Over the last years booming of cloud technologies created a lot of opportunities for business and together with IoT expansion established new niche: Edge Computing. Since it's one of the first speech within the UA community we will go through main points about the origin, business use cases, main frameworks, and challenges. Why DevOps people should start learning embedded programming aspects and why we shouldn't allow to register a cloud node after reboot? That's the questions what we'll also review with professional part of the audience.
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
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
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
