Using AIOps to Solve Three of IT’s Toughest Network Operational Challenges

AIOps can help IT manage the issues of the day while also working on critical return-to-work projects.

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By: Trent Fierro, Senior Marketing Manager for AIOps at Aruba, a Hewlett Packard Enterprise company

Until recently, troubleshooting network issues meant helping users face-to-face or huddling in a conference room while planning next steps. Now, most IT teams are working remotely, trying to solve issues for other remote workers with very little data and interaction with others on their team. Return-to-office projects are also proving difficult as everything from improved coverage planning to new environmental health and safety programs that include contact-tracing analysis and access control involve some level of guesswork without real-world data.

As a result, there’s very little time and limited resources to chase intermittent issues, respond to every user, and bounce between management and operations tools.

In these scenarios, embracing Artificial Intelligence for IT Operations (AIOps) can help over-burdened IT teams manage the issues of the day while also working on critical return-to-work projects. This concept combines the use of data analytics, machine learning, and automation to enhance IT operations ability to be successful, and in many cases, improve efficiency.

Problem #1: Solving Networking Issues Proactively

Troubleshooting problems today requires data and information that users or Internet of Things (IoT) devices often can’t convey on their own. For example, if a user complains that video calls keep dropping, IT has a starting point, but that data alone often isn’t enough to solve the issue. Often, it requires more troubleshooting such as conducting a video conference with the user to see what happens, or for IoT, diving into a management tool to spot anomalies—all tactics that take time and personnel resources.

This is where AIOps can help. Through effective AIOps, IT can often identify and preempt these types of issues before users are impacted. This can be achieved where data is collected from all wireless, switching, and SD-WAN Gateway devices to create an operational baseline across the entire ecosystem, including some work-from-home scenarios. If the performance of the network or an application deviates drastically, IT can take proactive steps to resolve the issue before end users even notice. AIOps should also include easy-to-use natural language processing-based search features that empowers IT personnel to quickly locate the user, the network device, or site-specific issues if there is problem.

Problem #2: Reducing Troubleshooting Effort

Trying to identify and fix a simple problem takes time, and that can eat into resources that could be used elsewhere. Most IT teams lose track of what percentage of time is spent on troubleshooting, looking through logs, or working with a user on an issue. With teams stretched thin by a network tasked to do more than ever before, reducing troubleshooting efforts is mission-critical.

Because AIOps continuously monitors key service levels to detect if something goes awry, AI-powered insights can automatically point the IT team to actual root causes in order to resolve common issues, such as incorrect 2.4Ghz and 5Ghz power settings on access points. The difference could mean solving an issue in minutes versus hours, simply knowing where to start, and what to change to fix the problem. This is key for solving intermittent issues.

Problem #3: Maintaining Visibility

Just as important to solving an issue, AIOps must continuously learn as the environment evolves and dynamically adjust critical baselines. While capturing the insights provided for similar issues that may arise in the future is valuable, the goal is to stay abreast of changes without setting up static thresholds or service-level expectations (SLEs).

This helps eliminate the need for trial and error based on old assumptions. Making a configuration change and then waiting to see if the change results in more help desk calls or a resolution doesn’t quite help if variables have changed. This is where networking knowledge captured by AIOps becomes an advantage – that additional context can be critical for a swift resolution or optimization effort.

Insights must provide IT teams with reasons for why Wi-Fi, switching, WAN, and issues with applications may be happening, as well as offer recommendations on what to change within a configuration. Eliminating needless guesswork from the start can thus drastically reduce guesswork and provide a better experience for all.

Saving Time for Innovation

Solving for these three core issues can afford IT improved efficiency – time to work on ways to get the most out of an existing infrastructure, time to tackle future projects that will further improve the network, and time to do more rewarding work that will add additional value to the business longer term. 

With automated anomaly detection, troubleshooting tips, and trusted optimization recommendations through AIOps, that additional efficiency will empower a happier, more productive workforce whether at home, in the office, or on the road.

To learn about Aruba’s approach to AIOps and Aruba ESP (Edge Services Platform), visit


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