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.