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  • The Inria's Research Teams produce an annual Activity Report presenting their activities and their results of the year. These reports include the team members, the scientific program, the software developed by the team and the new results of the year. The report also describes the grants, contracts and the activities of dissemination and teaching. Finally, the report gives the list of publications of the year.

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Section: New Results

Service Transparency

An Intelligent Sampling Framework for Controlled Experimentation and QoE Modeling

Participants: Muhammad Jawad Khokhar, Nawfal Abbasi Saber, Thierry Spetebroot, Chadi Barakat.

For internet applications, measuring, modeling and predicting the quality experienced by end users as a function of network conditions is challenging. A common approach for building application specific Quality of Experience (QoE) models is to rely on controlled experimentation. For accurate QoE modeling, this approach can result in a large number of experiments to carry out because of the multiplicity of the network features, their large span (e.g., bandwidth, delay) and the time needed to setup the experiments themselves. However, most often, the space of network features in which experimentations are carried out shows a high degree of similarity in the training labels of QoE. This similarity, difficult to predict beforehand, amplifies the training cost with little or no improvement in QoE modeling accuracy. So, in this work, funded by ANR BottleNet and IPL BetterNet, we aim to exploit this similarity, and propose a methodology based on active learning, to sample the experimental space intelligently, so that the training cost of experimentation is reduced. We validate our approach for the case of YouTube video streaming QoE modeling from out-of-band network performance measurements, and perform a rigorous analysis of our approach to quantify the gain of active sampling over uniform sampling. We first develop the methodology for an offline case where a pool of scenarios to experiment with is available. Then, we present an online variant that does not require a pool of scenarios, but finds automatically and in an online manner the best scenarios to experiment with. This latter variant outperforms the offline variant both in terms of accuracy and computation complexity. It is published in [22]. The overall methodology and its specification to both the offline and the online cases are published in [15].

A Methodology for Performance Benchmarking of Mobile Networks for Internet Video Streaming

Participants: Muhammad Khokhar, Thierry Spetebroot, Chadi Barakat.

Video streaming is a dominant contributor to the global Internet traffic. Consequently, gauging network performance w.r.t. the video Quality of Experience (QoE) is of paramount importance to both telecom operators and regulators. Modern video streaming systems, e.g. YouTube, have huge catalogs of billions of different videos that vary significantly in content type. Owing to this difference, the QoE of different videos as perceived by end users can vary for the same network Quality of Service (QoS). In this work, funded by ANR BottleNet and IPL BetterNet, we present a methodology for benchmarking performance of mobile operators w.r.t Internet video that considers this variation in QoE. We take a data-driven approach to build a predictive model using supervised machine learning (ML) that takes into account a wide range of videos and network conditions. To that end, we first build and analyze a large catalog of YouTube videos. We then propose and demonstrate a framework of controlled experimentation based on active learning to build the training data for the targeted ML model. Using this model, we then devise YouScore, an estimate of the percentage of YouTube videos that may play out smoothly under a given network condition. Finally, to demonstrate the benchmarking utility of YouScore, we apply it on an open dataset of real user mobile network measurements to compare performance of mobile operators for video streaming. This work is published in [21] and its extension to more sophisticated QoE models that consider other factors than interruptions is ongoing.

On the Cost of Measuring Traffic in a Virtualized Environment

Participants: Karyna Gogunska, Chadi Barakat.

The current trend in application development and deployment is to package applications and services within containers or virtual machines. This results in a blend of virtual and physical resources with complex network interconnection schemas mixing virtual and physical switches along with specific protocols to build virtual networks spanning over several servers. While the complexity of this setup is hidden by private/public cloud management solutions, e.g. OpenStack, this new environment constitutes a challenge when it comes to monitor and debug performance related issues. In this work carried out in collaboration with the Signet team of I3S with the support of the UCN@Sophia Labex, we introduce the problem of measuring traffic in a virtualized environment and focus on one typical scenario, namely virtual machines interconnected with a virtual switch. For this scenario, we assess the cost of continuously measuring the network traffic activity of the machines. Specifically, we seek to estimate the competition that exists to access the physical resources (e.g., CPU) of the physical server between the measurement task and the legacy application activity. This work was published in the IEEE Cloudnet 2018 conference [20] where it was awarded the Best Student Award. The collaboration with I3S is pursued towards a controlled configuration and deployment of measurements tools in a way to limit their impact on the legacy data plane of virtualized environments.

ElectroSmart

Participants: Arnaud Legout, Mondi Ravi, David Migliacci, Abdelhakim Akodadi, Yanis Boussad.

We are currently evaluating the relevance to create a startup for the ElectroSmart project. We are quite advanced in the process and the planned creation is June 2019. There is a "contrat de transfer" ready between Inria and ElectroSmart to transfer the PI from Inria to the ElectroSmart company (when it will be created). Arnaud Legout the future CEO of the company obtained the "autorisation de création d'entreprise" from Inria. ElectroSmart has been incubated in PACA Est in December 2018.

The three future co-founder of ElectroSmart (Arnaud Legout, Mondi Ravi, David Migliacci) are following the Digital Startup training from Inria/EM Lyon. This training helped formalize and improve the product market fit and the business model. We are also preparing the iLab competition.

The business model of ElectroSmart is to create an affiliation strategy to help companies selling product to reduce EMF exposure to find potential clients. Indeed, ElectroSmart users represent a highly qualified database of people concerned by EMF exposure. This database is invaluable to these companies as it is an emerging market and it is hard for these companies to make efficient marketing campaigns. The benefit for the ElectroSmart users is to have access to negotiated and validated solutions to reduce their EMF exposure. We are currently validating this market. We started our first affiliation campaign in December 2018 with the Spartan company that sells radiation blocking boxers. We already have two more planned campaigns in 2019, with a goal of 5 campaigns in 2019.