nokia bell labs - npatixeuil/m2r/uploads/main/nokia.2016.2.pdf · nokia bell labs internship @...

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© 2016 Nokia1 Public

Nokia Bell Labs

Internship @ Nokia Paris Saclay – UPMC master RES (M2)

• Marc-Olivier Buob, Massimo Gallo, Fabien Mathieu, Ludovic Noirie

• Bell Labs

• 21-11-2016

© 2016 Nokia2

Outline

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1. Nokia Bell Labs – General presentation

2. Internships in Algorithms

3. Internships in Networks

4. Internships in Internet of Things

5. Conclusion

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© 2016 Nokia3

Outline

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5. Conclusion

© 2016 Nokia4

“Through research and innovation, Nokia Bell Labs is changing the way

people connect with the world”

https://www.bell-labs.com/

Nokia Bell Labs – General presentation

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Nokia Bell Labs: Technology that Transforms

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© 2016 Nokia5

Bell Labs History: Unparalleled disruptive innovationNokia Bell Labs – General presentation

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© 2016 Nokia6

95%Focused on

5+ year future

1Game-

changer per lab/year

MURRAY HILL (NJ)

ANTWERP

STUTTGART & MUNICH

SHANGHAI

PARIS

DUBLIN

ISRAEL

CAMBRIDGE

SILICON VALLEY

1000+Innovators

CHICAGO

ESPOO

BUDAPEST

AALBORG

WROCLAW

BEIJING

Bell Labs Scope & Scale: A global innovation engineNokia Bell Labs – General presentation

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© 2016 Nokia7

Nokia Paris-SaclayNokia Bell Labs – General presentation

• Paris-Saclay location

– 20 km south of Paris, Nozay, Essonne(Paris-Saclay agglomeration)

• Research activities

– Security

– Algorithms

– Analytics

– Network

– IoT control

– III-V devices

– Optical networking & transmission

– E2E mobile networks

Public

https://networks.nokia.com/fr/l-innovation-en-france

© 2016 Nokia8

Outline

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5. Conclusion

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© 2016 Nokia9COPYRIGHT © 2013 ALCATEL-LUCENT. ALL RIGHTS RESERVED.

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Models and algorithms

GraphsMobilePropag

Alarmanonymcontent

Auctionvoting

Measure&control

Space-timepatterns

Game theory

© 2016 Nokia10

Efficient Pattern Matching for Large Networks

Marc-Olivier Buob

[email protected]

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© 2016 Nokia11

NEW NETWORKS, NEW PATTERNS, NEW TASKSDESIGNING SCALABLE DATA STRUCTURES AND ALGORITHMS IS THE KEY TO ACHIEVE HIGH SPEED PROCESSING

Key Goal: Create optimized data structures to achieve high

speed processing.

– Pattern matching is a frequent operation : routing,

firewalling, monitoring.

– More and more data must be processed.

– The game is changing : NFV and SDN.

– Existing algorithms are not always well-suited nor scalable.

Key Disruption: scalable processing.

– Finding the convergence between networking and

language theory.

– Offer new functionalities based on these fundations :

network forecasting, efficient SDN routing, improved

firewalling.

ENABLE SCALABLE PROCESSING – PROVIDE NEW FUNCTIONALITIES

10X

© 2016 Nokia12

TOWARD COMPLEX PATTERN MATCHINGDESIGNING SCALABLE DATA STRUCTURES AND ALGORITHMS IS THE KEY TO ACHIEVE HIGH SPEED PROCESSING

PERFORM COMPLEX PATTERN MATCHING ON NETWORKS (105x)

GENERALIZED ROUTING TABLES AND

FIREWALLS

Standard tries only operates on linear

sequences.

They are not well-suited to catch complex

patterns (e.g. regular expressions).

GRAPH PATH LABEL AUTOMATONS

A novel data structure able to test a set of

R regular expressions in a single pass

instead of evaluating each of them

independently.

Complexity : O(W) instead of O(W.R).

