Artificial Intelligence, advanced analytics, and next-generation monitoring are promising to deliver their benefits, but only when properly applied to the right problems. With that done, next-gen network analytics driven by Machine Learning (ML) and Artificial Intelligence (AI) will be able to revolutionize traditional infrastructure management models, reduce costs, simplify operations, and provide new insights. AI can be trained to pinpoint network shortcomings and failures (even before they happen) and detect where the problem is. Also, it can fix insufficient network utilization, provide bandwidth and delay estimation, and solve network congestion issues.

The first AI-based network analytics were identifying unusual behavior but were limited to collecting time-series data. Today, more specialized and advanced AI/ML algorithms can leverage when networking elements are controlled are providing any additional monitoring capabilities. With this type of AI approach, AI algorithms are moving from detecting to detect-and-act, meaning that the AI can analyze and take real-time actions on the network.

Benefits of AI-based Automation?

Before the utilization of AI, network providers typically used deep-packet inspection (DPI) or another sort of packet filtering. It allowed them to dissect individual network packets and collect information they needed to identify and fix network problems. However, this approach was inefficient because no entity had access to the entire provider-user networking path. It is limited what operators could understand from the DPI as well as what could be done about the network issue. Also, DPI is increasingly unable to decrypt the information from many packets due to expanding packet encryption. Therefore, the data is rendered almost useless.

AI tools can be trained to detect network problems by using metrics they can easily connect across different points on the delivery path. Also, AI can perform without the need for HTTP traffic analysis or DPI, which makes it a very suitable solution for encrypted packets traversing through multi-operator networks or from private to the public cloud (and vice versa).

AI enables the automation of detection and analysis, laying the ground for self-remediation actions. On the other hand, it also allows IT operators to spend their time on solving tasks that are less mundane. The profile of network problems will change by AI-based automation, as well as their impact on enterprises. Some network problems that we face today can often be urgent, but with AI, the root causes can be identified, mitigated and resolved much faster, or at least converted into less-urgent issues.

Improved Nextgen Monitoring

Enterprises and service providers need to prove that they’re able to deliver high-performance solutions to their network issues to stay competitive in the ever-changing IT landscape. Without the help of a comprehensive platform that allows for continuous network monitoring, it would be nearly impossible to rise to that challenge. The next-generation AI-based control will enable enterprises to proactively manage their elastic and dynamic workloads, as well as improve their cybersecurity, thanks to:

• Alerts that notify about automatically detected abnormal changes or unusual behaviors
• Full visibility into applications and network infrastructure
• Continuous compliance in the realm of infrastructure-as-code
• Gaining insights and developing innovative solutions by analyzing historical data

Lack of Skills in the Industry

Any organization that seeks to integrate automation and AI processes will need staff who understand and can work with the technology. The industry will need more domain experts and data scientists who understand the importance of the training datasets as they optimize their machine learning strategy. It is what will determine how intelligent the AI is.

Today, we see a change in network IT – from traditional-configured boxes to AP programming. To be able to maintain AI-powered systems, IT operators need to master new skill sets, while trust between man and machine must also develop. For enterprises that want to implement AI-based infrastructure, they need to make sure their data is ready for AI. For this; required is a central data hub for extracting and analyzing data from multiple sources to make effective use of the data. It is essential to building AI into the foundation of the enterprise network architecture – businesses need to reimagine and rebuild themselves on a foundation powered by AI.

AI-based tools can only be as right as their training, so IT operators need to make sure they develop their systems with the right ML algorithms, correct amount and type of data, and approach for their specific application. There is no doubt that AI will disrupt the ways we operate IT. With the advancements in deep learning, AI-based systems will be able to manage large networks dynamically and efficiently.