AI Developments for Network Health

Almost every big or small organization today depends largely on its network performance.

A healthy and secure network is the key to a successful organization now. Many industry insiders say that they have to tackle data breaches almost every day of the year.

In light of anomalies and security threats occurring, it is more important to carry out routine network performance and security monitoring.

What is a network health check or network monitoring?

Network monitoring refers to the constant monitoring of the network for any anomalies, like slow or failing components. The network administrator is notified immediately in case of any trouble.

A network health check does not necessarily have to be a constantly running activity. It is a routine checkup of the network’s functionality. An engineer may analyze and report on any bottlenecks in the network performance or reliability issues and suggest improvements.

There are several tools available for network monitoring. These tools mostly monitor the network’s bandwidth and availability, the network’s speed, the load on the network and CPU, memory and storage space available, among other things.

With the advent of artificial intelligence and machine learning, network management is going to become much easier and less tedious. Human effort in network management will be greatly reduced.

AI application in network monitoring

The application of AI and machine learning are widely sought-after in every field today. Who wouldn’t want a system that can learn and improve upon itself without much effort on the human side? That would make things much easier and faster. By combining the human intellect and creativity with the self-improving algorithms of an AI system, failproof networks will soon be a reality.

Many network monitoring tools have already begun implementing AI and machine learning in their systems. The use of AI in network performance monitoring isn’t very widespread yet, but companies now understand the benefits that AI will bring to their networks.

The most common application of AI in network monitoring is in data processing. Networks generate a huge amount of data daily. Network monitoring requires a system to process all this data to understand what is going on in the network. An AI system can scan through a huge stack of data very quickly and can provide an analysis of the performance.

The AI is constantly running diagnostics, which gives ample amount of current and historical data to compare. Through machine learning from historical data, the system can learn to look for network trends on its own.

From problematic data being encountered, the machine learning system can begin to detect similar problematic data in the future and can alert the network administrator about any potential threats. This makes the network management tool more pro-active than reactive.

The AI-driven machine learning system will begin to familiarize from experiences and provide better and quicker insight into the network performance.

In addition to this, the AI system can provide bandwidth and delay estimation to the administrator for better video or gaming experience and provide fair bandwidth allocation to end users. It can also fix insufficient network utilization.

Problem-solving with AI

An AI system can be used not only to identify problems and notify about them, but may also be trained to solve problems without human intervention.

The machine learning system can identify common or recurring problems that affect the network and figure out solutions to deal with them, from past records.

The system will gradually learn to solve these problems on its own when it has enough data to support its decision.

The AI can even patch out potential issues before they cause harm to your network.

Another big advantage of AI is that it can be trained. It is up to the network administrator to identify the most common problems and the biggest threats and train the system to detect these problems better.

AI in disaster recovery solution

It is not enough for the system to be able to detect and solve problems before they occur; the system also needs to be prepared for worst case scenarios.

There may be problems and anomalies that go undetected even in an AI-driven system. As already discussed, the AI system is ultimately a trained algorithm which will be taught by humans based on previously encountered problems. But what about problems that may not have occurred earlier? These kinds of anomalies will have no data for the system to learn from.

In case of a network failure resulting from such problems, the AI can be trained to save the data in the network from the backup data.

The AI can initiate disaster recovery if it is connected to cloud-based network backup and recovery sources. The speed of initiating these activities is equally important to avert any damage to the network data.

In fact, the AI system itself can also be used to backup network data.

Through network automation, the AI system can be trained to perform certain repetitive tasks, such as creating backups of the network data at regular intervals, without human intervention. This will make sure that the backup process is continuous and will prevent any loss of data in case of a disruption.

Other advantages of having an AI-driven system

Other than running diagnostics and problem solving, the AI system can also help the network in other related activities.

The AI system can analyze the traffic on the network. And instead of just monitoring the levels of traffic, the AI can also warn about malicious activities on the network, such as attempted hacks.

As the network undergoes rapid upgradation with advancements in technology, the machine learning system can also upgrade itself with data inputs from the network itself.

Without AI or machine learning, after every little development in the network, the human resource would have to be trained to adapt to the change. With machine learning, that will hardly be necessary as the system learns from its own experiences. There might only be the need for a little change in programming now and then.

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