W = length of the word (32 in Ipv4)

R = #rules (400k routes in IPv4 networks)

10X

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© 2016 Nokia13

Deep Learningfor Text Classification

Maria Laura Maag

[email protected]

© 2016 Nokia14

Goal� Apply Deep Learning (e.g neural networks) to analyze and classify text.

� State of the Art of Related Work

� Benchmark different techniques in a realistic setting.

� Further results can be forseen (proposal of new enhancements / techniques).

Skills

� Java, Python

� Machine learning

� Knowledge of Natural Language Processing (NLP)

� Techniques of clustering/classification (un)supervised

� MySQL, base de données big data

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© 2016 Nokia15

Detection of AnomaliesThrough Space-Time Correlations

Maria Laura Maag

[email protected]

© 2016 Nokia16

Goal

� Study how to incorporate space-time datas (network topology, timestamps) to an existing tool thatclusters informations from bug reports (based on machine learning)

� Enhance the existing tool

� Definition and implementation of a strategy base on space-time datas.

� Adaptation of existing learning algorithms to integrate the new data.

� Compare with related work

Skills

� Java, Python

� Machine learning

� Knowledge of Natural Language Processing (NLP)

� Techniques of clustering/classification (un)supervised

� MySQL, base de données big data

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© 2016 Nokia17

Space recognition via Deep Learning

Dimitrios Milioris

Nokia Bell Labs

© 2016 Nokia18

Train a DCNN to classify images from indoor/outdoor space Deep Convolutional Neural Network (DCNN)

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© 2016 Nokia19

Space feature learningrecognize the learned space features, with no need of additional assistive technology

Look into the capability in space feature learning and recognition, even under severe appearance changes.

© 2016 Nokia20

Expectation from the intern

Propose a robust approach for indoor navigation/wayfinding using DCNN.

Demonstrate that DCNN also has a potential capability in space feature learning and recognition, even under severe appearance changes.

Introduce a DCNN based approach to look into the visual similarity and visual distinctiveness of interior space.

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© 2016 Nokia21

Outline

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5. Conclusion

© 2016 Nokia22

Programmable Data PlaneMassimo Gallo

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© 2016 Nokia23

A programmable data planeInternships in Networks

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Specialized hardware for advanced L4-L7 packet processing

– Expensive, limited flexibility, and no room for consolidation

NFV for advanced L4-L7 packet processing

– I/O bottlenecks, scarce integration with hardware, little room for consolidation

Climb: Click for middleboxes

– Modularity, flexibility, reconfiguration, and hardware acceleration of L2-L7

Firewall

VPN Gateway

Proxy Server

Load Balancer

© 2016 Nokia24

Internships in NetworksNetwork Function Virtualization

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• Context: Network Function Virtualization main advantage is easy scale up/down of deployed service

• In the Network department, the intern will :

– Explore the possibility of deploying CliMB on top of existing lightweight Virtual Machines supporting Click, or Linux containers to simplify network functions’ scale up/down

– Design a mechanism for state sharing across different CliMB instances

– Achieve high-profile academic publication based on the results from this semester project.

• Contact: Massimo [email protected]

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© 2016 Nokia25

Internships in NetworksTraffic Generator

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• Context: Prototypes implementing new protocols are difficult to test due to the lack of flexible traffic generators


– Analyze state-of-the-art tools, techniques and best practices.

– Design of a more flexible traffic generator able to generate high speed traffic for non-standard protocol

– The intended design will ideally build on top of existing modular router architecture to develop the flexible framework needed to build a Protocol Independent Traffic generator.


• Contact: Massimo [email protected]

© 2016 Nokia26

Machine Learning for channel scheduling

Fabio Pianese

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© 2016 Nokia27

Internship in NetworksMachine Learning

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• Context: Machine learning can assist human designers in developing heuristics based on statistical insights that can be automatically extracted from the system. Resource allocation problems such as cellular scheduling offer an interesting opportunity for the integration of data-driven insights


– Experiment with a machine learning platform that uses the ns3/LENA simulator

– Explore the space of practical data-driven heuristics for application-aware scheduling.


