How AI is transforming Network Performance Management
As networks grow, network performance management (NPM) grows infinitely more complicated.
The days of IT departments having everything in their field of vision are long gone.
Organisations now have a wealth of software and assets – from staff laptops, to business-critical apps, to Internet of Things (IoT) devices – running globally and concurrently and generating a wealth of operational data every second. What’s more, many of these devices and data sources operate outside the WAN, in the realms of the cloud.
Frankly, this data is simply too distributed and too complex for human teams to capture, interrogate and remediate unaided. So it’s helpful and reassuring to know… that the machines are coming.
In the field of NPM, Artificial Intelligence (AI) and Machine Learning (ML) is certainly nothing to be feared. AI and ML-powered NPM systems allow network teams to quickly identify patterns and unusual behaviours, spot trends before they have a business impact, and record multi-variable anomalies that humans simply can’t see. That’s because they can analyse and learn from every single networked data source: from user and device data, through to application log data, infrastructure metrics and network packet / flow data.
Below, we explore five key ways in which AI and Machine Learning are transforming Network Performance Management.
- Predictive analysis
Making predictions is one of AI’s core superpowers. By collecting and analysing data over time, it becomes possible to predict, for example, that your network capacity will not be sufficient in the coming days, weeks, or precisely next Thursday at 11.14. Armed with this information, you can decide the best course of action well in advance.
- Spotting patterns
Some patterns may be easy to see. But undoubtedly, the more data you have, the harder it is to sift through it all and join the dots. Systems that employ Machine Learning, however, can intelligently gather and analyse vast quantities of data very quickly, interrogate it, analyse it, and identify patterns and (ir)regularities in network behaviours.
- Highlighting correlations
Similarly, using AI and ML to sift through all network performance data enables you to establish baselines and see correlated ‘spikes’ in metrics between different areas of the network, which will help to resolve mysterious issues when troubleshooting. This is especially useful, for example, when trying to pinpoint performance issues in distributed containerised / microservices environments featuring thousands of nodes.
- Revealing what’s not normal
A system that intelligently learns will not only help you understand what you should expect: it will also show you when network behaviours are not normal. Network Performance Management tools that use AI and Machine Learning will proactively watch for transactions and metrics that are not behaving normally and alert you accordingly. This can give advance notification of hardware or software problems that may be affecting business processes, productivity, network responses or customer service. Importantly, the more sophisticated systems will intelligently cater for expected variances, such as seasonal peaks in traffic flow, so you’ll get a fuller picture and won’t need to analyse this detail.
- Getting to the ‘why’
One of the biggest benefits of AI and ML is that the enhanced analysis can shed more light on the true cause of network issues. It can build greater context into system alerts and carry out cross-domain root cause analysis – saving you time. That, in turn, means you are better equipped to prioritise your incident management activities, because you know which problems most demand your attention.
Riverbed: delivering actionable insight
At Riverbed, AI and ML are key areas of focus for us, as we explore new ways to deliver valuable network insights to our customers. And, as we develop innovative capabilities, we weave them into our services – including Riverbed Visibility-as-a-Service End User Experience (VaaS EUX), delivered as a managed service by BT.
A managed service helps you to deliver improved customer and user experiences by showing you what works well and what doesn’t. The real-time dashboards offer insight and help you better understand application and device performance, resolve problems faster, and improve productivity. And that means you can reduce downtime, save money, and make sure your teams are always working on what matters most to your business.
Find out more about VaaS EUX from BT, and get in touch to discuss how it could give your business unparalleled insight into ever-changing security challenges.
To discover more about the value of AI and Machine Learning in network management read Riverbed’s white paper.
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