IoT Dynatrace
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This document discusses managing complex IoT ecosystems across enterprise clouds and the internet of things. It notes that digital transformation converges the physical and virtual world. It also mentions that Dynatrace works with many industries including manufacturing, healthcare, smart cities, energy, agriculture, and retail on projects involving digital twins, predictive maintenance, smart homes, connected cars, and more. Finally, it discusses trends and challenges in industrial IoT like new data sources, immediate actions, cloud to edge processing, complex architectures, and full-stack monitoring across sensors, devices, edge systems, and clouds.
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IoT Dynatrace
- 1. Manage complex IoT ecosystems in the enterprise cloud and the Internet of Things
- 5. converges physical and virtual world Digital Transformation
- 6. We are in projects with
- 10. Digital Twin - Augmented Reality
- 12. Smart Home
- 13. Healthcare
- 14. Connected Car
- 15. Smart City
- 16. Energy / Power Supply
- 17. Agriculture
- 18. Retail
- 19. Internet of Thingsof Everything
- 20. Edge devices Infrastructure Compute Platform Applications Solutions IoT Gateway MS Azure Cloud FoundryIBM SAPAWS Logistics Transportation Application Monitoring (Cloud) Platform Monitoring Application and Device Monitoring Business Monitoring Smart Metering Fault Detection Industry 4.0 Manufacturing Smart City Fleet Management Home Automation Traffic & Lighting Control Smart Home Entertainment OpenShift Energy Power Supply Predictive Maintenance Azure IoT AWS IoT Bosch IoTGE PredixThingworxIBM Watson IoT Platform Communication & Transaction Monitoring Google Google IoT Core Connected Car Automotive Patient & Hospital Management Healthcare SAP Leonardo C3 IoT
- 22. On average, a single transaction uses 82 different types of technology Browser Multi-geo Mobile Network Code Hosts Logs IoT (Sensors, Actors) 3rd parties Services Cloud SDN Containers It takes over a month to monitor a single application from end-to-end. This does not scale. Application complexity
- 23. Sensors Devices Edge Cloud Storage Compute Intelligence Insights Logic -> Action 3 pillars of IoT Enterprise Cloud
- 24. Mobile Application Code Database Network Container Micro-service Browser Synthetic Server Mainframe Log & Events API Cloud high fidelity, full stack data All transactions, all the time Connected end-to-end PurePath + Smartscape Real-time dependency detectionAuto instrumentation Automated workflows Automated problem detection Automated root cause analysis Causation gives answers Massive Automation Automate the effort Natural language interface Automated business impact OneAgent Advanced analytics Expert knowledge built-in Self learning A.I. Better data makes Dynatrace artificial intelligence and automation possible
- 26. Trends and Challenges in the Industrial Internet of Things
- 27. Trends and Challenges in the Industrial IoT ▪ New data sources, new types of data, rapidly increasing amounts of data ▪ New use cases require immediate actions, fast responses ▪ Cloud to the edge: process and analyze data at the origin ▪ Complex architectures and integrations demand new solution designs ▪ Full-stack monitoring of all dependencies
- 28. Gather data Data preparation Model training Evaluation / testing Deployment Operations Monitoring Optimization Predictive Maintenance Machine Learning
- 29. Gather data Data preparation Model training Evaluation / testing Deployment Operations Monitoring Optimization Predictive Maintenance Machine Learning
- 30. Predictive maintenance ▪ Model quality depends on data set and training ▪ Many machine breakdowns are not predictable ▪ Incomplete set and/or poor quality of data ▪ Handling errors ▪ Increase quality of PM by coupling with other services ▪ Process data monitoring / optimization ▪ Smart material management ▪ Digital assistance systems (to reduce handling errors)
- 31. Better predictive maintenance ▪ Assure high data quality ▪ Monitor data collection, communication and analysis ▪ Consider all dependencies ▪ Monitor model training and evaluation ▪ Monitor operations (consider all dependencies) ▪ Correlate all gathered information ▪ Feedback results into predictive maintenance lifecycle and external dependencies
- 32. Multi tier IoT Data aggregation Measurement Control Processing, Analytics Machine Learning Processing Analytics Machine Learning Shift Left Compute, Analytics, Machine Learning Virtualization, Containers, Microservices Systems Management, Controlled Deployment Remote Access, Monitoring Cloud to the Edge Sensors Actuators Gateways Access Points Sensor Hubs Edge IT Sensing Acting Connecting physical and virtual world Analytics Machine Learning Management Archive Integration Business Logic
- 33. Sensors Devices Edge Cloud Storage Compute Intelligence Insights Logic -> Action 3 pillars of IoT and smart monitoring Smart Analysis / Root Cause Detection End-To-End / Full Stack