Key takeaways
Network monitoring is about knowing exactly how your network performs and when something goes wrong. It is about delivering actionable insights into network traffic, infrastructure systems and the resulting impact on business operations.
This insight is gathered by collecting network traffic information and analyzing traffic trends in real-time. As a result of proactive and predictive network traffic analytics, network monitoring helps IT teams isolate network incidents before they can impact end-users.
Like most of technology today, the advent of AI and machine learning is likely to have a large impact on the traditional field of network monitoring. Used smartly, these can enable more sophisticated proactive and predictive capabilities.
Network monitoring is the practice of observing and analyzing network performance, health, and traffic patterns to ensure optimal network operations and identifying incidents proactively.
By tracking performance, availability, and uptime of an entire computer network, you can identify and troubleshoot problems before they impact users. This practice involves network asset discovery, mapping, data collection, and analysis.
Network monitoring is an important part of IT operations. The staff responsible for your network likely support your network operations center (NOC).
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Network monitoring helps IT teams identify anomalous behavior and optimize network efficiency in real-time. From a business perspective, network monitoring helps in the following ways:
Subsets of network monitoring can focus on specific areas of metrics.
Performance monitoring measures how the traffic and data workloads consume the available network resources. The measurements include metrics such as bandwidth usage, packet loss, jitter, and latency.
This information helps identify network bottlenecks, degraded connections or overloaded devices, allowing them to reconfigure resource distribution and traffic flows and therefore enable consistent end-user experience.
For organizations relying on cloud services, remote users, or AI-driven workloads, monitoring ISP performance is essential. It extends visibility beyond your internal network to the service provider layer, helping detect latency, packet loss, or throughput issues that originate upstream — before they affect user experience or business operations.
Availability monitoring ensures that all network components such as routers, switches, servers and services are up and running in accordance with metrics defined by the Service Level Agreement contracts (SLAs).
Availability monitoring helps identify outages, service disruptions and unresponsive devices. It involves SNMP status polling, uptime reporting, downtime incidents calculations including metrics such as MTTF and MTTR.
Configuration monitoring tracks changes to your network configurations. These changes usually represent user and traffic actions. The idea behind configuration monitoring is to:
Cloud monitoring focuses on the health, availability and performance of the cloud infrastructure, platforms and applications. Important metrics include the Service Response Time, API usage, resource utilization and cost tracking.
Sometimes considered a subset of networking monitoring, network security monitoring (NSM) detects and responds to security threats on a network. It collects data from network traffic patterns, unusual login attempts, and malware infections — all this data you can use to detect and respond to security threats.
Unlike network monitoring, it is not concerned with all network activity.
(Know the difference between network performance monitoring & application performance monitoring.)
A computer network transmits information in accordance with Protocols, which are the rules and standards for TCP/IP communication. These universal standards allow devices on a network to communicate with one another and transmit data.
In context of monitoring the performance and security of traffic flows, the following network monitoring protocols are important:
SNMP is an application layer protocol that maintains network elements. It exchanges and extracts management information shared between network devices such as switches, modems, WLAN controllers, and routers. This information is used to monitor the network's performance based on:
(Read our full guide to SNMP monitoring.)
ICMP reports errors. It's used in network devices to send error messages indicating that a requested service or host is unavailable — or that a router along the path to the destination is not functioning properly.
NetFlow is a protocol that captures IP traffic stats, aggregating packet records and preprocessing flow information and data export. It helps identify bottlenecks, analyze network bandwidth usage, track anomalous traffic patterns and perform diagnosis by analyzing traffic flows.
Your business can face diverse network monitoring challenges, and you’ll need the right strategies to prevent them. Here are a few problems to be aware of:
When issues arise, troubleshooting seems like a guessing game without proper visibility. You don't know where to look for the root cause of problems — this is the point where a lack of network visibility hurts: network admins can't visualize every area, including the devices and connections it comprises. (Observability is the solution to this problem. You might also hear it described as applied observability or full-stack observability.)
Use a network monitoring tool with a network mapping option to draw a visual representation of the entire network. This will help you to identify the devices and connections in your network.
