The Intelligent Network Analyzer (INA) uses the graph database by Neo4j to build a digital twin of the mobile telecommunications network. Based on this digital twin, INA can be used to efficiently perform various analyses to support network operators in their daily business. In our talk, we will show some features of INA and explain how they draw on the particular strengths of the Neo4j graph database.
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Sopra Steria: Intelligent Network Analysis in a Telecommunications Environment
1. Chart Library
ALL THE CHARTS TO ENRICH YOUR POWERPOINT
PRESENTATIONS
INTELLIGENT NETWORK
ANALYZER by Sopra Steria
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INA
• We do IT Strategy, Consulting, and Solution Development
• We are 2500 consultants in the Telecom & Media vertical
• We work with clients across Europe
Tamara Kreiling
Senior Consultant
Telco, Media & Tech
2
Franziska Schmidt
Data Science Consultant
Telco, Media & Tech
Who we are What we do
Banking
Public Sector
Insurance
Aerospace
Automotive & Industrial Goods
Telecom & Media
Energy & Utilities
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TELECOMMUNICATION,
MEDIA,
TECHNOLOGY
EVER TRIED TO
CHANGE A TIRE
WHILE DRIVING?
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INA
OUR CLIENTS CHANGE THEIR RUNNING NETWORK EVERY DAY
4
* Data from annual reports of Telekom, Vodafone und Telefónica from 2020
Of Network changes fail at first
iteration
10%
1000
80%
Network changes and manual
data entries every day
Of network outages due to
changes
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INA
Before: Manually maintained network documentation
With INA: Daily updated digital twin of network topology
Fragmented / spread data collection in silos
No end-to-end view on the network
Error-proneness in manual documentation
Outdated, static planning data
Building a meta inventory is cost and time intensive
Contextual influence e.g. nw changes not considered
Inventories not in sync with the real network status
Analysis of network traffic flows not possible
Prediction of topology changes and impacts not possible
THE CHALLENGES
WHEN MANAGING NETWORK CHANGES
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INA
• Aligning network interfaces to multi vendor networks
• Modeling and mapping multi vendor networks
• Ensuring data completeness
• On the fly calculation of end-2-end routing paths
• Very performant analysis on huge, layered graphs
• The use cases are endless
• Dry-runs, what-if analysis, impact and root cause analysis…
• Automated simulations to identify neuralgic points
6
Example real world network Complicating factors
No problem with neo4j
IT AIN‘T THAT EASY IN REALITY – IF YOU DON‘T USE NEO4J
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INA
…
Web-based application: INA
Explorer utilizes thin web-
based visualization services
Great performance: Very fast
rendering of network layout
and computing of complex
analyses
High customizability: Graph
layout, edge crossing
avoidance method, and node
icons customizable at runtime
Customizable icons allow user-
dependent visualization
The complex calculation for
preventing edge crossings can
be performed at runtime.
Deep analyses executable via
right click quickly expandable
INA EXPLORER VISUALIZES THE GRAPH IN AN INTERACTIVE WAY
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SIMULATION MODULE
Simulation of any
changes in the topology
Evaluation of different
simulated variants
INA BASIS
SYSTEM
Network discovery
including functional
network model
Storage and display in
graph database
Visualization interface
Result: End-to-End
network discovered
TIME TRAVEL MODULE
Before-and-after
change comparisons of
topology
Root cause-analysis of
network changes
AI
MODULE
Topology evaluation
and suggestions
Iterative recommend.
based on the as-is
situation
Saving of versions and
entering into the
inventory process
ANALYSIS
MODULE
Root-cause analysis
Impact analysis
Prepared NW analysis:
L2 L3 routing
Network performance
and data traffic analysis
Result: Digital Twin
SOPRA STERIA INTELLIGENCE LAYER
AVAILABLE OUT-OF-THE-BOX MODULES
INA
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INA
• INA uses near real-time data that includes real
network statistics, up-to-date topology and
routing information across all network layers.
• INA simulates topology changes and their
impact on data traffic and latency.
• Complex root-cause and impact analyses can
be applied at any granularity, including
subnetwork, site and individual devices down
to a single port.
SIMULATION BASED PLANNING
INA as digital twin represents the as is E2E
network and simulates planned network changes.
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INA
A new link should be added in order to
decrease the utilization on the
overloaded red link
If the link is inserted at this point, an
overload will occur at a new point.
SIMULATION BASED PLANNING
This location for the new link is
suitable as the traffic now runs via
different routes and thus no longer
causes congestion
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INA
TROUBLESHOOTING
INA: In case of an outage, not only directly
connected antennas can be identified, but also
wider effects on the network are easy to discover
• Impact Analysis is easily possible to conduct
- Identification of impacted sites
- Consideration of all routed traffic
- identification of all dependent antennas
• Use cases: Power outages, malfunctioning
network elements, environmental influences,
sabotage
• INA interfaces with ticketing systems and can
correlate to actual incidents or changes in near
real time.
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INA
x
PREDICTIVE MAINTANANCE
INA tests the robustness of the network without
your help. And helps to combat neuralgic points.
• A virtual network engineer (Chaos Monkey)
will run continuously in the background and
randomly simulate failures by consecutively
switching points in the network on and off.
• This will identify sensitive/neuralgic points in
the network. Similarly, the extent of the
damage and the impact on other parts of the
network will be revealed.
x
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INA
Simulated Annealing
• If you would want to assign the antenna to a
yellow backbone router through the blue
routers so that utilization is minimized.
• There are 800bn possible combinations:
517
= 800bn
• Brute force calculation of all combinations
would take ~25 years if we assume 1ms per
configuration.
• INA’s Annealer feature automatically analyses
possible routing options and suggests the best
option within the tested range.
• Simulated Annealing finds optimized solution
within 60 s after 1000 tested combinations.
AUTOMATED ROUTING OPTIMIZATION
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INA
A non optimal routing is selected. The
Annealer starts to test other routing
configurations (1000 combinations
within 60s).
The annealer proposes an optimized
routing configuration next to the
input configuration for better
comparison. A data table describes
the proposed changes.
AUTOMATED ROUTING OPTIMIZATION
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INA
SUSTAINABILITY
Make the network more
sustainable by discovering
particularly energy-
intensive components,
and reduce CO2
consumption in the entire
network
SECURITY
Detection of neuralgic
points in the network,
which e.g. supply large
parts of the population or
critical infrastructures
FUTURE NETWORK
Change analysis,
simulation and planning
to verify network changes
before they are applied to
the network
PREDICTIVE
MAINTANANCE Avoid
network outages and
misconfigurations as
mismatches will be
detected beforehand
COVERAGE
Measure network
coverage and evaluate
options to reach the
expansion targets and
increase service quality
Model further,
individualized cases and
find answers to questions
as you need them when
you need them
INA‘s potential
for the whole
telecom
business.
INA's technology allows users to combine data points that help to answer very
specific questions in a variety of use cases. In addition, INA offers ready-made use
cases, as well as the possibility to jointly develop even more.
SUSTAINABILITY
Discover particularly
energy-intensive
components, and reduce
CO2 consumption in the
entire network
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