Anomaly detection

Artificial intelligence automatically detects and prioritizes problems

The Dynatrace analytics-based approach to anomaly detection

  • Eliminate alert floods and alarm spam

    Getting hit with alarms every time some threshold is violated? Dynatrace consolidates all related performance issues into a single actionable notification. Less noise, more problem-solving.

  • Focus on the problems that really matter

    Not every threshold violation is a problem, and not all problems are created equal. Artificial intelligence determines whether an anomaly has an actual or potential impact on customers.

  • Automate dynamic problem detection

    Dynatrace integrates multidimensional baselining and predictive analytics to automatically detect when things aren't behaving as expected. Get notified only when something needs your attention.

Anomaly detection built for dynamic environments

The traditional reactive approach of identifying problems by responding to alerts based on static thresholds doesn't work for today's elastic cloud infrastructure, containers, and microservices. With so many components in perpetual motion and "normal" behavior constantly being redefined, these dynamic environments demand a new, proactive approach.

That's where artificial intelligence comes in. With deepest possible knowledge of your system's topology, dynamic baseline performance, and behavior, Dynatrace harnesses predictive analytics and continuous machine learning to auto-identify anomalies based on the metrics that matter for your particular environment.

  • Avoid nuisance alerts and false positives/negatives triggered by static thresholds.
  • End-to-end gap-free monitoring finds even the hardest-to-spot anomalies.
Through smart auto-baselining and predictive analytics, Dynatrace automatically detects anomalies, and AI determines whether they impact customers
Dynatrace diagnoses problems that are often difficult to pinpoint, including database, memory, threading, and CPU issues
With Dynatrace, we have shortened the time to identify and solve performance problems by 60%, and have achieved 100% application performance visualization.
Yunpeng Qiao Senior Manager, Global Application Operation at Lenovo

Prioritize problems automatically

Identify performance anomalies before they affect customers. Eliminate guesswork and stop spending time hunting down problems. Dynatrace automatically applies AI algorithms to determine whether a performance issue has an actual or potential impact on customers.

Because the anomaly detection engine understands the relationship between operational and business metrics, you get a single notification only when something impacts customers' user experience.

  • Focus on fixing problems, not finding them.
  • Problem detection based on 100% of customer transactions—no averages or samples.

Smart baselining and prediction-based anomaly detection

Dynatrace uses different methodologies to determine when anomalous behavior warrants a problem notification. Automatic multidimensional baselining detects violations of individual reference values that change over time (response times and error rates of application or services). Predictive analytics detect abnormalities in application traffic and service load—as traffic and load depend on business-model seasonal patterns (e.g., workweek vs. weekends, day vs. night, Black Friday).

  • Dynatrace learns application traffic patterns and raises a problem notification when a statistically relevant deviation is detected—including a quantified customer impact and insight into the possible root cause.
  • Analytics can predict upcoming traffic levels, and get smarter and smarter over time.
  • Automatic baselining can be fine-tuned for parameterized anomaly detection—lower thresholds for certain mission-critical services, or higher thresholds for apps and services still in development.
Dynatrace detects statistically relevant deviations between actual and predicted behavior; here, unexpected low traffic from one week to the next.
Notifications quantify customer impact and offer insight into potential root cause.

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