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Utilisation du cloud dans les systèmes intelligent

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Les "systèmes intelligents" constituent la nouvelle génération de systèmes embarqués, qui, en s'appuyant sur les caractéristiques de robustesse et de déterminisme de leurs aînés, se connectent au cloud afin d'enrichir l'expérience utilisateur, qu'il s'agisse d'entreprises (collectant des données ou surveillant des systèmes par exemple), de particuliers (à la maison ou dans un contexte médical, ou bien dans la voiture) ou bien d'autres machines (dans le cas de systèmes automatisés à grande échelle). Le cloud et particulièrement Windows Azure fourni les vecteurs de communication et les moyens de stocker massivement des données et de les traiter, déchargeant ainsi les installations locales et donc rendant le déploiement de ses systèmes plus simple. Cette session, riche en exemples concrets, présentera la stratégie qui est celle de Microsoft autour du futur des systèmes embarqués, et leur connexion au cloud, ainsi que les technologies et les partenariats mis en oeuvre pour accélérer ces déploiements de systèmes intelligents. avec un exemple qui parlera à tous: le futur de la voiture, avec Windows Embedded Automotive!

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Utilisation du cloud dans les systèmes intelligent

  1. 1. palais descongrèsParis7, 8 et 9février 2012
  2. 2. Utilisation du Clouddans les SystèmesIntelligentsMardi 7 févrierCharlie GrabiaudPartner Technology ManagerWindows Embedded, Microsoft
  3. 3. Agenda
  4. 4. Market opportunity Billions of systems $1.2 Trillion WW market 10.0 By 2015 $520 8.0 Billion 6.0 Today 4.0 2.0 0.0 Traditional Embedded Intelligent SystemsIDC, 2011
  5. 5. Stages of Intelligent Systems Stage A: Stage B: Stage C: Stage D: Discrete Technology Connected System Managed System Analytical System Solutions System for a specific business Data and information is shared between two or more systems purpose Limited automatic Connected devices allow data to be automatically updated in data-flow between devices and back end back end systems Two-way connectivity allows for remote management of devices System capable of analytics and BI
  6. 6. Microsoft ConfidentialData is the New Currency Connectivity 3 4
  7. 7. Device Systems Analytics Heartbeat (On, Off) Transactions Performance Logistics Efficiency Records Productivity Events Telemetry Health and System Performance Interaction Data Data System Contextual Related Data Data CRM (Customer) Weather ERP (Inventory, Employee) Traffic Market Intelligence GPS Fraud/Theft Detection Maps
  8. 8. Today’s Data Challenges Too slow Too much Results have outlived their value Vast amount of data, little information Too costly Too littleHigh integration costs, Missing the right data Complex toolsets Too vague No context to the data
  9. 9. Data Generation vs Capacity Data generation Data hardware capabilities Processing all the data centrally in premises becomes either a bottleneck or too costly: • Must bring some of the processing closer to the data source • Must use public cloud scaling Bandwidth / Server capacity 2010 2015 2020
  10. 10. Benefits of Public CloudComputing • Data & services accessible from anywhere – Office, Home and on the road • Almost unlimited resources – Internet-scale computing and services platform • Very high availability – Automatic data redundancy and distribution – Robustness of Microsofts datacenters and Windows Azure • Cost optimization – No huge CAPEX before development can start – Pay Per Use Model – Good Windows Azure Applications are scalable by definition
  11. 11. Microsoft Intelligent Systemssupport Windows Embedded Compact Windows Embedded Standard Windows Embedded Enterprise, … Windows Azure Windows Embedded Server Windows Embedded Storage Server, … Microsoft SQL Microsoft Dynamics Microsoft Sharepoint, ...
