The team will walk through the initial release of the Industrial Edge Blueprint which is a multi-product integration of a real world implementation based on OpenShift at the edge.
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Traditional N-Tier Apps Cloud Native Microservices ISV Packaged Apps Physical Virtual Private cloud Public cloud Red Hat Enterprise Linux Edge cloud Red Hat OpenShift Red Hat Open Hybrid Cloud Data, Analytics & AI/ML Edge computing with Red Hat OpenShift
Edge computing with Red Hat OpenShift 6 Edge gateway/ edge server Small bare metal footprint Infrastructure virtualization Public/private cloud A consistent edge platform to meet your needs Develop once, deploy anywhere Meet diverse use cases Consistent operations
▸ Available now ▸ Available now Edge computing with Red Hat OpenShift 7 Central data center Cluster management and application deployment Kubernetes node control Single node edge servers Low bandwidth or disconnected sites. Regional data center Far edge CONFIDENTIAL designator ▸ Available in 2021 S W Site 1 W Site 2 S S W Site 3 Remote worker nodes Environments that are space constrained 3 Node Small footprint with high availability Legend: S: Supervisor/control nodes W: Worker nodes
Edge computing with Red Hat OpenShift 8 Managing the edge, just like the core Red Hat Advanced Cluster Management for Kubernetes Multicluster lifecycle management Policy driven governance, risk, and compliance Advanced application lifecycle management
Edge computing with Red Hat OpenShift Connect applications and data at the edge 9 Use Cases ▸ Create fast and lightweight edge applications. ▸ Connect, aggregate and transform data for real-time data analysis and associated business actions. ▸ Container-native capabilities for better operational and scalable application infrastructure Red Hat Middleware Sample Industries ▸ Event triggered business ▸ Data aggregation and real-time analysis ▸ Data aggregation for AI-models Financial services Energy Healthcare Manufacturing
11 AI at the edge use cases Automotive Health–life science Manufacturing Energy Retail Public sector Patient diagnosis/treatment Quality assurance Sensor-based asset monitoring Digital in-store experience Predictive maintenance Edge computing with Red Hat OpenShift Monitoring and control
AI/ML Lifecycle and Edge Computing 12 Gather and prepare data Develop & train ML model Integrate ML models in app dev processes Deploy Apps & ML Inferencing ML models monitoring & management Execution Venue Data Center X X X X X Public Clouds X X X X X Edge X X X DataOps MLOps Edge computing with Red Hat OpenShift
13 ▸ Provides common, automated management across large-scale ▸ Lowers latency times with more distributed network architecture ▸ Uses remote worker node topology ▸ Deploy radio access network (RAN) functions where needed 5G DU Access 4G DU Aggregation 5G CU Red Hat OpenShift runs the most demanding workloads Telco RAN use case Edge computing with Red Hat OpenShift Aggregation 4G CU Telco core 4G/5G Core Legend: CU: Centralized Unit Access: also known as far edge DU: Distributed Unit Aggregation: also known as near edge Access
14 Edge computing with Red Hat OpenShift Solution Blueprints : Simplifying the creation of edge stacks Bringing the Red Hat portfolio and ecosystem together - from services to the infrastructure Blueprint as code From POC to production Open for collaboration Highly reproducible Go beyond documentation using GitOps process to simplify deployment So that you can scale out your deployments with consistency Ensure your teams are ready to operate at scale Anyone can suggest improvements, contribute to it
Edge computing with Red Hat OpenShift 16 Transforming industrial manufacturing Capitalize on industry 4.0 technologies To achieve successful optimization, planning and control of production Transition their IT-OT environment to next generation infrastructure Capitalize on edge computing, AI/ML, hybrid cloud, and software-defined technology Optimize production at the factory floor Use AI/ML intelligent applications for predictive maintenance and higher quality products Support future operating environments Accelerate the designing, developing, and deploying new apps and services Business initiatives creating new opportunities
