Circonus Applies Big Data Technology to IT Operations Monitoring for Automated Alerts and Machine-Learning Intelligence

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Automated anomaly detection leverages machine learning for complex operations at scale

Machine learning can be a very powerful tool when applied to operations in the right ways. It opens up new possibilities for analysis, automation, and intelligence that most operations teams are just beginning to explore.

Circonus, a leading provider of analytics and monitoring solutions for IT operations and DevOps, announces new automated anomaly detection using machine-learning intelligence.

Today’s complex IT environments require a new approach to monitoring and management. Virtualization, cloud computing, microservices, and continuous delivery are just a few of the factors that have contributed to an explosion in the volume, velocity, and variety of data that needs to be collected and processed for efficient operations.

“IT operations is a Big Data problem,” said Theo Schlossnagle, Founder and CEO of Circonus, “It’s simple - the more data operations has available to them, the better decisions they can make to optimize performance. Before Circonus, none of the monitoring tools available on the market were thinking about the problem this way.”

Circonus is built on proprietary technology that was architected to support the operations of some of the largest websites in the world. Data collection, storage, and processing at massive scale are the first requirements for tackling IT operations as a Big Data problem. It also requires integrated analytics tools that can efficiently go through all the data to provide machine-learning intelligence that is actionable and immediate.

“Machine learning can be a very powerful tool when applied to operations in the right ways,” said Schlossnagle, “It opens up new possibilities for analysis, automation, and intelligence that most operations teams are just beginning to explore. We are pleased to offer the new automated anomaly detection feature to our customers as we learn together what can be done and what should be done when it comes to applying Big Data technology to IT operations.”

Automated anomaly detection relies on machine-learning technology to analyze the behavior of any kind of metric and automatically create a continuously updated forecasting model of what that data should look like over time. While customers can still set their own thresholds for alerting, the unique value of this new feature is that the system can do it for them. Circonus can automatically alert them when the data being collected for the metric does not behave as expected.

Website traffic, for example, may be a key metric for the business but can have a very “noisy” data profile that makes it difficult to set usable alerts using the traditional manual threshold method. Either the threshold is too sensitive and produces too many alerts that are ignored, or the threshold is too high and operations does not get the alerts needed to fix issues before they impact the business. Automated anomaly detection takes a different approach, basing alerting on actual data behavior so it can account for the “noise” in the data signal and alert as needed, catching problems manual thresholds cannot.

Automated anomaly detection is available now. For more information, visit http://www.circonus.com

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Christian Madsen
Circonus, Inc.
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