@article {Cas2016, title = {Unveiling Network and Service Performance Degradation in the Wild with mPlane}, journal = {IEEE Communications Magazine - Network Testing Series}, year = {2016}, abstract = {

Unveiling network and service performance issues in complex and highly decentralized systems such as the Internet is a major challenge. Indeed, the Internet is based on decentralization and diversity. However, its distributed nature leads to operational brittleness and difficulty in identifying the root causes of performance degradation. In such a context, network measurements are a fundamental pillar to shed light and to unveil design and implementation defects. To tackle this fragmentation and visibility problem, we have recently conceived mPlane, a distributed measurement platform which runs, collects and analyses traffic measurements to study the operation and functioning of the Internet. In this paper, we show the potentiality of the mPlane approach to unveil network and service degradation issues in live, operational networks, involving both fixed-line and cellular networks. In particular, we combine active and passive measurements to troubleshoot problems in end-customer Internet access connections, or to automatically detect and diagnose anomalies in Internet-scale services (e.g., YouTube) which impact a large number of end-users.

}, author = {Pedro Casas and Pierdomenico Fiadino and Sarah Wassermann and Stefano Traverso and Alessandro D{\textquoteright}Alconzo and Edion Tego and Francesco Matera and Marco Mellia} } @inproceedings {Cas2015a, title = {On the Analysis of QoE in Cellular Networks: from Subjective Tests to Large-scale Traffic Measurements}, booktitle = {6th International Workshop on Traffic Analysis and Characterization (TRAC)}, year = {2015}, author = {Pedro Casas and Martin Varela and Pierdomenico Fiadino and Mirko Schiavone and Helena Rivas and Raimund Schatz} } @inproceedings {Fia2015b, title = {Challenging Entropy-based Anomaly Detection and Diagnosis in Cellular Networks}, booktitle = {ACM SIGCOMM}, year = {2015}, author = {Pierdomenico Fiadino and Alessandro D{\textquoteright}Alconzo and Mirko Schiavone and Pedro Casas} } @inproceedings {Cas2015, title = {QoMOSN - On the Analysis of Traffic and Quality of Experience in Mobile Online Social Networks}, booktitle = {European Conference on Networks and Communications (EuCNC)}, year = {2015}, author = {Pedro Casas and Pierdomenico Fiadino and Mirko Schiavone} } @inproceedings {Fia2015a, title = {RCATool - A Framework for Detecting and Diagnosing Anomalies in Cellular Networks}, booktitle = {27th International Teletraffic Congress (ITC)}, year = {2015}, author = {Pierdomenico Fiadino and Alessandro D{\textquoteright}Alconzo and Mirko Schiavone and Pedro Casas} } @inproceedings {Fia2015, title = {Towards Automatic Detection and Diagnosis of Internet Service Anomalies via DNS Traffic Analysis}, booktitle = {6th International Workshop on Traffic Analysis and Characterization (TRAC)}, year = {2015}, author = {Pierdomenico Fiadino and Alessandro D{\textquoteright}Alconzo and Mirko Schiavone and Pedro Casas} } @article {Dus2014a, title = {Algorithm and Scheduler Design and Implementation}, year = {2014}, month = {09/2014}, keywords = {algorithm design, job scheduler, mPlane software, repository tools}, isbn = {D3.3}, issn = {D3.3}, author = {Maurizio Dusi and Saverio Niccolini and Sofia Nikitaki and Daniele Apiletti and Elena Baralis and Alessandro Finamore and Luigi Grimaudo and Stefano Traverso and Francesco Matera and Edion Tego and V, Guchev and Zied Ben Houidi and Pietro Michiardi and Marco Milanesio and YiXi Gong and Dario Rossi and Ilias Leontiadis and G, Dimopoulos and Tivadar Szemethy and A, Bakay and Arian B{\"a}r and Pedro Casas and Alessandro D{\textquoteright}Alconzo and Pierdomenico Fiadino} } @inproceedings {Cas2014d, title = {On the Analysis of QoE-based Performance Degradation in YouTube Traffic}, booktitle = {10th International Conference on Network and Service Management, CNSM 2014}, year = {2014}, month = {11/2014}, publisher = {IEEE}, organization = {IEEE}, address = {Rio de Janeiro, Brazil}, abstract = {

