Page Comparison

Summary: Edge Scoping 

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• Cloud SW Capability example 
  • Cloud region "x" with SR-IOV, GPU, Min-guarantee support
  • Cloud region "y" with SR-IOV support
• Cloud HW Capability example 
  • Resource cluster "xa" in Cloud region "x" with SR-IOV and GPU support 
  • Resource cluster "xb" in Cloud region "x" with GPU support
  • Resource cluster "ya" in Cloud region "y" with SR-IOV support

Note 3:

• 5G Service/VNF placement example
  • Constraints used by Optimization Framework (OOF)

    • 5G CU-UP VNF location to be fixed to a specific physical DC based on 5G DU, bounded by a max distance from 5G DU

  • Optimization Policy used by OOF

    • Choose optimized cloud region (or instance) for the placement of 5G CU UP for subscriber group based on the above constraints

Note 4:

• For the 5G Service/VNF placement example in Note 3
  • 5G CU-UP VNF preferably maps to a specific Cloud region & Physical DC End Point 

Note 5:

• For the 5G Service/VNF placement example in Note 3
  • OOF will pass the Physical DC End Point to SO as a opaque data

Note 6:

• For the 5G Service/VNF placement example in Note 3
  
  • SO passes the Physical DC End Point to Multi-Cloud as a opaque data, besides the Cloud Region

Cloud-agnostic Placement/Networking & Homing Policies (Phase 1 - Casablanca MVP, Phase 2 - Stretch Goal)

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• Edge Infrastructure Analytics complementing 5G VNF Analytics

• Increase the accuracy of placement decisions
• Addresses gap in cloud provider solution – e.g. open source OpenStack does not have a comprehensive telemetry solution

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ONAP, as in R2, collects the statistics/alarms/events from workloads (VMs) and take any close loop control actions such as Heal a process, scale-out, restart etc.. In R3 and beyond, infrastructure related statistics/alarms/events will be collected, generate actionable insights and take life cycle actions on the workloads.  Infrastructure statistics normally include performance counters, NIC counters, IPMI information on per physical server node basis.  To reduce the load on the ONAP, it is necessary that aggregated (summarized) information is sent to the ONAP from edge-clouds. 

As part of this activity, intention is to create aggregation micro-service that collects the data from physical nodes (over collected and other mechanisms), aggregate the information (time based aggregation, threshold based aggregation, silencing etc.,..) based on the configurable rules and export the aggregate data to DCAE.  This micro service can be instantiated by ONAP itself - one or more instances for edge-clouds at the ONAP-central itself using OOM, it could be instantiated at the edge-cloud using their own deployment tools or it could be deployed edge service providers at the regional site level.  

In R3, functionality is limited to HPA features and visualization.  R3 stretch goal: It collects information from each compute node for all HPA features and keeps track of health and resource information. It would use this information in placement decisions by OOF for accurate results.

Even though the aggregation service is being developed in Multi-Cloud project, it is expected that this can be deployed at various places. The decision to deploy at various levels can be due to performance and regulatory reasons.  Following deployments are envisaged at this time:

• At the edge site level.
• At the regional site level (on behalf of set of edge sites).
• At the ONAP level (on behalf of set of edge sites)

Impacted projects (development activities)

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ONAP Edge Analytics with DCAE/DMaaP independent of closed loop (Beyond Casablanca)

Value

• 5G Analytics
  
  

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