2017-2018
Who?
Name: Chadi Barakat and Thierry Spetebroot
Mail: Chadi.Barakat@inria.fr and Thierry.Spetebroot@inria.fr
Telephone: 04 92 38 75 96
Web page: http://team.inria.fr/diana/chadi/
Where?
Place of the project: Inria
Address: 2004, route des lucioles, 06902 Sophia Antipolis, France
Team: Diana
Web page: http://team.inria.fr/diana/
What?
Pre-requisites if needed: Java and C++ programming skills, network programming skills, network traffic measurement and analysis skills
Description:
Context – ACQUA is a framework and mobile application for prediCting Quality of User Experience at Internet Access. It is developed by the Diana team at Inria Sophia Antipolis – Méditerranée and is supported by Inria under the ADT ACQUA grant. The scientific project around ACQUA is supported by Inria Project Lab BetterNet and the French National Project ANR BottleNet. ACQUA presents a new way for the evaluation of the performance of Internet access. Starting from network-level measurements as the ones we often do today (bandwidth, delay, loss rates, jitter, etc), ACQUA targets the estimated quality of experience related to the different applications of interest to the user without the need to run them (e.g., estimated Skype quality, estimated video streaming quality).
An application in ACQUA is a function, or a model, that links the network-level and device-level measurements to the expected quality of experience. Supervised machine learning techniques are used to establish such link between measurements both at the network level and the device level, and estimations of the Quality of Experience for different Internet applications. The required data for such learning can be obtained either by controlled experiments as we did in two recent communications on Skype and YouTube Quality of Experience, or by soliciting the crowd (i.e. crowdsourcing) for combinations (i.e. tuples) of measurements and corresponding application-level quality of experience.
The ACQUA mobile application is currently in its beta test. This application is supposed to be on one hand the reference application for QoE forecasting for end users at their Internet access, and on the other hand, the feedback channel that allows end users to report to us (if they are willing) on their experience together with the corresponding network measurements so as to help us calibrating better and more realistic models. For this calibration, we are currently performing extensive, efficient and automatic measurements in the laboratory, we will count on end users to help us completing this dataset with further applications and more realistic network and user conditions. Further information on the ACQUA project can be found at http://project.inria.fr/acqua/
Roadmap for the PFE – The project comes with different challenges to be solved. For the PFE, the first challenge that we would like to address is the validation of the network measurements done in ACQUA and the evaluation of the impact of these measurements on the predicted Quality of Experience. The candidate will have to master the project first. Second, she/he will have to run a set of controlled network experiments where the ground truth is known, have ACQUA run in this controlled network, and compare what ACQUA provides to known ground truth. In a second step, the candidate will have to compare what the QoE models predict with known ground truth to what ACQUA provides with these models. The expected output is an estimation of the accuracy of the ACQUA tool in carrying out network and QoE measurements.
Roadmap for the internship – More challenges will be covered in an internship that will follow the PFE. Analyzing the data provided sent by users’ mobiles is a first challenge, especially in terms of evaluating how people judge their quality of experience and the link between this subjective measurement and the network performance. The second challenge is regarding the scalability of the ACQUA measurement backend. Indeed, our backend consists of VMs that interact with the mobiles for their active measurements of network performances. We would like to propose an architectural solution that allows load balancing of measurement traffic across the VMs and the validation of this solution with real experiments over platforms as PlanetLab. One more challenge is regarding the introduction of passive measurements in ACQUA (signal, radio access technology, incoming and outcoming traffic counts, etc) and the evaluation of the impact of these measurements on predicting end user Quality of Experience. This prediction is done up to now with the help of active measurements. The internship will start by establishing a list of these different challenges, then addressing the most relevant ones regarding the expertise of the candidate and the available time frame for the internship.
Useful Information: http://project.inria.fr/acqua/
Who?
Name: Arnaud Legout (Inria)
Mail: arnaud.legout@inria.fr
Telephone: 04 92 38 78 15
Web page: http://www-sop.inria.fr/members/Arnaud.Legout/
Where?
Place of the project: Inria Sophia Antipolis
Address: 2004 route des Lucioles
Team: DIANA
Web page: https://team.inria.fr/diana/
What?
Pre-requisites if needed: Python programming, Android programming, statistical analysis
(depending on the task)
Description:
The Internet and new devices such as smartphones have fundamentally
changed the way people communicate, but this technological revolution
comes at the price of a higher exposition of the general population to
microwave electromagnetic fields (EMF). This exposition is a concern
for health agencies and epidemiologists who want to understand the
impact of such an exposition on health, for the general public who
wants a higher transparency on its exposition and the health hazard it
might represent, but also for cellular operators and regulation
authorities who want to improve the cellular coverage while limiting
the exposition. Despite the fundamental importance to understand the
exposition of the general public to EMF, it is poorly understood
because of the formidable difficulty to measure, model, and analyze
this exposition.
The goal of the ElectroSmart project is to develop the instrument,
methods, and models to compute the exposition of the general public to
microwave electromagnetic fields used by wireless protocols and
infrastructures such as Wi-Fi, Bluetooth, or cellular.
We currently have an Android application deployed in Google Play that
makes measurements of electromagnetic wavgqes. We have 25 000 downloads,
a mark of 4.5/5, and 200 million measurements. We have a team of 5
persons working full time on the project and we are in the process of
creating a startup. This PFE will take place in that context. We can
propose a broad spectrum of subjects that we will adapt depending on
the competencies of the candidate. Possible subjects are: i) making
data science on the huge amount of collected data to understand the
exposition of persons (requirements: Python, statistical analysis),
ii) make android development to improve the ElectroSmart application
(requirements: Android), iii) contribute to calibration experiments in
an anechoic chamber (requirements: Electromagnetic fields knowledge,
physical experimental skills)
You can find details on the ElectroSmart project on
https://es.inria.fr/
Useful Information:
This PFE can be continued with an internship and a Ph.D. thesis or
engineering position for excellent candidates.
Who?
Name: Christelle Caillouet, David Coudert
Mail: christelle.caillouet@unice.fr, david.coudert@inria.fr
Telephone: +33 4 92 38 79 29
Web page: http://www-sop.inria.fr/members/Christelle.Molle-Caillouet/
http://www-sop.inria.fr/members/David.Coudert/
Where?
Place of the project: COATI, joint project team between Inria and I3S lab
Address: Inria, 2004 route des lucioles, Sophia Antipolis
Team: COATI
Web page: https://team.inria.fr/coati/
What?
Pre-requisites if needed: Graph theory, Linear programming, Algorithmic
Description: Recent advances of technology have led to the development of flying drones
that act as wireless base stations to track objects lying on the ground. This kind of robots
(also called Unmanned Aerial Vehicles or UAVs) can be used in a variety of applications
such as vehicle tracking, traffic management and fire detection.
Deploying these Unmanned Aerial Vehicles to cover targets is a complex problem since each
target should be covered, UAVs should form a connected backbone with a base station in order
to collect and send information to the targets, while minimizing several parameters such that
deployment cost, UAV's altitudes to ensure good communication quality, energy consumed,
UAV's move, ...
The project direction is to provide an efficient and reliable drone placement and scheduling
by adjusting their position ensuring the surveillance of all the targets among time.
Theoreticaly, this problem is related to the set covering problem (and its dynamic version),
and the 3D packing problem.
The guideline of the proposed project is the following :
* Bibliographic analysis and understanding of papers [1] and [2]
* Development of a linear model extending [1] with scheduling constraints
* Implementation and analysis of obtained solutions
Useful Information:
[1] C. Caillouet, T. Razafindralambo, "Efficient Deployment of Connected Unmanned Aerial
Vehicles for Optimal Target Coverage", in IEEE Global Information Infrastructure and
Networking Symposium, Oct. 2017.
[2] L. Di Puglia Pugliese, F. Guerriero, D. Zorbas, T. Razafindralambo, "Modelling the mobile
target covering problem using flying drones", Optimization Letters, Springer Verlag, volume
10(5), pages 1021–1052, June 2016.
This project can be followed by an internship.
Who?
