Network Monitoring
with InfluxDB
Charles Mahler –
Technical Writer, InfluxData
This session looks at the Network Monitoring use case
and how InfluxDB enables these types of solutions.
Charles Mahler
Technical Writer, InfluxData
Charles Mahler is a Technical Writer at InfluxData where
he creates content to help educate users on the
InfluxData and time series data ecosystem. Charles’
background includes working in digital marketing and
full-stack software development.
Network Monitoring with InfluxDB
Agenda
1. Challenges of Network Monitoring
2. How InfluxDB helps solve those challenges
3. InfluxDB Network Monitoring use cases
Use Case Categories
5
IoT Monitoring Developer Tools Real-time Analytics
Industrial
Enterprise
Consumer
DevOps
Networks
Security
Cloud
Applications
APIs
Gaming
Renewable
Energy
FinTech
Crypto
Challenges of Network Monitoring
Collecting Data
• Legacy hardware
• Proprietary protocols
• Most important part
Storing Data
• Cost
• Complexity
Analyzing Data
• Query data efficiently
• Allow non-experts to
generate value from data
• Take action on data
How the InfluxDB Ecosystem can
help solve these challenges
Data Sources
Application
Workflows
Infrastructure
Insights
Telegraf
Client Libraries
HTTP
Syslog
Kubernetes
Apache Kafka
Python
Arduino
Node.js
JavaScript
Go
Data Systems
Mobile apps
Web apps
Cloud Services
Devices
Sensors
Databases
Networks
Message Queues
APIs
IoT Platforms
CRMs
InfluxDB Platform
IoT
Actions
InfluxDB
Purpose-Built Time Series Database
Visualization, Query & Task Engine
Collect
Downsample
Trigger
Alert
Transform
…
300+ Plugins
12 Languages
…
Native Ecosystems
JMeter
NiFi
AWS Kinesis
Azure Event Hubs
GCP PubSub
Java
.NET/C#
PHP
Ruby
Vector
Fluentd
Scrapers
Native Collectors
Telegraf
• Easy to deploy
• 300+ plugins
Ecosystem Integrations
• FluentD
• Apache NiFi
• Vector
• Line Protocol support
Client Libraries
• 12+ Languages
• Best practices out of the
box
Data Collection
Telegraf Deployment patterns
● Per device sidecar pattern
● single Telegraf instance for multiple devices
Telegraf processors for Network Monitoring
● Normalizing data
● Enriching data
● Filtering and rate limiting
Data Storage
Storage Costs
• Compression
• Data Management features
Management Complexity
• Scaling
• Integrating with other tools
• Deployment flexibility
Edge data architecture
Edge
Data
Replication
Local UI
and Alerts
Gateway
Device Metrics
Aggregated UI
and Alerts
InfluxDB
Bucket
InfluxDB
Bucket
InfluxDB
Bucket
InfluxDB
Bucket
InfluxDB
Bucket
InfluxDB
Bucket
Edges
External
Databases
Cloud Data
Sources
• Query performance
• Allowing non-experts to gain value from data
• Acting on data
Analyzing Data
Network Monitoring Use cases
Security Monitoring
• Behavior modeling
• Intrusion detection
Performance Monitoring
• Faster incident resolution
• Reduce toil
• Improve resource efficiency
Forecasting
• Hardware capacity estimates
• Predictive maintenance
InfluxDB Security Use Cases
InfluxDB Performance Monitoring Use Cases
● Cisco
○ Use InfluxDB for E-commerce platform performance monitoring and
Cisco Live network monitoring
○ 2300 wireless access points
○ 650 switches
○ 76TB of network data over 5 days
● Red Hat
○ Use InfluxDB for internal network monitoring
○ 60+ locations
○ 1600 network devices
○ 14000 network interfaces
InfluxDB Forecasting Use Cases
• Hardware capacity planning
• Renewable energy and power grid optimization
• Predictive maintenance
T H A N K Y O U

Charles Mahler [InfluxData] | Use Case: Networking Monitoring | InfluxDays 2022

  • 2.