  12. 12. Windows Embedded+Azure Scenarios• Industrial Automation• Automotive• Public Services• Energy• Medical
  13. 13. Industrial cloud services • Storage of auditable data – Small and mid size companies without own DC – Long-term backup and availability • Device Monitoring and remote maintenance – Machines and equipment in remote locations • Web based engineering – More computing power for compiling – Team engineering across multiple locations
  14. 14. Siemens/Intel/Microsoft POC• Cooperation of – Siemens – Intel – Microsoft• Data in – SQL Azure• Services on – Windows Azure• Siemens Devices – Windows Embedded – Intel CPUs
  15. 15. Global Infotainment Trends Today 2015 Device Types Market Share End User Cost Market Share End User Cost Video/3D Nav/ <10% $3000 20% $2000 Online Services Color Screen/Speech 30% $1000 50% < $1000 UI/Navigation USB/BT Telephone/ Standard or 40% < $500 70% Media $250 Radio/CD/ 100% Standard (Free) 100% Standard (Free) MP3 Playback 60 Million Cars/Light Trucks 80 Million Cars/Light Trucks
  16. 16. The Automotive Design Lifecycle Today  Long Lead Times and Fixed Functionality 3-5 yr 1-2 yr 2+ years 7 years 10-15 years… Emerging  Faster Development Cycles, Annual Releases, Continuous New Functionality 3-4 3-4 Mo 9 Mo 7 years 10-15 years… Mo
  17. 17. Daimler Project: eMobility • Enables drivers remote access to vehicle information • Monitor charging state and possible range • Combine car data with other information • Access data at any time from every device
  18. 18. eMobility: Visualize data • Use Bing routing service to calculate possible range • Combine additional information and charging spot location for exact calculation • Increase confidence in vehicle possibilities
  19. 19. Giletta, Italy : Intelligent SaltSpreadingSituation• Fleet of Trucks spreading salt on the road when snowing• Truck drivers control the spreading manually, using predefined route Intelligent System solution Objectives - Spreading performance •On-board navigation and cost and control system - Safety on the roadChallenges - Environmental impact •Back-end system• Salt is expensive - Better alignment to aggregating and• Unnecessary pollution created by trucks weather conditions. computing data• Slow in some areas – no dynamic system to chose spreading location• System not effective Spreading Parameters Maps view iMx27 with WinCE6 R3 Dedicated CAN Bus Snowplough Controls
  20. 20. Technology Enablers• Windows Embedded To power the on-board controller (ARM, Real Time & connectivity)• Windows Azure Cloud-based application to analyze data and enable decision Creation of Additional business value • Hundreds of tons of salt saved • Improved security on the road • Reduced maintenance costs • Reduced environmental impact • Planned extension to otherNew Usage Scenario services (transport or recycling)• Provide accurate directions to the driver• New Data collection of highway infrastructures and services, weather, truck location data and traffic data
  21. 21. Home Energy GatewayArchitecture
  22. 22. Home Energy GatewayArchitecture Integration & Analytics: • Base Services (ex. Authentication) • Backoffice system integration • Services Directory (reusability) • Business Analytics Internet Portal: • Secure Access • Customizable Content • Services Catalog • Services & Product Search • Client Data Back-End Systems Home PLC Home Energy Mobile, PC, TV, Other Smart Meters Gateway Multi Channel and Multi Device
  23. 23. Towards Intelligent MedicalSystems Health drivers –Aging population –Increasing costs –Prevalence of chronic disease –Consumer expectations of service quality and life style continuity –Significant and accelerating staffing shortages Health Intelligent Systems –Intelligent/connected medical devices (glucometers, blood pressure monitors) –Electronic medical record (EMR)/personal health record (PHR) systems –Care management systems (enables remote care by clinicians) –Telemedicine and remote patient monitoring –Telepresence/video conferencing –Patient portals
  24. 24. Medical Proof Of Concept
  25. 25. HealthVault in Medical POC
  26. 26. Analytics from Edge to Cloud
  27. 27. Understanding Streaming Data(1) Question: “how many red cars are in the parking lot”. Answering with a relational database: • Walk out to the parking lot. • Count vehicles that are Red Cars SELECT COUNT(*) FROM ParkingLot WHERE type = ‘AUTO’ AND color = ‘RED’
  28. 28. Understanding Streaming Data(2) What about: “How many red cars took the I-80 interchange to San Francisco in the last hour”? Answering with a relational database: • Pull over and park all vehicles in a lot, keeping them there until the end of the hour. • At the end of the hour, count vehicles that Doesn’t seem like a are in the lot. great solution… • Then deliver the answer
  29. 29. Understanding Streaming Data(3) Different kinds of questions require different ways of answering them. The last questions we looked at are best answered with a stream data processing engine, or complex event processing engine. How would a streaming engine do the processing for this scenario? • Stand by the freeway, count red cars as they pass by. • Keep updating the answer internally, keep delivering the answer as needed by the consumers. This is the streaming data paradigm in a nutshell – ask questions about data in flight.
