Apply the Right Network Traffic Monitoring Tool
Beyond a network topology mapper, monitoring network traffic generally requires four additional essential network monitoring tools:
NetFlow Analyzer
While was created by Cisco, the term, “Netflow Analyzer” is now a generic term used to describe flow data from any vendor, such as Juniper, a.k.a., JFlow. IPFIX is a flow standard used with many vendors. In short, flow analysis of network traffic is essential to see the full picture, such as network traffic from site-to-site or device-to-device. Most network traffic issues can be resolved through flow analysis.
Packet Analyzer
A packet analyzer is used to decode the actual packets of network traffic. While NetFlow Analyzers are useful for most network traffic issues, packet analyzers allow you to analyze each packet for deep packet inspection (DPI) and troubleshoot more difficult application issues, especially those related to voice over IP (VoIP) and video conferencing, such as Cisco WebEx conferencing.
Network Performance Dashboard
Most network traffic monitoring toolsets come complete with a performance dashboard. These dashboards provide a high-level overview of what’s happening with network traffic. Enterprise-level tools, such as LiveNX, allow for the consolidation of all data sources, so you truly have a complete picture of your entire network, across all domains.
Network Monitoring Reports
Network traffic monitoring usually requires both real-time and historic reporting. Real-time reports are visual analytics to monitoring what going on with network traffic now. Historic reports are useful for planning, providing updates to key stakeholders, and even forensic troubleshooting of network incidents. More complex network environments require reporting processing at scale as network data sizes can be massive and bog down many monitoring tools not up to the task.
Proactive Alerts
Alerts, especially proactive alerts, are vital for tuning into network traffic issues that need immediate attention, separating the relevant issues from the noise. Increasingly, these alerts are powered by AI and machine learning so that variances in network traffic are correlated and isolated to produce meaningful alerts, a.k.a., anomaly detection.