Contact: Srinivasa.r.addepalli@intel.com Analytics as a Service Contact: Srinivasa.r.addepalli@intel.com

Requirements Network Analytics Closer to the data source Traditional Analytics ML/DL analytics Training Inferencing Support for various types of analytics Analytics on infrastructure level statistics : NFVI, VIM, K8S etc… Analytics on VNF level statistics Support for Shared analytics services (Analytics services shared across multiple NFVIs, Multiple VNFs or Multiple VNF instances) Dedicated analytics services (Per VNF instance) Dynamic configuration (Day0 + Day 2) in case of shared analytics services Static configuration (Day0) in case fo dedicated analytics services

Service Assurance – E2E stack ONAP Glue (with DCAE and CLAMP) Configuration & Deployment App Dispatcher (using K8S) Model Dispatcher E2E configurator, Mapper (Day0 config and Day2 config) Packaging and installation Analytics App Distribution Model Distribution Data sources & mechanisms Collection layer : Data Collection, transformation & Distribution Closed loop actions Spark Apps App1 App2 App3 ONAP - LCM Collectd ONAP Action processor NFVI Ticket system VIM Node exporter Protocol termination, AVRO encodinig & Kafka output SDN Cntrl Other process entities Alarms cAdvisor Spark/PNDA framework Workloads SNMP Network Fabric Spark ML frameworks Local LCM actions Kafka OpenTSDB Syslog HDFS Zookeeper VES Consul for Dynamic App Config, App & Model Deployment Manager (from PNDA) Analytics framework Collection agents Distribution Analytics Actions

Inferencing/transform Apps E2E flow Training (Estimator – Spark term) Training action (Batch) Continuous NFVI Collection: Extract and Storing Apps Training Apps Models VNFs Collect Distribute models Net Apache Spark App HDFS, OpenTSDB NFVI streaming Collection: Extract and stream Inferencing/transform Apps GUI VNFs Collect Result Ticket Generation Net Apache Spark Closed loop App Models ONAP-central Inferencing (Transformer – Spark term)

Analytics as a Service(An example) Instantiate inferencing framework on a cloud region Instantiate Standard Analytics framework (input – Cloud region) Onboard ONAP-Analtyics framework packages Using PNDA & Open source, create packages & helm charts Two packages Standard package consisting of Apache Spark, HDFS, OpenTSDB, Kafka And ML packages including MLLib, spark-scikit-learn, spark-tensorflow Inference package consisting of Kafka, Apache Spark Notes: Not shown, but standard package can be instantiated in ONAP-central and edges too ONAP-Central Instantiate standard analytics instance Instantiate inference analytics instance Cloud-region Cloud-region Standard Analytics framework Cloud Region Cloud Region Cloud Region Cloud Region Inference Analytics framework Use existing facilities of ONAP (SDC and SO) - No changes in ONAP

Standard Analytics engine Onboarding of Analytics App and Uploading/Distributing spark app images & Models As a operator figure out the analytics applications that need to be brought up for various use cases Create CSAR with helm charts (Analytics Application BP) Example: Say NFVI analytics require one collection service, two spark applications and one action processing service. It would have three helm charts. Onboard CSAR to ONAP SDC Spark applications and ML Models are not a micro service and hence dockerhub (public/private) can’t be used. Add “App & Model management and Distribution service in ONAP which is used to upload spark app images and models ’ - new service in ONAP Upload Spark app images, Distribute to various cloud regions Upload ML Models and distribute to various cloud regions Onboard Analytics App BP ONAP-Central Upload spark app and model Upload app Cloud-region Cloud-region Standard Analytics engine Cloud Region Cloud Region Cloud Region Cloud Region Inference engine

Configuration Profiles (for Day0) Operator configures various configuration profiles for analytics applications. Operator instantiates analytics application in a cloud region with one of the configuration profiles. ONAP brings up services as per the app CSAR. Upload analytics App configurations (Multiple profiles are possible) ONAP-Central Instantiate Analytics App with one of config profiles Cloud-region Cloud-region Standard Analytics engine Cloud Region Cloud Region Cloud Region Cloud Region No change in ONAP Uses existing facilities Inference engine

VNF Specific configuration – Part of Day2 configuration Instantiate VNF and also pass reference VNF specific configuration for Analytics Prepare VNF specific configuration on per analytics app While instantiating VNF, add reference to analytics configuration specific to that VNF ONAP will push the configuration consul of analytics framework. ONAP-Central Upload app, if does not exist Run application Cloud-region Cloud-region Standard Analytics engine Cloud Region Cloud Region Cloud Region Cloud Region Inference engine

Automation of Manual steps (Integrate with DCAE & CLAM) Creation of BP and uploading BP to SDC. Auto upload of images and models to remote analytics frameworks. Auto detecting VNF and configuring analytics services automatically.

BACKUP

Current state of Analytics in ONAP – R3 DCAE is Analytics framework. TCA is one of the analytics applications Current use case Collects statistics from VNFs using VES DCAE runs statistics using TCA. TCA generates alarms Policy listens for alarms Generate LCM actions on APPC APPC in turn work with MC to take action (Heal / Scale-out /Scale-in/Migrate ) APPC Policy (Drools) DCAE APP Statistics Site1 Site n VNF VNF VNF VNF VNF VNF NFVI NFVI

R4 – Integration with PNDA APPC DCAE is moving from CDAP to PNDA PNDA uses Apache Spark. Spark is most popular data analytics framework. Apache Spark has many features (Need to be ensured that version selected can support following). Support for MLLib Support for tensorflow Support for SciKit-Learn Support for Kafka Support for datasets Support for HDFS/S3/Normal file system. Apache Spark has many third party analytics applications Policy (Drools) DCAE Deployment Client/GUI Kafka Deployment Manager Apps (e.g TCA) Apache Spark HDFS Kafka APP Statistics Site1 Site n VNF VNF VNF VNF VNF VNF NFVI NFVI

Current status of PNDA integration with DCAE Very early stages Work that was done so far A bridge & bootstrap container that brings up PNDA as VMs using Openstack. OOM brings up the PNDA bootstrap and Mirror containers. PNDA bridge containers assume that Openstack is installed. PNDA bridge container bring up HDFS, KAFKA, OpenTSDB, Apache Spark in VMs using HEAT templates On collection layer Usage of VES Agents in VNFs VES transport to Kafka VES model to AVRO model (Not very clear on the status) No integration with DCAE and CLAMP framework yet.

