project team mascotte common project i3s (cnrs/université nice sophia-antipolis) inria...

Project Team MASCOTTE

Common projectI3S (CNRS/Université Nice Sophia-Antipolis)

INRIA Sophia-Antipolis Méditerranée

(CRC CORSO with Orange Labs)

J-C. BERMOND 19/01/2009

Project team Evaluation13-14/11/2007 - Paris

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MEMBRES 01/2009

• J-C. Bermond CNRS• D. Coudert INRIA• O. Dalle Univ. Nice-Sophia• F. Giroire CNRS• F. Havet CNRS• J. Moulierac Univ. Nice-Sophia• S. Pérennes CNRS• B. Reed CNRS• H. Rivano CNRS• M. Syska Univ. Nice-Sophia

• Collaborateurs externesA. Ferreira CNRS /CEEJ. Galtier France Télécom R&DA. Laugier France Télécom R&D

11 PhD

2 Post-Doc

2 Ingénieurs Experts

2 secrétaires temps partiel

Visiteurs

IP IST/FET Aeolus

IST/FET CRESCCO

CRC CORSO I & II

ACI SI Presto

Cost 293 Graal

Multi-layer network design, provisioning

Traffic grooming

Constrained routing

Reconfiguration

IP IST/FET Aeolus

IST/FET CRESCCO

CRC CORSO I & II

ACI SI Presto

Cost 293 Graal

Multi-layer network design, provisioning

Traffic grooming

Constrained routing

Reconfiguration

ANR JC-JC OSERA

ARC CARMA

CRC CORSO I & II

I/F Regal Mobidyn

Medium access

Energy awareness

Network design

Evolving graphs

Joint LP/simulation

ANR JC-JC OSERA

ARC CARMA

CRC CORSO I & II

I/F Regal Mobidyn

Medium access

Energy awareness

Network design

Evolving graphs

Joint LP/simulation

IP IST/FET Aeolus

Color Arigatoni

P2P semantics

Resource discovery

Resource placement

Simul large systems

Model Driven Archi.

IP IST/FET Aeolus

Color Arigatoni

P2P semantics

Resource discovery

Resource placement

Simul large systems

Model Driven Archi.

IP IST/FET Aeolus

CRC CORSO I & II

ACI SI Presto

Connectivity

Satellite boarded nets

Reliability

Shared Risk Resource Groups combinatorics

IP IST/FET Aeolus

CRC CORSO I & II

ACI SI Presto

Connectivity

Satellite boarded nets

Reliability

Shared Risk Resource Groups combinatorics


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A multilayer networked world

Software development

• Mascotte’s on-going developments– Optimization: MASCOPT– Simulation: OSA

• Goals– To provide experimental tools

• Validation and verification of theoretical results

• Experiments on virtual/future configurations– To address important but seldom considered

issues• Enforce/ensure reproducibility• Support experimental methodology


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5

European collaborations

7 contracts (3 french)

29 partners (1/3 french)

Students and post-docs exchanges

Bilateral visits

Common experiments (recent)


6

Worldwide collaborations

3 formalized collaborations

6 North america3 South americaIsraëlChina (recent)

3 PhD studentsSeveral post-docs


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Visibility (for ministries)

2004 2005 2006 2007 2008

PhD Thesis 1 1 3 2 1

H.D.R. 1

Journals 18 10 12 22 28

Conferences 15 32 22 37 34

Chapters

Books 1 1 2

SoftwaresPROSIT/ASIMUT OSA

MASCOPT


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Visibility (scientific animation)

• Participation in 18 Editorial Boards

• Participation in more than 50 Program Committees

• Organization of 17 scientific events– Next coming

Nice, september 10 - 12 Marseille, march 3 - 7


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4 years research perspectives

Design and deployment of access networks


Routing and network provisioningRouting and network provisioning

Sim

ulat

ion

of h

ypot

hetic

net

wor

ksS

imul

atio

n of

hyp

othe

tic n

etw

orks

Overlay networksOverlay networks

Routing and network provisionning

• Multilayer networks (Embedding of virtual topologies with congestion and dilation)

• Dynamic Grooming • Compact Dynamic Routing • (Contract with Alcatel Lucent Bell Belgium)• Reconfiguration and Rerouting– Group communication• RELIABLITY and FAULT TOLERANCE(probabilistic failures)


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• Reliability and security (wireless, FTTH, …)• Optimization access protocols WiFi/WImax• Wireless mesh networks for backhaul

communications– AP (Access Point) location– Crosslayer routing and call scheduling

• Sustainable networking – Model energy consumption of all components/techno– Sustainable development or OpEx minimization

• Unknown demands (mobile and/or versatile users)


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Project RAISOM

P2MPP2MP

P2P

P2MPP2MP

P2P

• Réseaux de Collecte IP Sans Fil Optimisés• PME’s: 3-ROAM & Avisto

Modeling and simulation of hypothetic networks

• OSA Project– Started late 2005 (see http://osa.inria.fr/ & INRIA gforge)– New Software Engineering techniques

• Based on Fractal (INRIA Sardes, INRIA ADAM, FT R&D)

– Advanced Support for Simulation Methodology• Implement good practices (take time to)

• Large-scale Distributed Simulation – Challenge: 106 nodes and 109 events– ARC just submitted: deployment (with INRIA ADAM and

INT ACMES)– Workshop on Net-Centric Simulation @ SIMUTools

• Work on FP7 Proposal with 10 European partners• European part of a Worldwide GRID for Dist. Simulation


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http://osa.inria.fr/

Projet Mascotte05/07/07

SPREADS – Safe P2P-

based REliable

Architecture for Data Storage(ANR –

Programme Télécoms

[2007-2010])

SPREADS: Safe P2P-based REliable Architecture for Data

Storage• ANR Project, 3 Years Project, 5 teams

– 2008-2010– INRIA Sophia, UbiquitousStorage, INRIA/LIP6

REGAL, Eurecom, LACL

• Mascotte's tasks– Simulations

• OSA Project– Optimizations/Trade-off– Experimentations

• Private GRID• GRID5000

MERCI !

