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Orientation in the fog: differences between stream processing in edge and cloud

Original (german) title: Orientierung im Nebel: Unterschiede zwischen Stream Processing in Edge und Cloud. See https://www.buildingiot.de/veranstaltung-7812-orientierung-im-nebel%3A-unterschiede-zwischen-stream-processing-in-edge-und-cloud.html?source=0&id=7812

Orientation in the fog: differences between stream processing in edge and cloud

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Orientierung im Nebel:
Unterschiede zwischen Stream
Processing in Edge und Cloud
• IIoT vs IoT
• (Stream) processing at the Edge
• How Apache Edgent can help
What I’ll talk about
• Julian Feinauer
• Phd in mathematics
• Founder of pragmatic minds GmbH and pragmatic industries GmbH
• Apache PLC4X & Apache Edgent (P)PMC member
• Twitter: @jfeinauer2
Who am I?
Industry 4.0, Digitalization and the
Industrial Internet of Things?!
• Everybody wants Industrie 4.0 or Digitalization
• „Can we do something with Big Data?“
• „We need a Data Lake“
• Can AI solve this for us?“
• This means that the Shopfloor is confrontend with
• ... connectivity
• ... modern IT infrastructure
• ... Cloud technology
Current Trend in the Industry
• #Neuland for the Industry
• Industry is always conservative regarding IT infrastructure
• Suppliers try to close the gap but are themselves faced with a
completly new situation and new technologies
• Very dynamic situation but Industry likes to plan
• In many industrial sites we have bad bandwidths
But...

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Orientation in the fog: differences between stream processing in edge and cloud