YouTube is the most popular service in today{\textquoteright}s Internet. Google relies on its massive Content Delivery Network (CDN) to push YouTube videos as close as possible to the end-users to improve their Quality of Experience (QoE), as well as to pursue its own optimization goals. Adopting space and time variant traffic delivery policies, Google servers handle users{\textquoteright} requests from multiple geo-distributed locations at different times. Such traffic delivery policies can have a relevant impact on the traffic routed through the Internet Service Providers (ISPs) providing the access, but most importantly, they can have negative effects on the end-user QoE. In this paper we shed light on the problem of diagnosing QoE-based performance degradation events in YouTube{\textquoteright}s traffic. Through the analysis of one month of YouTube flow traces collected at the network of a large European ISP, we particularly identify and drill down a Google{\textquoteright}s CDN server selection policy negatively impacting the watching experience of YouTube users during several days at peak-load times. The analysis combines both the user-side perspective and the CDN perspective of the end-to-end YouTube delivery service to diagnose the problem. On the one hand, we rely on the monitoring of YouTube QoE-based Key Performance Indicators (KPIs) to detect performance degradation events affecting the end-customers. On the other hand, we analyze the temporal behavior of the Google CDN traffic delivery policies, by tracking the activity of the Google servers providing the videos. The analysis is supported by time-series analysis, entropy-based approaches, and clustering techniques to flag the aforementioned anomaly. The main contributions of the paper are threefold: firstly, we provide a large-scale characterization of the YouTube service in terms of traffic characteristics and provisioning behavior of the Google CDN servers. Secondly, we introduce simple yet effective QoE-based KPIs to monitor YouTube videos from the end-user perspective. Finally and most important, we analyze and provide evidence of the occurrence of QoE-based YouTube anomalies induced by CDN server selection policies, which are somehow normally hidden from the common knowledge of the end-user. This is a main issue for ISPs, who see their reputation degrade when such events occur, even if Google is the culprit.

}, keywords = {clustering, Content Delivery Networks, Empirical Entropy, Performance Degradation, Quality of Experience, YouTube}, author = {Pedro Casas and Alessandro D{\textquoteright}Alconzo and Pierdomenico Fiadino and Arian B{\"a}r and Alessandro Finamore} } @inproceedings {Fia, title = {Characterizing Web Services Provisioning via CDNs: The Case of Facebook}, booktitle = {5th International Workshop on TRaffic Analysis and Characterization}, year = {2014}, month = {08/2014}, publisher = {IEEE}, organization = {IEEE}, address = {Nicosia, Cyprus}, abstract = {

Today{\textquoteright}s Internet consists of massive scale web\ services and Content Delivery Networks (CDNs). This paper sheds\ light on the way major Internet-scale web services content is\ hosted and delivered. By analyzing a full month of HTTP traffic\ traces collected at the mobile network of a major European ISP,\ we characterize the paradigmatic case of Facebook, considering\ not only the traffic flows but also the main organizations and\ CDNs providing them. Our study serves the main purpose of\ better understanding how major web services are provisioned\ in today{\textquoteright}s Internet, paying special attention to the temporal\ dynamics of the service delivery and the interplays between the\ involved hosting organizations. To the best of our knowledge, this\ is the first paper providing such an analysis in mobile networks.

