Smart Scaler

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.