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Cloud & Infrastructure
AWS Blog·Jun 18·5 min read
AWS Blog·Jun 18·5 min readAmazon ECS introduces new high-resolution metrics for faster service auto scaling
TL;DR
Previously, Amazon ECS struggled with slow response times for service auto scaling, which adjusts task counts based on workload demand. The introduction of high-resolution metrics allows for faster scaling decisions using Amazon CloudWatch data.
✦ Why It Matters
Engineers can implement high-resolution metrics to enhance the responsiveness of their ECS services under varying workloads.
Key Takeaways
- Enable high-resolution metrics for faster scaling decisions.
- Use target tracking scaling policies with high-resolution metrics.
- Leverage Amazon CloudWatch for real-time performance monitoring.
- Apply these improvements across AWS Fargate and EC2 services.
Full Summary
Amazon Elastic Container Service (ECS) faced challenges in quickly adjusting task counts to meet fluctuating workload demands, which is crucial for maintaining application performance. To address this, AWS introduced high-resolution metrics that allow ECS to detect load changes every 20 seconds, significantly enhancing the service auto scaling capabilities.
This feature utilizes Amazon CloudWatch metrics, including CPU and memory usage, to inform scaling decisions. Benchmark tests showed that the time to trigger scale-out improved from 363 seconds to 86 seconds, and the total time to scale and provision new tasks decreased from 386 seconds to 109 seconds.
These improvements are achieved through advanced machine learning algorithms and metric publishing optimizations. Engineers can enable these metrics when creating or updating their ECS services, making it applicable across various compute options like AWS Fargate and Amazon EC2.
This advancement allows for more responsive and efficient resource management in cloud applications.