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: https://wiki.onap.org/download/attachments/8225716/ONAP-edge-automation-update-arch-use-case-10-23-2018.pdf?api=v2 

• 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)
     

Architecture Scope:

• 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) 

  
  

• References: https://wiki.onap.org/download/attachments/41422290/ONAP-edge-automation-F2F.pptx?version=1&modificationDate=1540934276000&api=v2; https://wiki.onap.org/download/attachments/28379482/ONAP-edge-automation-update-arch-10-29-2018-followup-11-07-2018.pptx?api=v2

ONAPARC-280 - Getting issue details... STATUS

ONAP-based Analytics as a Service Details:

• 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)