Smart Scaler

Видавець: Avesha, Inc.

Smart Scaler guarantees SLAs and reduces cloud costs by autonomous autoscaling of K8s with AI and RL

Smart Scaler uses Reinforcement Learning (RL) to guarantee application SLA (for example: response time, error rate) and reduce cloud costs by precisely scaling the K8s application resource needs just in time. It will:
  • Extract application and infrastructure performance data from the cluster’s monitoring data sources (e.g., Prometheus, Datadog, NewRelic, etc).
  • Use historical application performance data to predict pod capacity requirements and the number of pods needed for a given load.
  • Build AI/ML/RL models of application behavior and traffic loading patterns to achieve the application SLA by the collaboration of the microservice scaling models, taking into account the service graph.
  • Apply these models toward monitoring the application and increasing or decreasing resources in anticipation of the application's need.
  • Free up top kubernetes, devops, engineering talent from worrying about SLA and eliminate performance tuning headaches and overprovisioning.
  • Smart Scaler deals with event-based traffic spikes by autonomous autoscaling.

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