Strategy for building data science solutions on edge computing with improved security features in RHEL 8.3

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Locationless Data
Science on a Modern
Secure Edge
John Archer
Chief Architect Energy
Red Hat

2. Source:
Giancarlo Revolledo
Unsplash
Islas Ballestas, Peru
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5. Edge computing with Red Hat
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The complexities of edge computing
Need to manage up to
hundreds of thousands nodes
and clusters remotely
Ensure support for a
heterogenous hardware and
software environments
Provide a consistent approach
for developer and IT
operations teams
Scale Interoperability Consistency

6. Reduced downtime
Lower OpEx and CapEx
Lower workforce risk
Less environmental impact
Process control
Environment monitoring
Autonomous vehicles
Edge computing with Red Hat
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Edge computing is already happening...but...
EnergyManufacturing
Telecommunication
s
Private Networks
vRAN
Distributed Core
Predictive maintenance
Factory automation
AR + remote export
Use cases
Benefits
Better user experience
Scale to meet demand
Greater network flexibility
Improved resilience
Reduced downtime
Increased productivity
Longer asset lifetime
Improved factory safety
Industrial use cases with AI/ML

7. OS Security Not Taken Seriously By Control System Companies
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8. Evolving Red Hat Enterprise Linux to
extend to the edge

9. ● Measured Boot
● Secure Boot
● Green Boot
● Full End-to-End Disk Encryption
● Push and Pull Signed Containers
● Transactionally Safe Patching - rpm-ostree
● OS and Firmware Over-the-Air Style updates
● Composable OS - osbuild
What is needed for a Modern Secure Edge OS?
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10. Red Hat Enterprise Linux
▸ Proven success with Edge workloads
▸ Enterprise life cycle, security, support
▸ Stable kABI/API & Application APIs
▸ Rich content of Application Streams,
Container engines, & Container images
EDGE COMPUTING
▸ Massive RHEL IHV/ISV ecosystem
▸ Foundation for the Red Hat portfolio
▸ Upstream leadership and customer advocate
▸ Simplified subscriptions w/ Smart Mgt
▸ Broad partner network
Value Proposition for Edge - Today
Trusted Platform Ecosystem and Expertise

11. Industrial application classification as defined by IEC62734 per TMEE 263
Edge application class types and scope mapping
Safety Class 0 Emergency action Always critical
Control
Class 1 Closed loop regulatory control Often critical
Class 2 Closed loop supervisory control Usually non-critical
Class 3 Open loop control Human in the loop
Monitoring
Class 4 Alerting
Short-term operational
consequence (e.g.,
event-based
maintenance)
Class 5 Logging and downloading/uploading
No immediate operational
consequence (e.g.,
history collection,
sequence-of-events,
preventive maintenance)

12. Open Edge Computing requires work across many communities, partners &
ecosystems to deliver a unified platform for distributed environments
● Software lifecycle for devices with 10+ year lifetimes
● Install, deploy and manage millions of devices with minimal human effort
● Efficient support for thousands-to-millions of identical devices
● Application development lifecycle that encompasses edge devices and core compute
● Long-term security for devices in physically and logically unsecured environments
● Engaging upstream communities to build, adopt, support, and advance these solutions
EDGE COMPUTING
What Challenges Can the Device Edge Solve?

13. We believe Red Hat can help
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Hybrid cloud provides
operational consistency across
your architecture-all the way
to the edge
Open source harnesses the
power of collaboration to
drive innovation and
interoperability
“If edge computing is going to be a
realistic future for enterprise IT,
it needs the hybrid cloud and open
source to thrive.”
Paul Cormier
Red Hat President and CEO
Edge computing with Red Hat

14. Edge computing with Red Hat
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Edge is part of our open hybrid cloud strategy
Any workload, any footprint, any location
App App App App
Physical Virtual Private cloud Public cloud
Device edge Enterprise edge Provider edge Enterprise coreProvider core

15. STRICTLY INTERNAL ONLY
End-User
Premises
Edge
Provider Edge Provider/Enterprise Core
“last mile”
FOOTPRINT
SCALE
Red Hat’s focus
Edge
Server/Gateway
Regional
Data Center
Infrastructure
Edge
Provider
Far
Edge
Provider
Access
Edge
Provider
Aggregation
Edge
Core
Data Center
Device or
Sensor
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Edge Tiers
WHAT IS THE EDGE
Device Edge
Centralize where you can, distribute where you must

16. STRICTLY INTERNAL ONLY
End-User Premises Edge Provider Edge Provider/Enterprise Core
Edge
Server/Gateway
Regional
Data Center
Infrastructure
Edge
Provider
Far
Edge
Provider
Access
Edge
Provider
Aggregation
Edge
Core
Data Center
Device or
Sensor
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Oil and Gas Data FlowPartners Red Hat’s focus
Remote Decision
Support
Real-time Data
Hub
Third Party
Sensor Data
TankerLand Rig
Deepwater Rig
Innovatio
Collaboration
OSDU
Field Location
Real-time Data
HubField Crews
Water Trucks Seismic Streamers
Refinery
Wind Turbine
Solar Farm

17. STRICTLY INTERNAL ONLY
End-User Premises Edge Provider Edge Provider/Enterprise Core
Edge
Server/Gateway
Regional
Data Center
Infrastructure
Edge
Provider
Far
Edge
Provider
Access
Edge
Provider
Aggregation
Edge
Core
Data Center
Device or
Sensor
17
Oil and Gas Data FlowPartners Red Hat’s focus
Remote Decision
Support
Real-time Data Hub
Modern SCADA
Model Training
Third Party
Sensor Data
Innovation
Collaboration
OSDU
Field Location
Real-time Data
HubNVIDIA
Jetson and Xavier
NVIDIA
P40 and V100
NVIDIA
P100 and V100
NVIDIA
P100 and V100NVIDIA
P100 and V100
Inference
Model Training
Retraining

