• 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)
     

• 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: Service Assurance with Big Data Analytics OpenPreview

ONAPARC-317: Edge Computing Functional Requirements for Dublin - Analytics as a ServiceClosedPreview

• What does ONAP-based Analytics Service encompass?

  • Support analytics-as-a-service in the cloud-regions that have K8S site orchestrator.

  • Use same analytics framework to have analytics even in ONAP-Central.

  • Two packages - Standard package and inferencing package.

  • Use existing analytics applications - TCA to prove this framework.

  • As a stretch - Showcase one ML based applications

    • Training application

    • Inferencing application

• How to Develop?

  • Use PNDA as a base

  • Create/adapt Helm charts

  • Ensure that no HEAT based deployment is necessary.

  • Use components that are needed for normal analytics as well ML based analytics (Apache Spark latest stable release, HDFS, OpenTSDB, Kafka, Avro schema etc..)

  • Use some PNDA  specific packages - Deployment manager as one example.

  • Develop new software components

    • that allow distribution of analytics applications to various analytics instances

    • that allow onboarding new analytics applications and models.

    • that integrates with CLAMP framework (if needed)

• Impacted ONAP Projects

  • DCAE, CLAMP, A&AI (TBD), Multi-VIM/Cloud (TBD)

• How to Test?
  
  

  • TCA (Changes - Convert this as a spark application) 

  • New Machine learning models for KPI (packet loss) prediction (New use case)