Name: Christelle Caillouet, FrÈdÈric Giroire
Mail: christelle.caillouet@unice.fr, frederic.giroire@cnrs.fr
Telephone: +33 4 92 38 79 29
Web page: http://www-sop.inria.fr/members/Christelle.Molle-Caillouet/
http://www-sop.inria.fr/members/Frederic.Giroire/
Where?
Place of the project: COATI, joint project team between Inria and I3S lab
Address: Inria, 2004 route des lucioles, Sophia Antipolis
Team: COATI
Web page: https://team.inria.fr/coati/
What?
Pre-requisites if needed: Linear programming, Algorithmic, Wireless Networks
Description: Wireless sensor networks are capable of periodically monitoring their vicinity and
reporting important information about the integrity and security of their
environment. The sensor nodes are powered by batteries and depending on how often
they take measurements and communicate with other devices, their energy may be
depleted fast. The replacement of the battery may be a hard task since the nodes
are often positioned in inaccessible places or the cost of replacement may be high.
To tackle this problem, a new technology has been recently developed by harvesting
energy from the transmitted RF signals. This technology uses a new type
of antenna which can convert part of the received signal power to electricity.
Depending on the transmitted power and the distance between the transmitting source
and the receiver, a node can harvest from some uW to some mW of power [1].
We assume that drones can fly over the sensor area and directionally emit energy towards
the ground nodes in order to recharge their battery. Taking into account the RF-power
harvesting limitations, the problem considered is to minimize the number
of deployed drones while maintaining the network operation for a given amount of time.
The guideline of the proposed project is the following :
* Bibliographic analysis of harvesting technology and related optimization models
* Development of a linear programming model for the above problem (related to [2])
* Implementation and analysis of obtained solutions
Useful Information:
[1] Shashank Priya and Daniel J. Inman. 2008. Energy Harvesting Technologies (1st ed.). Springer Publishing Company
[2] L. Di Puglia Pugliese, F. Guerriero, D. Zorbas, T. Razafindralambo, "Modelling the mobile
target covering problem using flying drones", Optimization Letters, Springer Verlag, volume
10(5), pages 1021ñ1052, June 2016.
This project can be followed by an internship.
Who?
Name: David Coudert, Nicolas Nisse
Mail: david.coudert@inria.fr, nicolas.nisse@inria.fr
Telephone: +33 4 92 38 79 81
Web page: http://www-sop.inria.fr/members/David.Coudert/
http://www-sop.inria.fr/members/Nicolas.Nisse/
Where?
Place of the project: COATI, joint project team between Inria and I3S lab
Address: Inria, 2004 route des lucioles, Sophia Antipolis
Team: COATI
Web page: https://team.inria.fr/coati/
What?
Pre-requisites if needed: Graph theory, Algorithmic, Optimization
Description: Mobility is an important aspect of smart-cities. Consequently, there is a growing demand for services offering efficient itinerary planning. Typically, a traveler wants to be informed of the best ways to reach its destination, using any combination of the possible means of transportation (buses, tram, metro, bicycles, etc.), and with a simple query. The main difficulty of such multi-modal itinerary computation, apart from the number of possibles modes of transportation that have to be combined, is to propose realistic itineraries. Indeed, if the announced travel-time of an itinerary is 15min and that the real travel-time is 25min, the traveller is right to be unhappy.
Nowadays, even in major French cities where real-time data are available on all channels, itinerary calculations are always based on theoretical timetables (e.g., in Paris). Therefore, the proposed itinerary does not take into account the actual state of the network (delay of a bus, traffic jam, unavailability of a bicycle, etc.), and the announced travel-time is often underestimated. In medium-scale cities (e.g., Nice), better solutions are now proposed. For instance, SMEs like Instant-System integrates and continuously refreshes the position of all buses, subways, trams, etc. on the network and uses them in the itinerary calculations. Nonetheless, the proposed solution is not scalable and many improvements are necessary.
The project direction is to study and develop algorithms for computing itineraries combining bicycle (e.g., vélo bleu, vélib’) and walk (and possibly other transportation means). The main objective is to better estimate the overall travel-time, taking into account: the cycling speed of the user, the probability that a bicycle is available at a station and that a slot will be free to return it, the number of red-lights along the path, the slope, etc.
Therefore, the main task of this project is to compare existing algorithms both theoretically and experimentally.
The long term objectives of this project (internship, PhD) are to design new algorithms offering better tradeoffs between pre-processing time, query-time, flexibility to handle events (blocked street, etc.), quality of the proposed itinerary, gap with real travel-time, specific constraint such as forbidden areas, computation of alternate routes, etc. Electric bicycles will require a particular attention.
This project will be done in the context of a collaboration with SMEs Instant-System (http:// www.instant-system.com) and Benomad (http://www.benomad.com).
Useful Information:
[1] H. Bast et al.: Route Planning in Transportation Networks. https://arxiv.org/pdf/1504.05140, 2015
[2] S. Storandt: Route Planning for Bicycles - Exact Constrained Shortest Paths made Practical via Contraction Hierarchy. ICAPS 2012
[3] R. Geisberger, C. Vetter: Efficient Routing in Road Networks with Turn Costs. SEA’11.
[4] D. Delling, A. Goldberg, T. Pajor, R. Werneck: Customizable Route Planning. Transportation Science, INFORMS, 2015.
[5] M. Baum, J., T., D. Wagner: Energy-Optimal Routes for Electric Vehicles. ACM SIGSPATIAL 2013.
[6] M. Baum, J. Dibbelt, L. Huebschle-Schneider, T. Pajor, and D. Wagner: Speed-Consumption Tradeoff for Electric Vehicle Route Planning. ATMOS’14.
[7] A. Kosowski, L. Viennot: Beyond Highway Dimension: Small Distance Labels Using Tree Skeletons. SODA 2017: 1462-1478
This project can be followed by an internship.
Who?
Name: Lucile Sassatelli, Ramon Aparicio-Pardo, Anne-Marie Pinna-Déry
Mail: {first.last}@unice.fr
Telephone: 0492942772
Web page:
http://www.i3s.unice.fr/~sassatelli/
http://www.i3s.unice.fr/~raparicio/
Where?
Place of the project: I3S (UCA,CNRS)
Address: 2000, route des Lucioles – Sophia Antipolis
Team: SigNet and S3 groups
Web page:
http://signet.i3s.unice.fr/
http://sparks.i3s.unice.fr/themes#scalable_software_systems
What?
Pre-requisites if needed:
Programming skills, Android programming is a plus
Description:
VR is growing fast with different companies rolling out cheap and not-so-cheap head-mounted sets in early 2016, from dedicated headsets like Oculus Rift and HTC Vive down to smartphone-dependent headsets (e.g., Samsung Gear VR, Google Cardboard and alike, to watch the phone screen an inch away from the eyes with magnifying lenses). VR platforms are also on the rise, such as YouTube 360 (YT 360) for distribution or Daydream presented at the last Google I/O conference in May 2016.
On the one hand, VR represents a tremendous revolution in the user’s experience, but VR also entails a daunting challenge for streaming transmission over the Internet (that is, Youtube-like, without download). The bit rates entailed by 360° videos (even H.265-compressed) are indeed much higher than for conventional videos (immersive smartphone apps [1] require about 28Mbps). These network speeds are hardly available in home accesses (of ADSL-type), forcing to offer the download option to avoid interruptions and low definitions. To tackle the challenge of streaming VR, a pre-selection of portions of the scene to be sent in priority can be made. This is enabled by the Spatial Relationship Description (SRD) amendment to the MPEG DASH standard [4,5], following pioneering works [6-9]. The decision problem of what to send is then made much more complex because it must take the user’s motion into account.
In the context of a local project, we are designing innovative streaming strategies for 360°-videos, which are meant to both decrease the required bandwidth and improve the user experience. These strategies however need to be tested and refined, and this usually requires user experiments, i.e., with a pool of human testers. In order to assess more rapidly the performance of the designed strategies, we want to simulate user’s motion to assess, in a first step, the performance in terms of objective metrics (quality, freezes, etc.). Then user experiments can be run on tuned strategies.
The goal of this PFE is to add a motion generator module in our platform, then test, analyze and possibly improve the streaming strategies.
