Habilitation Defence - Complexity of Ambient Software 2016

Transcript

1. 1.

   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
2. 2.

   Agenda - Biography - Middleware & Ambient Intelligence - Towards

   Dynamic, Scalable, Autonomous Middleware - Conclusions & Perspectives 2
3. 3.

   Career Path 3 1998 2002 2016 2014 2012 2010 2008

   2006 2004 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
4. 4.

   Teaching 4 1998 2002 2016 2014 2012 2010 2008 2006

   2004 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
5. 5.

   Research 5 1998 2002 2016 2014 2012 2010 2008 2006

   2004 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
6. 6.

   Projects 6 2016 2014 2012 2010 2008 2006 2004 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 & Trust Internet of Things Autonomic ITS Ambient Intelligence Smart City
7. 7.

   Animation 7 2016 2014 2012 2010 2008 2006 2004 Internship

   Officer Service Laboratory / Department / CS Councils
8. 8.

   Animation 8 2016 2014 2012 2010 2008 2006 2004 Teaching

   SPE-T Program Leader (INSA / SJTU / EM) Double PhD Degree (INSA / SJTU)
9. 9.

   Animation 9 2016 2014 2012 2010 2008 2006 2004 Research

   Dynamid Team Creation & Animation Open Source Open Data Laboratory Scientific Seminars Digital Communication Rhône-Alpes Region ARC 7 board & axe Responsible
10. 10.

    PhD co-supervising 10 2016 2014 2012 2010 2008 2006 2004

    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)
11. 11.

    None
12. 12.

    Middleware is the link

13. 13.

    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]
14. 14.

    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]
15. 15.

    The Beginning 15 Heterogeneity Single Machine API Hardware Issues Application

    Domain Evolution Software Challenges Multi standards Gateways Internet Providers 1990 Impacts
16. 16.

    The Breakthrough 16 Heterogeneity Single Machine API Hardware Issues Application

    Domain Evolution Software Challenges Multi standards Gateways Internet Providers Jini Web Services SOA 2000 REST Impacts
17. 17.

    Dynamism 17 Heterogeneity Dynam ism Single Machine M2M API Mobile

    Objects VANET Home Automation Hardware Issues Application Domain BANET Sensors Complexity Evolution Software Challenges Multi standards Gateways Internet Providers Service Composition Context-Oriented Impacts
18. 18.

    Scalability 18 Heterogeneity Dynam ism Scalability Single Machine M2M User

    Social Group API Mobile Objects Context-oriented VANET Home Automation Hardware Issues Application Domain CRAN Cloud Computing Data Centers Smartphone Fleet Deployment BANET Sensors Complexity Evolution Software Challenges Multi standards Gateways Internet Providers Discovery Cloudlets Message-Oriented Middleware Event-based Processing Impacts
19. 19.

    Autonomy 19 Heterogeneity Dynam ism Scalability Autonom y Single Machine

    M2M User Social Group Society API Mobile Objects Context-oriented VANET Home Automation Hardware Issues Application Domain CRAN Cloud Computing Big Data Data Centers Smartphone Fleet Deployment Drone Fleets Autonomous Vehicles BANET Sensors Service Robotics Event-based Processing Internet of Things Complexity Evolution Software Challenges Multi standards Gateways Internet Providers Active Assisted Living Message-oriented Middleware Discovery Cloudlets Machine Learning Self-Managed Distributed Systems Impacts
20. 20.

    20 Heterogeneity Dynam ism Scalability Autonom y Single Machine M2M

    User Social Group Society API Mobile Objects Context-oriented VANET Home Automation Hardware Issues Application Domain 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 Complexity Evolution Software Challenges Multi standards Gateways Internet Providers Active Assisted Living Message-oriented Middleware Discovery Cloudlets 1 2 3 Impacts
21. 21.

    1. How to deal with dynamism? 2. How to overcome

    scalability issues? 3. How to distribute decision-making? Research Contributions 21
22. 22.

    Dynamism - Why is dynamism a challenge? - Services /

    Devices Heterogeneity ↗ - Mobility / Adaptation Needs ↗ - Complementary Proposals - Contextual Spontaneous Service Composition 22
23. 23.

    23 Ambient Environment

24. 24.

    24 Execution Environment Heterogenous Not Reliable Mobile

25. 25.

    25 Application Execution Flow

26. 26.

    26 Where? Application Ambient Environment

27. 27.

    27 What? Fine-grained Method-level Coarse-grained Bundle & Service-level [Golchay 2016]

    [Ibrahim 2008, Ben Hamida 2010]
28. 28.

