The network monitoring teams can now tap into Internet-of-Things, software-level network, and cloud-based services to ensure maximum uptime and optimal network performance.

However, adapting to these technologies would mean defining new practices for legacy architecture integration, reengineering the monitoring workflow, and evaluating the toolkit for enhancing comprehensive and layered network management.

This guide has been designed to help network monitoring teams redefine their modus operandi to have a more effective, data-based, efficient, and responsive NMS practice.

Network Monitoring: Best Practices

The very need for having a defined network monitoring practice grows into the need to update it with time. As networks grow complex, interconnected, and integrated into the core business, the dependencies of different business functions make network uptime critical for productivity.

Teams, people, and operations will work every minute with the assumption that the network would be up and running. Having network issues even in smaller episodes can erode the collaboration between teams, bring down customer trust, and cause obvious damage to the business’ bottom line.

Hence, as networks have become denser and complex, the need for having an adaptive and heuristics-based approach for monitoring them has only become more critical.

Here is how you can reconfigure your NMS practices for a better understanding of the network and eventually, more effective management of the network:

1. Defining a Problem: Baselining Mean Network Performance.

The first step of understanding whether the network is performing at its designed levels is by having a quantitative benchmark to compare existing network performance with ideal network performance. The challenge comes in defining – what should be the ideal network performance?

Network administrators can observe network performance for a few weeks to a few months across different business activity levels. At the end of the observing period, the network administrator will have a mean network performance benchmark. This can be used to establish a threshold of performance across the network.

Setting the threshold is only one part of the solution. The other part focuses on getting alerts as soon as the threshold has been breached.

This way, the baselined mean performance of one node or element in the network can stand as a proxy to show issues in some other part of the network. For instance, if CPU usage grows at an aggressive rate against the baseline usage, some change is worth studying in the network.

Such baselining helps the network administrators become proactive to resolve an issue, instead of being reactive and waiting for someone to raise a complaint. More time and resources are saved that would have gone into handling the downtime and managing customers waiting on the line.

2. Defining Issue-Ownership to Expedite Resolutions.

The first step sets the second one in momentum. Once you have established the baseline, you have the alerts coming in. Now, all you have to do is define – who should be informed at what point.

This is a critical step in controlling MTTR. Often, enterprises with large IT teams end up getting the alerts at the right time, but the solution is not dispatched for a long time.

This can be due to several reasons – erroneous priorities, misallocated technicians, and so on. Many of these challenges can be diffused even before they arise, simply by creating a hierarchy of ownership across the network. This hierarchy decides who gets alerted when based on an incoming alert that indicates a threshold breach.

This exercise reduces the gap between alert monitoring and acting on it. Since ownership across the network has already been divided, the rule-based alerting approach helps the network administrators focus on the problem at hand instead of getting distracted by a cohort of issues that they might not be equipped to solve.

3. Layer-Sensitive Report Generation.

Communication across a complex network is often dictated by an open system interconnection model. This allows teams to focus on the interoperability of the system instead of focusing on the underlying technology. The same prioritization has to happen in terms of report generation.

Data flow can fail at any point or points in the system. The monitoring system should be able to detect and report failures across different technologies.

Essentially, the network monitoring system should be flexible for detecting errors across the physical layer, data link layer, network packet forwarding, host-to-host communication, sessions, syntaxes, and applications.

Hence, the network monitoring system that understands the varied nature of nodes and elements in the network and tags each alert with the right source can help the NSM team launch troubleshooting protocols efficiently. Issues that are on the verge of being detected as problems can be detected early in the process.

4. Solving the Problem of NMS Data Availability’s Dependency on the Network Uptime.

Generally, network monitoring teams prefer having NMS within the network for efficient data collection and faster reporting. However, this creates an unhealthy dependency between the NMS and the network.

If the network faces an error and shuts down, the team wouldn’t have access to the data embedded in the NMS, no matter how sophisticated it is.

High Availability (HA) can solve this problem by ensuring that the NMS is running even if the network is monitors goes down for any reason. While HA may seem like a secondary measure, it can save you from the circular problem of network downtime.

5. Availability of Data Across a Timeline.

Just the availability of alerts across a timeline can help in filtering the problems form the issues and aid the RCA process. Getting a notification and solving it is the everyday idea of monitoring.

But, having a repository of alerts with the right source of the issue tagged in them can help build intelligent systems that help in expediting the resolution process.

Your network monitoring practices should have data available for the past hours, days, weeks, and months to give you a visually accessible picture of how a network problem is exacerbated.

6. Have a Unified View.

As companies scale, their network monitoring practices have to scale with them. A small business with a dedicated network setup and the onsite team will not run into immediate crisis since a basic tool can report on the entire network.

As businesses scale, they add new nodes in the network in the form of new offices in varied locations and cloud infrastructure.

Your network monitoring system has to be engineered in a way that allows you to have a centralized view of the entire network, available in an accessible manner on one platform.

This will give you a clear understanding of large-scale network trends as well as how each node in the network interplays with the other nodes across the network.

In Conclusion

Some network monitoring teams may feel that while these best practices are designed for enhanced network monitoring efficacy, they can be ‘too much to ask for’ in terms of resources allocated to the NMS. That problem can be easily solved with a tool that has been engineered on the foundation of these best practices.

Motadata brings each of the best practices as its native features. You can have layer-based reporting, HA, historical records, and a federated view of the entire network, including different locations, nodes, and IT assets in one place. You won’t have to spend more time in reengineering the network monitoring process. Motadata’s features singlehandedly make your process more responsive, efficient, and systematic.