- 1st phase: Getting familiar with the literature, the approaches developed and the available tools:
- the considered principles for streaming 360°-videos
- the testbed, made of 2 Android applications, a virtual network, multimedia toolboxes [6,7] and cinematographic editing tools
- 2nd phase: Design and implementation of a module in the Android client app to generate user motion, so as to automatize tests:
- Design of different types of head motion: fully random, impacted by saliency maps, and impacted by the editing strategy.
- Implementation and tests with the new module and assessment of quality metrics for different strategies.
References :
[1] Within application. Available: http://with.in/
[2] CNET. Everyone wanted a piece of virtual reality at this year's CES. CES 2016. Available: http://tinyurl.com/jr9cz7h
[3] Bo Begole. Why The Internet Pipes Will Burst When Virtual Reality Takes Off. Forbes, Feb. 2016.
[4] ISO/IEC 23009-1:2014/Amd 2:2015, "Spatial relationship description, generalized URL parameters and other extensions".
[5] O. A. Niamut, E. Thomas, L. D'Acunto, C. Concolato, F. Denoual, and S. Y. Lim, "MPEG DASH SRD: spatial relationship description," ACM Int. Conf. on Multimedia Systems (MMSys), May 2016.
[6] FFMPEG. Available: https://ffmpeg.org/
[7] MP4box. Available: https://gpac.wp.mines-telecom.fr/mp4box/
Useful Information:
Any possibly interested candidate is invited to come visit our demo booth in Antibes on October 7-8:
https://www.fetedelascience.fr/pid35201/fiche-evenement.html?identifiant=25917144
https://www.fetedelascience.fr/pid35201/fiche-evenement.html?identifiant=19873794
Who?
Name: Johan Montagnat
Mail: johan.montagnat@cnrs.fr
Telephone: 04 92 96 51 03
Web page: http://www.i3s.unice.fr/~johan
Where?
Place of the project: I3S
Address: Templiers 1 building, 930 route des Colles, 06903 Sophia Antipolis
Team: SPARKS
Web page: http://sparks.i3s.unice.fr
What?
Pre-requisites if needed: OS (Linux), CUDA and learning algorithms background appreciated
Description:
This project will study the feasibility and performance of parallel computing using standard machine learning libraries on Jetson TK1 embedded computing platforms with CUDA (nVidia GPU) accelerators. Jetson TK1 are ideal target for low-power / constrained application exploiting machine learning technologies. However, there specific hardware and limited capability (compared to mainframe / cloud solutions) needs to be investigated. This traineeship will investigate the ability to interconnect several Jetson modules over the Ethernet network and the opportunities for parallel computing on several units. Performance tests will be defined using well-known benchmarks such as the MNIST handwritten numbers database.
Useful Information:
Stages:
- Common learning software libraries review
- Linux kernel installation on Jetson TK1
- Target software libraries installation and testing
- Benchmark design and execution
- Parallel computing opportunities study
References:
- https://www.semanticscholar.org/paper/Are-Very-Deep-Neural-Networks-Feasible-on-Mobile-D-Rallapalli-Qiu/d7896d6be118386a1f76f389210ca4e3a87b0d4a
- http://engineering.skymind.io/distributed-deep-learning-part-1-an-introduction-to-distributed-training-of-neural-networks
- http://www.deeplearningbook.org/
Who?
Name: Damien Saucez
Mail: damien.saucez@inria.fr
Telephone: +33 4 89 73 24 18
Web page: https://team.inria.fr/diana/team-members/damien-saucez/
Where?
Place of the project: Inria Sophia Antipolis
Address: 2004 route des Lucioles, 06902 Sophia Antipolis
Team: Diana
Web page: https://team.inria.fr/diana/
What?
Pre-requisites if needed:
Description:
The Internet was thought to ease communications and it definitely outperformed the expectations to become the essential piece of our society. It was also thought to be distributed and independent but unfortunately almost 50 years after its debut we can say that it miserably failed with this objectives with only a few actors controlling the infrastructure and services. Recently we have indeed seen the emergence of platforms (a technology + an eco-system) such as Uber, Airbnb, or Doctolib that are all centralised. These platforms gain a major importance in our life and they are not centralised only for technological reasons but also for economical reasons as it eases the creation of monopoles or oligopoles and thus increase the power of the platform at the expenses of our freedom.
In contrast, the blockchain concept is emerging and promises the possibility to move back centralised systems to decentralised ones.
In this work, we will take the case of the centralised Uber platform and implement it using a blockchain. We will then study the technological and economical changes that such reorganisation would cause.
To achieve this goal, the students will first study the platforms to categorise and abstract them formally. They will then study the concept of blockchain and identify a blockchain that could be used to implement a platform. If such blockchain does not exist yet, they will design a new blockchain. Afterwards, the students will implement a proof-of-concept platform in a centralised way and on blockchain and study the differences.
As platform total eco-systems where the technology is just a part, the study will not be limited to the technological aspects but instead it will be extended to an economical study.
The work is open for either one or two students. In case this work is done in group, one student will work more on the technical aspects (e.g., simulations) while the other will work more on the theoretical aspects.
Useful Information:
The economical study will be performed in collaboration with researchers from the GREDEG (http://unice.fr/laboratoires/gredeg).
This work is a subject of internships and we do not expect the students to complete the
entire work within the PFE. Instead it would be followed by internships if the candidates
are excellent.
This work is part of a join IDEX project with the GREDEG.
Who?
Name: Walid Dabbous & Thierry Turletti
Mail: walid.dabbous@inria.fr & thierry.turletti@inria.fr
Telephone: 0492387718 & 0492387879
Web page: https://team.inria.fr/diana/team-members/walid-dabbous/ & https://team.inria.fr/diana/team-members/thierry-turletti/
Where?
Place of the project: Inria
Address: 2004 route des Lucioles, 06902 Sophia Antipolis
Team: Diana project-team
Web page: https://www.inria.fr/equipes/diana
What?
Pre-requisites if needed: Good network and programming skills
Description:
Upcoming 5G networks will leverage programmability of the network offered by Software Defined Networking (SDN) [1] and Network Function Virtualization (NFV) [2]. Recently we proposed the L2BM (Lazy Load Balancing Multicast) framework [3] for Software Defined ISP networks to deploy efficient and scalable high quality video streaming to end-users. In a nutshell, L2BM implements two main functionalities: (1) a solution to handle multicast group management in a scalable way on Software Defined ISPs using multicast Virtual Network Functions (VNFs) and (2) a smart multicast routing algorithm for large scale live video streaming applications that runs on SDN controllers using a threshold-based traffic engineering policy for capacity sharing. We have implemented L2BM and tested it on a testbed made of mesh of Grid nodes emulating the infrastructure of a wired ISP network [4].
In this PFE, the candidate will learn the mechanisms used to program future networks to support efficient high quality video streaming. She/he will experiment L2BM on the R2lab testbed [5] and propose an evaluation of the framework in a scenario including both wired and wireless end-users. This work is done in the context of an ongoing collaboration with NICT, Japan [6].
A possible follow up internship will concern the design of L2BM extensions to enhance the Quality of Experience (QoE) of wireless end-users with for instance the placement of caching virtual network functions (VNFs) at the edge of the ISP.
References:
[1] Nunes, Bruno Astuto A., et al. "A survey of software-defined networking: Past, present, and future of programmable networks." IEEE Communications Surveys & Tutorials 16.3 (2014): 1617-1634. URL: https://hal.inria.fr/hal-00825087v5/
[2] Mijumbi, Rashid, et al. "Network function virtualization: State-of-the-art and research challenges." IEEE Communications Surveys & Tutorials 18.1 (2016): 236-262. URL: https://arxiv.org/pdf/1509.07675
[3] H. Soni, W. Dabbous, T. Turletti, H. Asaeda, “Scalable Guaranteed-Bandwidth Multicast Service in Software Defined ISP networks”, IEEE International Conference on Communications (ICC), May 2017, Paris, France. 2017. URL: https://hal.inria.fr/hal-01400688
[4] H. Soni, W. Dabbous, T. Turletti, H. Asaeda, “NFV-based Scalable Guaranteed-Bandwidth Multicast Service for Software Defined ISP networks”, IEEE Transactions on Network and Service Management, to appear, 2017. URL: https://hal.archives-ouvertes.fr/hal-01596488v1
[5] FIT Reproducible Research Lab (R2lab), URL: http://fit-r2lab.inria.fr/
[6] UHD-on-5G Associated Team, URL: https://team.inria.fr/diana/uhd-on-5g/
Who?