18. If you can do it once, you can do it 1000s of times.
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EDGE COMPUTING
Full
Stack
Reproducible Everywhere Needed
Advanced Cluster Management
Mirror
SW images
config “blueprints”
credentials
Upload
telemetry
resource status
Across smaller footprint
hardware for device edge
(x1 mil)

19. Red Hat Enterprise Linux
▸ Zero-touch provisioning + Ignition
▸ Hardware Attestation w/ advanced IMA
policies
▸ Auto-disk encryption w/ TPMv2
▸ Read-only OS binaries
Security Enhancements Operational Enhancements
Value Proposition for Edge
▸ Image deployments w/ rpm-ostree updates
▸ Optimized OTA updates (static-deltas)
▸ Flexible update/upgrade model
▸ LVFS via fwupdmgr
EDGE COMPUTING

20. Initial Deliverables for Edge
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Bringing the technology from Fedora IoT to the enterprise
Greenboot provides additional
resilience and peace of mind by
allowing custom health checks to
trigger node rollbacks.
Delivered as a Flexible BlueprintIntelligent OS Rollbacks
RHEL’s Image Builder enables the
right level of flexibility for OS content,
configuration, update cadence, and
image life cycle.
Transactional Updates
rpm-ostree ensures a known-good
state for each update. OTA payloads
are optimized for low-bandwidth
environments.
EDGE COMPUTING

21. Looking Further into the Future of
Red Hat Enterprise Linux

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Reduced Footprint
Fedora/RHEL Minimization objective & pipeline
▸ Optimization of distribution content for small
footprints and containers
▸ https://tiny.distro.builders/
Management & toolbox containers
▸ Optionally move bash and other common
packages from the host into a management
container
EDGE COMPUTING

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EDGE COMPUTING
Measuring & Attestation w/ TPM 2.0
Local system measurement:
▸ Enhancement to rpm and IMA on the local system to
ensure only trusted binaries can be executed.
▸ Ensure important files that are loaded or executed are
measured.
Keylime project:
▸ Remotely ensure a device is running trusted code with
the correct configuration
▸ Require remote attestation in order to connect to
back end services
Trusted Computing Group:
▸ Standards to ensure compliant implementations can
verify published measurements from vendors

24. Production Data Science at the Edge

25. MLOps Everywhere

26. Edge computing with Red Hat
M
W
M W
M W
Central Data center
Regional DC
High BW Site
Low BW or
Disconnected Site
or
Cluster Management and
Application deployment
Kubernetes node
control
Edge deployments with OpenShift
Far Edge26

27. Data Science Workflow
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29. OpenDataHub Roadmap
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● ODH JupyterHub
Forked JupyterHub repos under Open Data Hub github repo for maintaining new changes. Added notebook images to
Thoth Station for building. Added ability to launch JupyterLab images.
● Notebooks to Pipelines with Elyra
Added a JupyterLab notebook image that includes Elyra. Elyra converts notebooks to Argo or Kubeflow pipelines.
● Mixing ODH & KF components- Distributed Training
Added Pytorch operator to work with Open Data Hub components.
● Mixing ODH & KF components- Kubeflow Monitoring
Added monitoring to Kubeflow components by enabling monitoring to Argo and adding Prometheus and Grafana to
Kubeflow installation.
● CI/CD
Added more tests to Open Data Hub components including Kafka and Superset and enhanced JupyterHub testing by
adding Selenium for web portal testing

30. Data Science Edge Production Challenges
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● Legacy and brownfield equipment
● Network design and support at edge
● Many efforts stuck in the data center
● Cost of moving data and identifying what to backhaul
● Data Quality generation at edge - uncalibrated sensors
● Most teams still maturing their ML/AL/DL/RL workflows
● Enablement of field edge personnel
● At Risk deployments

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Compressor Monitoring with PerceptiLabs on OpenShift

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LocationlessDataScienceonaModernSecureEdge
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e-mail me at archer@redhat.com
Thank You
Questions?

35. A small footprint deployment
with long-life release support.
Key building blocks are Red
Hat Enterprise Linux and a
container runtime.
Red Hat OpenShift deploy-
ment on a single box (master
+ worker) with resources to
run full Kubernetes cluster as
well as application workloads.
Red Hat OpenShift masters
and workers reside on the
same node. High Availability
(HA) setup with 3 servers.
Red Hat OpenShift masters
reside in a central location,
with reliably-connected
workers distributed at edge
sites sharing a control plane.
Single node 3+ node HA
Small footprint device edge
Memory-constrained edge
servers and gateways
Telco 5G
near edge and MEC
Smart manufacturing
Remote office
Disconnected clusters
Asset monitoring
Telco 5G
far edge - RAN
IoT / data
collection gateways
Telco 5G
in-vehicle field operations
Telco 5G
sparsely populated areas
Autonomous vehicles
Intermittently connected IoT /
data collection gateways
uCPE
(customer premise equipment)
Edge AI & data pipelining
ExamplesDefinition
Single node edge servers Remote worker nodes Edge clusters (3+ node HA)
M
W
M W
M W
3WSR
EDGE ARCHITECTURES
Telco 5G
far edge - RAN
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