The worker tier consists of an Auto Scaling group configured with a target tracking policy. Latency refers here to the time required for any queue message to be consumed and fully processed.Ĭonsider the example of a customer using a worker tier to process image files (e.g., resizing, rescaling, or transformation) uploaded by users within a target latency of 100 seconds. The key challenge that this post addresses is applications that fail to honor their acceptable/target latency in situations where the MPD varies over time.

We also cover the utilization of Amazon EC2 Spot instances, mixed instance policies, and attribute-based instance selection in the Auto Scaling Groups as well as best practice implementation to achieve greater cost savings.

Specifically, we demonstrate how to dynamically update the target value of the Auto Scaling group’s target tracking policy based on observed changes in the MPD. This post builds on that guidance to focus on latency-sensitive applications where the MPD varies over time. For latency-sensitive applications, AWS guidance describes a common pattern that allows an Auto Scaling group to scale in response to the backlog of an Amazon SQS queue while accounting for the average message processing duration (MPD) and the application’s desired latency. For example, an EC2 Auto Scaling Group can be used as a worker tier to offload the processing of audio files, images, or other files sent to the queue from an upstream tier (e.g., web tier). Scaling an Amazon EC2 Auto Scaling group based on Amazon Simple Queue Service (Amazon SQS) is a commonly used design pattern in decoupled applications. Specialist Solution Architect, EC2 Flexible Compute. This blog post is written by Wassim Benhallam, Sr Cloud Application Architect AWS WWCO ProServe, and Rajesh Kesaraju, Sr.

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