  • 1. Orientierung im Nebel: Unterschiede zwischen Stream Processing in Edge und Cloud
  • 2. • IIoT vs IoT • (Stream) processing at the Edge • How Apache Edgent can help What I’ll talk about
  • 3. • Julian Feinauer • Phd in mathematics • Founder of pragmatic minds GmbH and pragmatic industries GmbH • Apache PLC4X & Apache Edgent (P)PMC member • Twitter: @jfeinauer2 Who am I?
  • 4. Industry 4.0, Digitalization and the Industrial Internet of Things?!
  • 5. • Everybody wants Industrie 4.0 or Digitalization • „Can we do something with Big Data?“ • „We need a Data Lake“ • Can AI solve this for us?“ • This means that the Shopfloor is confrontend with • ... connectivity • ... modern IT infrastructure • ... Cloud technology Current Trend in the Industry
  • 6. • #Neuland for the Industry • Industry is always conservative regarding IT infrastructure • Suppliers try to close the gap but are themselves faced with a completly new situation and new technologies • Very dynamic situation but Industry likes to plan • In many industrial sites we have bad bandwidths But...
  • 7. • „Universal Protocol Adapter“ • Connectivity for PLCs • Bridging the GAP between Shopfloor and IT However...
  • 9. The Industrial Internet of Things PLCs Sensors Actors
  • 10. The Industrial Internet of Things PLCs Sensors Actors Hundreds of internal variables • 10-100 Sensors • Scaling • Data types • 10-100 Actors • Different technologies • Metadata is important
  • 11. The Industrial Internet of Things PLCs Sensors Actors • No discrete Events • Timeseries! • Events have to be extracted based on • ... absolute values (thresholds) • ... (bit-)triggers / value changes • ... derivatives • ... • è Stream Processing at the Edge!
  • 13. (Classic-)Definition: “A data stream can be seen as a continuous and potentially infinite stochastic process in which events occur independently from another”
  • 15. The Industrial Internet of Things PLCs Sensors Actors • No discrete Events • Timeseries! • Events have to be extracted based on • ... absolute values (thresholds) • ... (bit-)triggers / value changes • ... derivatives • ... • è Stream Processing at the Edge!
  • 17. Example: Cycle Detection • A (boolean) marker is true during cycle, false otherwise • We want to detect cycles as events, and record their start / end
  • 19. Example: Cycle Detection • A (boolean) marker is true during cycle, false otherwise • We want to detect cycles as events, and record their start / end • „Oh, we sometimes jitter, so a cycle is only a real cycle if its longer than 10 seconds“
  • 21. Example: Cycle Detection • A (boolean) marker is true during cycle, false otherwise • We want to detect cycles as events, and record their start / end • „Oh, we sometimes jitter, so a cycle is only a real cycle if its longer than 10 seconds“ • „Ah, we forgot, it would be good to also store the pressure at the end of the cycle“
  • 23. Example: Cycle Detection • A (boolean) marker is true during cycle, false otherwise • We want to detect cycles as events, and record their start / end • „Oh, we sometimes jitter, so a cycle is only a real cycle if its longer than 10 seconds“ • „Ah, we forgot, it would be good to also store the pressure at the end of the cycle“ • „In fact, we also need the current, rpm and temperature for the cycle“
  • 25. Example: Error Logging • Exceptions are stored as bit mask • About 2300 bits / exceptions on the machine • Task • Read all bits • See which toggled • Generate Event for Exception on / off
  • 27. Example: Complex Actions • We need production parameters, at defined points • First, wait until bit0 is set, then take the start time • Then, wait until bit1 is set, then take the pressure • Then, wait until bit2 is set, then take the rpm • ...
  • 29. Part II – Which Engine to use?
  • 31. Stream Processing is not Stream Processing Property Cloud Edge Distribution Yes No Scalable Yes No Fast Yes Easy Stateless Yes* No Message Delivery Exactly-Once At least once sufficient
  • 32. From Imperative to Declarative
  • 33. CRUNCH it! • CRUNCH - A framework for declarative industrial data analysis • Not another streaming framework, ABOVE the streaming layer • Created by pragmatic minds GmbH • Open Source sind end of 2018 • Apache 2.0 License • Visit: https://github.com/pragmaticminds/crunch
  • 34. CRUNCH it! • CRUNCH - A framework for declarative industrial data analysis • Not another streaming framework, ABOVE the streaming layer • Created by pragmatic minds GmbH • Open Source sind end of 2018 • Apache 2.0 License • Visit: https://github.com/pragmaticminds/crunch
  • 39. • Incubating project at the Apache Software Foundation • Established 2016 • Edge runtime with • a streaming runtime • real-time analytics capabilities • and cloud communication for constrained devices (i.e., with small footprint), at the edge. Apache Edgent
  • 43. But... • All have their own specific API • Partly open source, but usually very vendor driven • Vendor-Lock in • Entrance barrier is low but this should be a strategic decision • Can become very expensive, long term
  • 44. Edgent to the rescue! • All Open Source (Apache 2.0 Licence) • Cloud Connectors for different providers, no vendor lock-in • Tight integration with other Apache Frameworks, like • Apache PLC4X (incubating) • Apache IoTDB (coming soon) • Complete toolkit to build analytic applications at the edge!
  • 45. What‘s (probably) Next? • More Cloud Connectors! • Tight integration of CRUNCH • Edge Integration Patterns! • Improve Routing / Rule Engine at the Edge („Camel at the Edge“) • Revive the Community
  • 46. Interested? • Visit www.edgent.apache.org • Sign up to our mailing-list: dev@edgent.apache.org • Follow us on twitter: @ApacheEdgent • Start using Edgent • File bugs: https://issues.apache.org/jira/projects/EDGENT • Source code: https://github.com/apache/incubator-edgent • Examples: https://github.com/apache/incubator-edgent-samples • Join our meetup in Frankfurt on Mai 6th in Frankfurt (am Main)
  • 47. Questions? Julian Feinauer T +49 7022 24933 01 M +49 176 2488 3977 E j.feinauer@pragmaticminds.de PRAGMATICMINDS GmbH Zementwerk 1 72622 Nürtingen www.pragmaticminds.de