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Authors: Maksim Chudnovskii, Igor Gustomyasov
2021-10-13

tldr - powered by Generative AI

The presentation discusses various methods and technologies used in machine learning cooperation systems for anomaly detection, root cause analysis, and predictive auto-scaling in Kubernetes clusters.
  • The system is divided into two main parts: preparation and evaluation of models, and real-time execution of trend models
  • Istio is used as a service mesh to collect service mesh metrics, and Permittelsa is used as a data layer to collect time series data
  • Combining workloads in schedule groups can reduce network resource consumption and optimize overall latency
  • Anomaly detection methods can significantly reduce the flow of notifications and automate the process of establishing monitoring thresholds
  • Predictive auto-scaling can proactively predict the required number of service ports using time series data and feature generation