Why Network Traffic Prediction Matters To Network Managers


Network managers and operations teams have a lot of work on their plate. They’re often under immense pressure trying to keep up with the flow of new tickets, while doing all they can to enhance the resilience of the network and stay one step ahead of emerging threats. 

Fortunately, the right tools can help them stay on top of their workload. There’s growing awareness of the value of using predictive solutions for network management. Here’s why they matter, and what you could achieve with predictive network management tools in your corner. 

Why network managers need predictive capabilities for both short- and long-term 

With predictive network management tools, network managers can work more effectively to prevent downtime in the short term, and proactively boost network resilience to increase uptime in the long term. 

Short-term network traffic predictions spot anomalies that, when correlated against each other, indicate that a site is liable to go down in the near future. An alert can enable the network operations team to prevent the outage from occurring and maintain site availability. For example, a site might be connected to a router which is showing high CPU utilization, rising temperatures, and intermittent packet loss. A predictive network management tool can understand the implications and issue an alert about impending outage, together with the information that engineers need to fix the situation. 

Long-term predictions enable network ops teams to prepare in advance for upcoming conditions and make the network more resilient. For example, the tool might warn the team that there’s a rise in traffic every Wednesday morning at 10am, and it’s expected to occur this Wednesday too. If caught unprepared, the site might go down or run slowly, but with advance warning the team can investigate the situation beforehand. This might involve ensuring that all hands are on deck to carry out root cause analysis immediately, or talking to their service provider to get more bandwidth, even if the network has enough bandwidth for a typical weekday. If more traffic than usual is expected this week, it’s likely to exceed the network supported capacity and it will be critical to add extra bandwidth in advance. 

Predictive network management tools also save teams from wasting time on minor or unnecessary issues. Non-predictive tools tend to rely on overly-rigid thresholds which can generate an alert because a metric crossed the baseline, even if this event has no impact on user experience. For example, the network might be running slowly, but it’s 3am and nobody’s in the office, so no one is affected by the temporary drop in speed. 

The state of traffic prediction for network management today

Most network managers have all the data they need to achieve this level of insight into network traffic, but their tools can’t cope with the complexity of correlating information and generating meaningful predictions. The result is that very few organizations have predictions; they just have data.

There are many reasons why predictive insights are so challenging. Predicting network traffic requires more information than just traffic levels. Device performance, operational and resource KPIs, applications, and services all provide data that impact traffic predictions. This amounts to a massive amount of data which is way beyond the capabilities of manual analysis, and also too much for most existing tools. 

The challenge is compounded by the fact that data comes from so many different sources. Every switch, firewall, and router measures its own conditions and sends incidents to its own server; other servers measure top KPIs; and still more track and verify configurations. It’s no simple task to assemble all these metrics and KPIs on a single graph for correlation. 

Today’s solutions typically carry out network performance monitoring, providing real time information and sometimes limited logging. They only store data for a limited time, and without historical data it’s extremely difficult to spot patterns and even harder to make predictions about them moving forward. They lack the artificial intelligence (AI) capabilities to see the bigger picture. They can aggregate information, but can’t take the final step of understanding what is happening or predicting what will happen. The best you can get is to study the data for each KPI or metric in isolation, make a prediction for each one, and then bring those predictions together to try to correlate them. 

Given the current state of network management tools, customers seeking traffic predictions need to work out for themselves what data to take from each source, what to look for within the data, how to put it together, and what to look at in the results. This is time-consuming work that’s even more difficult to do in real time, and network operations teams are overstretched and constantly putting out fires. They don’t have the time to run laborious traffic predictions. 

The pains of not having good solutions for network traffic prediction

The lack of predictive capabilities hinders network management teams in a number of ways. Investigating the root causes of a performance issue is much slower. Network Operations teams want to correlate traffic levels and various KPIs to the exact second when the issue arises. Doing this manually is slow and reactive, requiring some trial and error to work out what is causing the issue. With an automated predictive solution, you gain deeper visibility into the root cause, and far more quickly. 

Existing monitoring solutions can add to the workload instead of lightening it. The rigid thresholds can automatically open unnecessary tickets or generate false alerts, using up network ops time and energy in unnecessary work. For example, a device might cross the threshold for high CPU use and automatically open a ticket. But if the users are office workers opening documents, the fact that the network is running a little slowly may not be a big deal. Smart predictions take expected behavior for the network and impact on users into account, so notifications are only sent when the predicted issue is likely to affect user experience. 

At the same time, network problems can go unseen without predictive capabilities. Management teams need to know not just about the unexpected incident, but also about the expected situation which didn’t occur. For example, a large online retail site may predict that they’ll see a certain level of traffic every Sunday evening, but this Sunday, traffic levels are down by 70%. That’s a sign that something is wrong, but without the earlier prediction of expected traffic levels, the team wouldn’t know that current levels are an anomaly, and there’d be no cause for the solution to issue an alert.

What NetOp does

Thankfully, NetOp can help. NetOp correlates all anomalies across a wide number of KPIs and considers more indicators than simply traffic levels, so it can produce automated predictions using powerful AI. By employing predictive capabilities, NetOp can spot the problem that isn’t there, such as when traffic is lower than expected. Finally, NetOp deploys dynamic thresholds after analyzing and learning the norms for each individual network’s baseline, so as to provide more relevant and accurate alerts. 

Predictive capabilities can save the day

With the help of predictive network management tools like NetOp, network management teams can reduce downtime and slowdowns, proactively improve network resilience, save time on root cause analysis, and avoid unnecessary alerts, allowing them to work more efficiently and deliver a stronger, more reliable network for their organization.

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