Traffic grooming

Group C requests (lightpaths) on the same container (wavelength)

• Idea: Use ADM only at initial and terminal nodes of requests (lightpaths)

• Objective: Minimize the number of ADM’s

Example: without grooming

• G= undirectional C4

• All-to-all communications– All the couples of

requests

• 12 requests =>6 wavelengths and 12 ADMs

Example: with grooming

• (0,1) (1,0)• (1,2) (2,1)• (2,3) (3,2)• (3,0) (0,3)• (0,2) (2,0)• (1,3) (3,1)

• (0,1) (1,0)• (1,2) (2,1)• (2,3) (3,2)• (3,0) (0,3)• (0,2) (2,0)• (1,3) (3,1)

Example: with grooming cont'd

• N=4 and C=3

Grooming (Results)

• Modelization using graph decomposition and design theory

• Inapproximability for the path and cycle within a

factor log n

• Results for All to All set of requests:

– Constructions for the unidirectional cycle C= 2, 3, 4, 5, 6

– Constructions for the path C=2

– Constructions for the bidirectional cycle

– Approximations for undirectional ring

– Two period grooming

• Mobile clients– 802.11– 802.16e ?

• Backhaul– Hertzian

directionnal– 802.16– 802.11a

Two distinct worlds

Call scheduling

• Interferences in the network modeled by a distance of interference dI = (1+Δ) dT

• Set of compatible calls = calls not interfering

• Formulation as a MILP problem difficult to solve

• Approximations using max coloring ?• Distributed algorithms ?• Case of gathering (also for sensor

networks)

Gathering in radio networks

Objective & Results

• Goal: minimize the number of steps (i.e. set of admissible calls) to gather (collect) information in a central vertex

• Two cases with or without buffering• General 4-approximation algorithm• Almost solved for paths

– lower bound + 1, when dT = 1, dI= 1

– lower bound + 3, when dT = 1, dI= 2 or 3

• Solved for trees dT = 1, dI= 1

Fault tolerance

• Kinds of failure: – Broken pipe

• E.g. between Japan and Corea (earthquake)

– Blocked amplifiers• Detailed example : onboard satellite network

– …

• Shared Risk Resource Groups (SRRG)– Combinatorial model for multiple failures


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Goal: Route simultaneously via disjoint paths signals (inputs) to amplifiers

(outputs)• Switches with 4 links • Amplifiers can fail (permanently!)


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0 2

1

3

0 2

1

3

20

1

3

0 2

1

3

Objective :minimize the number of switches Objective :minimize the number of switches

Example: 4 signals, 4 failures, 8 amplifiers

Results

• Before 2003 : - Exact results for small values of k (number of failures)- Methodology to construct solutions

- Good practical results (Astra 1K save 50

sitches) but plouf • 2003-2007

- Extensions p signals among k inputs

- Priority signals- Asymptotic results (New parameter Robustness)


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Another type of failure at INRIA


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Shared Risk Resource Group (SRRG)

• One failure may affect two disjoint paths simultaneously

• No 1+1 protection for request CD on upper layer

Shared Risk Resource Group (SRRG)

S T

X

Path between S and T with minimum number of colors

Set of resources failing simultaneously with a single failure

Complexity Results on SRRG


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Polynomials Algorithms

• Span 1 = Each risk/color formed a connected subgraph

• Few colors of span larger than 1

Software development

Mascopt: Java Mascotte Optimization Library :

Combinatorial Optimization Network Oriented.

Goals• Abstraction of CO algorithms• Experiments and Validation• Education (Master 2)Publish results with data and program

Challenges• Interface with simulation• KISS : Keep it simple and small

RootsFollow-up to previous Mascotte project: PORTO (RNRT with FT R&D and

ALCATEL)

Simulation: OSA Open Simulation Architecture

Goals• Support experimental

methodology• Framework for experimental

``good practices'‘• Open platform• Recent software engineering

techniques

Challenges• Study new simulation techniques

(with 1 million P2P nodes ?)• Enforce model reuse

RootsFollow-up to previous Mascotte

projects: ASIMUT (used by CNES), PROSIT

(used in OSSA EU project)Project team Evaluation13-14/11/2007 - Paris

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Graph theory

• Graph decomposition• Structural properties of digraphs• New parameter: Robustness (expansion)• Coloring problems: related to frequence

assignments• Example L(2,1) labellings

– If d(u,v) = 1 |f(u) – f(v)| > 1– If d(u,v) = 2 |f(u) – f(v)| > 0

L(2,1) colorings with Δ2 + 1 colors for Δ large enough

old conjecture (probablistic and discharging method)


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