This document discusses using Splunk to monitor and debug a real-time self-adapting traffic management system. It includes 4 sections that discuss using Splunk to analyze traffic profiles from probe data, monitor errors, system load, and performance measurements. It also provides a tip for using Splunk with Windows Azure and sending data to Splunk over UDP for better efficiency with large data volumes.

1. Copyright © 2014 Splunk Inc.
Splunk at Graphmasters

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Worldwide traffic

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Selfish Routing

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Collaborative Routing

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Sebastian Heise, MSc
• COO & Founder
• Pattern Recognition &
Machine Learning

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Our Challenge
We are running a
real-time self adapting system.
How to debug?

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Many Data Connectors
Log data from
the app
Log data from
traffic feeds
(probes)
Internal log
data from
infrastructure
NUNAV Driver
NUNAV Cloud
City Traffic Management
Sensor Data
GPS DataMap Data
Incident Data

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traffic profiles from probe data
#1

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Lane Identification

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Traffic Prediction & Pattern

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Road Sampling

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Always-On Data-Processing
#2

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Errors

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System Load

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Performance Measurements
#3

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Graphs showing predicted arrival time
accuracy

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Top Tip
We use Splunk with Windows Azure (all our core systems are there)
When doing timespan queries from Azure Table Storage encode the
time in the partition/row key (Azure Diagnostics dumped to table
storage also have this encoding – use it).
If you are pushing a lot of data into Splunk, do it over UDP - it’s much
more efficient

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Let‘s make the world a better place!
#4
Without Splunk, this
would be even harder!

20. Thank You