Nokia Bell Labs

© 2016 Nokia

1 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

Outline

1. Nokia Bell Labs – General presentation 2. Internships in Algorithms

3. Internships in Networks

4. Internships in Internet of Things

5. Conclusion

© 2016 Nokia 3

Outline

Public

1. Nokia Bell Labs – General presentation 2. Internships in Algorithms

3. Internships in Networks

4. Internships in Internet of Things 5. Conclusion

“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

Nokia Bell Labs: Technology that Transforms

© 2016 Nokia 5

Bell Labs History: Unparalleled disruptive innovation Nokia Bell Labs – General presentation

Public

95 ^%

Focused on 5+ year

future

1

Game- 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 engine

Nokia Bell Labs – General presentation

© 2016 Nokia 7

Nokia Paris-Saclay

Nokia Bell Labs – General presentation

•

Paris-Saclay location

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

•

Research activities

– Algorithms – Analytics – Network – IoT control – III-V devices

– Optical networking & transmission – E2E mobile networks

Public

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

Outline

1. Nokia Bell Labs – General presentation 2. Internships in Algorithms

3. Internships in Networks

4. Internships in Internet of Things

5. Conclusion

© 2016 Nokia

9 COPYRIGHT © 2013 ALCATEL-LUCENT. ALL RIGHTS RESERVED. 9

Models and algorithms

Graphs Mobile Propag Alarm

anonym content

Auction voting Measure

&control

Space-time patterns

Game theory

Efficient Pattern Matching for Large Networks

Marc-Olivier Buob

marc-olivier.buob@nokia.com

© 2016 Nokia 11

NEW NETWORKS, NEW PATTERNS, NEW TASKS

DESIGNING 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

TOWARD COMPLEX PATTERN MATCHING

DESIGNING SCALABLE DATA STRUCTURES AND ALGORITHMS IS THE KEY TO ACHIEVE HIGH SPEED PROCESSING

PERFORM COMPLEX PATTERN MATCHING ON NETWORKS (10

x)

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

© 2016 Nokia 13

Deep Learning

for Text Classification

Maria Laura Maag

Maria_Laura.Maag@nokia.com

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

© 2016 Nokia 15

Detection of Anomalies

Through Space-Time Correlations

Maria Laura Maag

Maria_Laura.Maag@nokia.com

Goal

Study how to incorporate space-time datas (network topology, timestamps) to an existing tool that clusters 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

© 2016 Nokia 17

Space recognition via Deep Learning

Dimitrios Milioris Nokia Bell Labs

Train a DCNN to classify images from indoor/outdoor space

Deep Convolutional Neural Network (DCNN)

© 2016 Nokia 19

Space feature learning

recognize 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.

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.

© 2016 Nokia 21

Outline

Public

1. Nokia Bell Labs – General presentation 2. Internships in Algorithms

3. Internships in Networks

4. Internships in Internet of Things 5. Conclusion

Programmable Data Plane

Massimo Gallo

© 2016 Nokia 23

A programmable data plane Internships in Networks

Public

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

Internships in Networks

Network 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 Gallo

© 2016 Nokia 25

Internships in Networks Traffic Generator

Public

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

• In the Network department, the intern will :

– 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.

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

• Contact: Massimo Gallo massimo.gallo@nokia-bell-labs.com

Machine Learning for channel scheduling

Fabio Pianese

© 2016 Nokia 27

Internship in Networks Machine Learning

Public

•

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

•

In the Network department, the intern will :

Experiment with a machine learning platform that

uses the ns3/LENA simulator

–

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

–

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

•

Contact: Fabio Pianese

Bitcoin rewarding system

Fabio Pianese

© 2016 Nokia 29

Internships in Networks Bitcoins

Public

•

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.

• In the Network department, the intern will : – Experiment with alternative incentive models for

Bitcoins

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

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

• Contact: Fabio Pianese fabio.pianese@nokia-bell-labs.com

Recommendation system

Zied Ben Houidi

© 2016 Nokia 31

Internships in Networks Recommendation systems

Public

• 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

•

In the Network department, the intern will :

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 Houidi

Outline

1. Nokia Bell Labs – General presentation 2. Internships in Algorithms

3. Internships in Networks

4. Internships in Internet of Things

5. Conclusion

© 2016 Nokia 33

General context of IoT

Internships 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?

Public

IoT control & service management Internships in Internet of Things

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

Majord’Home solution (IoT network

Application plane

App. #1 App. #N

Software-Defined LAN Controller(s) IoT service

management layer

IoT service management functions IoT service

catalog

IoT service recommendation

Other functions

© 2016 Nokia 35

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 newsletter May 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...

Full OpenFlow and Open vSwitch Internships 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 :

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.

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

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

•

Contact: Richard Douville

© 2016 Nokia 37

Tunneled IoT protocol

Internships 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.

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

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

• Contact: Richard Douville

richard.douville@nokia-bell-labs.com

Public

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

•

In the IoT-C department, the intern will:

– 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

Universal IoT service characterization Physical

interfaces = ?

IoT service signature = n ×

Adaptive IoT service modeling

Example of application:

IoT service

Cindy Dave

Alice Bob

© 2016 Nokia 39

Cognitive Networking Management Internships in Internet of Things

•

In the IoT-C department, the intern will:

– 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 Ciavaglia

Public

Intent-Based Networking

Internships in Internet of Things

•

In the IoT-C department, the intern will:

– 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 Ciavaglia

© 2016 Nokia 41

Outline

Public

1. Nokia Bell Labs – General presentation 2. Internships in Algorithms

3. Internships in Networks

4. Internships in Internet of Things 5. Conclusion

Internship in Nokia Bell Labs @ Paris Saclay Conclusion

•

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

• Marc-Olivier.Buob@nokia-bell-labs.com

• Massimo.Gallo@nokia-bell-labs.com

• Fabien.Mathieu@nokia-bell-labs.com

© 2016 Nokia 43

Confidential