Amazon ECS introduces new high-resolution metrics for faster service auto scaling

Amazon Elastic Container Service (Amazon ECS) service auto scaling automatically adjusts task counts to meet workload demand using comprehensive scaling policies including predictive scaling for recurring traffic patterns, scheduled scaling for planned events, and target tracking to scale dynamically on real-time metrics. Users can choose proactive scaling with predictive or scheduled scaling, or reactive scaling with target tracking based on Amazon CloudWatch metrics such as average CPU/Memory usage, request count per target, or custom metrics like queue depth. The new launch supports high-resolution (20-second) metrics and metric publishing optimizations, enabling faster detection and response to load changes. Benchmarking shows time to trigger scale-out improved from 363 seconds to 86 seconds (76% faster) and total time to scale and provision new tasks improved from 386 seconds to 109 seconds (72% faster). Key benefits include improved performance and reliability during demand surges, cost reduction by right-sizing task counts without preemptive padding, and simpler scaling configuration by using target tracking with high-resolution metrics instead of custom step-scaling policies. To use faster service auto scaling, enable high-resolution metrics for the ECS service and configure a target tracking scaling policy using metrics ECSServiceAverageCPUUtilizationHighResolution or ECSServiceAverageMemoryUtilizationHighResolution. This works across AWS Fargate, ECS Managed Instances, and Amazon EC2. Metrics can be enabled during service creation or update via the Amazon ECS console, AWS SDKs, AWS CLI, or AWS CloudFormation. High-resolution metrics incur additional CloudWatch costs while standard 60-second metrics remain free. The feature is available now with no additional cost for the feature itself, but with CloudWatch pricing applying to high-resolution metrics. Feedback can be sent via AWS re:Post or AWS Support.