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Edge Computing:
Appliance and
Challenges
DevOps Fest 2020
What I will share with you today
● What is Edge Computing?
● Where to use in business ?
● Why it's going to be next big thing?
● What it means for DevOps?
● What were the practical Challenges?
● Our vision of the future
● Q&A
What is Edge Computing?
Main aspects
● resources virtualization
● close to data origin
● low latency for real time apps <20ms
● distributed computing resources
● autonomous operation capabilities
Typical Device Edge Computing approach
What is Edge Computing?
Base Resources
virtualization
● CPU
● GPU
● RAM
● Network
● Flash memory
Periphery access
management
● GPIO
● Southbound radio
● PCIe
● USB
● Audio/Video
● Power
● Industrial Bus(-es)
● Sensor ports
https://www.pine64.org/rockpro64/
Where to use in business?
Application areas and use cases
● Telecommunications
● Manufacturing
● Automotive
● Mining, Oil & Gas
● Energy
● Agriculture
● SpaceTech
https://www.gartner.com/smarterwithgartner/what-edge-computing-means-for-infra
structure-and-operations-leaders/
Where to use in business? CASE 1: Video processing
Video analysis & enhancements
Why I need to send video stream to cloud to extract plate ID?
Where to use in business? CASE 2: Factory 4.0
Robotics & Manufacturing
I need to communicate locally, resiliently and at lowest cost
Where to use in business? CASE 3: Software Defined Satellite
Space Tech
● Camera and sensors storing
data to local storage on
satellite
● Different data models
processing the data and supply
it to the own infrastructure on
the ground
Why going to be the Next Big Thing?
Key factors of business success and why it’s serious
● Market needs to connect clouds and IoT
● Powerful governance by Linux Foundation & LF Edge
● Major investments from key tech players
Trillions of sensors & controls All kind of clouds
You Missed The Cloud Revolution?
Edge is 4X the Size & Will Hit 4 Trillion Economy*
http://www.chetansharma.com/publications/edge-internet-economy/
Arpit Joshipura, General Manager LF Networking, Edge and IoT at Linux Foundation. ONS 2019, Antwerp Belgium
Edge Computing: Key DevOps specifics
What to take care specially
● Resources management
(scaling and periphery control)
● Autonomous payload execution
(especially for the Device Edge)
● Security aspects: Secure boot
Edge Computing: our challenges
Practical aspects: AgTech
Why Edge Computing but not pure
Embedded approach?
Solution dev process - need to know
IoT & domain specifics
QA process - traditional vs reality
Support&Maintanace aspects (SW
upgrade, debug, monitoring)
Upcoming challenges:
Connectivity-as-a-Service (eSIM)
Proof of data origin
CV integration for non-AI specific
HW
Practical aspects: AgTech
➔ It’s NOT “Embedded vs cloud” it’s
“Embedded PLUS Cloud”
➔ CI/CD & QA automation possibilities
➔ Applications scalability
➔ No chip dependency
➔ Allows 3rd party applications
➔ Imagine it’s like IOS or Android vs
legacy firmware phone
Why Edge Computing but not pure
Embedded approach?
Practical aspects: AgTech
Solution development process
➔ Need to know IoT and domain specifics
➔ GPS is not precise, not stable and needs to be
filtered
➔ Want to have stable precise positioning (RTK)?
Stable connectivity required
➔ Sensors (self-) calibration
➔ Environmental conditions: outdoor, UV,
Vibrations, chemicals, thermal management
Practical aspects: AgTech
QA process: Load testing
Traditional vs Edge
Practical aspects: AgTech
QA: Staging environment
Traditional vs Edge:
Practical aspects : AgTech
Support & Maintenance aspects
➔ Low qualified operational personnel -
make it plug&play or disable it
➔ Operating in the middle of nowhere
➔ Low speed & unstable connectivity:
don’t expect stable telemetry & console
➔ Foolproof user design is a must
➔ If mobile network used - who is
managing account balance?