}, keywords = {Akamai, Content Delivery Networks, Facebook, HTTP Traffic, mobile networks}, author = {Pierdomenico Fiadino and Alessandro D{\textquoteright}Alconzo and Pedro Casas} } @inproceedings {Fia2014, title = {On the Detection of Network Traffic Anomalies in Content Delivery Network Services}, booktitle = {ITC26}, year = {2014}, month = {09/2014}, address = {Karlskrona, Sweden}, author = {Pierdomenico Fiadino and Alessandro D{\textquoteright}Alconzo and Arian B{\"a}r and Alessandro Finamore and Pedro Casas} } @inproceedings {Cas2014, title = {Understanding HTTP Traffic and CDN Behavior from the Eyes of a Mobile ISP}, booktitle = {Passive and Active Measurements Conference (PAM)}, year = {2014}, author = {Pedro Casas and Pierdomenico Fiadino and Arian B{\"a}r} } @inproceedings {Fiaa, title = {Vivisecting WhatsApp through Large-Scale Measurements in Mobile Networks}, booktitle = {SIGCOMM 2014}, year = {2014}, month = {08/2014}, publisher = {ACM}, organization = {ACM}, address = {Chicago, USA}, abstract = {

WhatsApp, the new giant in instant multimedia messaging in mobile networks is rapidly increasing its popularity, taking over the traditional SMS/MMS messaging. In this paper we present the first large-scale characterization of WhatsApp, useful among others to ISPs willing to understand the impacts of this and similar applications on their networks. Through the combined analysis of passive measurements at the core of a national mobile network, worldwide geo-distributed active measurements, and traffic analysis at end devices, we show that: (i) the WhatsApp hosting architecture is highly centralized and exclusively located in the US; (ii) video sharing covers almost 40\% of the total WhatsApp traffic volume; (iii) flow characteristics depend on the OS of the end device; (iv) despite the big latencies to US servers, download throughputs are as high as 1.5 Mbps; (v) users react immediately and negatively to service outages through social networks feedbacks.

}, keywords = {Large-Scale Measurements, mobile networks, WhatsApp}, author = {Pierdomenico Fiadino and Mirko Schiavone and Pedro Casas} } @inproceedings {A2014, title = {Who to Blame when YouTube is not Working? Detecting Anomalies in CDN Provisioned Services}, booktitle = {TRAC}, year = {2014}, month = {08/2014}, publisher = {IEEE}, organization = {IEEE}, address = {Nicosia, Cyprus}, author = {Alessandro D{\textquoteright}Alconzo and Pedro Casas and Pierdomenico Fiadino and Arian B{\"a}r and Alessandro Finamore} } @inproceedings {Cas2014b, title = {YouTube All Around: Characterizing YouTube from Mobile and Fixed-line Network Vantage Points}, booktitle = {EuCNC}, year = {2014}, month = {06/2014}, address = {Bologna, IT}, author = {Pedro Casas and Pierdomenico Fiadino and Arian B{\"a}r and Alessandro D{\textquoteright}Alconzo and Alessandro Finamore and Marco Mellia} } @inproceedings {Cas2014c, title = {YouTube in the Move: Understanding the Performance of YouTube in Cellular Networks (BEST PAPER AWARD RUNNER UP)}, booktitle = {Wireless Days 2014}, year = {2014}, month = {11/2014}, publisher = {IEEE}, organization = {IEEE}, address = {Rio de Janeiro, Brazil}, abstract = {

YouTube is the most popular and volume-dominant service in today{\textquoteright}s Internet, and is changing the way ISPs manage their networks. Understanding the performance of YouTube traffic is paramount for ISPs, specially for mobile operators, who must handle the huge surge of traffic with the constraints and challenges of cellular networks. In this paper we present an empirical analysis of the performance of YouTube flows accessed through a national-wide cellular network, considering download throughput as well as end-user Quality of Experience (QoE) metrics. The analysis considers the characteristics and impacts of the Content Delivery Network hosting YouTube, and compares its behavior with other popular HTTP video streaming services accessed through cellular networks. The QoE analysis is performed through end-user device measurements, which directly reflect the experience of the end-users. Our study additionally shows the potentiality of monitoring YouTube performance in cellular networks directly from the smart-phones of the users, bypassing the traffic visibility loss at the core of the network introduced by traffic encryption (e.g., HTTPS).

