How AI Could Have Prevented the “Great Google Outage”
How AI Could Have Prevented the “Great Google Outage”

The Black Mirror-esque Google outage.

Still from Black Mirror.

Google went down for millions, if not billions of people on December 14th, 2020. Using a Chromebook, I was locked out of my laptop, and couldn’t access any Google services on my phone. In attempts to recover my accounts, I was told that none of my Google accounts existed.

As it turns out, the Great Google Outage impacted many countries.

For many, this was a wake-up call to how dependent we are on Google. Without access to the Internet, it feels like that Black Mirror episode where people can be locked out of society at the click of a button.

Why Outages Happen

Outages are nothing new — it’s just surprising when it happens to a behemoth like Google.

That said, Google suffered a 5-minute outage back in 2013, causing Internet traffic to drop by 40%. Most of the outage was solved in just 1 minute. Before then, one of the last outages was in 2009.

There are many potential causes of outages, including:

Server power supply failures.

Hardware problems like overheating.

External actions like malware.

How AI Can Prevent Outages

Google is an enormous network, with literally millions of servers around the world. It’d be impossible to have manual oversight into the status of these servers. At that scale, automation is needed to predict outages and monitor server status.

Machine learning is great at finding the proverbial needle in the haystack. Given historical server performance data, including a column on outages, you could train a machine learning model to predict outages, allowing companies to reduce maintenance costs and prevent these catastrophes.

With automated machine learning tools like Obviously.AI, companies can build and deploy AI systems in minutes, rather than the months it would traditionally take.

Google’s Great Outage could indeed be used by Google as “training data” to make predictive AI models more accurate. AI finds patterns in data, and with more historical data to look back on, it can more accurately find patterns.

Binary Classification

This is an example of a binary classification task in machine learning, as you’re predicting a binary outcome: Server live, or server down.

This has been commonly used for tasks like predicting power grid outages, and the same concept applies to servers.

Potentially predictive data attributes include processor usage, memory usage, disk read/write, latency, throughput, network bandwidth, and more.
['Frederik Bussler']
['Artificial Intelligence', 'Google', 'AI', 'Servers', 'Information Technology']