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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

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

ONAPARC-280 - Getting issue details... STATUS

ONAPARC-317 - Getting issue details... STATUS

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

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  • 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
          • 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)

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