Auto-scaling with Elastic Load Balancing (ELB) in Amazon SageMaker involves dynamically adjusting the number of instances based on workload demands while efficiently distributing incoming traffic across these instances. Here's a detailed overview of how this integration works:
Auto-Scaling in SageMaker
Amazon SageMaker supports auto-scaling for real-time endpoints, allowing the system to dynamically adjust the number of instances provisioned for a model in response to changes in the inference workload[3][7]. This feature ensures that resources are optimized by scaling out during peak hours and scaling in during low-demand periods, thereby maintaining optimal performance while minimizing costs[1][3].
SageMaker offers several auto-scaling options, including Target Tracking Scaling, Step Scaling, and Scheduled Scaling. Target Tracking Scaling is commonly used, where you set a target metric (e.g., CPU utilization) and SageMaker adjusts the instance count to maintain that target[3][5].
Elastic Load Balancing (ELB) Integration
While SageMaker's auto-scaling primarily focuses on adjusting instance counts based on workload metrics, integrating with Elastic Load Balancing enhances the distribution of traffic across these instances. ELB ensures that incoming requests are optimally routed to available instances, improving responsiveness and reducing bottlenecks[9].
In a typical setup, ELB registers instances in an Auto Scaling group and distributes traffic across them. When instances are added or removed by Auto Scaling, ELB automatically adjusts its configuration to include or exclude these instances, ensuring that traffic is always directed to active instances[9].
How Auto-Scaling Works with ELB in SageMaker
1. Workload Monitoring: SageMaker monitors workload metrics such as CPU utilization or concurrent requests per instance. If these metrics exceed predefined thresholds, the auto-scaling policy is triggered[2][3].
2. Scaling Actions: When the workload increases, SageMaker scales out by provisioning additional instances. ELB automatically registers these new instances and begins distributing traffic to them. Conversely, when the workload decreases, SageMaker scales in by removing unnecessary instances, and ELB deregisters these instances to prevent them from receiving traffic[2][9].
3. Traffic Distribution: Throughout this process, ELB ensures that incoming requests are efficiently distributed across the active instances. This not only enhances performance but also helps maintain a consistent user experience by minimizing response times and avoiding bottlenecks[9].
4. Cost Optimization: By scaling instances based on actual demand and using ELB to manage traffic distribution, organizations can optimize their costs. They only pay for the resources they use, reducing unnecessary expenses during periods of low demand[3][7].
In summary, the integration of auto-scaling with Elastic Load Balancing in SageMaker provides a robust and efficient way to manage real-time inference workloads. It ensures that resources are dynamically adjusted to meet changing demands while maintaining optimal performance and cost efficiency.
Citations:
[1] https://businesscompassllc.com/efficiently-managing-traffic-for-amazon-sagemaker-real-time-endpoints-with-autoscaling-and-xgboost/
[2] https://randomtrees.com/blog/auto-scaling-for-generative-ai-models-with-amazon-sagemaker/
[3] https://aws.amazon.com/blogs/machine-learning/optimize-your-machine-learning-deployments-with-auto-scaling-on-amazon-sagemaker/
[4] https://repost.aws/questions/QUDPxlldhzS1GNaSLn4ebRxw/sagemaker-inference-recommendation
[5] https://www.restack.io/p/real-time-ai-inference-answer-sagemaker-autoscaling-cat-ai
[6] https://stackoverflow.com/questions/71344215/how-does-scaling-policy-work-with-sagemaker-endpoints
[7] https://docs.aws.amazon.com/sagemaker/latest/dg/endpoint-auto-scaling.html
[8] https://docs.aws.amazon.com/sagemaker/latest/dg/endpoint-auto-scaling-policy.html
[9] https://jayendrapatil.com/aws-auto-scaling-elb/