As networks grow and evolve, traffic and resource utilization patterns can change. What was considered normal in the past might not be applicable anymore. So, your one-size-fits-all baseline will not work.
Invest in monitoring tools offering automated baseline settings to analyze historical data and trends to generate baselines more efficiently. And test their accuracy by making controlled changes and observing how the monitoring system responds.
Baselines are not static. So be prepared to adapt and refine them as your network evolves and its usage patterns change.
Networks generate a massive amount of data every second, including:
This sheer volume overwhelms monitoring systems and leads to performance bottlenecks. Since not all data is equally important, you should:
AI/ML algorithms are crucial for sifting through massive volumes of network data, identifying meaningful patterns, and prioritizing actionable insights, making sense of the 'data deluge'.
The complexity and dynamic nature of modern networks makes locating faults difficult because they consist of many interconnected components, devices and protocols. And when you can't locate faults, you fail to pinpoint the exact source of performance issues.
To avoid this, implement end-to-end monitoring to track data flows from source to destination. Doing so will help identify bottlenecks or disruptions along the path and help your teams to determine the root cause of an issue.
AI significantly accelerates root cause analysis by correlating events across complex, interconnected systems, reducing the 'guessing game' often associated with troubleshooting.
Network diagrams display the relationships and connections between network components, devices, and infrastructure elements. But this architecture's complex and technical nature makes it challenging to interpret network diagrams.
To overcome his problem, establish standard guidelines, including consistent symbols, colors, and terminology. Then, create diagrams with different layers of information, such as logical layers, physical layers and operational layers.
This focuses your team on the relevant aspects without letting unnecessary details cloud the view.
Networks host diversified applications, each with different bandwidth and resource requirements. And their traffic changes over time (depending on factors such as time of day, day of the week, and special events).
This variability makes it difficult to predict future capacity requirements accurately. So, you should employ AI-driven predictive modeling for more accurate forecasts for future capacity needs, moving beyond simple trend analysis.
Encryption and security protocols for data transmission bring in encrypted traffic that monitoring tools cannot quickly inspect. This creates blind spots — critical points where potential security threats and performance issues might occur but go undetected because of low monitoring coverage.
To eliminate blind spots, ensure your monitoring strategy covers all critical points and deploy monitoring tools at key network junctions, data centers, and entry/exit points. Use AI to analyze metadata from encrypted traffic or use behavioral patterns to detect anomalies and potential threats even when deep packet inspection (DPI) is not possible, thereby reducing blind spots
If you support distributed systems and networks, the lack of centralized point of control makes implementing consistent monitoring and management practices difficult. Distributed networks have numerous sections, including:
You should implement a centralized AI-powered monitoring platform can unify data and insights from diverse distributed environments, providing a single, intelligent view of the entire network — so you can consolidate data, analyze performance, and identify issues more effectively.
So, you’ve read this far and you’ve probably realized: it’s impossible to monitor complex networks easily, so long as you’re trying to do it manually or piece by piece. There are plenty of solutions available to help organizations monitor networks.
The smartest approach is to explore modern observability platforms that increasingly leverage AI/ML to provide deeper insights and automation across all parts of your network and systems together. (The alternate, and more common, approach is that separate components each require their own monitoring.) Splunk Observability Cloud is here to help you do just that.
Network monitoring means tracking a computer network's performance. No matter what your business size is — you should monitor networks to identify and troubleshoot problems before they impact business and user activity.
By integrating ISP monitoring into your network monitoring strategy, teams can correlate internal network metrics with ISP-level insights. This holistic view helps reduce mean time to resolution (MTTR), identify recurring provider issues, and ensure reliable connectivity for distributed users and cloud workloads.
Say goodbye to blind spots, guesswork, and swivel-chair monitoring. With Splunk Observability Cloud and AI Assistant, correlate all your metrics, logs, and traces automatically and in one place.
See an error or have a suggestion? Please let us know by emailing splunkblogs@cisco.com.
This posting does not necessarily represent Splunk's position, strategies or opinion.
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