Towards a Carrier Grade ONAP Platform FCAPS Architectural Evolution

Transcription

1 Towards a Carrier Grade ONAP Platform FCAPS Architectural Evolution Key Contributors: Ramki Krishnan, Sumit Verdi, Xinhui Li, Danny Lin, Bin Hu, Gil Hellmann, Bin Yang, Shankar Narayanan, Sastry Isukapalli, Rajesh Gadiyar, Vimal Begwani

2 Agenda Operational Intelligence for Dynamic Orchestration MC FCAPS Architecture Common Data Model & Data Distribution MC Impact - VoLTE, vcpe VNF Placement (Homing) Use Case MC<->OF Interaction for VNF Placement (Homing)

3 Data Distribution Function Operational Intelligence for Dynamic Orchestration Application and Infrastructure correlated context is key DCAE Analytics Functions Learning Outliers Adaptive on-demand service blue-printing and workflows Service Access Virtual Physical Transport VNF Aggregation Correlation VES Collection NFVI Multi-Cloud FCAPS SDC Policy Opt. Framework Dynamic policies using utilization intelligence, Policies using batched data Cloud-aware workload placement using capability and resource utilization Planning & Forecast Common Data Model Aggregation Normalization

4 Holmes CDAP MC FCAPS Architecture Standardized Common Data Model across Cloud Providers - Various cloud providers have disparate data structures, representations, middle-wares and more for infrastructure telemetry collection and management Composite NFVI Intelligence at atomic, aggregate and infrastructure capability granularities to enable cloudaware decisioning by OF, DCAE, SO Enable Continuous Service Deployment with run-time resource reservations and utilization telemetry FCAPS Data Distribution to enable simplified and scalable many-many communications using DMaaP pub-sub VES Collector Multi Cloud OpenStack SDN-C DCAE SDC Policy Capability, Capacity, Topology per cluster Real-time Faults Metrics per Host, VM, Network etc. Replication Active Sync DMaaP MSB Endpoints.. Infra Class - MC instance, Clusters within MC instance etc. VMware Wind River Kubernetes Opt. Frwk. Azure A&AI

5 FCAPS Common Data Model, Distribution & Integration * Joint collaboration between VMware, Intel, AT&T, China Mobile, WindRiver Host OpenStack VMware Wind River Azure Google Cloud Mirantis Host OS Hypervisor Virtual Machine Guest OS Switching Data Store Resource Pool/Provider Data Center Capacity Alert Metrics & Infrastructure Class * Telemetry EPA Resource Reservations Resource Utilization VES Collector NFVI Collector DMaaP DCAE OF Policy SDN-DC Recommendation

6 Infrastructure Class Example Cloud Provider Multi-Tenancy Multi-Device Access U/P Disaggregation Business Model Cost Cluster Topology Network Fabric type - CLOS etc., 2 level, 3 level Interconnect BW - Max, Min Cloud Agnostic Data Model Capability Resource Utilization Hardware encryption Hardware transcoding NUMA nodes available Storage class CPU, Memory, NIC class WAN interconnects and protocols Resource Reservation Number of tenants MC instances per data center Maximum number of VM s Total number of clusters Total number of hosts Total number of CPU, MEM Total number of data stores Number of hosts currently active Number of running VMs Total number of vcpu s powered VM density per host Available bandwidth per host NUMA utilization

7 VoLTE: Distributed DC VNF Placement (Homing) Use Case Workflow: Continuous Deployment - Day 1 & Beyond FCAPS, VNF [i] EPC [u] IMS [u] Multi-Cloud Instance 1 FCAPS, VNF [p] ONAP OF Edge Site 1 EPC [c] IMS [c] FCAPS, VNF [i] EPC [u] IMS [u] Multi-Cloud Instance 1 Multi-Cloud Instance 1 Core Site 1 Edge Site 2 Enterprise FCAPS, VNF [i, p] EPC [c, u] IMS [c, u] Multi-Cloud Instance 2 Edge Site 3 Cloud Interconnect IPSEC VPN etc. Historical Analytics Multi-Cloud Instance 3 Web Conf. Voice Translate Public Cloud DC, e.g. Azure Current ONAP Challenge: Static MC instance selection for workload placement leading to higher cost due to under-utilization or poor application QoE due to over-subscription of infrastructure Value Proposition: OF to deliver the best VNF placement solution in terms of Cost, Security and Application QoE by dynamically determining the appropriate multi-cloud instances leveraging aggregate infra data from MC [u] User Plane [c] Control Plane [i] Data Ingestion [p] Data processing