• Contact: Fabio [email protected]

© 2016 Nokia28

Bitcoin rewarding systemFabio Pianese

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© 2016 Nokia29

Internships in NetworksBitcoins

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• Context: Bitcoin network is designed to incentivize participation rewarding with Bitcoins the first node that solves a proof-of-work challenge. However, the reward is decreasing asymptotically to zero to ensure a maximum amount of Bitcoins exist, discouraging nodes to participate to the system.


– Experiment with alternative incentive models for Bitcoins

– Bitcoin client will be extended to support new reward system.


• Contact: Fabio [email protected]

© 2016 Nokia30

Recommendation systemZied Ben Houidi

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© 2016 Nokia31

Internships in NetworksRecommendation systems

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• Context: Users today are lost in an ever increasing tangle of Web content from diverse sources with only few means to learn fast what is relevant. We invented a new approach for relevant web content discovery to complement the existing solutions


– Improve system’s scalability

– Add more features to it (e.g; natural language processing to identify topics, and automated clustering of articles into stories).

– Evaluate a solution for https traffic.

• Contact: Zied Ben [email protected]

© 2016 Nokia32

Outline

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5. Conclusion

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© 2016 Nokia33

General context of IoTInternships in Internet of Things

Internet of Things (IoT) growth …

• Unprecedented growth of connected devices: 20~46 billions(*) devices expected in 2020(*) The Future X Network book, §11 – The future of the Internet of Things

• Promise of a new era of digital services

… but limited usage of IoT devices

• People buy connected devices for a limited IoT service experience

• Association between connected devices is limited / restricted (lack of skills, unawareness, silos, …)

How to better leverage available connected devices in personalized services?

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© 2016 Nokia34

IoT control & service managementInternships in Internet of Things

Nokia Internal Use

Need for digital assistance in IoT: first focus on communication control between IoT devices

• Majord’Home paper @ ManSDN/NFV’14

• SD-LAN paper @ ITC’15

• …

We also address IoT services

• IoT service modeling, characterization and classification focusing on physical interfaces:IoT service catalog & recommendation

• We assume a SD-LAN-like solution to set up the connections between connected objects

ManSDN/NFV’14, ITC’15

Smart spaces(IoT data plane)

Majord’Homesolution(IoT networkconfiguration layer )

Applicationplane

CO Controller(s) NE Controller(s)

App. #1 App. #N

NE #1 NE #jCO #i CO #M

Software-Defined LAN Controller(s)

Connected Objects (COs) Network Elements (NEs)CO #1 NE #K

IoT servicemanagementlayer

IoT service management functions

IoT service catalog

IoT service recommendation

Other functions

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© 2016 Nokia35

Some of our publications…Internships in Internet of Things

Nokia Internal Use

1. Mathieu Boussard, Dinh Thai Bui, Richard Douville, Nicolas Le Sauze, Ludovic Noirie, Pierre Peloso, Rémi Varloot, Martin Vigoureux, The Majord'Home: a SDN Approach to Let ISPs Manage and Extend Their Customers' Home Networks, ManSDN/NFV’14 - http://www.cnsm-conf.org/2014/proceedings/mansdn-program-detail-inner.html

2. Mathieu Boussard, Dinh Thai Bui, Laurent Ciavaglia, Richard Douville, Michel Le Pallec, Nicolas Le Sauze, Ludovic Noirie, Serge Papillon, Pierre Peloso, Francesco Santoro, Software-Defined LANs for Interconnected Smart Environments, ITC’15 - http://dx.doi.org/10.1109/ITC.2015.33

3. Mathieu Boussard, Nicolas Le Sauze, SDN in LANs: Programming the Network to Secure IoT Traffic, IEEE SDN newsletterMay 2016 - http://sdn.ieee.org/newsletter/may-2016/sdn-in-lans-programming-the-network-to-secure-iot-traffic

4. Dinh Thai Bui, Richard Douville, Mathieu Boussard, Supporting Multicast and Broadcast Traffic for Groups of Connected Devices, IEEE NetSoft 2016 - http://dx.doi.org/10.1109/NETSOFT.2016.7502441

5. Dinh Thai Bui, Kahina Aberkane, A Generic Interface for Open vSwitch, IEEE NetSoft 2016 -http://dx.doi.org/10.1109/NETSOFT.2016.7502442

6. Michel Le Pallec, Mohamed Omar Mazouz and Ludovic Noirie, Physical-Interface-Based IoT Service Characterization, IoT’16 - http://www.iot-conference.org/iot2016/program/

7. Ludovic Noirie, Michel Le Pallec, Nesrine Ammar, Towards Automated IoT Service Recommendation, submitted as a demo paper...

