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Transform Your Telecom Operations with Graph Technologies

The telco industry faces an ever-increasing expectation from their customers on quality and availability of the services offered; any interruption or degradation of the service has a tremendously negative impact on their business and can lead to customer churn.

It’s no wonder this industry was one of the first to realize the power of graphs, especially in the areas of network and service management. Two of the three largest Telcos in the world, three of the five largest telco equipment vendors, and leading OSS vendors and major MVPDs have been using Neo4j in mission-critical solutions for years.

Join us to hear how graph technology is helping differentiate a primary OSS domain: Network and service assurance. We’ll look at why graph technology is used to help optimize network and service performance in critical areas:

•Performance management
•Fault and event management
•Service quality management
•Discovery and reconciliation

The performance, flexibility, and expressivity of a native graph platform are truly transformative for these challenging disciplines. Register and learn how you can leverage graph technology for your next generation service assurance solution.

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Transform Your Telecom Operations with Graph Technologies

  1. 1. Transform Your Telco Operations with Graph Technologies Neo4j: The #1 Platform for Connected Data
  2. 2. Jesús Barrasa Director Telecom Solutions,
 Neo4j @barrasaDV Transform Your Telco Operations with Graph Technologies Amy E. Hodler Analytics Program Manager, Neo4j @amyhodler
  3. 3. Why Graphs in Telco?
  4. 4. Requirements Capture Complexity Allow Flexibility High Performance Bridge Business - IT gap Rich, Dynamic, human friendly Graph Model on a Native Graph Platform
  5. 5. “Give me solutions that provide me and my customers with accurate and timely visibility into the state of the network and the services riding on that network” Tier1 Service Provider (*) (*) Simplified view of a graph representation of a service based on the SID model (
  6. 6. Structural vs. Discrete analysis Structural representation of composition, dependencies, recursion… A B C D E F G .25 .25 .25 .25 .5 .5 MATCH (:Device)-[*]->(x:Port) WHERE NOT (x)--() Device Shelf Shelf Card Card Slot Card Port Port Port Port
  7. 7. Structural vs. Discrete analysis Data integration 123 456 789 A_123 F_456 F_789
  8. 8. “Graph data structures provide enormous advantages over relational databases overall. Ericsson has chosen to take this approach because of the tremendous advantage it has to service providers”(*) Graeme Jones. Strategic Product Manager (*)
  9. 9. The Property Graph Model Optical MUX Filter Card
  10. 10. Use Cases
  11. 11. IoT OSS/BSS Governance & Metadata Mgmnt IAM & Fraud Analysis Common Graph Use Cases in Telco Digital Transformation • Smart Homes • Assurance • Fulfilment • CRM/Support • Planning & Optimisation • Regulatory compliance • Data Lineage • Consent Mgmnt • Identity & Access Mgmnt • CEM • Customer 360 • Graph Rules Engine
  12. 12. In Network Operations Network Service Assurance White Paper
  13. 13. Fault Management
  14. 14. Fault Management (Deep) Impact/Root Cause Analysis 🏦 :DEPENDS_ON :DEPENDS_ON :DEPENDS_ON IF/AX2431 💥 Customer Event Correlation Event Prioritisation
  15. 15. MATCH (fe:Link { linkId: $id})<-[:DEPENDS_ON*]-(s:Service) RETURN max(s.priority) AS severity { alarmType: “LOS”, notifyingEntity: “IF/AX/0/3”, …} Fault Management (Deep) Impact/Root Cause Analysis GET http://localhost:11001/engine/ia/s1_361_sdh {"severitySummaryCode": 4, "severitySummaryDesc": "MAJOR", "detail": [{"elem":"2217/ol",
 "impact" : 1, ...
  16. 16. Graph Size: ~50M nodes (avg depth: 6)Graph Size: ~1K nodes (avg depth: 5) Simulation: 128 clients, synchronous requests with1ms wait between requests 50000x increase in size of dataset -> 1.14x impact in query performance Graph Native Matters! Fault Management at Scale Matters (Deep) Impact/Root Cause Analysis
  17. 17. Algorithms – Community Detection • Label Propagation ○ Spreads labels based on neighbors to infer clusters • Union Find / Connected Components ○ Finds groups of nodes that all have a path to each other
 • Strongly Connected Components ○ Finds groups of nodes that are all connected 
 to each other following the 
 direction of relationships • Louvain Modularity ○ Measures the presumed accuracy of community grouping • Triangle-Count & Clustering Coefficient ○ Measures the degree that nodes tend to cluster together Source: “Fast unfolding of communities in large networks” – Blondel, Guillaume, Lambiotte, Lefebvre
  18. 18. Change Management & What-If Analysis
  19. 19. Source: Network Science - Barabasi
  20. 20. Understanding Influence Source: “Robustness of the European power grids under intentional attack.” - R.V. Sole, M. Rosas-Casals, B. Corominas-Murtra, and S. Valverde. Source: “Network Science” - Barabasi Preventing 
 Cascading Failures with 
 4 Nodes Removed
  21. 21. Traffic Engineering
  22. 22. Traffic Engineering Diverse Routing ?
  23. 23. Traffic Engineering Diverse Routing Path Analysis Least cost path from A to B + Dependency Analysis No shared underlying resources
  24. 24. • Single-Source Shortest Path ○ Calculates “shortest” path between a node and all other nodes Algorithms - Pathfinding & Search • All-Pairs Shortest Path ○ Finds all shortest paths between all nodes
  25. 25. • Parallel Breadth-First Search & Depth-First Search ○ Traverses tree structure by exploring nearest neighbors (BFS) or down each branch (DFS) • Single-Source Shortest Path ○ Calculates path between a node and all other nodes Algorithms - Pathfinding & Search • All-Pairs Shortest Path ○ Calculates shortest path group with all shortest paths between nodes • Minimum Weight Spanning Tree ○ Calculates the path with the smallest value for visiting all nodes Least Cost Routing
  26. 26. Conclusions
  27. 27. The Neo4j Graph Platform
  28. 28. Neo4j Adoption Highlights MVPD 2 of the Top 5 US multichannel video service providers have chosen Neo4j SW & Eqpmnt Vendors 3 of the 5 largest Telco software and equipment vendors in the world embed Neo4j in key products 2 of the 3 world’s largest CSP use Neo4j in mission critical solutions Largest Telcos (March 2018)
  29. 29. Magic Quadrant for Operations Support Systems. March 2018
  30. 30. Telecom is All About Connections Accurate, real-time visibility for your network and services with a native-graph platform Uncover hidden dependencies for planning and predictions with optimized graph algorithms Let’s Get Started Together Telecom page Network Service Assurance Paper optimize-network-services-neo4j/ Desktop Download
  31. 31. Questions?