Question

A company runs an application as an Amazon Elastic Container Service (Amazon ECS) task that processes messages from an Amazon Simple Queue Service (Amazon SQS) queue. The ECS task operates on a schedule, processing the SQS queue every 10 minutes. A large volume of messages was delivered to the SQS queue. The application required several hours to process every message. The company's new service level objective (SLO) requires the application to process messages within 10 minutes of delivery. A DevOps engineer must configure the application to meet the SLO while minimizing idle running resources. Which solution satisfies these requirements with the **MOST** operational efficiency?

Accepted Answer

B. Configure a target tracking scaling policy for the ECS task. Calculate the number of messages for each task by using the SQS ApproximateNumberOfMessagesVisible metric and the ECS RunningTaskCount metric. Scale the number of tasks based on the calculated attribute.

Answer

Configure a step scaling policy for the ECS task. Configure the step scaling policy to use the ECSServiceAverageMemoryUtilization metric. Set the maximum number of tasks to equal the number of messages the application receives at peak hours.

Answer

Create a scheduled scaling policy to increase the number of ECS tasks available during peak volume periods. Set the schedule as a cron for the application's peak hours. Set the maximum number of tasks to equal the number of messages the application receives at peak hours.

Answer

Create a predictive scaling policy to increase the number of available ECS tasks during peak volume periods. Configure the predictive scaling policy to use the ECSServiceAverageCPUUtilization metric. Set the maximum number of tasks to equal the number of messages the application receives at peak hours.