Discover how to optimize costs during a game launch by configuring an Auto Scaling group with memory-optimized instances. Set the capacity based on demand and leverage CPU and memory monitoring. Ensure a good player experience while balancing costs for the entertainment company.
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Question
An entertainment company recently launched a new game. To ensure a good experience for players during the launch period, the company deployed a static quantity of 12 r6g.16xlarge (memory optimized) Amazon EC2 instances behind a Network Load Balancer. The company’s operations team used the Amazon CloudWatch agent and a custom metric to include memory utilization in its monitoring strategy.
Analysis of the CloudWatch metrics from the launch period showed consumption at about one quarter of the CPU and memory that the company expected. Initial demand for the game has subsided and has become more variable. The company decides to use an Auto Scaling group that monitors the CPU and memory consumption to dynamically scale the instance fleet. A solutions architect needs to configure the Auto Scaling group to meet demand in the most cost-effective way.
Which solution will meet these requirements?
A. Configure the Auto Scaling group to deploy c6g.4xlarge (compute optimized) instances. Configure a minimum capacity of 3, a desired capacity of 3, and a maximum capacity of 12.
B. Configure the Auto Scaling group to deploy m6g.4xlarge (general purpose) instances. Configure a minimum capacity of 3, a desired capacity of 3, and a maximum capacity of 12.
C. Configure the Auto Scaling group to deploy r6g.4xlarge (memory optimized) instances. Configure a minimum capacity of 3, a desired capacity of 3, and a maximum capacity of 12.
D. Configure the Auto Scaling group to deploy r6g.8xlarge (memory optimized) instances. Configure a minimum capacity of 2, a desired capacity of 2, and a maximum capacity of 6.
Answer
C. Configure the Auto Scaling group to deploy r6g.4xlarge (memory optimized) instances. Configure a minimum capacity of 3, a desired capacity of 3, and a maximum capacity of 12.
Explanation
To dynamically scale the instance fleet based on CPU and memory consumption and optimize costs, the solutions architect should choose option C: Configure the Auto Scaling group to deploy r6g.4xlarge (memory optimized) instances. Set the minimum capacity to 3, the desired capacity to 3, and the maximum capacity to 12.
Here’s the rationale behind this choice:
- Memory utilization monitoring: The operations team has already included memory utilization in their monitoring strategy using the CloudWatch agent and a custom metric. By selecting memory-optimized instances, the Auto Scaling group can effectively monitor and scale based on memory consumption.
- Right-sized instances: The initial analysis showed that the game’s CPU and memory consumption was only about one quarter of what was expected. Deploying larger and more expensive instances would not be cost-effective. The r6g.4xlarge instances strike a balance between performance and cost, making them a suitable choice for the Auto Scaling group.
- Scaling capacity: To handle variable demand, the Auto Scaling group should be configured with a minimum capacity, desired capacity, and maximum capacity. Setting the minimum and desired capacity to 3 ensures that there are a sufficient number of instances available to handle the workload. The maximum capacity of 12 allows for scaling up when necessary, providing flexibility while controlling costs.
By configuring the Auto Scaling group to deploy r6g.4xlarge instances with appropriate capacity settings, the company can dynamically scale its instance fleet based on CPU and memory consumption, effectively meeting demand while optimizing costs.
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