Complexity of Ambient Software:from Dynamic Composition to Distributed, Contextual, Autonomous, Large-scale Execution

November, 28 2016

Frédéric Le Mouël University of Lyon - INSA Lyon

@flemouel

Habilitation Defense

Agenda

- Biography

- Middleware & Ambient Intelligence

- Towards Dynamic, Scalable, Autonomous Middleware

- Conclusions & Perspectives

2

Career Path

3

1998 2002 2016201420122010200820062004

PhD University of Rennes 1 IRISA / INRIA Solidor

Assistant Professor EMN Nantes

OCM

Associate Professor INSA Lyon

INRIA CITI / Ares - Amazones - Dynamid

Invited Professor Shanghai Jiao Tong University

Computer Science Department

Teaching

4

1998 2002 2016201420122010200820062004

Object-Oriented Programming Software Engineering

Compilation

Operating Systems Networks

System & Network Administration

Dynamic Web Middleware

Software Engineering

Distributed Computing Ambient Intelligence

~ 275h/year 3-4-5y ‘Grande Ecole’

University International

Master

Research

5

1998 2002 2016201420122010200820062004

Laptop

Middleware Mobile Computing

Distributed Computing

Smart Cities

Autonomic & Social Computing

Mobile Cloud Computing

Intelligent Transportation Systems

Internet of Things

Context-awareness Adaptation

Home Automation

Service-Oriented Approaches Ambient Intelligence

Offloading

Projects

6

2016201420122010200820062004

European IP 7 Amigo

(WP leader - 220k€)

ANR ACI KAA

(member)

ARC INRIA Priam

(member)

BQF INSA Smart Chappe (leader - 20k€)

Rhône-Alpes Region COOPERA

(leader - 40k€)

Rhône-Alpes Region ARC 7

(co-leader - 32k€)

VALEO CIFRE

(leader - 110k€)

Security

& TrustInternet

of ThingsAutonomic

ITS

Ambient

Intelligence

Smart

City

Animation

7

2016201420122010200820062004

Internship Officer

Service

Laboratory / Department / CS Councils

Animation

8

2016201420122010200820062004

Teaching

SPE-T Program Leader (INSA / SJTU / EM)

Double PhD Degree (INSA / SJTU)

Animation

9

2016201420122010200820062004

Research

Dynamid Team Creation & Animation

Open Source Open Data

Laboratory Scientific Seminars Digital Communication

Rhône-Alpes Region ARC 7 board & axe Responsible

PhD co-supervising

10

2016201420122010200820062004

Noha Ibrahim - « Spontaneous Integration of Services in Pervasive Environments »

(National & Europe)

Amira Ben Hamida - « A Middleware for a Contextual and Autonomic Deployment of

Services in Pervasive Environments » (Europe)

Roya Golchay - « From Mobile to Cloud : Using Bio-inspired Algorithms for Collaborative Offloading  »

(National)

Trista Lin - « Smart Parking : Network, Infrastructure and

Urban Service » (Regional)

Marie-Ange Lèbre - « Impact of a Local and Autonomous Decision

on Intelligent Transportation Systems at different Scales »

(CIFRE)

Middleware is the link

Research Domains

Middleware is a third-party computer software allowing to abstract, publish and interconnect services to exchange and process information.

13

[Le Mouël 2016]

Research Domains

Ambient Intelligence is an IT vision focusing on an efficient and ergonomic support to human well-being and society concerns - anywhere, anytime - by using communicating, invisible, non-intrusive everyday-life

embedded objects.

14

[Le Mouël 2016]

