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Fine Grain Placement Service (F-GPS) Edge Automation (Dublin)

Fine Grain Placement Service (F-GPS) Edge Automation (Dublin)

Jan 23, 2019



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.

    • 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

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)]


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

https://lf-onap.atlassian.net/browse/ONAPARC-280

https://lf-onap.atlassian.net/browse/ONAPARC-317

ONAP-based Analytics as a Service Details: (see Distributed_analytics_v3.pptx in Edge Automation through ONAP)



  • 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)

3rd Party Analytics Application - Dublin Scope

  • Register VMware VIO (OpenStack-based) edge cloud region(s) in ONAP Central (Note: These Edge Cloud Regions support K8S)

  • Deploy VMware vROps 3rd party infra analytics framework/application in target edge cloud region to monitor multiple edge cloud regions 

  • Deploy microservice in target edge cloud region for cloud infra Event/Alert/Alarm/Fault Normalization & Dispatching to ONAP Central

    • Note: This microservice uses the Multi-VIM/Cloud project repository for development

    • ONAP project impact:

      • Multi Cloud project impact

        • Cloud infra Event/Alert/Alarm/Fault Normalization & Dispatching microservice development

          • Integrate DMaaP (Kafka) client for communication to ONAP Central 

          • Receive Event/Alert/Alarm/Fault from 3rd party infra analytics application

          • Normalize from cloud specific Event/Alert/Alarm/Fault format to cloud agnostic (ONAP internal) Event/Alert/Alarm/Fault format

            • ONAP internal format references

            • Alert examples (Note: Host CPU Threshold & Memory Contention Threshold in a cloud region are defined separately)

          • Dispatch Event/Alert/Alarm/Fault to ONAP central using DMaaP (Kafka) client

        • Cloud infra Event/Alert/Alarm/Fault Normalization & Dispatching microservice deployment

          • Develop K8S Helm chart

        • Note: 

          • This microservice is focussed on operational workflow & and independent of the current deployment focused micoservices in multi vim/cloud.

  • Register 3rd party infra analytics application in ONAP Central (Stretch Goal)

    • ONAP project impact:

      • Multi Cloud impact - Below

        • Populate Intent in A&AI 

          • Infra Analytics as service exemplary intent -- “Infrastructure Analytics as service for Alerts at Cluster Level and Host Level for a Cloud Region”

        •  

          • Capabilities (not exhaustive) corresponding to intent in A&AI (Note: Cluster CPU Threshold & Memory Threshold are defined separately) 

            • Cluster has memory contention caused by more than half of the virtual machines 

            • Cluster has memory contention caused by less than half of the virtual machines

            • Cluster has unexpected high CPU workload

            • ...

        • Populate Cloud Region List in A&AI corresponding to Intent

      • A&AI: Leverage existing HPA/Intent key-value pair schema 

Fine Grained Placement Service (F-GPS)