A company runs a high-traffic e-commerce application on Amazon EC2 instances in an Auto Scaling group behind an Application Load Balancer. The application reads and writes data to an Amazon Aurora MySQL DB cluster. During seasonal sales events, the application experiences sudden spikes in traffic, leading to CPU exhaustion on the EC2 instances and increased read replication lag on the Aurora database due to a surge in read queries. Which combination of actions should a solutions architect implement to optimize compute and storage performance? (Select TWO.)
- Deploy Amazon Aurora Replicas in the DB cluster and configure Aurora Auto Scaling to dynamically add reader instances as read demand increases.Cevap
- Transition the EC2 instances to a compute-optimized instance family and configure a target tracking scaling policy based on average CPU utilization.Cevap
- CEnable Amazon RDS Multi-AZ replication and configure the Application Load Balancer to route read traffic directly to the standby DB instance.
- DSubmit a request to AWS Support to pre-warm the Application Load Balancer to resolve the CPU exhaustion on the backend EC2 instances.
- EConfigure the Auto Scaling group cooldown period to be shorter than the instance boot time to speed up the launch of new compute resources.
Cevap
Deploy Amazon Aurora Replicas in the DB cluster and configure Aurora Auto Scaling to dynamically add reader instances as read demand increases. Transition the EC2 instances to a compute-optimized instance family and configure a target tracking scaling policy based on average CPU utilization.
The correct options recommend deploying Aurora Replicas with Auto Scaling to handle the database read load, and migrating the EC2 instances to compute-optimized instance types with target tracking policies to handle the compute load.
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Anahtar Kavram
Scaling compute resources using target tracking and scaling database read capacity using Aurora Replicas with Auto Scaling.