    Network Monitoring with InfluxDB CharlesMahler – Technical Writer, InfluxData
  • 3.
    This session looksat the Network Monitoring use case and how InfluxDB enables these types of solutions. Charles Mahler Technical Writer, InfluxData Charles Mahler is a Technical Writer at InfluxData where he creates content to help educate users on the InfluxData and time series data ecosystem. Charles’ background includes working in digital marketing and full-stack software development. Network Monitoring with InfluxDB
  • 4.
    Agenda 1. Challenges ofNetwork Monitoring 2. How InfluxDB helps solve those challenges 3. InfluxDB Network Monitoring use cases
  • 5.
    Use Case Categories 5 IoTMonitoring Developer Tools Real-time Analytics Industrial Enterprise Consumer DevOps Networks Security Cloud Applications APIs Gaming Renewable Energy FinTech Crypto
  • 6.
    Challenges of NetworkMonitoring Collecting Data • Legacy hardware • Proprietary protocols • Most important part Storing Data • Cost • Complexity Analyzing Data • Query data efficiently • Allow non-experts to generate value from data • Take action on data
  • 7.
    How the InfluxDBEcosystem can help solve these challenges
  • 8.
    Data Sources Application Workflows Infrastructure Insights Telegraf Client Libraries HTTP Syslog Kubernetes ApacheKafka Python Arduino Node.js JavaScript Go Data Systems Mobile apps Web apps Cloud Services Devices Sensors Databases Networks Message Queues APIs IoT Platforms CRMs InfluxDB Platform IoT Actions InfluxDB Purpose-Built Time Series Database Visualization, Query & Task Engine Collect Downsample Trigger Alert Transform … 300+ Plugins 12 Languages … Native Ecosystems JMeter NiFi AWS Kinesis Azure Event Hubs GCP PubSub Java .NET/C# PHP Ruby Vector Fluentd Scrapers Native Collectors
  • 9.
    Telegraf • Easy todeploy • 300+ plugins Ecosystem Integrations • FluentD • Apache NiFi • Vector • Line Protocol support Client Libraries • 12+ Languages • Best practices out of the box Data Collection
  • 10.
    Telegraf Deployment patterns ●Per device sidecar pattern ● single Telegraf instance for multiple devices
  • 11.
    Telegraf processors forNetwork Monitoring ● Normalizing data ● Enriching data ● Filtering and rate limiting
  • 12.
    Data Storage Storage Costs •Compression • Data Management features Management Complexity • Scaling • Integrating with other tools • Deployment flexibility
  • 13.
    Edge data architecture Edge Data Replication LocalUI and Alerts Gateway Device Metrics Aggregated UI and Alerts InfluxDB Bucket InfluxDB Bucket InfluxDB Bucket InfluxDB Bucket InfluxDB Bucket InfluxDB Bucket Edges External Databases Cloud Data Sources
  • 14.
    • Query performance •Allowing non-experts to gain value from data • Acting on data Analyzing Data
  • 15.
    Network Monitoring Usecases Security Monitoring • Behavior modeling • Intrusion detection Performance Monitoring • Faster incident resolution • Reduce toil • Improve resource efficiency Forecasting • Hardware capacity estimates • Predictive maintenance
  • 16.
  • 17.
    InfluxDB Performance MonitoringUse Cases ● Cisco ○ Use InfluxDB for E-commerce platform performance monitoring and Cisco Live network monitoring ○ 2300 wireless access points ○ 650 switches ○ 76TB of network data over 5 days ● Red Hat ○ Use InfluxDB for internal network monitoring ○ 60+ locations ○ 1600 network devices ○ 14000 network interfaces
  • 18.
    InfluxDB Forecasting UseCases • Hardware capacity planning • Renewable energy and power grid optimization • Predictive maintenance
  • 19.
    T H AN K Y O U