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To enhance the ability of the playbook, we develop a time series pattern and anomaly detection service that a playbook step can use to analyze time series metrics.
First, time series pattern detection identifies key signals: spikes or dips for abrupt changes, level shifts for persistent alterations, and trends for gradual issues like memory leaks.
Second, profile-based anomaly detection uses regression models to build profiles of our services, for example, predict CPU usage based on the number of requests per second in a normal operation, which can be used to indicate inefficiencies or misconfigurations of services.
These algorithms help investigating system bottlenecks quickly.
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IEEE/ICACT20260142 Slide.15
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