Name: Thierry Turletti & Walid Dabbous
Mail: thierry.turletti@inria.fr & walid.dabbous@inria.fr
Telephone: 0492387879 & 0492387718
Web page: https://team.inria.fr/diana/team-members/thierry-turletti/ & https://team.inria.fr/diana/team-members/walid-dabbous/
Where?
Place of the project: Inria
Address: 2004 route des Lucioles, 06902 Sophia Antipolis
Team: Diana project-team
Web page: https://www.inria.fr/equipes/diana
What?
Pre-requisites if needed:
Description:
The Internet of Things (IoT) is playing an increasingly role today and more than half of major new business systems are expected to incorporate IoT elements by 2020.
LoRa is an emerging communication technology for Low Power Wide Area Network (LPWAN) which is known to be particularly efficient for long range communication links (several kilometers) at very low cost. However, first studies on the LoRaWAN MAC protocol developed by the LoRa Alliance for this technology reported important performance issues [2,3].
In this PFE, the candidate will first study the principles of the LoRa LPWAN technology and its possible usages. Then she/he will have the opportunity to run live experiments with a few LoRa devices based on Arduino that have been developed by our colleagues at LEAT [4]. A benchmark will be performed using different metrics (e.g., packet loss, RSSI, SNR) for various scenarios (e.g., w/ and w/o Line Of Sight (LOS/NLOS), indoor and outdoor and possibly within the R2lab anechoic testbed[5]).
Following this PFE study, a possible internship will focus on the design of enhanced transmission mechanisms and their theoritical and experimental evaluation. Another extension concerns the design of a particular LoRa application such as geolocalization.
References:
[1] N. Sornin, M. Luis, T. Eirich, T. Kramp, O.Hersent , “LoRa Specification 1.0,” LoRa Alliance Standard specification., 2016. https://www.lora-alliance.org/
[2] Augustin, A., Yi, J., Clausen, T., & Townsley, W. M. (2016). A study of LoRa: Long range & low power networks for the internet of things. Sensors, 16(9), 1466. http://www.mdpi.com/1424-8220/16/9/1466/pdf
[3] Adelantado, Ferran, et al. "Understanding the limits of LoRaWAN." IEEE Communications Magazine 55.9 (2017): 34-40. URL: https://arxiv.org/pdf/1607.08011
[4] Pham, C., Ferrero, F., Diop, M., Lizzi, L., Dieng, O., & Thiaré, O. (2017, June). Low-cost antenna technology for LPWAN IoT in rural applications. In Advances in Sensors and Interfaces (IWASI), 2017 7th IEEE International Workshop on (pp. 121-126).
[5] FIT R2lab Wireless Tesbed : http://fit-r2lab.inria.fr/
Contact: Joanna Moulierac and Frédéric Giroire.
Emails: joanna.mouliearac@unice.fr, frederic.giroire@cnrs.fr
Phone: 04 92 38 50 98
Laboratory: INRIA Sophia Antipolis, COATI team-project, https://team.inria.fr/coati/
Context
Software-defined or Software-Driven Networks (SDN) is a new networking paradigm enabling innovation, centralization of network management and preventing the so-called ossification of the Internet. SDN decouples the control plane from the data plane in network equipments, which means that a switch or a router is transformed into a simple forwarding device that applies rules sent by a remote controller using a normalized protocol. This simple approach allows network administrators to get a better control on the traffic in their network, e.g., Google has recently presented an SDN-based re-design of its core backbone where it is able to reach nearly 100% utilization of links under stringent QoS constraints [1]. SDN also enables the academic community to experiment with flexible as well as high performing equipments to test new or existing protocols. The OpenFlow protocol is the leading instantiation of the SDN concept at the moment and is supported by major manufacturers, e.g., HP, Juniper, IBM as well as open-source virtual switches like Open vSwitch [2], which is at the heart of cloud management solutions like OpenStack [3]. In particular, the SDN technology allows to optimize dynamically the placement of tasks in servers which are executed by virtual machines and of routes in networks.
Objective
We consider the problem of optimizing jointly the servers and network of a datacenter. A data center has a set of tasks to be executed by the servers (backup, computations, gaming, video streaming). Some of these tasks (backup, video streaming, computation with map reduce) generate some traffic which has to be routed throughout the data center networks.
We consider the problem of jointly affecting the tasks to servers and the network demands to routes in a network with limited capacity in order to minimize the time to carry out all the tasks.
Requirements: taste for algorithmics and network optimization.
References
[1] JAIN, S. et al, B4: experience with a globally-deployed software defined wan. In Proceedings of the ACM SIGCOMM 2013 conference on SIGCOMM (SIGCOMM '13). ACM, New York, NY, USA, 3-14.
Who?
Name: Ramon Aparicio-Pardo, Lucile Sassatelli
Mail: {first.last}@unice.fr
Telephone: 0492942772
Web page:
http://www.i3s.unice.fr/~raparicio/
http://www.i3s.unice.fr/~sassatelli/
Where?
Place of the project: I3S (UCA,CNRS)
Address: 2000, route des Lucioles – Sophia Antipolis
Team: SigNet group
Web page: http://signet.i3s.unice.fr/
What?
Pre-requisites if needed:
Knowledge on networking and routing
Knowledge of video streaming distribution
Programming skills (Matlab, Python, C++)
Experience in network simulations tools (ns3, omnet) would be welcome
Experience in virtualization environments would be welcome
Description:
I P video will progress up to the 82% in 2021 [1] . One of the main forces pushing up this video traffic growth is the explosion of live streaming that will grow 15-fold from 2016 to 2021, accounting for 13% of Internet video traffic by 2021 [1] . One of the major changes driving this rise of live streaming is the emergency of live social networks like Younow, Snapcha, Twitch, Periscope, Facebook live, streaming app Live.ly or Youtube Live. These platforms allow a variety of users from live broadcasting influencers up to video gamers (an increase of 50%, up to half-million, of monthly streamers for Twitch and YouTube Live in first half of 2017 [2] ) to produce, monetize and broadcast their own videos, that are consumed by an increasing number of viewers (more than 1.2 million of concurrent viewers this year [2] ). On the other hand, classical Video on Demand (VoD) by “Over The Top” and Internet-enabled set-top box providers will continue playing a major role in video delivery.
Therefore, the diversity and dynamicity of both live video and VoD connections in terms of contents, providers and consumers make challenging the design of a control plane for the video and network management. Network Function Virtualization (NFV), Software Defined Networking (SDN) and data analytics appears as key-enablers for such control plane. NFV and SDN allow fully exploiting the flexibility and scalability of virtualized resources. Data analytics implies to make use of Machine Learning techniques to exploit the Big Data sets associated to the video streams. In fact, the video sessions in this heterogeneous scenario of VoD and live video are characterized by the “5Vs” of Big Data: volume, variety, velocity, value, and veracity [3] . Then, the management of video delivery networks could benefit significantly from the collaboration between Big Data and SDN as suggested in [4]
The goal of this PFE is the development of a SDN platform suitable to test such a control plane management for video streaming flows. To do that, the Mininet network emulator [5] , OpenDayLight switch controller [6] and the TAPAS client [7] are proposed to build the testbed. Mininet is a network emulator that can work with virtual switches like OpenVSwitch (OVS) [8] using protocols to customize at fine grain the behavior of the network; the virtual switch can be instructed through a remote controller
(OpenDayLight, in our case) to apply the network management rules and algorithms designed to deal with the video streams.
The PFE will be composed by two main phases:
1. Development of the SDN platform based on Mininet and OpenDayLight.
2. Validation of the platform with naïve algorithms on testing VoD and, eventually, live video scenarios.
Useful Information:
This project could be followed by an internship, whose objective would be the development and testing in the SDN platform of the mentioned control plane integrating optimization algorithms with data analytics. An example of such joint kind of control plane can be found in [9] .
