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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)
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ONAP-based Analytics as a Service Details:
- What?
- 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?
- 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)
- How to Test?
- 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)
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