8 Residential vcpe: Distributed DC Placement (Homing) Use Case Workflow: Continuous Deployment - Day 1 & Beyond TunnelXConn AllottedResource: ONAP OF Requirement: TunnelXConnCapability Physical resources at Customer Premise (BRG) Logical subscriber link Policy: Latency, Capacity check {Customer_Location, TunnelXConn} < X ms Allotted resource has capacity for new order BRG vbng vg MUX vg Resources in Cloud (vg) Policy: Co-location {TunnelXConn, vg} in same Cloud Home BRG vg vg VNF Resource: vg (VFC) Policy: Capacity check Cloud has capacity for new vg BRG Broadband Residential Gateway BNG Broadband Network Gateway vg Virtual Gateway vg Mux Virtual Gateway Multiplexer Infrastructure resource Per-customer resource ONAP OF targeted for R2 to address R1 use cases (VoLTE, CPE) and upcoming use cases (5G etc.)

9 MC <-> OF Interaction Key Contributors: Shankar Narayanan, Sastry Isukapalli, Ramki Krishnan

10 Policy Driven Homing Cluster Capabilities, Capacity, Topology Multi Cloud (MC) Infra Class - Resource Utilization/Reservation DMaaP Replication A&AI Available Cloud/service instances DCAE Decomposed Service components (Demands) from SO Cloud Agnostic Std. Data Model from MC, Other plugins HOMING (OF-HAS) Constraint Model plugins Algorithm plugins to SO Optimized Homing recommendations Constraint Categories Service Requirements Runtime Metrics Optimization Objectives Runtime Queries Cloud Capabilities Latency Regulatory Security Distance Proximity/Colocation (virtual & physical) Diversity (Disaster Zones) Bandwidth from Policy Homing Constraints Latency (Customer) Distance (Customer) Cost (Provider) Load balancing (Provider) Site Reliability Cluster SW Capabilities (Availability zones, Affinity/Anti-Affinity) Cluster HW Capabilities (SRIOV, Hardware encryption, Transcoding, NUMA boundaries) Aggregate Capacity (Tenant, Hosts etc.) Aggregate Utilization (Tenant, Hosts etc.) Capacity check Quota

11 OF Model-Driven Approach Model Specification Data Specification Data Sources SDN-C Goal: provide a platform that facilitates model-driven optimization Constraint Model Repository Translators Interfaces Policy Engine AAI DCAE Multi Cloud Model-driven (Declarative) Library of building blocks (as SDC models, policy models, etc.) Reuse models from OF-contrib library Recipes for model composition Adapt at operation time (no new code) (new constraints, objectives, runtime flags) Op-ex benefits reducing software dev costs Rapid analyses what-if scenarios via config Application- Specific API Solution Environment Optimization Results Platform can seamlessly provide new functionality/advances to application

12 ONAP-OF Based on Standardized Constraint Modeling ONAP-OF Contributions Data Interfaces Model/Constraint Translators Building Blocks Recipes and Knowledge Base Operational Environment Available Extensions Contributed Models Global Constraint Catalog Stochastic Minizinc Uncertainty Considerations MiningZinc Constraint-Based Mining MiniBrass Soft Constraints Available Technology LibMzn (Embeddable Library) Minizinc Standard Library MiniZinc Model Flat Zinc Data (dzn format) Chuffed

13 BACKUP

14 Example Problem: Budget-Constrained Max Flow

15 Example Problem: Budget-Constrained Max Flow Model Templating Support from OSDF Macros from OSDF Data Template Constraints from model designer Data from A&AI, SDN, DCAE or Multi-Cloud Constraints from developer or from service provider/vendor (can be from SDC or Policy Engine)