References :
[1] Cisco Visual Networking Index: Forecast and Methodology, 2016–2021, June 6, 2017
[2] https://blog.streamlabs.com/streamlabs-live-streaming-report-q217-53-growth-100m-twitch-youtube-crushing-it-1b9048efb4e2
[3] M. Kantardzic, Data Mining: Concepts, Models, Methods, and Algorithms, IWiley, IEEE, 2011.
[4] CUI, Laizhong, YU, F. Richard, et YAN, Qiao. When big data meets software-defined networking: SDN for big data and big data for SDN. IEEE network, 2016, vol. 30, no 1, p. 58-65.
[5] http://mininet.org/
[6] https://www.opendaylight.org
[7] https://github.com/ldecicco/tapas
[8] http://openvswitch.org
[9] Ramon Aparicio-Pardo, Lucile Sassatelli, “A Green Video Control Plane with Fixed-Mobile Convergence and Cloud-RAN,” in Proc. IEEE ComSoc-ACM SIGCOMM 29th Intl. Teletraffic Congress, ITC 2017, Genoa, Italy, Sept. 2017
Who?
Name: Eric MADELAINE
Mail: eric.madelaine@inria.fr
Telephone: +33 - 87 47 99 80
Web page: http://www-sop.inria.fr/members/Eric.Madelaine/
Where?
Place of the project: INRIA
Address: 2004 Rte des Lucioles Sophia-Antipolis
Team: Kairos
Web page: https://www.inria.fr/equipes/kairos
What?
Pre-requisites if needed: Java, knowledge of either distributed systems, or logical specification techniques, would be appreciated
Description:
We are creating a new framework for modeling, analysing, and verifying the behavior of large systems, composed of a number of
processes, running in parallel, distributed, or embedded environments. Our approach is meant to address "open" systems, e.g.
process composition architectures, generic algorithms, or parallel skeletons, where we want to analyse and prove global properties
without knowing the details of the basic components of the system. One of the original feature of the approach is to deal explicitely
with the data part of these systems, mixing finite algorithms for the control part, with SMT techniques for the data part, and avoiding
the usual state-explosion problem of classical verification approaches.
Such techniques can be applied in various application domains, and be used to give a behavioral semantics to various programming
languages or modeling formalisms. As an example we are currently working on parts of the on-board software of nano-satellites,
extending their existing data-less model with data-dependant properties.
We have already setup and published the theoretical basis of this framework, and also implemented a prototype algorithm for
the analysis part.
The PFE subject lies in the modeling part of our work: We want the student(s), after a short bibliographic period,
and getting basic knowledge with our formalism, to model a small set of new use-cases presenting features beyond the examples
we already have treated or published (different kind of communication or synchronisation, more complex data constraints,
and scaling up in size or complexity), build a behavioral model of these examples using our tools, and make a critical
analysis of these results.
Useful Information:
This PFE can be done by either 1 or 2 students. The set of use-cases adressed will be adapted (more examples, and
more complex features), if 2 students are working together.
If the project is successful, and if the student(s) are interested, this subject can lead to an intership in the team, and
eventually to a PhD subject.
Who? Luigi Liquori
Name:
Mail: Luigi.Liquori@inria.fr ,Telephone: 04 92 38 79 41 --- 04 92 38 71 93
Web page:
http://www-sop.inria.fr/members/Luigi.Liquori/
Where? Inria
Place of the project: Inria Sophia Antipolis Mediterranean
Address: 2004 route des lucioles, 06903 Sophia Antipolis
Team: KAIROS
Web page: https://team.inria.fr/kairos/
What?
Pre-requisites if needed: knowledge of distributed systems
Description:
In his seminal paper, Times, Clocks and the Ordering of Events in a Distributed System
http://research.microsoft.com/users/lamport/pubs/time-clocks.pdf
Leslie Lamport introduced the concept of ``one event is happening before another'' and formalize it as a partial order over events. He also presented a distributed algorithm for synchronizing a system of logical clocks used to totally order the events.
The aims of this PFE is a deep understanding of the Lamport algorithm and its implementation in Real Time MAUDE (RTMaude) a language and tool supporting the formal specification and analysis of real-time and hybrid systems.
http://heim.ifi.uio.no/peterol/RealTimeMaude/
The specification formalism in RTMaude is based on rewriting logic, emphasizes generality and ease of specification, and is particularly suitable to specify object-oriented real-time systems.
Useful Information: This PFE could lead to a Phd in the Kairos Team.
Who?
Name: Konstantin Avrachenkov
Mail: K.Avrachenkov@inria.fr
Telephone: 04 92 38 77 51
Web page: http://www-sop.inria.fr/members/Konstantin.Avratchenkov/me.html
Where?
Place of the project: Inria Sophia Antipolis, Lagrange building
Address: 2004 Route des Lucioles, 06902
Team: NEO
Web page: https://team.inria.fr/neo/presentation/
What?
Pre-requisites if needed: Good knowledge of probability theory;
some knowledge of machine learning or distributed computing is a plus
Description:
Unsupervised learning is a type of machine learning tasks that draw
inferences from datasets consisting of input data without labeled responses
(no training set is given). The most common unsupervised learning method
is cluster analysis, which is used for exploratory data analysis to find
hidden patterns or grouping in data. It is very common to represent
dataset as a weighted graph where the weights correspond to some proximity
measure of two data points. There is a number of classical approaches
to unsupervised learning such as K-means, Principal Component Analysis
or spectral clustering. However, most of the classical approaches are not
easily distributed to make computations among distributed agents or on
a cluster of processing units. We feel that statistical physics methods
such as Gibbs sampling and Generalized Potts Model are particularly well
suited to design light complexity, distributed unsupervised machine
learning methods.
A student or a group of students first will do a literature review,
identify (with my help) the most perspective approaches to distributed
unsupervised learning and implement one or two methods.
Useful Information: Some example references:
[1] Blatt, M., Wiseman, S. and Domany, E.
Clustering data through an analogy to the Potts model.
Advances in Neural Information Processing Systems, pp.416-422, 1996.
[2] Eaton, E. and Mansbach, R.
A Spin-Glass Model for Semi-Supervised Community Detection.
In Proceedings of AAAI 2012.
[3] Mezard, M. and Montanari, A., 2009. Information, physics, and computation.
Oxford University Press.
Who?
Name: Frederic Mallet
Mail: Frederic.Mallet@unice.fr
Telephone: 04 92 38 79 66
Web page: http://www-sop.inria.fr/members/Frederic.Mallet/
Where?
Place of the project: Batiment Lagrange INRIA
Address: 2004 route des Lucioles
Team: Kairos (I3S/INRIA)
Web page: https://team.inria.fr/kairos/
What?
Pre-requisites if needed: Strong motivation
Description:
This project should explore the different formalisms used to express uncertain discrete/Continuous behaviors (Markov Chains, Stochastic Petri Nets, Stochastic Hybrid Automata) and explore other formalisms to express logical time properties (Signal, Kieler Graph, CCSL). The goal is to propose a unification by proposing a logic that can express both uncertain behaviors (through probabilities, stochastic processes, distributions, stastical information) and logical time constraints.
The case studies will be in the context of connected objects and IoT for smart transportation systems, homes, factories.
Useful Information:
Logical Time: Leslie Lamport: Time, Clocks, and the Ordering of Events in a Distributed System. Commun. ACM 21(7): 558-565 (1978)
Statistical Model Checking
Statistical Model Checking – Plasma Lab
Yuxin Deng: Book Introduction by the Author: Semantics of Probabilistic Processes An Operational Approach. Bulletin of the EATCS 116(2015)
Kieler: http://www.rtsys.informatik.uni-kiel.de/en/research/kieler
Signal and Polychrony: http://www.irisa.fr/espresso/Polychrony/
CCSL:
Julien DeAntoni, Frédéric Mallet: TimeSquare: Treat Your Models with Logical Time. TOOLS (50) 2012: 34-41
http://www.sciencedirect.com/science/article/pii/S0167642317301740
Jing Liu, Ziwei Liu, Jifeng He, Frédéric Mallet, Zuohua Ding: Hybrid MARTE statecharts. Frontiers of Computer Science 7(1): 95-108 (2013)
Who?