    28 When? Automatic Proximity Cloud Spontaneous Semantic Service Composition [Golchay

    2016] [Ibrahim 2008]
29. 29.

    29 How? Graph Cut with Multiple Destinations Collaborative Decision Cache

    Graph Coloring ACO Algorithms [Golchay 2016] [Ben Hamida 2010, Golchay 2016]
30. 30.

    30 ~10 devices ~50-100 services [Ben Hamida 2010] [Golchay 2016]

    [Ibrahim 2008] Time (ms) Node Number Execution Time AxSel with adaptation AxSel without adaptation Efficient ~25-55ms
31. 31.

    Guidelines - Dynamism - Engineering Granularity - good offloading performance

    - Environment Volatility - good reactivity - Service & Semantics - bad scalability? 31
32. 32.

    1. How to deal with dynamism? 2. How to overcome

    scalability issues? 3. How to distribute decision-making? Research Contributions 32
33. 33.

    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. 34.

    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. 35.

    35 Subscribers Topic Ranges Geometric Point Enclosure Problem

36. 36.

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

    REIN
37. 37.

    37

38. 38.

    38 Pri-REIN S3 will be served before S1 + Matching

    Time intervals
39. 39.

    39

40. 40.

    Guidelines - Scalability - Message-oriented Middleware - asynchronous - Distributed

    Publish/Subscribe - efficient, QoS - Engineering - genericity - Content Relevancy? 40
41. 41.

    1. How to deal with dynamism? 2. How to overcome

    scalability issues? 3. How to distribute decision-making? Research Contributions 41
42. 42.

    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. 43.

    43 Short-Path Problem Guidance Service Fuel Consumption Travel / Waiting

    Time Optimization
44. 44.

    44 Local vs Global Optimization Decision Partial Data

45. 45.

    45 When moving & service connected, what data to exchange?

    Path modification decision?
46. 46.

    Ant-inspired Distributed Decision-making 46 ACO (Ant Colony Optimization) Vehicle Ant

    Pheromone Evaporation
47. 47.

    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
48. 48.

    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. 49.

    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
50. 50.

    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]
51. 51.

    Concluding Remarks - Technology is here! - Middleware Dynamism, Scalability,

    ok! - Smart Middleware: Natural Receptacle for Autonomy! - Engineering 51
52. 52.

    Concluding Remarks - Why are not Middleware & Ambient Intelligence

    in production ? - (when Middleware & Cloud Computing are main trend!) 52 & Internet of Things & Vehicular Networks
53. 53.

    53 Heterogeneity Dynam ism Scalability Autonom y Single Machine M2M

    User Social Group Society API Mobile Objects Context-oriented VANET Home Automation Hardware Issues Application Domain 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 Software Challenges Multi standards Gateways Internet Providers Active Assisted Living Message-oriented Middleware Discovery Cloudlets Complexity Evolution 2000 Impacts
54. 54.

    54 Heterogeneity Dynam ism Scalability Autonom y Single Machine M2M

    User Social Group Society API Mobile Objects Context-oriented VANET Home Automation Hardware Issues Application Domain 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 Software Challenges Multi standards Gateways Internet Providers Active Assisted Living Message-oriented Middleware Discovery Cloudlets Complexity Evolution 2006 2007 iPhone Facebook Impacts
55. 55.

    Perspectives - User & Society acceptance ↗ - Hot Research

    Issues: - IoT Security - IoT Automatic Provisioning & Deployment - IoT Safety with Distributed Behavior Checking 55
56. 56.

    Perspectives - Planetary-scale Middleware & Distributed Systems - Interconnecting Smart

    Cities - Internet of People 56 Birds W ater Understanding Earth Macro-behavior Distributed really anywhere
57. 57.

    Future 57 Heterogeneity Dynam ism Scalability Autonom y Single Machine

    M2M User Social Group Society API Mobile Objects Context-oriented VANET Home Automation Hardware Issues Application Domain 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 Software Challenges Multi standards Gateways Internet Providers Active Assisted Living Message-oriented Middleware Discovery Cloudlets Complexity Evolution Impacts
58. 58.

    Future 58 Heterogeneity Dynam ism Scalability Autonom y Single Machine

    M2M User Social Group Society API Mobile Objects Context-oriented VANET Home Automation Hardware Issues Application Domain 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 Software Challenges 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 Complexity Evolution Ethical Software Life-cycle Impacts
59. 59.

    Thanks - The Dynamid Team 59 Julien Nicolas Mark Noha

    Amira François Roya Trista Marie-Ange Stefan
60. 60.

    Questions? frederic.le-mouel@insa-lyon.fr @flemouel http://www.le-mouel.net http://dynamid.citi-lab.fr 60

61. 61.

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