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.

1. Transform Your Telco
Operations with Graph
Technologies
Neo4j: The #1 Platform for Connected Data

2. Jesús Barrasa
Director Telecom Solutions,
Neo4j
jesus.barrasa@neo4j.com
@barrasaDV
Transform Your Telco Operations with Graph Technologies
Amy E. Hodler
Analytics Program Manager,
Neo4j
amy.hodler@neo4j.com
@amyhodler

3. Why Graphs in Telco?

4. Requirements
Capture Complexity
Allow Flexibility
High Performance
Bridge Business - IT gap
Rich, Dynamic, human
friendly Graph Model on
a Native Graph Platform

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 (*)
(*) https://www.tmforum.org/resources/quick_insights/maximizing-network-agility-effective-resource-management/
Simplified view of a graph representation of a service
based on the SID model (http://www.tmforum.org)

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. Structural vs. Discrete analysis
Data integration
123
456 789
A_123
F_456 F_789

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
(*) https://www.tmforum.org/resources/quick_insights/maximizing-network-agility-effective-resource-management/

9. The Property Graph Model
Optical MUX Filter Card

10. Use Cases

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. In Network Operations
Network Service Assurance White Paper
neo4j.com/whitepapers/white-paper-optimize-network-services-neo4j/

13. Fault Management

14. Fault Management
(Deep) Impact/Root Cause Analysis
🏦 :DEPENDS_ON
:DEPENDS_ON
:DEPENDS_ON
IF/AX2431
💥
Customer
Event Correlation
Event Prioritisation

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. 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. 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. Change Management
& What-If Analysis

19. Source: Network Science - Barabasi

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. Traffic Engineering

22. Traffic Engineering
Diverse Routing
?

23. Traffic Engineering
Diverse Routing
Path Analysis
Least cost path from A to B
+
Dependency Analysis
No shared underlying
resources

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. • 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. Conclusions

27. The Neo4j Graph Platform

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. Magic Quadrant for Operations
Support Systems. March 2018

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
neo4j.com/industries/telecom/
Network Service Assurance Paper
neo4j.com/whitepapers/white-paper-
optimize-network-services-neo4j/
Desktop Download
neo4j.com/download/

31. Questions?