Name: Dimitra Politaki (I3S - Inria)
Mail: dimitra.politaki@inria.fr
Name: Sara Alouf (Inria),
Mail: sara.alouf@inria.fr
Web page: http://www-sop.inria.fr/members/Sara.Alouf/
Where?
Place of the project: Inria Sophia Antipolis
Address: 2004 route des Lucioles, 06902 Sophia Antipolis
Team: NEO
Web page: https://team.inria.fr/neo/
What?
Description:
In the past decade, there has been an awareness raising concerning the energy cost and environmental footprint of the fastly growing information and communication technology (ICT) sector, including data centers, wireless cellular networks, etc. Green deployment strategies aim at reducing energy costs and the environmental impact of the ICT sector. Such deployment strategies include the use of renewable energy sources to power the equipment. The solar energy production is increasingly being considered when modeling computer and communication systems (see e.g. [1], [2]). There is a however a lack of a unified stochastic model for the solar energy to be used in the mathematical analysis of communication/computer systems. The recent study [3] develops such stochastic models for the solar power at the surface of the earth.
The rate of solar energy that arrives at a surface per unit of time and per unit area is the solar irradiance and is expressed in W/m2. The global irradiance I_G (t) accounts for all radiations arriving at a surface at time t except for the ground-reflected ones. During a clear sky day without any perturbations due to a change in the meteorological conditions, the solar irradiance exhibits a predictable pattern that is called the clear sky solar irradiance I_CS(t). Weather conditions affect the solar irradiance. The induced perturbations can be captured by a multiplicative noise denoted α(t) and called clear sky index in the literature. We have I_G(t) = α(t)I_CS(t).
In [3], we propose a 4-state semi-Markov process to model the clear sky index α(t). State sojourn times and clear sky index values in each state have phase-type distributions. We use per-minute solar irradiance data [4] to tune the model, hence we are able to capture small time scales fluctuations.
This model has been tuned and validated using data for the city of Los Angeles. The objective of this PFE is to investigate whether the same 4-state semi-Markov model can be tuned and validated using data related to another city. Each state in this model refers to a given weather condition. Can this model be universal? Specific tasks to be performed during this PFE are to:
- read and comprehend the relevant literature,
- retrieve per-minute measurements of the global solar irradiance for some locations on earth,
- find the empirical distributions of sojourn times and values in each state from the retrieved traces,
- test the robustness of the model by computing the autocorrelation function and the periodogram of generated trajectories,
References:
1. Ioannis Dimitriou, Sara Alouf, and Alain Jean-Marie. A markovian queueing system for modeling a smart green base station. In Proceedings of EPEW: European Performance Evaluation Workshop, volume 9272 of LNCS, pages 3–18, Madrid, Spain, August 2015.
http://dx.doi.org/10.1007/978-3-319-23267-6 1.
2. Giovanni Neglia, Matteo Sereno, and Giuseppe Bianchi. Geographical Load Balancing across Green Datacenters. ACM SIGMETRICS Performance Evaluation Review, 44(2):64–69, September 2016. http://dx.doi.org/10.1145/3003977.3003998.
3. Dimitra Politaki and Sara Alouf. Stochastic models for solar power. In Proceedings of EPEW: European Performance Evaluation Workshop, volume 10497 of LNCS, pages 282–297, Berlin, Germany, September 2017. http://dx.doi.org/10.1007/978-3-319-66583-2 18.
4. Afshin Andreas and Stephen Wilcox. Solar Resource and Meteorological Assessment Project (SOLRMAP): Rotating Shadowband Radiometer (RSR); Los Angeles, California (Data), 2012. http://dx.doi.org/10.5439/1052230.
Who?
Name: Alain Jean-Marie (Inria)
Mail: alain.jean-marie@inria.fr
Web page: http://www-sop.inria.fr/members/Alain.Jean-Marie/me.html
Name: Sara Alouf (Inria),
Mail: sara.alouf@inria.fr
Web page: http://www-sop.inria.fr/members/Sara.Alouf/
Where?
Place of the project: Inria Sophia Antipolis
Address: 2004 route des Lucioles, 06902 Sophia Antipolis
Team: NEO
Web page: https://team.inria.fr/neo/
What?
-Pre-requisites if needed: C++ programming.
-Description:
The marmoteCore software is a C++ environment for modeling with Markov chains. It consists in a reduced set of high-level abstractions for constructing state spaces, transition structures and Markov chains (discrete-time and continuous-time). It provides the ability of constructing hierarchies of Markov models, from the most general to the particular, and equip each level with specifically optimized solution methods. Currently, marmoteCore has the form of a C++ API providing tools for the construction and the analysis (numerical or by Monte-Carlo simulation) of Markov chains, together with a library of the Markov models most known in the literature.
The objective of this PFE is to implement quasi-birth-and-death (QBD) processes in marmoteCore. QBDs are a generalization of birth-and-death processes. A QBD process moves up and down in the levels similarly to birth-and-death processes, but unlike these the transitions between states may have a distribution that is not exponential but be described by a block-encoded distribution. The transition probability matrix of a QBD process has a tridiagonal block structure where the elements in the diagonal are matrices.
References:
1. Alain Jean-Marie and Issam Rabhi, “marmoteCore: a software platform for Markov modeling.” ROADEF 2016, 17th congress of the French Society for Operations Research and Decision, Compiègne, February 2016.
Who?
Name: Guillaume Urvoy-Keller, Quentin Jacquemart
Mail: urvoy@unice.fr, quentin.jacquemart@unice.fr
Telephone: +33 (0)4.92.94.27.64
Web page: http://www.i3s.unice.fr/~urvoy/ and http://www.qj.be/
Where?
Place of the project:
Address:
Laboratoire I3S
2000, route des Lucioles
Les Algorithmes
Bât. Euclide B - BP 121
06903 Sophia Antipolis Cedex
Team: I3S/SigNet project
Web page: http://signet.i3s.unice.fr/
What?
Pre-requisites if needed: networking
Description:
Fog computing constitutes a natural extension of the cloud computing models, where processing tasks can be spawned either on compute nodes within private data center (e.g., Openstack), or on public data centers (e.g., Amazon Web Services or Windows Azure), or on remote devices, e.g., Android or Raspberry Pis. A typical example are data stream applications that process data originated from sensors. These sensors are physically distributed, and the data is aggregated at the closest possible data-center. In addition, tasks can be offloaded/onloaded on sensors (in the form of Java applications or Linux containers), e.g., to perform data pre-processing. The recently started European project Prestocloud [1] is devising an architecture to enable such distributed application deployment, provisioning the VMs over the different data centers, distributing the tasks and monitoring the global platform performance to scale it up/down based on the devops constraints and the actual application load.
To enable a seamless cooperation between tasks, tasks migration and data streams aggregations, one needs to deploy a dynamic network overlay to interconnect the different sets of resources (in the large sense, i.e., VMs, containers, edge devices). A first prototype has been developed in the SigNet project [4]. It enables building a VPN mesh spanning over public (AWS, Azure) and private clouds. The objective of this PFE is to improve this prototype along these two directions:
1) Improving its resilience, testing its scalability and ability to connect multiple edge devices and building an adequate monitoring infrastructure.
2) Develop an active bandwidth measurement tool able to infer the actual bandwidth available on the different links that form the mesh. Preliminary results with Pathload [2] have been obtained [3], and we would like to extend our prototype to account for specific constraints of cloud provider environments, esp. multi-pathing, which is heavily used to distribute load within the cloud provider network.
References:
[1] http://prestocloud-project.eu/new/
[2] Jain, Manish, and Constantinos Dovrolis. "Pathload: A measurement tool for end-to-end available bandwidth." In Proceedings of Passive and Active Measurements (PAM) Workshop. 2002.