© 2016 Nokia36

Full OpenFlow and Open vSwitchInternships in Internet of Things

• Context: Interface and switch (even Open vSwitch) designs are constrained by standards (IEEE, IETF...)

• In the IoT-C department, the intern will :

– Push the limits of Software-Defined Network (SDN) to support any bit stream formats (even unknown) on network interfaces.

– Investigate and develop an extended-OVS design to enable the configuration of different data frame formats.

– Propose OpenFlow extensions to handle such capability.


– The work will lead to internal technical report(s), publications and proof of concept.

• Contact: Richard [email protected]

Public

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© 2016 Nokia37

Tunneled IoT protocolInternships in Internet of Things

• Context: Lot of different narrow band protocols used by the IoT (Bluetooth LE, Zigbee, Weave, etc.)

• In the IoT-C department, the intern will:

– Allow the access of IoT remotely transparently as if they are local for the both extremities.

– Investigate and develop a method to tunneled such protocols at the appropriate layer.


– The work will lead to internal technical report(s), publications and proof of concept.

• Contact: Richard [email protected]

Public

© 2016 Nokia38

Virtual function to identify/control/manage the physical interfaces of a connected device

Internships in Internet of Things

• Targets

– Identify the physical interfaces involved in IoT services

– Identify the network flows associated with each physical interface of a connected device for a fine-grained control


– Study the state-of-the-art on existing standards / methods to describe objects with their physical interfaces

– Define methodology / tools to identify the physical interfaces from existing connected object description + the data flows generated by these interfaces

– Define solutions to identify traffic flows from different physical interfaces in an aggregated traffic flow

– Validate these solutions (simulations/experiments…)

• Contact: Michel Le [email protected]

Public

Universal IoT service characterization

Physical interfaces = ?

IoT service signature = n ×

Adaptive IoT servicemodeling

Example of application:IoT service matching

CindyDave

AliceBob

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© 2016 Nokia39

Cognitive Networking ManagementInternships in Internet of Things


– Develop innovative autonomic mechanisms and protocols to conduct and document appropriately a set of experiments (functional and performance tests, proof of concepts validation...).

– Design the infrastructure of the Cognitive Networking Management platform in line with the user, operational and technical requirements. The intern will support its proposal with an analysis of state-of-the-art tools, techniques and best practices.

– The work will lead to internal technical report(s), publications and contributions to standardization (IETF, IRTF, ETSI).

• Contact: Laurent [email protected]

Public

© 2016 Nokia40

Intent-Based NetworkingInternships in Internet of Things


– Investigate how to identify the user’s and operator’s intentions from structured and unstructured set of information in order to translate automatically these intents into network configuration instructions.

– Artificial Intelligence (AI), information modeling and semantics, policy-based management, programming of NFV- and SDN-based infrastructure.

– Application on specific IoT networks scenario and use cases with evaluation and demonstration of the proposed approaches.

– The work will lead to internal technical report(s), publications and contributions to standardization (IETF, IRTF, ETSI).

• Contact: Laurent [email protected]

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© 2016 Nokia41

Outline

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5. Conclusion

© 2016 Nokia42

Internship in Nokia Bell Labs @ Paris SaclayConclusion

• How to know about Nokia internships ?

– Nokia web site => “careers”:

• http://company.nokia.com/en/careers/open-jobs

• http://company.nokia.com/en/careers/open-jobs/legacy-alcatel-lucent-jobs

(URL may change with Nokia integration…) (look at English ad French proposals)

• Note: internship proposals, thesis proposals (CIFRE), etc…

– The presenters

• [email protected]




– And your professors…

• They have contacts with several Alcatel-Lucent Bell Labs researchers…

Public

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