The Beginning

15

Heterogeneity

Single Machine

API

HardwareIssues

ApplicationDomain

Evolution

SoftwareChallenges

Multi standards

Gateways

Internet Providers

1990

Impacts

The Breakthrough

16

Heterogeneity

Single Machine

API

HardwareIssues

ApplicationDomain

Evolution

SoftwareChallenges

Multi standards

Gateways

Internet Providers

JiniWeb Services

SOA

2000

REST

Impacts

Dynamism

17

Heterogeneity

Dynam

ism

Single Machine

M2M

API

Mobile ObjectsVANET

Home Automation

HardwareIssues

ApplicationDomain

BANET Sensors

ComplexityEvolution

SoftwareChallenges

Multi standards

Gateways

Internet Providers

Service Composition

Context-Oriented

Impacts

Scalability

18

Heterogeneity

Dynamism

Scalability

Single Machine

M2M

User Social GroupAPI

Mobile Objects

Context-oriented

VANET

Home Automation

HardwareIssues

ApplicationDomain

CRAN

Cloud Computing

Data Centers

Smartphone Fleet Deployment

BANET Sensors

ComplexityEvolution

SoftwareChallenges

Multi standards

Gateways

Internet Providers

Discovery

Cloudlets

Message-Oriented Middleware

Event-based Processing

Impacts

Autonomy

19

Heterogeneity

Dynamism

Scalability

Autonomy

Single Machine

M2M

User Social Group

Society

API

Mobile Objects

Context-oriented

VANET

Home Automation

HardwareIssues

ApplicationDomain

CRAN

Cloud Computing

Big Data

Data Centers

Smartphone Fleet Deployment

Drone Fleets

Autonomous Vehicles

BANET Sensors

Service Robotics

Event-based Processing

Internet of Things

ComplexityEvolution

SoftwareChallenges

Multi standards

Gateways

Internet Providers

Active Assisted Living

Message-oriented Middleware

Discovery

Cloudlets

Machine Learning

Self-Managed Distributed Systems

Impacts

20

Heterogeneity

Dynamism

Scalability

Autonomy

Single Machine

M2M

User Social Group

Society

API

Mobile Objects

Context-oriented

VANET

Home Automation

HardwareIssues

ApplicationDomain

CRAN

Cloud Computing

Big Data

Data Centers

Smartphone Fleet Deployment

Drone Fleets

Autonomous Vehicles

BANET Sensors

Service Robotics

Deep Learning

Event-based Processing

Internet of Things

Self-managed distributed systems

ComplexityEvolution

SoftwareChallenges

Multi standards

Gateways

Internet Providers

Active Assisted Living

Message-oriented Middleware

Discovery

Cloudlets

1 2

3

Impacts

1. How to deal with dynamism?

2. How to overcome scalability issues?

3. How to distribute decision-making?

Research Contributions

21

Dynamism

- Why is dynamism a challenge?

- Services / Devices Heterogeneity ↗

- Mobility / Adaptation Needs ↗

- Complementary Proposals

- Contextual Spontaneous Service Composition

22

23

Ambient Environment

24

Execution Environment

Heterogenous

Not Reliable

Mobile

25

Application

Execution Flow

26

Where?

ApplicationAmbient

Environment

27

What? Fine-grained Method-level

Coarse-grained Bundle & Service-level

[Golchay 2016]

[Ibrahim 2008, Ben Hamida 2010]

28

When?

Automatic Proximity Cloud

Spontaneous Semantic

Service Composition

[Golchay 2016]

[Ibrahim 2008]

29

How?

Graph Cut with Multiple Destinations

Collaborative Decision Cache

Graph Coloring ACO Algorithms

[Golchay 2016]

[Ben Hamida 2010, Golchay 2016]

30

~10 devices ~50-100 services

[Ben Hamida 2010]

[Golchay 2016]

[Ibrahim 2008]

Tim

e (m

s)

Node Number

Execution Time

AxSel with adaptation AxSel without adaptation

Efficient ~25-55ms

Guidelines - Dynamism

- Engineering Granularity - good offloading performance

- Environment Volatility - good reactivity

- Service & Semantics - bad scalability?

31

1. How to deal with dynamism?

2. How to overcome scalability issues?

3. How to distribute decision-making?

Research Contributions

32

Scalability

- Why is scalability a challenge?

- Services / Devices ↗

- Discovery, Information Dissemination?

- One proposal

- Pri-REIN - Prioritized Event Matching in Pub/Sub

33

[Qian 2015]

34

Subscriber S1 channel.subscribe( (topic = « Temperature », value = [25,35]), (topic = « Location », value = [7,13]) )

Publisher P1 channel.publish( (topic = « Temperature », value = 28), (topic = « Location », value = 12) )

Matching

Time?

