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The goal of Analytics as a Service closer to edges is address edge Scalability, Constrained Environment and Service Assurance Requirements.
- Avoid sending large amount of data to ONAP-Central for training, by letting training happen near the data source (Cloud-regions).
- ONAP scale-out performance, by distributing some functions out of ONAP-Central such as Analytics
- Letting inferencing happen closer to the edges/cloud-regions for future closed loop operations, thereby reducing the latency for closed loop.
Reference: ONAP-edge-automation-update-arch-use-case-10-23-2018.pdf
5G use case relevance
- 5G/performance Analysis and Optimization: High Volume and RT Data Collection/Analytics/Closed Loop of performance metrics at the Edge Cloud
- Reference: ONAP-edge-automation-update-arch-10-29-2018-followup-11-07-2018.pptx
- Reference: 5G_UseCase_for_Dublin_v4.pptx (reference slide adapted from this deck)

Dublin Requirements Summary: Distributed Analytics as a Service (Dublin Summary) - Edge Automation

Instantiation of edge and connectivity to ONAP central (out of scope for ONAP)
Edge Cloud Registration [Ref. Arch. Impact Details (1)]
- Automation of registration when scale (>100s)
ONAP edge functions or 3rd party edge functions deployed at edge (e.g. Analytics, Closed Loop Control) [Ref. Arch. Impact Details (21 , 22)]
- Registration of the edge functions to ONAP central (Intent, capabilities, capacity)
  - Intent Example: “Infrastructure Analytics as service for Alerts at Cluster Level and Host Level”
Deploy Network Services in an optimal way to the edges using edge/central functions [Ref. Arch. Impact Details (3)]
- Includes multiple VNFs on multiple edges/core which make a service
- Cloud region (means one control plane) choice
- Connect the service to the functions
Networking of ONAP Central and edge functions [Ref. Arch. Impact Details (5)]

Reference: ONAP-edge-automation-update-arch-10-29-2018-followup-11-07-2018.pptx

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