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Application Acceleration

By using machine learning technologies of CrystalClear Time Series Analysis Engine, Federator.ai scales the number of containers/pods (replicas) based on predictions capturing the dynamics of application workloads to meet the resource demands by providing Just-in-Time Fitted allocation recommendations.

Based on the application-aware insight that digs into the metrics of individual applications and CPU/memory usage, Federator.ai helps users achieve much better performance (Kafka: reduce latency; NGINX: reduce average response time & HTTP response error rate ) with much fewer resources (Kafka: reduced number of Kafka consumers; Generic: CPU & memory management).

Effective workload predictions
Federator.ai focuses on more accurate indications to reflect real application workloads, such as message production rate of a Kafka topic, so that accurate predictions can be produced and desired performance can be accomplished by autoscaling in time.
Cost-effective application deployments
Federator.ai integrates the workload metrics, workload predictions, and application KPI in deciding the right number of replicas and achieves more cost-effective application deployments.
Achieving desired performance
Without guessing or experimenting on what metric threshold to set in Kubernetes native HPA, Federator.ai achieves better use of resources for desired performance automatically.
A presentation in Kafka Summit on why intelligent autoscaling is better than Kubernetes native HPA
A demo of how to configure an application on Federator.ai for autoscaling Kafka consumer

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