Smart Scaler

de 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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