35

Subscribers

Topic Ranges

Geometric

Point Enclosure

Problem

36

Index Structure: H-Tree - Interval-based - Tagging non-matching subscribers

REIN

37

38

Pri-REINS3 will be served before S1

+ Matching Time intervals

39

Guidelines - Scalability

- Message-oriented Middleware - asynchronous

- Distributed Publish/Subscribe - efficient, QoS

- Engineering - genericity

- Content Relevancy?

40

1. How to deal with dynamism?

2. How to overcome scalability issues?

3. How to distribute decision-making?

Research Contributions

41

Autonomy

- Why is distributing decision-making a challenge?

- Partial Knowledge

- Local vs Global Optimization

- One solution for one use-case

- Ant-inspired Guidance Service in Smart City

42

[Lèbre 2016]

43

Short-Path

Problem

Guidance

Service

Fuel Consumption

Travel / Waiting Time

Optimization

44

Local vs Global

Optimization Decision

Partial Data

45

When moving

& service connected,

what data to exchange?

Path modification

decision?

Ant-inspired Distributed Decision-making

46

ACO (Ant Colony Optimization)

Vehicle Ant

Pheromone

Evaporation

47

Data exchange:

Pheromone map of vehicle m :

Travel time at the maximum allowed speed

Travel time measured by m at time t 0

The more is high, the more the information is old

Pheromone evaporation:

Pheromone validity time

Evaporation gradient

0.5 init for unknown places

PKP KPP PDLAIS PPE CS

11,6 %

1,8 %

3,7 %

7,9 %

3,7 %

Travel Time Gain

k-path without

pheromone

k-path with

pheromone

Autonomous Intersections

Local Pheromone

Centralized Solution

Normal

Traffic

48

49

k-path without

pheromone

k-path with

pheromone Autonomous Intersections

Local Pheromone

Centralized Solution

Earthquake

PKP KPP PDLAIS PPE

80 %

40 %

20 %20 %

Arrival Percentage

PKP KPP PDLAIS PPE CS

98 %85 %

78 %89 %

78 %

Accident

Arrival Percentage

Guidelines - Autonomy

- Local decisions - can be globally efficient

- Local decisions - robustness

- Greatly depends on the use-case

- Smart City: traffic ≠ parking

50

Tradeoff in favor

of local decisions

[Lin 2015, Lèbre 2016]

Concluding Remarks

- Technology is here!

- Middleware Dynamism, Scalability, ok!

- Smart Middleware: Natural Receptacle for Autonomy!

- Engineering

51

Concluding Remarks

- Why are not Middleware & Ambient Intelligence in production ?

- (when Middleware & Cloud Computing are main trend!)

52

& Internet of Things& Vehicular Networks

53

Heterogeneity

Dynamism

Scalability

Autonomy

Single Machine

M2M

User Social Group

Society

API

Mobile Objects

Context-oriented

VANET

Home Automation

HardwareIssues

ApplicationDomain

CRAN

Cloud Computing

Big Data

Data Centers

Smartphone Fleet Deployment

Drone Fleets

Autonomous Vehicles

BANET Sensors

Service Robotics

Deep Learning

Event-based Processing

Internet of Things

Self-managed distributed systems

SoftwareChallenges

Multi standards

Gateways

Internet Providers

Active Assisted Living

Message-oriented Middleware

Discovery

Cloudlets

ComplexityEvolution

2000

Impacts

54

Heterogeneity

Dynamism

Scalability

Autonomy

Single Machine

M2M

User Social Group

Society

API

Mobile Objects

Context-oriented

VANET

Home Automation

HardwareIssues

ApplicationDomain

CRAN

Cloud Computing

Big Data

Data Centers

Smartphone Fleet Deployment

Drone Fleets

Autonomous Vehicles

BANET Sensors

Service Robotics

Deep Learning

Event-based Processing

Internet of Things

Self-managed distributed systems

SoftwareChallenges

Multi standards

Gateways

Internet Providers

Active Assisted Living

Message-oriented Middleware

Discovery

Cloudlets

ComplexityEvolution

2006 2007

iPhoneFacebook

Impacts

Perspectives

- User & Society acceptance ↗

- Hot Research Issues:

