Networking

Benefits of AI and Machine Learning in Network Monitoring

AI&ML are becoming more prevalent across the network, from edge to core. They’re being used for everything from security threat detection to traffic analysis. But what does this mean for your organization? How can you leverage these technologies effectively? And how do they affect existing processes and procedures?

Read on as we explore some of the latest trends in artificial intelligence and machine learning. We’ll also discuss their impact on network operations and management.

How Important is AI and ML in Managing and Monitoring Today’s Networks

AI is a technology that has been around for decades but only recently started making its way into network monitoring solutions. The reason why it’s taken so long for this type of solution to be implemented is that there were many challenges associated with implementing these types of technologies.

However, as time goes on, we will see more and more networks being monitored using AI and ML techniques. Here are some reasons why you should consider adding an AI or ML system to your existing network monitoring tool:

1. It can make better decisions than humans.
2. It doesn’t get tired like human operators do.
3. It learns over time.
4. It does not require any manual intervention.
5. It provides instant alerts when something happens.
6. It reduces false positives.
7. It helps reduce costs by automating repetitive tasks.
8. It improves efficiency.
9. It increases productivity.
10. It makes sure everything runs smoothly.

Data Processing and Analysis

AI is a powerful tool for analyzing large amounts of data quickly. It’s able to process information at an incredible rate compared to humans or even other computers. This makes it ideal for examining massive amounts of data generated by networking equipment such as switches, routers, firewalls, intrusion detection/prevention devices, etc. By using AI-based algorithms, you’ll be able to identify trends and patterns within this data. You may then be able to take action before problems arise.

For example, if you notice that traffic between two specific IP addresses is increasing rapidly, you could block access to those IP addresses until a further investigation reveals what’s going on.

Machine learning is another form of artificial intelligence that uses statistical methods to analyze data without programing each individual algorithm yourself. Instead, you use pre-programmed models that learn how to solve certain problems based on previous experiences. These models can also adapt to new situations very easily.

Automatic Problem Solving

AI can be used to solve many different types of networking problems. One example would be automatic troubleshooting. When something goes wrong on your network, you need someone who understands how things work to fix the issue. However, if you’re using an NPM, then you don’t necessarily need to call up a technician or engineer. Instead, you can let the AI do the heavy lifting by analyzing the data sent from your devices.

If it detects anything unusual, it will automatically send out notifications via email or SMS. In addition, it might suggest possible fixes in order to resolve the issue. For instance, if you have a router that isn’t forwarding packets properly, the AI may recommend changing the settings. Or maybe it suggests upgrading firmware. Either way, it saves you money while ensuring all your devices run correctly.

Customizable Response Capabilities

AI doesn’t just provide answers; it gives you options when dealing with network issues. You may not want to take action immediately after receiving alerts from an NPM. Instead, you might need some additional time to think things through or consider other factors. In these cases, you’ll still receive notifications, but you won’t necessarily see any immediate results.

This flexibility makes AI ideal for situations where you don’t always know how to react to certain events.

What are the Challenges Associated with Using AI/ML in Network Monitoring?

There are two main issues:

Data quality refers to whether the information provided by the sensors is accurate enough to provide meaningful insights.

Scalability relates to the ability to collect large amounts of data quickly without compromising accuracy. Both of these problems are exacerbated when dealing with IoT devices.

For example, if you want to monitor thousands of servers, each one needs its own sensor. If any single server fails, then all of those other sensors stop working too.

Why Does it Matter How we Monitor our Networks?

It matters because it affects the way we operate our business. In fact, according to Gartner, “By 2022, 80% of enterprises will rely heavily on AI-powered analytics to drive operational efficiency.” As mentioned earlier, AI helps reduce human error while automating tedious tasks. So, not only does it save money, it makes people happier!

Are there any examples where AI/ML helped improve Network Management?

Yes! Here are three ways AI/ML improved network monitoring:

1) Automated Threat Detection –

With AI, we can detect threats faster than ever before. By analyzing millions of events per second, we can identify anomalies and take action immediately.

2) Real-Time Analytics –

Using AI, we can analyze massive volumes of data within seconds instead of minutes. This allows us to spot patterns and predict future behavior.

3) Predictive Maintenance –

AI enables predictive maintenance through intelligent anomaly detection.

What Can be Done with AI/ML to Help us Better Understand, Manage and Monitor our Networks?

AI has a lot of potential to transform the way that we think about managing our networks. We could start by looking at what happens after something goes wrong. Instead of having to wait weeks or months to get answers from IT staff, we could ask questions directly to the device itself.

Imagine asking Siri, “Hey Siri, did my router reboot?” Or “Hey Cortana, did I lose connectivity?”. Then, based on the answer, we would know exactly what happened and who’s responsible.

What are some examples of what AI/ML could provide for Network Management?

Here are just a few ideas:

• Detecting outages as they happen.
• Identifying root causes of an outage.
• Predicting failures before they occur.

How Do we Make Sure That We don’t End Up Creating Another Layer of Complexity?

We need to ensure that we’re building systems that work well together. That means making sure that we have good interfaces between layers so that we can easily share information across them. It also requires thinking carefully about which parts should remain manual. And finally, we must consider how much automation is appropriate. There may come a point where we simply cannot automate everything.

Is there anything else that should be considered when thinking about using AI/ML for Network Monitoring?

We need to keep in mind that this technology isn’t perfect yet. For example, if you use ML to classify images, you’ll find that your system might misclassify certain objects. You’d want to check these results against known samples to see whether they match expectations. Also, remember that even though AI is getting smarter every day, it still doesn’t always perform perfectly. Sometimes, humans are needed to correct mistakes made by machines.

Can AI/ML Replace Humans or Augment Them?

It depends on the task. If you’re trying to build a self-driving car, then yes, AI will definitely replace human drivers. But if you’re trying to diagnose why someone’s computer crashed last night, then no, AI won’t be able to handle all tasks. Humans are still necessary because they can look past surface-level problems and dig deeper into underlying issues.

Do you recommend starting off small or going big right away?

Start small and grow slowly. The more data you collect, the easier it becomes to train models. So, instead of jumping straight into large scale deployments, try collecting data first. Once you’ve got enough data, you can begin training machine learning algorithms.

AI can certainly improve many aspects of networking, but it shouldn’t be seen as a replacement for people.

Where Can we go Next?

There are lots of exciting things happening right now in terms of applying machine learning techniques to networking. Here are just a few areas worth exploring:

• Deep Learning – A new type of neural net architecture that uses multiple hidden layers to learn complex relationships.
• Reinforcement Learning – An approach to training algorithms that rewards success rather than punishing failure.