Analytics as a Service Closer to Edges

Problem Statement:

The goal of Analytics as a Service closer to edges is address edge Scalability, Constrained Environment and Service Assurance Requirements.

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)

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

ONAPARC-280 - Getting issue details... STATUS

Deploy using Helm charts (PNDA+ : to address large number of Cloud regions, Machine learning workloads) at ONAP-Central as well as in K8S based data centers.
Use existing analytics frameworks that have already instantiated.
Deploy analytics applications from ONAP
How to Test:
- TCA (Changes - Convert this as a spark application)
- New Machine learning models for KPI (packet loss) prediction (New use case)