AI & ML in Network Performance Monitoring

If you’re a tech geek you must have come across disrupting technologies like Artificial Intelligence, Machine Learning, Big Data, and IoT.

These are the key buzz words since many years.

With this blog we plan on kickstarting 2020 with the most sought-after question amongst IT administrators & DevOps team, “How will AI & ML benefit us?”, “What’s the role of AI in Networks?” & more. Well, let’s get started!

2019 began with the anticipation around Artificial Intelligence (AI) and Machine Learning (ML) for network monitoring.

This year, MSPs want to know exactly how Artificial Intelligence (AI) & Machine Learning (ML) will help them monitor IT Infrastructure to make customers life easier.

As per gartner’s market guide for AIOps platforms, “Such tools shall improve the decision-making process for IT leaders with a contextual insight on large volume of machine data. It will significantly help in automation.”

Certainly, such platforms have the capability to process large volume of data.

But there’s much more to it than just data processing like automation. With the introduction of AI in network monitoring software (NMS) some use cases will revolutionize the way you monitor!

Some of the features that we will cover in this blog are: anomaly detection, observability, correlation, predictive analytics & outliers.

Benefit of AI & ML in Network Monitoring

An IT network which is capable of fixing and optimizing itself without any human’s intervention has now become a reality.

The following are some of the major contributions that these revolutionary technologies will make:

1. Predictive Analytics

AI along with machine learning can be used to study historical machine data.

Users can take smarter decisions by knowing what has been happening in the past with the help of past trends, NMS can predict future networks better.

2. Anomaly Detection

Anomaly detection in a network monitoring tool, that can alert users whenever a metric deviates from an expected pattern.

The algorithm can be either basic or agile, depending on the flexibility offered by the vendor.

3. Baseline Alerts

AI-powered NMS offers the flexibility to setup baselines, which can be hourly, weekly or monthly.

Whenever the baseline is violated i.e. if defined threshold value goes out from the defined baseline, the tool shall generate an alert.

Best network performance monitoring tools have an option to configure the threshold value into either in Absolute or Percentage or both with number of occurrences.

4. Outlier Detection

Outlier policy detects one member in a group behaves differently from its peers. It is in a way pin pointing the odd one out of the crowd (in metrics of course!).

If we go more technical then the algorithms used to configure this by the vendor are – DBSCAN, MAD, SCALEDDBSCAN, and SCALEDMAD.

5. Forecast

Forecasting suggests when a metric is likely to cross a particular threshold in the near future.

Again, if we speak technically then the algorithms that tools use for this is either Linear or Seasonal.

6. Smart Log Parsing

Machine learning brings Log Parsing functionality on the table that too via UI. Users can use OOB available log parsers or else they can even build a new one as per their requirement

Embracing the Change

With the help of AI & ML, NMS vendors can bring in automation by using IT operational data. It is all about embracing the change when networks grow with technological advancements.

There are many organizations who still prefer to stick to the legacy architecture. It’s not wrong but evolution is necessary.

Now, there are not many vendors in the network monitoring space which are offering AIOps platform or AI-powered network monitoring systems. You need to be selective about the tool that you’re going ahead with.

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