Use Case: Predictive maintenance of machinery Edge computing with Red Hat OpenShift Enable software-driven production optimization with full visibility Leverage big data & ML technology for traceability and analytics Declarative configuration management to simplify operations at scale How edge computing and AI/ML can ensure production by reducing downtime and costs Accelerate software development Automate configuration management of production lines Use data from the factory floor to react proactively
SENSOR DATA & INFORMATION 19 W Remote worker node Sensor Simulators Core HQ Data Center S W 3 node cluster Factory #1 Factory #2 Sensor Simulators MQTT MQTT Git Quay Eclipse Che GitOps VSCode Quarkus CODE & CONFIGURATION High Level Overview of a customer Ceph AI/ML
Edge computing with Red Hat OpenShift 21 Solution Blueprint: Edge and AI/ML in industrial manufacturing Accelerating time to value using OpenShift at the edge Coding, simulation & deployment to production Container based CI/CD from data center the edge Automated configuration management Consistent roll-outs using end to end GitOps for distributed environments Data processing from sensors to analytics Open source middleware and AI/ML stacks ML model training and deployment to production Open Data Hub enabled CI/CD Bringing OpenShift, the Red Hat portfolio and ecosystem together from the core to the factory floor
22 https://youtu.be/mZ71V8RfXSw?t=315 ● Full blueprint demonstration ● Stay tuned to OpenShift TV for a live demo (12/10 1PM EST) https://www.twitch.tv/redhatopenshift/ ● Explore / Contribute Next Steps https://github.com/redhat-edge-computing
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Reduced downtime Lower OpEx and CapEx Lower workforce risk Less environmental impact Process control Environment monitoring Autonomous vehicles Edge computing with Red Hat OpenShift and ACM 25 Edge computing is already being looked into Energy Manufacturing Telecommunications 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
We believe Red Hat can help 27 Hybrid cloud provides operational consistency & flexibility across your architecture - including 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 OpenShift and ACM Portfolio comprised of powerful building blocks integrated with our partner ecosystem
▸ Today ▸ Today Red Hat platforms for the edge 28 W Central data center S W S Cluster management and application deployment Kubernetes node control Single-node edge servers Low bandwidth or disconnected sites Regional data center Far edge S W CONFIDENTIAL , under NDA Remote worker nodes Space-constrained environments 3 node Clusters Small footprint with high availability ▸ 2021 Small footprint edge OS Memory-constrained edge servers/Internet of Things (IoT) Gateways ▸ Today Supervisor node Worker node S W
A common platform across hybrid, multi-cloud and edge architectures 29 Private cloud Public cloud App App App App Edge Develop once, deploy anywhere - at scale
Edge computing with Red Hat OpenShift Delivering consistency and flexibility 30 Cloud-native apps AI/ML, Functions Communities of Innovation | Ecosystems of Solutions Secure & Automated Infrastructure and Operations Traditional apps Edge Private Cloud Hybrid Multi-Cloud
31 Vertically integrated Continuous open source innovation Open and interoperable Edge computing with Red Hat OpenShift Two approaches to building an edge architecture Dedicated point solutions
Edge computing with Red Hat OpenShift 32 Agenda ▸ Business goals ▸ Red Hat OpenShift and edge computing ▸ Edge computing architectures ▸ Red Hat OpenShift edge computing use cases ▸ Demo
Business goals 33 ▸ Innovation ▸ Speed ▸ Flexibility Organizations are under pressure to offer hybrid customer experiences delivered by a hybrid mix of apps, deployed in hybrid environments, and developed by hybrid teams. Edge computing with Red Hat OpenShift
Edge computing with Red Hat OpenShift 34 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
36 Transforming industries with edge computing Telecommunications Health–life science Manufacturing Automotive Retail Public sector Financial Energy Hospitality Edge computing with Red Hat OpenShift
Edge computing and the telecommunications industry A transformational opportunity Enable the connected economy Provide services to increasing number of people, devices, and things Deliver enhanced customer experiences Respond faster, increase customer satisfaction Create new revenue stream opportunities Offer cloud services to enterprises at network edge 37 Telehealth Mobile gaming 5G Smart cities Industry 4.0 Video Analytics Edge computing with Red Hat OpenShift
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