Credit: NASA/JPL-Caltech/MSSS
Upcoming challenges: Connectivity-as-a-Service
Upcoming challenges:
➔ Define TPM to be used
➔ Implement secure boot by
utilizing Keylime project
➔ Enable digital signature for
upstreaming data
https://keylime.dev/
Proof of data origin & secure boot using TPM
Upcoming challenges:
Computer Vision integration for
non-AI specific HW
➔ Training dataset collection (another
use case for EdgeGW )
➔ Linking container with data model
to specific GPU
➔ Integration with autopilot for route
correction for rows tracking
DevOps Fest 2020. Pavlo Repalo. Edge Computing: Appliance and Challanges
DevOps Fest 2020. Pavlo Repalo. Edge Computing: Appliance and Challanges
DevOps Fest 2020. Pavlo Repalo. Edge Computing: Appliance and Challanges

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DevOps Fest 2020. Pavlo Repalo. Edge Computing: Appliance and Challanges

  • 2. What I will share with you today ● What is Edge Computing? ● Where to use in business ? ● Why it's going to be next big thing? ● What it means for DevOps? ● What were the practical Challenges? ● Our vision of the future ● Q&A
  • 3. What is Edge Computing? Main aspects ● resources virtualization ● close to data origin ● low latency for real time apps <20ms ● distributed computing resources ● autonomous operation capabilities
  • 4. Typical Device Edge Computing approach
  • 5. What is Edge Computing? Base Resources virtualization ● CPU ● GPU ● RAM ● Network ● Flash memory Periphery access management ● GPIO ● Southbound radio ● PCIe ● USB ● Audio/Video ● Power ● Industrial Bus(-es) ● Sensor ports https://www.pine64.org/rockpro64/
  • 6. Where to use in business? Application areas and use cases ● Telecommunications ● Manufacturing ● Automotive ● Mining, Oil & Gas ● Energy ● Agriculture ● SpaceTech https://www.gartner.com/smarterwithgartner/what-edge-computing-means-for-infra structure-and-operations-leaders/
  • 7. Where to use in business? CASE 1: Video processing Video analysis & enhancements Why I need to send video stream to cloud to extract plate ID?
  • 8. Where to use in business? CASE 2: Factory 4.0 Robotics & Manufacturing I need to communicate locally, resiliently and at lowest cost
  • 9. Where to use in business? CASE 3: Software Defined Satellite Space Tech ● Camera and sensors storing data to local storage on satellite ● Different data models processing the data and supply it to the own infrastructure on the ground
  • 10. Why going to be the Next Big Thing? Key factors of business success and why it’s serious ● Market needs to connect clouds and IoT ● Powerful governance by Linux Foundation & LF Edge ● Major investments from key tech players Trillions of sensors & controls All kind of clouds
  • 11. You Missed The Cloud Revolution? Edge is 4X the Size & Will Hit 4 Trillion Economy* http://www.chetansharma.com/publications/edge-internet-economy/ Arpit Joshipura, General Manager LF Networking, Edge and IoT at Linux Foundation. ONS 2019, Antwerp Belgium
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  • 26. Edge Computing: Key DevOps specifics What to take care specially ● Resources management (scaling and periphery control) ● Autonomous payload execution (especially for the Device Edge) ● Security aspects: Secure boot
  • 27. Edge Computing: our challenges Practical aspects: AgTech Why Edge Computing but not pure Embedded approach? Solution dev process - need to know IoT & domain specifics QA process - traditional vs reality Support&Maintanace aspects (SW upgrade, debug, monitoring) Upcoming challenges: Connectivity-as-a-Service (eSIM) Proof of data origin CV integration for non-AI specific HW
  • 28. Practical aspects: AgTech ➔ It’s NOT “Embedded vs cloud” it’s “Embedded PLUS Cloud” ➔ CI/CD & QA automation possibilities ➔ Applications scalability ➔ No chip dependency ➔ Allows 3rd party applications ➔ Imagine it’s like IOS or Android vs legacy firmware phone Why Edge Computing but not pure Embedded approach?
  • 29. Practical aspects: AgTech Solution development process ➔ Need to know IoT and domain specifics ➔ GPS is not precise, not stable and needs to be filtered ➔ Want to have stable precise positioning (RTK)? Stable connectivity required ➔ Sensors (self-) calibration ➔ Environmental conditions: outdoor, UV, Vibrations, chemicals, thermal management
  • 30. Practical aspects: AgTech QA process: Load testing Traditional vs Edge
  • 31. Practical aspects: AgTech QA: Staging environment Traditional vs Edge:
  • 32. Practical aspects : AgTech Support & Maintenance aspects ➔ Low qualified operational personnel - make it plug&play or disable it ➔ Operating in the middle of nowhere ➔ Low speed & unstable connectivity: don’t expect stable telemetry & console ➔ Foolproof user design is a must ➔ If mobile network used - who is managing account balance? Credit: NASA/JPL-Caltech/MSSS
  • 34. Upcoming challenges: ➔ Define TPM to be used ➔ Implement secure boot by utilizing Keylime project ➔ Enable digital signature for upstreaming data https://keylime.dev/ Proof of data origin & secure boot using TPM
  • 35. Upcoming challenges: Computer Vision integration for non-AI specific HW ➔ Training dataset collection (another use case for EdgeGW ) ➔ Linking container with data model to specific GPU ➔ Integration with autopilot for route correction for rows tracking