How machine learning can solve wireless network issues

shutterstock 308854598 780x520 How machine learning can solve wireless network issues

Wi-Fi is crucial to the way we work today. Fast, reliable, and consistent wireless coverage in an enterprise is business-critical. Many day-to-day operations in the enterprise depend on it. And yet, most of the time, IT teams are flying blind when it comes to individual experience. This springs from two main challenges.

The first challenge is data collection. We want to know the state of every user at every given time. But these states change constantly as network conditions and user locations change. With tens of thousands of devices being tracked, there is a huge amount of information to be collected. This volume of data simply cannot be handled in an access point or a controller running on an appliance with fixed memory and CPU.

The second challenge is data analysis. It takes considerable time and effort to sort through event logs and data dumps to get meaningful insights. And significant Wi-Fi intelligence is required to actually make heads or tails out of the data.

Someday soon, I believe, big data and machine learning will solve the above hurdles. It will allow me to ask my network how it is feeling, it will tell me where it hurts, and it will provide detailed prescriptions for fixing the problem (or will automatically fix it for me). While this seems to be a futuristic vision, the foundation to achieve it is already being laid out through big data tools and machine learning techniques like unsupervised training algorithms.

Using these technologies, we can now continuously update models that measure and enforce the experience for our wireless users. For example, we can ensure specific internet speeds in real time (i.e throughput) with a high level of accuracy. This allows the IT staff to know a wireless user is suffering before they even realize it — and thus before they have to log a call with the help desk.

Once a user problem is detected, machine learning classification algorithms can isolate the root cause of the problem. For example, is the throughput issue due to interference, capacity, or LAN/WAN issues? After isolating the problem, machine learning can then automatically reconfigure resources to mediate the issue. This minimizes the time and effort IT teams spend on troubleshooting, while delivering the best possible wireless experience.

I’ve written before how artificial intelligence will revolutionize Wi-Fi. I would love to be able to just unleash IT teams on sifting through hordes of data so they can glean meaningful information. But it is like finding a needle in a haystack. Machine learning is key to automating mundane operational tasks like packet captures, event correlation, and root cause analysis. In addition, it can provide predictive recommendations to keep our wireless network out of trouble.

Also key to this vision is the elastic scale and programmability that modern cloud elements bring to the table. The cloud is the only medium suitable for treating Wi-Fi like a big data problem. It has the capacity to store tremendous amounts of data, with a distributed architecture that can analyze this data at tremendous speed.

Wi-Fi isn’t new. But how we use Wi-Fi has evolved. And now more than ever, Wi-Fi needs to perform flawlessly. We are in an era where wireless needs to be managed like a service, with all the flexibility, agility, and reliability of other business-critical platforms. With machine learning, big data, and the cloud, this new paradigm is quickly becoming a reality.

Ajay Malik is a wireless technology expert at Google.

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