[3] A. Pagliari, Q. Jacquemart, G. Urvoy-Keller . "Towards non-intrusive measurements of available bandwidth for multi-cloud applicationsPoster" ACM IMC 2017
[4] A. Paglari, "Network as an On-Demand Service for Multi-Cloud Workloads "Master thesis, UNS, 2017.
Who?
Name: Quentin Jacquemart, Pierre-Antoine Vervier, Guillaume Urvoy-Keller
Mail: quentin.jacquemart@unice.fr, pierre-antoine_vervier@symantec.com, urvoy@i3s.unice.fr
Telephone: +33 4 92 94 26 95, +33 4 93 00 82 06, +33 4 92 94 27 64
Web page: http://www.qj.be/ and http://www.eurecom.fr/en/people/vervier-pierre-antoine and http://www.i3s.unice.fr/~urvoy/
Where?
Place of the project:
Address:
Laboratoire I3S
2000, route des Lucioles
Les Algorithmes
Bât. Euclide B - BP 121
06903 Sophia Antipolis Cedex
Team: I3S/SigNet project
Web page: http://signet.i3s.unice.fr/
What?
Pre-requisites if needed: networking, data processing
Description:
Even though the IPv6 address space is slowly gaining popularity [1], basic network-level security mechanisms appear to be lacking [2] due to lack of awareness and/or inappropriate production processes.
At the same time, one of the main motivations for an IPv6-based Internet is the belief that new networking paradigms, such as the Internet of Things (IoT), will drastically increase the number of devices connected to the network.
Unfortunately, over the past year, IoT devices have been increasingly targeted by tailored malware [3], resulting, at least, in the violation of privacy [4] and large-scale Internet attacks [5].
This PFE aims at exploring whether IoT devices are currently reachable through the IPv6 address space, in order to assess the IPv6 threat landscape with respect to IPv6 and IoT, e.g. to know if purpose-built “IoT honeypots” would be useful in defending against these attacks.
Staring from IPv4-wide scans of devices, such as those provided by ZMap [6], the student is expected to locate IPv4 subnets mostly containing IoT devices.
From there, the student shall locate the equivalent IPv6 subnets.
Based upon these findings, the student shall study the coherence of the security policies applied to both networks, and to the end-devices when contacted from the different address stack.
References
[1] IPv6 - Google, https://www.google.com/intl/en/ipv6/statistics.html
[2] J. Czyz, M. Luckie, M. Allman, M. Bailey. Don't Forget to Lock the Back Door! A Characterization of IPv6 Network Security Policy. NDSS 2016, 23nd Annual Network and Distributed System Security Symposium.
[3] D. Goodin. Brace yourselves—source code powering potent IoT DDoSes just went public. Ars Technica, February 2016. https://arstechnica.com/information-technology/2016/10/brace-yourselves-source-code-powering-potent-iot-ddoses-just-went-public/
[4] J.M. Porup. “Internet of Things” security is hilariously broken and getting worse. Ars Technica, January 2016. https://arstechnica.com/information-technology/2016/01/how-to-search-the-internet-of-things-for-photos-of-sleeping-babies/
[5] D. Goodin. Record-breaking DDoS reportedly delivered by >145k hacked cameras. Ars Technica, September 2016. https://arstechnica.com/information-technology/2016/09/botnet-of-145k-cameras-reportedly-deliver-internets-biggest-ddos-ever/
[6] The ZMap Project, https://zmap.io/
SUPERVISOR
Name: Giovanni Neglia
Name: Alain Jean-Marie Telephone: +33 (0) 4 92 38 7906
Mail: giovanni.neglia@inria.fr
Mail: alain.jean-marie@inria.fr
Web page: http://www-sop.inria.fr/members/Giovanni.Neglia/
LOCATION
Inria Sophia-Antipolis Méditerranée
Address: 2004 route des Lucioles, 06902 Sophia Antipolis Team: Neo, https://team.inria.fr/neo/
DESCRIPTION
This internship is in the framework of Neo’s research cooperation with Akamai Technologies, the world leader in the field of Content Delivery Networks.
Caching policies try, implicitly or explicitly, to estimate the popularities of the different contents, in order to store those more likely to be requested in the near future. [1] advocates that popularity estimation will play a fundamental role in future cellular networks, while [2] stresses the importance in such scenario to perform the estimation at the right level of the cache hierarchy.
Efficient estimation of popularites can be done with counting extensions [3] of Bloom filters. The specific variant in [4] is conceived to quantify request rates through an auto-regressive filter that can track also time-variant popularities. [5] suggests that the counting error floor (due to false positives) does not allow to evaluate correctly the popularity but for the most popular m contents, where m is the number of counters used. A similar remark on how memory affects estimation quality is in [6]. In [7], the request rate for content i is estimated simply as λi = 1/Ti where Ti is the most recent time-interval between two consecutive requests. [8] proposes a new caching policy relying on more sophisticated estimation techniques. [9] suggests a novel approach to implicitly estimate popularities that does not require additional memory. [10] presents an interesting framework to estimation techniques by looking at both the learning rate and the learning accuracy.
The student will read the papers below and compare the popularity estimation techniques proposed in these papers in terms of their algorithmic complexity as well as of their caching performance (e.g. their hit rate). The last aspect will be carried on by simulations using synthetic traffic traces, but also real ones provided by Akamai Technologies.
PRE-REQUISITES
The student should have good programming and analytical skills (probability, algorithms).
OTHER INFORMATION
This subject is research oriented and can be continued with a longer internship.
REFERENCES
[1] E. Zeydan, E. Bastug, M. Bennis, M. Abdel Kader, I. Alper Karatepe, A. Salih Er, and M. Debbah, “Big Data Caching for Networking: Moving from Cloud to Edge,” IEEE Communications Magazine, Volume: 54 Issue: 9, 2016
[2] M. Leconte, G. Paschos, L. Gkatzikis, M. Draief, S. Vassilaras, S. Chouvardas Placing, “Dynamic Content in Caches with Small Population,” in Proc. of IEEE INFOCOM 2016, San Francisco, USA
[3] A. Broder and M. Mitzenmacher, “Network applications of bloom filters: A survey,” Internet Math., vol. 1, no. 4, pp. 485–509, 2003. [Online]. Available: http://projecteuclid.org/euclid.im/1109191032 [4] G. Bianchi, N. d’Heureuse, and S. Niccolini, “On-demand time-decaying bloom filters for telemarketer detection,” Computer Communication Review, vol. 41, no. 5, pp. 5–12, 2011. [Online]. Available: http://doi.acm.org/10.1145/2043165.2043167
[5] G. Bianchi, K. Duffy, D. J. Leith, and V. Shneer, “Modeling conservative updates in multi-hash approximate count sketches,” in 24th International Teletraffic Congress, ITC 2012, Krakow, Poland, September 4-7, 2012, 2012, pp. 1–8.
[6] G. Neglia, D. Carra, P. Michiardi, Cache Policies for Linear Utility Maximization, Proc. of INFOCOM 2017, Atlanta, GA, USA, 1-4 May 2017
[7] M. Dehghan, L. Massoulie, D. Towsley, D. Menasche, and Y. Tay, “A Utility Optimization Approach to Network Cache Design,” in Proc. of IEEE INFOCOM 2016, San Francisco, USA.
[8] S. Li, J. Xuy, M. van der Schaarz, W. Li, “Popularity-Driven Content Caching,” in Proc. Of IEEE INFOCOM 2016, San Francisco, USA
[9] G. Neglia, D. Carra, M. D. Feng, V. Janardhan, P. Michiardi, and D. Tsigkari, “Access-time aware cache algorithms,” Proceeding of ITC 28, Würzburg, September 2016, a longer version is available as Inria Research Report RR-8886 at http://profs.sci.univr.it/∼carra/downloads/RR-8886.pdf.