What we want to achieve Dockerization & Helm based analytics framework Help in any integration needed between CLAMP/DCAE and PNDA Machine Learning support in analytics framework Distribution of Analytics applications

Dockerization & Helm based deployment Why? Uniformity across ONAP (Be deployed using OOM) Avoid HEAT based deployment - Current PNDA integration (based on gerrit), seems to be deploying mirror container using OOM and then mirror container brings up various analytics services using HEAT. A way to realize “Analytics as a Service” (Ability to bring up analytics framework anywhere – near edges such as regional sites or at the cloud regions. How? Identify all software packages that constitute analytics framework (PNDA has nice list) & micro-services. Apache Spark, HDFS, OpenTSDB, Kafka broker etc… DockerFiles to create container images (See whether existing container images in dockerHub are good enough, if not create docker images) Create helm charts (Adapt helm charts that are available in public – Ensure that parameterization is done to provide flexibility of deploying multiple instances. Training and Inferencing packages

NFV Technical Requirements – Service Assurance service assurance, failover and recovery are high on the list as industry transitions to NFV/SDN Use cases Fault Correlation and Root cause analysis Performance management Anticipation of service failure & Auto trouble ticket management Anticipation of SLA failure & QOS management Traffic prediction Some use cases require ML. ML based analytics require various metrics, logs. Maintain historical data for training (Big data framework) ML based Analytics applications

Collection & Schema Support for both Pull and push from/to scrape targets Collection from - Node-exporter, cAdvisor CollectD logStash VES Encoding - Conversion to AVRO schema Export to next level application - Via Kafka

Analytics Applications – Training/inference/normal Support analytics applications that are created elsewhere for Apache Spark Input Kafka HDFS data OpenTSDB data Model Output

On-boarding Analtyics-App images/models/configurations Collection Applications Analytics Applications Analytics Environment (common across multiple applications) Authentication credentials (for Kerberos) Connectivity information Tunable parameters App Configurations (Multiple) Collection apps Output Kafka topic (for streaming) Scrape targets Metrics to be collected/parsed Storage related information (for storing apps) Analytics Apps App specific configuration Kafka topics to listen to Kafka to use for sending events Run time (Need to be taken care) - In some cases (inferencing cases), Scrape targets are only known at run time.

Offloading Analytics (One of ONAP-Edge workloads) Analytics App Repo & Mapping with VNF & Service management APPC Analytics instance registration management Policy/CLAMP Distributed Deployment Manager Model Management Conversion API ONAP-Edge workload (Analytics) ONAP-Edge workload (Analytics) Third party analytics framework PNDA+ PNDA+ Kafka Kafka Deployment Manager Deployment Manager Apps Apps Training at the regional sites and inference at the edges. Both at the edges Both at the regional sites Apache Spark Apache Spark HDFS Kafka HDFS Kafka Collection & transformation Collection & transformation Edge1 Edge2 Edge n Edge1 Edge2 Edge m

May need to develop Kafka proxy Activities Activity Description ML framework libraries in PNDA distribution Ensure that PNDA packaging includes Machine learning libraries such as MLLib, spark sci-kit learn integration and spark-tensorflow integration packages. PNDA packaging as Helm charts To allow deployment of PNDA anywhere (using OOM in case of ONAP-central) and as set of containers in case of regional sites or edge sites. Packaging for training and inference (inference packaging should have minimal components to allow deploying at the edges) – Functional decomposition Collection mechanisms Logs/events collection(logstash), metrics/stats (Collectd, Node-exporter, cAdvisor etc…) Distribution of models, analytics applications To various instances – near edges/edges. New enhancements in DCAE framework To allow distribution of models, applications, application environment, application configuration. A&AI changes To register analytics instances Closed loop actions at service level (conversions) Edge analytics instances sending the alerts/insights and getting them converted to rest of ONAP for closed loop actions at the service level. Distributed deployment On per VNF/Service instance basis, run the right applications, provide mapping of events from collectors to analytics applications and also providing configuration of applications/ May need to develop Kafka proxy

R4 Proposal Analytics-as-a-Service Distributed Deployment Manager Deploy analytics applications and collection applications using API. Configuration using analytics and collection applications. Mapping of VNFs and telemetry to collection and analytics applications. Dynamic support (once the applications are running). Prove with simple analytics service at the edge (Modify ZTT – Zero Touch Telemetry?) Stretch Model onboarding Model deployment Network analytics models

TODO/Questions Need to understand DCAE to PNDA integration AND suggest changes Docker containers and helm charts. Sequence diagrams between ONAP-central, Analytics workload Features of new components ML/DL use cases – Public models. Collection mechanisms Transformation open source projects (Cisco JOY? Open source project) Auto-Creation of models. Inferencing framework suitable for edges (Should be lean) APIs between ONAP-Central & Analytics framework workloads (ONAP based or third party)