Optimizing ElastiCache CPU Utilization for Improved Website Performance

Best Solution for High ElastiCache CPU Utilization

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Question

Your application is using ElastiCache to handle session state and cache for frequently accessed data.

However, during peak times, users are complaining that your website is running very slowly.

You checked the CloudWatch metrics for your application servers, and databases and cannot find any single issue.

Moreover, you notice that your Redis Cluster is showing 40% CPU utilization.

You also see that the other metrics are just at the normal ranges, and you get convinced that this issue is with CPU utilization.

In this case, which is the best way to go with this issue?

Answers

Explanations

Click on the arrows to vote for the correct answer

A. B. C. D.

Correct Answer: A.

EBS volume metrics.

Elasticache usually gets two metrics to measure the CPU utilization.

That is the EngineCPUUtilization and the CPU utilization.

The CPU Utilization does not give complete visibility.

In that, it gives us insights into the CPU utilization for instance, whereas the EngineCPUUtilization provides additional visibility to the CPU utilization of the Redis process level.

Option C and D are incorrect since we have to scale based on certain metrics to ensure that there are no underutilized resources hence saving costs.

So, we have to establish a good metric and scale based on the metric, not to scale based on guesswork.

Option B is incorrect.

It's just a distractor.

https://aws.amazon.com/elasticache/redis/faqs/

Based on the scenario presented, the best way to address the issue of slow performance during peak times is to scale up the Redis Cluster by using a larger node type.

Here's why:

  • The scenario highlights that CPU utilization on the Redis Cluster is at 40%, which is relatively high. If CPU utilization is consistently high during peak times, this can indicate that the cluster is not able to handle the incoming workload effectively.
  • Increasing the Connection Overhead parameter value may help reduce network overhead, but it is unlikely to address the root cause of high CPU utilization on the Redis Cluster.
  • Scaling up the application server by adding more EC2 instances may help distribute the workload, but it will not necessarily address the issue of high CPU utilization on the Redis Cluster.
  • Scaling up the Redis Cluster by using a larger node type can increase the available compute resources, which can help handle the incoming workload more effectively. This can lead to lower CPU utilization and improved performance.

It is also worth noting that using both the CPU Utilization and EngineCPUUtilization metrics together can provide a more detailed understanding of CPU utilization for the Redis clusters. However, this is not the best solution to address the issue of slow performance during peak times. Instead, it is more appropriate for monitoring and troubleshooting purposes.

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