}, keywords = {Cellular Networks, Content Delivery Networks, End-device Measurements, QoE, Traffic Measurements, YouTube}, author = {Pedro Casas and Pierdomenico Fiadino and Andreas Sackl and Alessandro D{\textquoteright}Alconzo} } @inproceedings {Fia2013, title = {HTTPTag: A Flexible On-line HTTP Classification System for Operational 3G Networks}, booktitle = {INFOCOM{\textquoteright}2013 Demo/Poster Session (INFOCOM{\textquoteright}2013 - Demo/Poster Session)}, year = {2013}, address = {Turin, Italy}, abstract = {

The popularity of web-based services and applications like YouTube and Facebook has taken HTTP back to the pole position on end-user traffic consumption. We present HTTPTag, a flexible on-line HTTP classification system based on pattern matching and tagging. HTTPTag recognizes on the fly and tracks the evolution of more than 280 applications running on top of HTTP in an operational 3G network, representing more than 70\% of the total HTTP traffic volume consumed by its customers. HTTPTag improves the network traffic visibility of an operator, performing tasks such as top-services ranking, long-term monitoring of applications popularity, and trend analysis among others.

}, keywords = {3G Networks, HTTP, Pattern Matching, Traffic Classification}, author = {Pierdomenico Fiadino and A B{\"a}r and P. Casas} } @inproceedings {Cas2013, title = {IP Mining: Extracting Knowledge from the Dynamics of the Internet Addressing Space (BEST PAPER AWARD)}, booktitle = {25th International Teletraffic Congress, ITC 25}, year = {2013}, abstract = {

Going back to the Internet of one decade ago, HTTP-based content and web services were provided by centralized or barely distributed servers. Single hosts providing exclusive services at fixed IP addresses was the standard approach. Current situation has drastically changed, and the mapping of IPs to different content and services is nowadays extremely dynamic. The adoption of large CDNs by major Internet players, the extended usage of transparent content caching, the explosion of Cloud-based services, and the decoupling between content providers and the hosting infrastructure have created a difficult to manage Internet landscape. Understanding such a complex scenario is paramount for network operators, both to control the traffic on their networks and to improve the quality experienced by their customers, specially when something goes wrong. Using a full week of HTTP traffic traces collected at the mobile broadband network of a major European ISP, this paper studies the associations between web services, the hosting organizations-ASes, and the content servers{\textquoteright} IPs. By mining correlations among these, we extract useful insights about the dynamics of the IP addressing space used by the top web services, and the way content providers and hosting organizations deliver their services to the mobile endusers. The extracted knowledge is applied on two specific use-cases, the former on hosting and service delivery characterization, the latter on automatic IP-based HTTP services classification.

}, keywords = {Content Delivery Networks, HTTP Traffic, IP Addressing Space, Traffic Classification and Analysis}, doi = {10.1109/ITC.2013.6662933}, author = {Pedro Casas and Pierdomenico Fiadino and Arian B{\"a}r} } @inproceedings {Cas2013c, title = {Mini-IPC: A Minimalist Approach for HTTP Traffic Classification using IP Addresses}, booktitle = {4th International Workshop on Traffic Analysis and Classification, TRAC 2013}, year = {2013}, abstract = {

The popularity of web-based services and multimedia applications like YouTube, Google Web Search, Facebook, and a bewildering range of Internet applications has taken HTTP back to the pole position on end-user traffic consumption. Today{\textquoteright}s Internet users exchange most of their content via HTTP. In this paper we address the problem of on-line HTTP traffic classification from network measurements. Building on the results provided by HTTPTag, a flexible system for on-line HTTP classification, we present and explore Mini-IPC. Mini-IPC is a minimalist approach for classifying HTTP flows using only the IP addresses of the servers hosting the corresponding content. Using one full week of HTTP traffic traces collected at the mobile broadband network of a major European ISP, we investigate to which extent the most popular HTTP-based services are hosted by well-defined sets of IP addresses, and evaluate the performance of Mini-IPC to classify these services using IPs only.

}, keywords = {CDNs, HTTP Traffic, IP Addressing Space, Mobile Networks{\textquoteright} Traffic, Traffic Classification and Analysis}, doi = {10.1109/IWCMC.2013.6583537}, author = {P. Casas and Pierdomenico Fiadino} }