[10] J. Li, S. Shakkottai, J. Lui, V. Subramanian, “Accurate Learning or Fast Mixing? Dynamic Adaptability of Caching Algorithms,” CoRR abs/1701.02214 (2017)
SUPERVISOR
Name: Giovanni Neglia
Eitan Altman
Telephone: +33 (0) 4 92 38 7906
Mail: giovanni.neglia@inria.fr eitan.altman@inria.fr
Web page: http://www-sop.inria.fr/members/Giovanni.Neglia/
LOCATION
Inria Sophia-Antipolis Méditerranée
Address: 2004 route des Lucioles, 06902 Sophia Antipolis Team: Neo, https://team.inria.fr/neo/
DESCRIPTION
Multiple choice questions (quizzes) are largely adopted worldwide for tests and examinations. With this methodology, students are called to answer TRUE or FALSE, or, more generally, choose one right answer over k possible answers to demonstrate their knowledge on a specific topic. It is not a rare case that examiners give to students in advance the complete list of all the possible quizzes (questions, the right answer and all the wrong answers) while, on the examination day, students are asked to provide the correct responses to a subset of them. This is the case of German and Austrian examinations for Driver License, or the many public competitive exams in Italy.
The purpose of this project is to evaluate if it is possible to use simple machine learning algorithms to correctly guess most of the answers at the examination, without no preliminary study of the subject. The student will carry on experiments on real datasets. He/she is also invited to develop a mathematical analysis of the results.
PRE-REQUISITES
The student should have good programming and analytical skills (probability, algorithms) as well as knowledge of the most common classifier systems.
OTHER INFORMATION
This subject is research oriented. It could lead to a longer internship.
Who?
Name: Damien Saucez
Mail: damien.saucez@inria.fr
Telephone: +33 4 89 73 24 18
Web page: https://team.inria.fr/diana/team-members/damien-saucez/
Where?
Place of the project: Inria Sophia Antipolis
Address: 2004 route des Lucioles, 06902 Sophia Antipolis
Team: Diana
Web page: https://team.inria.fr/diana/
What?
Description:
Industrial systems such as valve actuators, monitoring systems, or energy control
need specific communication mechanisms with real time, robustness, and safety
constraints and the failure of a communication can lead to catastrophes. This is why
factories and industrial units deploy a multitude of independent communication systems,
each one with different hardware and protocols as each system is designed specifically
for its usage and requirements.
With the advent of Industry 4.0 and thanks to the recent advances in commodity network
system, we are expecting to witness a progressive convergence of the communication systems
toward off-the-shelf standards coming from the Internet world. The drawback of Internet
technologies is that they don’t provide any form of guaranty such as delay, loss or safety
however, they provide bandwidth that are typically several order of magnitude higher than
industrial system and are cheap.
In this work, we will design a Software Defined Network (SDN) network to allow industrial
networks composed of multiple distinct physical systems to be merged on one single
multipurpose high speed Ethernet/IP based backbone. The student will first characterise
the main elements of industrial networks and protocols (e.g., deadline, delay constraints,
bandwidth, resiliency level). They will then propose a mapping model that defines how
the embedding of multiple independent real-time systems can be done in one unified
best-effort system. Finally, the student will validate their work either through formal
proof, numerical evaluation, simulations, or a real implementation.
Useful Information:
This work is a subject of internship and we do not expect the student to complete the
entire work within the PFE. Instead it would be followed by an internship if the candidate
is excellent.
This work is part of a multi-year large project with a leading industrial partner.
Knowledge in network protocols and architecture, and in embedded systems is a plus
but is not an absolute requirement.
Advisors : Frédéric Giroire and Joanna Moulierac
Emails : frederic.giroire@cnrs.fr, Joanna.moulierac@unice.fr
This PFE is also co-supervised by Jérémie Leguay, head of the Network and Traffic Optimization team at Huawei’s French Research Center (FRC) in Paris.
Laboratory : INRIA Sophia Antipolis, COATI team-project, https://team.inria.fr/coati/
Description :
A network slice is a virtual network which is implemented on top of a physical network in a way that creates the illusion of the slice tenant of operating its own dedicated physical network. A virtual link between virtual nodes can be realized as a multi-hop path with reserved bandwidth on all physical links constituting the path. A virtual node can implement specific network functions that can be installed on physical node (e.g., firewalls, DPI probes). Virtual links and virtual nodes can be easily established by an Software Defined Network (SDN) controller or network orchestrator. Network slicing functionalities are foreseen to be a key component of 5G to provision isolated and personalized network services to different applications (e.g., connected vehicles, smart factories) [2].
The first step is a thorough study of the research literature [1] on network slicing with a focus on optimization methods used to map virtual networks on top physical resources. The idea is to learn how to model the problem of the embedding a single slice and formulate it as an ILP. And to understand from the literature how the problem is generally decomposed and efficiently solved.
The second step is the implementation of the optimization model within a solver such as ILOG CPLEX (available in Python, Matlab, C++, Java).
References :
[1] Vassilaras, S., Gkatzikis, L., Liakopoulos, N., Stiakogiannakis, I. N., Qi, M., Shi, L., ... & Paschos, G. S. (2017). The Algorithmic Aspects of Network Slicing. IEEE Communications Magazine, 55(8), 112-119.
[2] Huawei’s whitepaper on Slicing:
http://www-file.huawei.com/~/media/CORPORATE/PDF/white%20paper/5g-service-guaranteed-network-slicing-whitepaper.pdf
Who?
Name: Fabrice Huet
Mail: fabrice.huet@unice.fr
Telephone: +33 4 92 94 26 91
Web page: https://sites.google.com/site/fabricehuet/
Where?
Place of the project: I3S Laboratory, Sophia Antipolis
Address: 2000 route des lucioles
Team: Scale
Web page: https://scale-project.github.io/
What?
Pre-requisites if needed: Motivation for reading papers and learning new programming languages
Description:
A lot of data is represented in graphs which are often too large to be loaded and processed on one machine.
A common approach to perform computation on large graphs is to partition them into smaller sub-graphs. These
sub-graphs can then be processed by different machines. Depending on the algorithm (PageRank, Shortest Path...), some
communication will take place during execution. Partitioning the graph is a problem in itself, with already a lot of
different solution. They can be classified into two different approaches, whether we place the edges (aka vertex-cut based)
the vertices (edge-cut based) on the different machines. The partitioning can have a major impact on the execution time
of an algorithm so using the best one is of paramount importance. However, there is no single best partitioner. It depends
on a lot of different parameters, including the graph considered and the algorithm executed.
In a previous work (https://hal.inria.fr/hal-01401309), we have investigated which metrics were the most significant for
choosing the best partitioning using a linear model. We want to pursue this work in the context of machine learning and
see if we can achieve better results. Overall, we want, given a tuple (graph, algorithm, environment), predict which
partitioner will lead to the best performance. Of course the graph, the algorithm and the environment will be
represented by carefully chosen metrics.
The aim of this PFE will be twofold
- Get familiar with machine learning and choose a techniques suited for the problem
- Perform experiments to build a model and evaluate its quality
Useful Information:
https://hal.inria.fr/hal-01401309
https://spark.apache.org/graphx/
Who?
Name: Julien Deantoni
Mail: julien.deantoni@inria.fr
Telephone: +33492387766
Web page: http://www.i3s.unice.fr/~deantoni/
Where?
Place of the project: INRIA Lagrange
Address: 2004 route des Lucioles
Team: Kairos
Web page: https://team.inria.fr/kairos/
What?
Pre-requisites if needed: Curiosity, open mind-ness, knowledge about operational semantics and/or metamodeling is a plus
Description:
Nowadays, it is possible to use meta environments to specific the syntax and operational semantics of Domain Specific Languages (http://gemoc.org/studio.html, https://github.com/gemoc/MODELS2017Tutorial). The execution is only possible only when the input model (or program) is "complete", meaning that there is enough information to execute it. This means that this program cannot be executed:
int main(){
if ( ){
return 0;
}else{
return 1;
}
}
However, when specifying a complex system, starting at a high level of abstraction (https://www.polarsys.org/capella/) it is usual to manipulate partial models in the sense that they do not specify all the information to make it deterministically executable. Such non deterministic execution could be a great help to have a first understanding of the models under development (in the simple example above, one could decide to randomly choose the "if" or "then" branch of the conditional statement). In a first step, the student will study and classify the different ways for a model to be "partial" and how it impacts the operational semantics of a language. In a second step, she/he will propose one or several (theoretical or technical) ways to allow execution of such partial models and the condition of validity for such execution.