The Dynatrace analytics-based approach to anomaly detection
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.
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 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.
Learn more about anomaly detection
How are new problems evaluated and raised?
Prediction-based anomaly detection
Anomaly detection is an effective means of identifying unusual or unexpected events and measurements within a web application environment.
Anomaly Detection for Monitoring
Monitoring is currently undergoing a significant change. Until two or three years ago, the main focus of monitoring tools was to provide more and better data. Interpretation and visualization has too often been an afterthought.
Parameterized anomaly detection settings
One key feature of Dynatrace is its ability to continuously monitor every aspect of your applications, services, and infrastructure and to automatically learn all the baseline metrics related to the performance of these components.