Path Transparency Measurements from the Mobile Edge with PATHspider

Postprin

Tracing Internet Path Transparency

This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 688421, and was supported by the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract number 15.0268. The opinions expressed and arguments employed reflect only the authors’ views. The European Commission is not responsible for any use that may be made of that information. Further, the opinions expressed and arguments employed herein do not necessarily reflect the official views of the Swiss Government.Peer reviewedPublisher PD

Exploring DSCP modification pathologies in the internet

This work is funded by the European Unions Horizon 2020 research and innovation programme under grant agreement no. 644399 (MONROE) through the Open Call and grant agreement no. 644334 (NEAT). The views expressed are solely those of the author(s). The European Commission is not responsible for any use that may be made of that information.Peer reviewedPublisher PD

Exploring usable Path MTU in the Internet

This work is funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 644399 (MONROE) through the Open Call. Additionally this work was partially supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 688421 (MAMI). The opinions expressed and arguments employed reflect only the authors’ view. The European Commission is not responsible for any use that may be made of that informationPostprin

An Observation-Based Middlebox Policy Taxonomy

peer reviewedRecent years have seen the rise of middleboxes, such as NATs, firewalls, or TCP accelerators. Those middleboxes play an important role in today's Internet, including enterprise networks and cellular networks. However, despite their undisputable success in modern network architecture, their actual impact on packets, traffic, and network performance is not that much understood. In this paper, we propose a path impairment oriented middlebox classification that aims at categorizing the initial purpose of a middlebox policy as well as its potential complications

Exploring DSCP modification pathologies in mobile edge networks

Electronic ISBN: 978-3-901882-95-1 Print on Demand(PoD) ISBN: 978-1-5386-0405-2Peer reviewedPostprin

Performance Evaluation And Anomaly detection in Mobile BroadBand Across Europe

With the rapidly growing market for smartphones and user’s confidence for immediate access to high-quality multimedia content, the delivery of video over wireless networks has become a big challenge. It makes it challenging to accommodate end-users with flawless quality of service. The growth of the smartphone market goes hand in hand with the development of the Internet, in which current transport protocols are being re-evaluated to deal with traffic growth. QUIC and WebRTC are new and evolving standards. The latter is a unique and evolving standard explicitly developed to meet this demand and enable a high-quality experience for mobile users of real-time communication services. QUIC has been designed to reduce Web latency, integrate security features, and allow a highquality experience for mobile users. Thus, the need to evaluate the performance of these rising protocols in a non-systematic environment is essential to understand the behavior of the network and provide the end user with a better multimedia delivery service. Since most of the work in the research community is conducted in a controlled environment, we leverage the MONROE platform to investigate the performance of QUIC and WebRTC in real cellular networks using static and mobile nodes. During this Thesis, we conduct measurements ofWebRTC and QUIC while making their data-sets public to the interested experimenter. Building such data-sets is very welcomed with the research community, opening doors to applying data science to network data-sets. The development part of the experiments involves building Docker containers that act as QUIC and WebRTC clients. These containers are publicly available to be used candidly or within the MONROE platform. These key contributions span from Chapter 4 to Chapter 5 presented in Part II of the Thesis. We exploit data collection from MONROE to apply data science over network data-sets, which will help identify networking problems shifting the Thesis focus from performance evaluation to a data science problem. Indeed, the second part of the Thesis focuses on interpretable data science. Identifying network problems leveraging Machine Learning (ML) has gained much visibility in the past few years, resulting in dramatically improved cellular network services. However, critical tasks like troubleshooting cellular networks are still performed manually by experts who monitor the network around the clock. In this context, this Thesis contributes by proposing the use of simple interpretable ML algorithms, moving away from the current trend of high-accuracy ML algorithms (e.g., deep learning) that do not allow interpretation (and hence understanding) of their outcome. We prefer having lower accuracy since we consider it interesting (anomalous) the scenarios misclassified by the ML algorithms, and we do not want to miss them by overfitting. To this aim, we present CIAN (from Causality Inference of Anomalies in Networks), a practical and interpretable ML methodology, which we implement in the form of a software tool named TTrees (from Troubleshooting Trees) and compare it to a supervised counterpart, named STress (from Supervised Trees). Both methodologies require small volumes of data and are quick at training. Our experiments using real data from operational commercial mobile networks e.g., sampled with MONROE probes, show that STrees and CIAN can automatically identify and accurately classify network anomalies—e.g., cases for which a low network performance is not justified by operational conditions—training with just a few hundreds of data samples, hence enabling precise troubleshooting actions. Most importantly, our experiments show that a fully automated unsupervised approach is viable and efficient. In Part III of the Thesis which includes Chapter 6 and 7. In conclusion, in this Thesis, we go through a data-driven networking roller coaster, from performance evaluating upcoming network protocols in real mobile networks to building methodologies that help identify and classify the root cause of networking problems, emphasizing the fact that these methodologies are easy to implement and can be deployed in production environments.This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Matteo Sereno.- Secretario: Antonio de la Oliva Delgado.- Vocal: Raquel Barco Moren