23919/TMA.2017.8002898","url":null,"abstract":"People are getting more and more conscious and worried about privacy issues that arise when browsing the Web. Ad-blockers, anti-tracking extensions, privacy and anonymity plug-ins, etc. promise to protect users and their privacy from third-party tracking systems. But how effective are they? In this paper, we present the first experimental campaign aimed at benchmarking popular plug-ins for web privacy preservation to date. We select 7 different plug-ins and setup a testbed to automatically browse regular web pages, while collecting navigation data. We analyze this data to compare each plugin, considering both privacy-protection and performance angles. Our results show that the picture is very variable, with no plugin being able to guarantee complete protection while improving performance as promised. By considering different experimental setups, we also observe that the European ePrivacy Directive is ignored by the majority of considered web sites. The directive prevents web services from installing tracking and profiling cookies before explicit consent is given by the user, but apparently this is not observed for most of services. To favor reproducibility, and repeatability, we share both the software and the data used to conduct this study with the community. Our aim is to let researchers and developers better understand the privacy threats in the Internet, possibly toward better performing privacy-preserving tools.","PeriodicalId":118082,"journal":{"name":"2017 Network Traffic Measurement and Analysis Conference (TMA)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123717038","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}
Roberto Gonzalez, Lili Jiang, Mohamed Ahmed, Miriam Marciel, R. C. Rumín, H. Metwalley, S. Niccolini
{"title":"The cookie recipe: Untangling the use of cookies in the wild","authors":"Roberto Gonzalez, Lili Jiang, Mohamed Ahmed, Miriam Marciel, R. C. Rumín, H. Metwalley, S. Niccolini","doi":"10.23919/TMA.2017.8002896","DOIUrl":"https://doi.org/10.23919/TMA.2017.8002896","url":null,"abstract":"Users online are commonly tracked using HTTP cookies when browsing on the web. To protect their privacy, users tend to use simple tools to block the activity of HTTP cookies. However, the “block all” design of tools breaks critical web services or severely limits the online advertising ecosystem. Therefore, to ease this tension, a more nuanced strategy that discerns better the intended functionality of the HTTP cookies users encounter is required. We present the first large-scale study of the use of HTTP cookies in the wild using network traces containing more than 5.6 billion HTTP requests from real users for a period of two and a half months. We first present a statistical analysis of how cookies are used. We then analyze the structure of cookies and observe that; HTTP cookies are significantly more sophisticated than the name=value defined by the standard and assumed by researchers and developers. Based on our findings we present an algorithm that is able to extract the information included in 86% of the cookies in our dataset with an accuracy of 91.7%. Finally, we discuss the implications of our findings and provide solutions that can be used to improve the most promising privacy preserving tools.","PeriodicalId":118082,"journal":{"name":"2017 Network Traffic Measurement and Analysis Conference (TMA)","volume":"306 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116228494","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}