Zero One: Predictive Maintenance Comes to the Cloud
If managed service providers could foresee problems in the cloud and act proactively instead of reactively, their job would be a lot easier and customers would be happier. But if they gaze a little further into the future, they might not like what they see.
This week, Datadog, a monitoring service for cloud environments, unveiled a forecasting feature that predicts when performance and stability issues will arise within cloud applications. Datadog claims this feature taps into massive data sets and runs on artificial intelligence and machine learning technology to make its predictions.
“Our forecasting algorithms have been fine-tuned based on trillions of data points across hundreds of thousands of servers daily,” says Brad Menezes, product manager for artificial intelligence and machine learning at Datadog. “We can predict where a metric will be in the future, taking into account historical patterns, and notify users with plenty of time to prevent any negative impact.”
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Predictive maintenance, especially in industrial Internet of Things scenarios, has had a profound impact on both vendors and service providers. It has led to new “power by the hour” business models, operational upheaval, and service staff makeovers.
That’s still a ways off.
For now, predictive maintenance entering the world of cloud monitoring is a good thing, says Kreece Fuchs, vice president at Trek10, a consulting partner for AWS and managed services provider.
Earlier this year, Fuchs jury-rigged a predictive tool and put in a support ticket to Datadog. That’s how he found out about Datadog developing a beefy forecasting tool, which led to Fuchs becoming a beta tester.
It’s too early to assess the tool’s accuracy, but Fuchs has high expectations. As many as one out of five site outages might be avoided thanks to the forecasting tool, he says.
“Just looking at the few things we’re trying to monitor right now, it’s forecasting the way I would forecast,” Fuchs says. “I’m also optimistic based on how [Datadog] developed the anomaly detection feature. It works really well.”
In late 2016, Datadog released an anomaly detection feature, which analyzes a metric’s historical behavior to distinguish between normal and abnormal trends.
As sophisticated, data-driven prediction tools make their way to the cloud, it begs the questions: How far can it go? How much can be handled through automation and artificial intelligence? Where does this leave the managed services provider?
“There’s still going to be a need on how to use these tools, how to deploy these tools,” Fuchs says. “But in five to 10 years, you could envision plugging in your whole environment into a system that just monitors it.”
Tom Kaneshige writes the Zero One blog covering digital transformation, AI, marketing tech and the Internet of Things for line-of-business executives. He is based in Silicon Valley. You can reach him at firstname.lastname@example.org.