Applying AI to network problems (Reader Forum)

Organizations receive a ton of network-related data every minute of every day. There has been a massive increase in devices, applications, bandwidth, IoT equipment, and cloud-based applications to manage. Add to this the recent rapid expansion of the network environment (due to the pandemic and the rise of remote working), and it is no longer humanly possible to know everything that crosses the network.

Relying on the traditional troubleshooting approach of sifting through log files or manually finding issues is no longer viable.

Today’s digital world requires relying on artificial intelligence (AI) to ensure the best possible user experience on the network and to quickly solve problems that are akin to finding a needle in a haystack.

The importance of experience

The success of networking is increasingly measured in terms of the experience provided to end users – i.e. bandwidth, availability, etc. End users remember if their internet connection is down, their video call is dropped, or they can’t download and download documents quickly. . They don’t remember the hundreds of times when everything worked as it should. In today’s anywhere work environments, delivering the right user experience is even more critical.

The other aspect of the modern business where experience matters is in an organization’s internal teams. Are their jobs filled with mundane, repeatable, and difficult tasks? Are they able to add value or are they wasting the majority of their time troubleshooting network issues?

Network managers and IT teams are faced with increasing complexity of network management and, at the same time, are under increasing pressure to accelerate the time to value of all processes, such as network refreshes or deployment and operating a network of branches. The combination of these issues also presents challenges for organizations looking to retain talented employees in the face of a hot labor market.

By recognizing the importance of the end-user and network management team experience and striving to improve it, the operational burden on staff can be reduced, freeing up their time for more projects digital transformation tools that better support business initiatives.

Improving the experience with AI

Applying AI tools to modern network problems can go a long way toward making these experience improvements a reality. AI will by no means completely replace the role played by an experienced network engineer, but it can provide the ability to scale and keep up with the ever-expanding environments and responsibilities that people in this role face.

In some cases, the AI ​​application may even be able to eliminate repetitive and mundane tasks from the job description. AI can help improve the experience in several ways:

• Visibility – The application of artificial intelligence technologies to the network allows greater visibility into the traffic passing through it. This allows network managers to see what’s going on at all times and be alerted to issues that need attention. They could then, for example, prioritize traffic as needed, in order to establish the best possible connection. The AI ​​could be configured to only alert if certain conditions are met, or even to make adjustments itself if the variables are correct.

• Service Level Monitoring – AI-powered visibility can help ensure the best possible user experience at all times. Once SLEs (Service Level Experiences) are in place, network managers can use technology to determine if target SLE levels are being met. For example, it’s important to know that 92% vs. 99% of user minutes meet Zoom’s requirements – and that could tell the team that there’s a potential issue that needs to be fixed. AI can help separate traffic by specific service, allowing teams to separate less important traffic (Windows Update Traffic) from significant traffic (Zoom) and see if one affects the other.

• Onboarding and Onboarding Automation – Onboarding and offboarding devices, equipment and applications are often the first things that come to mind when you hear the phrase “mundane tasks”. These actions are critically important, but they are repetitive and time-consuming. Applying AI to the problem can erase the task from the job description, as the rules and requirements are set and the AI ​​takes care of the rest, allowing the employee to perform tasks of greater value.

• Security – Using AI, network managers can be automatically alerted only to important issues, instead of having to sift through unimportant alerts or false positives. The AI ​​can learn which issues are critical and which are not, and either take action on its own or bring something to the attention of the team that needs immediate attention. The ability to have an automated ally to monitor potential security threats can go a long way in preventing breaches or worse.

• Compliance – Protecting compliance with industry regulations and safety requirements – and reporting on the success and failure of compliance efforts – is another way AI can help and remove an important task, but repetitive, scorecards. AI can learn which guardrails should be applied and prevent non-compliance.

• Troubleshooting – Due to the enhanced network visibility that AI technology can provide, it can also help the team identify potential and existing issues and troubleshoot the cause. Traditionally, teams had to manually undertake trial and error to determine why a problem was occurring. Is there a loss of signal? Then check the software and settings for each device. If that wasn’t the issue, then maybe it’s the Wi-Fi coverage area, which leads to wandering around trying to connect from different locations. The bottom line is that time would be wasted, as well as valuable effort expended in locating a problem. With AI, the problem can be contained and identified immediately – instead of taking days (or weeks) – leaving the team to fix the problem, reducing repair time and speeding up the return to work.

• Repair Overview – Troubleshooting with AI shouldn’t stop when the problem is discovered. It can also help in cases where the problem is identified, but the team has no idea how to make the necessary repairs. Perhaps senior team members have left the company or are unavailable; or maybe this is a problem that has never been encountered before. Either way, with the AI ​​involved, steps to fix the problem can be recommended, allowing even a junior staff member to quickly make a correction. In some cases, the AI ​​itself might make the corrections.

AI solves network problems

AI can provide network managers with better visibility into network performance, anomaly detection that can ensure application performance and protect network availability, and even automated troubleshooting that can self-heal the network before it fails. failure occurs.

The AI-driven future is fast approaching and will forever change the way network management teams and IT teams operate, as well as their experiences.