cruise control
OpsCruise’s Machine Learning Platform Explained
Predictive Behavior Models
OpsCruise automatically builds ML-based predictive models of all services or components of the application. These learned models are then used in predictive mode in real-time to detect early onset of performance problems without the need to set thresholds whether manually or from statistical outliers. Such preemptive problem detection enables Ops to take proactive actions, reducing MTTD and MTTR.
Knowledge Augmented ML
OpsCruise uses ML techniques tailored to the analytical needs in different stages of processing. To understand the context, curated knowledge relevant to the application, Kubernetes and infrastructure is incorporated in each stage. These techniques augment the known facts, e.g., application structure, or the meaning of container metrics and implications of Kubernetes configuration. Knowledge-augmented ML creates more transparent white or gray box models that allows Ops to execute granular corrective actions not possible with black box models.
Automated Reasoning
OpsCruise provides automated causal reasoning that builds on insights from its behavior models, application topology and dependencies, and an extensible knowledge base of fault isolation diagnostics. The root cause analysis (RCA) process includes both local microservice analysis as well as a global dependency analysis to isolate the problem source reducing significant manual war room efforts.
Cruise Control is executed in a real-time multi-stage pipeline . . . from the ingestion of different metrics and application related data to fault isolation enabling corrective actions.