- IoT Security

- IoT Automatic Provisioning & Deployment

- IoT Safety with Distributed Behavior Checking

55

Perspectives

- Planetary-scale Middleware & Distributed Systems

- Interconnecting Smart Cities

- Internet of People

56

BirdsWater

Understanding

Earth

Macro-behavior

Distributed

really anywhere

Future

57

Heterogeneity

Dynamism

Scalability

Autonomy

Single Machine

M2M

User Social Group

Society

API

Mobile Objects

Context-oriented

VANET

Home Automation

HardwareIssues

ApplicationDomain

CRAN

Cloud Computing

Big Data

Data Centers

Smartphone Fleet Deployment

Drone Fleets

Autonomous Vehicles

BANET Sensors

Service Robotics

Deep Learning

Event-based Processing

Internet of Things

Self-managed distributed systems

SoftwareChallenges

Multi standards

Gateways

Internet Providers

Active Assisted Living

Message-oriented Middleware

Discovery

Cloudlets

ComplexityEvolution

Impacts

Future

58

Heterogeneity

Dynamism

Scalability

Autonomy

Single Machine

M2M

User Social Group

Society

API

Mobile Objects

Context-oriented

VANET

Home Automation

HardwareIssues

ApplicationDomain

CRAN

Cloud Computing

Big Data

Data Centers

Smartphone Fleet Deployment

Drone Fleets

Autonomous Vehicles

BANET Sensors

Service Robotics

Deep Learning

Event-based Processing

Internet of Things

Self-managed distributed systems

SoftwareChallenges

Ethics

Humanity

Privacy by design

Affective Computing

Neural Connectivity

Human Enhancements

Quantum Computers

Avatars

Augmented Reality

Nano Robots

Smart Dust

Multi standards

Gateways

Internet Providers

Active Assisted Living

Message-oriented Middleware

Discovery

Cloudlets

ComplexityEvolution

Ethical Software Life-cycle

Impacts

Thanks - The Dynamid Team

59

Julien

Nicolas

MarkNoha

Amira

François

Roya

Trista

Marie-Ange

Stefan

Questions?

frederic.le-mouel@insa-lyon.fr

@flemouel

http://www.le-mouel.net

http://dynamid.citi-lab.fr

60

Bibliography[Ibrahim 2008] N. Ibrahim, Spontaneous Integration of Services in Pervasive Environments, PhD Thesis, INSA Lyon, Lyon, France, September 2008.

[Ben Hamida 2010] A. Ben Hamida, AxSeL : un intergiciel pour le déploiement contextuel et autonome de services dans les environnements pervasifs, PhD Thesis, INSA Lyon and ENSI, University of La Manouba, Lyon, France, February 2010.

[Qian 2015] S. Qian, J. Cao, F. Le Mouël, M. Li, and J. Wang, Towards Prioritized Event Matching in a Content-based Publish/Subscribe System. In Proceedings of the 9th ACM International Conference on Distributed Event-Based Systems (DEBS'2015), pp. 116–127, Oslo, Norway, June 2015.

[Lin 2015] T. Lin, Smart Parking : Network, Infrastructure and Urban Service, PhD Thesis, University of Lyon, INSA Lyon, Lyon, France, December 2015.

[Golchay 2016] R. Golchay, From Mobile to Cloud : Using Bio-Inspired Algorithms for Collaborative Application Offloading, PhD Thesis, University of Lyon, INSA Lyon, Lyon, France, January 2016.

[Lèbre 2016] Marie-Angle Lèbre, De l’impact d’une décision locale et autonome sur les systèmes de transport intelligent à différentes échelles, PhD Thesis, University of Lyon, INSA Lyon, Lyon, France, January 2016.

[Le Mouël 2016] Frédéric Le Mouël, Complexité du logiciel ambient : de la composition dynamique à l’exécution distribuée, contextuelle, autonome et large-échelle, Habilitation Thesis, University of Lyon, INSA Lyon, Lyon, France, November 2016.

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— “Family” Extract of “Ellyn’s Elements of Style” 07/08/2010