Ontology, NFV and the Future OSS September 2015
As NFV moves from labs and trials into production, the need to assure the services it delivers has become urgent, but legacy tools struggle to deliver. Ontology 5 builds a real-time service topology across legacy, physical, logical and virtual service components and uses it to power next-generation service inventory and assurance functions for NFV, hybrid and multi-domain services
E2E Service Assurance Requires: OSS/BSS 3. Links to the OSS/BSS (e.g. to correlate Trouble Tickets) 3 rd Party 1. A complete view of the logical and physical resources upon which a service depends (network, compute and storage) 2. Visibility of and links to their specific performance measures We call this a Structural Dependency Model or simply Topology Model 3
E2E Topology Must Span the Classical and the (new) Virtual Domains OSS/BSS 3 rd Party Regardless of whether services are designed as hybrids, some elements will be delivered by the classical network e.g. inter-dc WAN connectivity, last mile etc. 4
These Domains are Described in Separate, Complex Systems OSS/BSS Real-time Topology Data Orchestration The interface between these domains is complex and described by entirely separate data sets 5
E2E Service Assurance Topologies are Challenging to Create Orchestration Real-time Topology Data Joining up data for a real-time E2E model of the: logical, virtual, and physical elements, is an non-trivial problem because the data has: high variety, changes quickly and comes from a very wide range of sources. 6
Structural Model: This Gap is Acknowledged by ETSI Reference Architecture ETSI NFV Architectural Framework, 7.2.8: This data-set provides information regarding the VNF deployment template, VNF Forwarding Graph, service-related information, and NFV infrastructure information models Comprehensive model of logical and physical elements is required by ETSI NFV architecture ETSI NFV architecture does not specify how this model is provided Using graph data and semantic models, Ontology is able to build, maintain and publish this model
The Assurance Process in NfV Requires This Model
Real-Time Inventory: Example 1 Impact analysis during activation cycle BUT this can deliver service because VFN1 is a redundant instance of VNF3! OSS/BSS 2. Not yet ready according to orchestrator Service Orchestrator(s) 1. Expected but not yet provisioned VNF 1 VNF 2 VNF 3 Cloud Platform DC Management VM1 Host1 VM2 Host2 What is impact of failure here?
Real-Time Inventory: Example 2 Impact analysis in hybrid environments Service VNF 1 How does NFVI find out about this problem? VNF 2 VNF 3 VM1 VM2 Host1 Host2
Beyond the NFV Event Horizon: Future OSS OSS Today Fast Large Complex Fault Management Performance Management Capacity Planning Currently: Different OSS (and BSS) functions require data delivery with different characteristics Hence stovepipes are built for acquisition and delivery
Beyond the NFV Event Horizon: Future OSS OSS Evolution N F V I Big Data Infrastructure Live Streaming Real-Time Query Batch Query OSS Functions NFV moves fast and generates telemetry Modern big data infrastructure can acquire, store and deliver with any characteristics OSS functions no longer require individual stovepipes
Beyond the NFV Event Horizon: Future OSS The role of inventory N F V I Big Data Infrastructure Live Streaming Real-Time Query Batch Query OSS Functions Consistent, universal, timely model of current state required to enable other OSS functions: impact analysis, fault recovery (stranded asset recovery becomes a garbage collection function), coordination of multiple orchestration components, retrospective incident investigation etc This is what Ontology provides
Deep Historical Query Real Time Query Big Data Driven OSS TM Forum Catalyst SNMP Monit, Collectd, Logstash, Ceilometer Metric aggregation Open Data Platform Live stream Stream processing Fault Analysis Perf Analysis Log Analytics Ontology recently participated in a TM Forum to demonstrate this idea Multi-vendor project with Cisco, Moogsoft and SevOne SNMP Logs Event aggregation Log Aggregation Real Time Data Store Master Data Store Security and Threat Analysis Capacity Analytics Inventory Architecture of platform uses only open source in the Open Data Platform Consequently, real-time inventory is shown external to platform Billing (Mediation) Netflow Network Telemetry Data Distribution Batch Processing Data Store & Processing Business Intelligence Data sources Publishers: Data aggregation Consumers: Data analysis Applications 14
Big Data Driven OSS TM Forum Catalyst Deep Historical Query Real Time Query SNMP Monit, Collectd, Logstash, Ceilometer SNMP Cisco Orchestrator Openstack VNFs Cisco Metric aggregation Event aggregation Log Aggregation Open Data Platform Apache Kafka Live stream Stream processing Real Time Data Store Master Data Store Fault Moogsoft Analysis Perf Analysis SevOne Elastic Log Analytics Search Security and Threat Analysis Capacity Analytics Ontology Inventory Ontology recently participated in a TM Forum to demonstrate this idea Multi-vendor project with Cisco, Moogsoft and SevOne Architecture of platform uses only open source in the Open Data Platform Consequently, real-time inventory is shown external to platform Billing (Mediation) Netflow Network Telemetry Data Distribution Batch Processing Data Store & Processing Business Intelligence Data sources Publishers: Data aggregation Consumers: Data analysis Applications 15
Existing Solutions Don t Fill the Gap Network Inventory Systems Too slow, too inflexible Vendor-specific NMS Not interoperable Are vendor specific Orchestration components Can only see what they manage Assume clean interfaces to OSS No interaction with BSS A solution to the problem must: handle complex, irregular data from many sources handle very dynamic services and networks be able to bridge to the existing OSS and BSS not be vendor-specific
Why Ontology Ontology provides the always current model of all logical and physical assets required for successful NFV deployments and orchestration Unparalleled real-time, end-to-end view of customers, services and network Addresses gap in ETSI architecture for model of services and infrastructure Addresses challenge of end-to-end visibility in hybrid environments Agility and adaptability via semantic modelling delivers low TCO and low-risk rapid deployment in complex hybrid environments
ONTOLOGY COMPONENTS 1) Modeller is used to set up Ontology Runtimes. Eclipse based Product Ontology Modeller Configuration Ontology Semantic Core Runtime Ontology Semantic Core Runtime Topology & Dependency Model 5) REST API for 3 rd parties (Optional) 4) Add Applications (optional) Any Config EMS NMS IMS Data Performance Management Topology & Dependency Model API To Ontology Applications Reports and Files Real time NFV and Topology App Real-Time Service Impact Analysis Change Planning and workflow CCA Common/ Root ause Analysis KPIs / KQIs Ontology Visualization Applications Alarms Faults Events Alarms / Change Events Dynamic Data (EMS, Orchestrators) 2) Semantic Core Runtimes are deployed to provide topology and inventory information for all VMS components and to support reporting 3) dynamic data sources are enabled by new functionality launched in Ontology 5 s RT Source Module
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