  30. 30. Event-Driven Applications Analytical results need to reflect important changes in business reality immediately and enable responses to them with minimal latency Database-driven Applications Event-driven Applications Query Paradigm Ad-hoc queries or requests Continuous standing queries Latency Seconds, hours, days Milliseconds or less Data Rate Hundreds of events/sec Tens of thousands of events/sec or more Query Semantics Declarative relational analytics Declarative relational and temporal analytics request Event output stream input stream response
  31. 31. Example: Microsoft CampusShuttle Bus Tracking • Plot current position for Redmond campus shuttles • Track specific shuttles • Identify when shuttles approach specific destinations • Proximity queries with SQL Spatial Libraries
  32. 32. Scenarios for Event-DrivenApplications Latency Months Days Relational Database Applications Hours Operational Analytics Applications, e.g., Logistics, etc. Minutes Data Warehousing Applications Web Analytics Applications Seconds 100 ms Monitoring Applications Manufacturing Applications Financial trading Applications < 1ms 0 10 100 1000 10000 100000 ~1million Aggregate Data Rate (Events/sec.)
  33. 33. StreamInsight™ Rich Analytics • Continuous processing of event streams from multiple sources • Based on rich declarative query language • Optimized for analytics over time-series data Intelligent Processing • Express and detect complex pattern and device profiles • Push richer analytics down to the device (pattern redeployment) Unified Experience • Provide uniform semantics & development experience from server to the edge • Seamlessly transition between historical and real- time data Microsoft Optimize data traffic • Send only relevant information from device StreamInsight™ • Eliminate bottleneck at the mid-tier
  34. 34. StreamInsight™ Platform StreamInsight™ Application Development Event sources StreamInsight™ Application at Runtime Event targets Input OutputDevices, Sensors Adapters StreamInsight™ Engine Adapters Pagers & Monitoring devices Standing Queries KPI Dashboards, ` SharePoint UIWeb servers Query Query Logic Logic Trading stationsEvent stores & Databases Query LogicStock ticker, news feeds Event stores & Databases
  35. 35. Analytics Platform Assets Process Sensors & Control SI SI Auto Robots SI SI Global, cross-asset analytics for SI aggregation and correlation of in-flight SI SI events; analytics on historical data Cross-asset Analytics & Per-asset analytics for lightweight Mining SI processing and filtering, computed close to the asset Hosted in the Embedded in the asset Integrated with .NET cloud/on-premise • Creates adaptable, • Gather insight from large • Extensible to incorporate network friendly, remotely collections of assets domain specific analytic manageable assets • Mine historical data to needs create/validate new models • Rich development tools to reduce total cost of ownership
  36. 36. Connected Car Scenario Exploratory analysis of historical data across cars to identify problems or enhance driver experience OEM Engr. New Analytics models, updates, etc. Computation for deployment Significant Operational Data (Battery level, engine status, Recommendations speed etc.) (Route, recharging station, business location, etc.) Location Data (GPS coordinates) Servicing/ Diagnostics (Service Contextual Data recommendation, Updates, etc (Destination, address, etc.) .) Asset Analytic Car Operation High Customer Satisfaction
  37. 37. World of Windows Embedded
  38. 38. Pour aller plus loin Prochaines sessions des Dev Camps Chaque semaine, les 10 Live Open Data - Développer des applications riches avec le février DevCamps 2012 16 Meeting protocole Open Data ALM, Azure, Windows Phone, HTML5, OpenData février Live Meeting Azure series - Développer des applications sociales sur la plateforme Windows Azure 2012 http://msdn.microsoft.com/fr-fr/devcamp 17 Live Comprendre le canvas avec Galactic et la librairie février Meeting three.js 2012 Téléchargement, ressources 21 février Live La production automatisée de code avec CodeFluent Meeting Entities et toolkits : RdV sur MSDN 2012 2 mars Live Comprendre et mettre en oeuvre le toolkit Azure pour http://msdn.microsoft.com/fr-fr/ 2012 Meeting Windows Phone 7, iOS et Android 6 mars Live Nuget et ALM 2012 Meeting Les offres à connaître 9 mars 2012 Live Meeting Kinect - Bien gérer la vie de son capteur 90 jours d’essai gratuit de Windows 13 mars 2012 Live Meeting Sharepoint series - Automatisation des tests Azure 14 mars Live TFS Health Check - vérifier la bonne santé de votre www.windowsazure.fr 2012 Meeting plateforme de développement 15 mars Live Azure series - Développer pour les téléphones, les 2012 Meeting tablettes et le cloud avec Visual Studio 2010 Jusqu’à 35% de réduction sur Visual 16 mars Live Applications METRO design - Désossage en règle dun Studio Pro, avec l’abonnement MSDN 2012 Meeting template METRO javascript 20 mars Live Retour dexpérience LightSwitch, Optimisation de www.visualstudio.fr 2012 Meeting laccès aux données, Intégration Silverlight 23 mars Live OAuth - la clé de lutilisation des réseaux sociaux dans 2012 Meeting votre application