Graph-enabled network automation solutions with Neo4j
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1) Graph databases like Neo4j are well-suited for network automation solutions because they can flexibly model complex network dependencies and relationships. 2) Key benefits of using Neo4j include flexibility to dynamically change schemas, developer productivity through an intuitive query language, and improved performance over traditional databases. 3) Common use cases for graph databases in telecommunications include assurance, orchestration, customer experience management, identity and access management, and smart home/city applications.
![MATCH (a:Element { id: “A”})
MATCH p = (a)-[:DEPENDS_ON*]->(n { id: “N”})
RETURN p
SELECT
d1.ElementId, d2.ElementId, d3.ElementId
FROM dpndncs AS d1
INNER JOIN dpndncs AS d2
ON d1.dependsOnElemId = d2.ElemId
INNER JOIN dpndncs AS d3
ON d2.dependsOnElemId = d3.ElemId
… <arbitrary number of joins>…
WHERE d1.ElementId = “A”
AND d3.ElementId = “N”
Element Depends On
A B
A C
A D
C H
D J
E F
E G
F J
G L
H I
J N
J M
L M
Does X depend on Y ?](https://image.slidesharecdn.com/totaltelecom-neo4j-jbarrasa-191104164231/85/Graph-enabled-network-automation-solutions-with-Neo4j-25-320.jpg)
![MATCH (fe:Link { linkId: $id})<-[:DEPENDS_ON*]-(s:Service)
RETURN max(s.priority) AS severity
{ alarmType: “LOS”,
notifyingEntity: “IF/AX/0/3”, …}
Service Assurance
IA/RCA
GET http://localhost:11001/engine/ia/s1_361_sdh
{"severitySummaryCode": 4,
"severitySummaryDesc": "MAJOR",
"detail": [{"elem":"2217/ol",
"impact" : 1,](https://image.slidesharecdn.com/totaltelecom-neo4j-jbarrasa-191104164231/85/Graph-enabled-network-automation-solutions-with-Neo4j-32-320.jpg)
Graph-enabled network automation solutions with Neo4j
- 1. Graph-Enabled Network Automation Solutions with Neo4j Dr. Jesús Barrasa - @BarrasaDV Director Telco&Media - Neo4j London - 29th October 2019
- 2. The #1 Platform for Connected Data
- 4. Not this type of graphs!
- 5. The Property Graph Model Optical MUX Filter Card
- 7. Are you still NOT using Graphs? Here are a few reasons why you should
- 8. ”Graph analysis is possibly the single most effective competitive differentiator for organisations pursuing data-driven operations and decisions“
- 9. 1. Adoption
- 10. Adoption Highlights Top MVPD 2 of the Top 5 US multichannel video service providers have chosen Neo4j Leading OSS Vendors Half of the leaders in the 2018 Gartner MQ for OSS embed Neo4j in their products 2 of the 3 world’s largest CSP use Neo4j in mission critical solutions Largest Telcos
- 11. IoT OSS/BSS Governance & Metadata Mgmnt IAM & Fraud Analysis Common Graph Use Cases in Telco • Assurance • Orchestration • CRM/Support • Planning & Optimisation • Regulatory compliance • Data Lineage • Consent Mgmnt • Identity & Access Mgmnt • Smart Home • Smart Cities • Industrial /Manufacturing Customer Experience Mgmt • Personalisation/Rec ommendation • Customer Journey Analysis • Customer 360 - KYC
- 12. 2. Key Business Benefits
- 13. • Flexibility: Dynamic Schema • Developer Productivity: Agility, Declarative query language Faster Time-To-Market (Solution/Production) • New data paradigm • Graph representation of data Innovation Enabler • Timeliness • Accuracy Better decisions drive better business outcomes • 10x less CPU with index-free adjacency • 10x less hardware than other platforms Hardware efficiency Neo4j: Graph Platform Benefits
- 14. why?
- 15. Need to capture complexity Flexibility Performance
- 16. Need to capture complexity Flexibility Performance
- 18. Need to capture complexity Flexibility Performance
- 19. Need to capture complexity Flexibility Performance
- 21. Look at this data… Element Depends On A B A C A D C H D J E F E G F J G L H I J N J M L M
- 22. Element Depends On A B A C A D C H D J E F E G F J G L H I J N J M L M Time challenge #1: Does A depend on F ? ?
- 23. Look at this data again…
- 24. Time challenge #2: Does E depend on M ? ? M E
- 25. MATCH (a:Element { id: “A”}) MATCH p = (a)-[:DEPENDS_ON*]->(n { id: “N”}) RETURN p SELECT d1.ElementId, d2.ElementId, d3.ElementId FROM dpndncs AS d1 INNER JOIN dpndncs AS d2 ON d1.dependsOnElemId = d2.ElemId INNER JOIN dpndncs AS d3 ON d2.dependsOnElemId = d3.ElemId … <arbitrary number of joins>… WHERE d1.ElementId = “A” AND d3.ElementId = “N” Element Depends On A B A C A D C H D J E F E G F J G L H I J N J M L M Does X depend on Y ?
- 26. Things get more complicated: Route diversity, SPOF detection…
- 29. Graph-Based Network Dual Model Route Oriented Model Least cost path from A to B Diverse routes + Dependency Oriented Model No shared underlying resources IA/RCA
- 31. 🏦 :DEPENDS_ON :DEPENDS_ON :DEPENDS_ON IF/AX2431 💥 Customer Event Correlation Event Prioritisation Service Assurance IA/RCA
- 32. MATCH (fe:Link { linkId: $id})<-[:DEPENDS_ON*]-(s:Service) RETURN max(s.priority) AS severity { alarmType: “LOS”, notifyingEntity: “IF/AX/0/3”, …} Service Assurance IA/RCA GET http://localhost:11001/engine/ia/s1_361_sdh {"severitySummaryCode": 4, "severitySummaryDesc": "MAJOR", "detail": [{"elem":"2217/ol", "impact" : 1,
- 33. Graph Size: ~50M nodes (avg depth: 6)Graph Size: ~1K nodes (avg depth: 5) Simulation: 128 clients, synchronous requests with1ms wait between requests AT SCALE 50000x increase in size of dataset -> 1.14x impact in query performance Graph Native Matters!!! (Deep) Impact/Root Cause Analysis
- 34. Smart Homes
- 37. 100 requests/day x 11 devices/zone x 1.3 Mill Zones = 1.43 Bill w/req to Neo4j/day
- 38. The future…
- 41. Come meet us at Stand 30 in the Exhibition Area to see Neo4j in action Join us at 12:50 for the Data Driven Technologies roundtable on “How to bring Graph Analytics into your projects: From Network Operations to Customer Journey Analysis” Thank you!