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A live-events company is planning to enhance the scalability of its ticket application on AWS, which experiences significant traffic spikes during scheduled sale events. The application operates on Amazon EC2 instances within an Auto Scaling group and utilizes PostgreSQL for its database. To ensure high availability during these peak periods, the company requires an effective scaling solution. What approach should be adopted to meet these requirements?
A
Implement a predictive scaling policy for the EC2 instances. Deploy the database on an Amazon Aurora PostgreSQL Serverless v2 Multi-AZ DB instance with auto-scaling read replicas. Develop an AWS Step Functions state machine to execute parallel AWS Lambda functions for pre-warming the database prior to a sale event. Establish an Amazon EventBridge rule to trigger the state machine.
B
Adopt a scheduled scaling policy for the EC2 instances. Utilize an Amazon RDS for PostgreSQL Multi-AZ DB instance with auto-scaling read replicas for the database. Set up an Amazon EventBridge rule to invoke an AWS Lambda function for creating a larger read replica before a sale event, and failover to this replica. Configure another EventBridge rule to invoke a Lambda function to scale down the read replica post-event.
C
Apply a predictive scaling policy for the EC2 instances. Host the database on an Amazon RDS for PostgreSQL Multi-AZ DB instance with auto-scaling read replicas. Construct an AWS Step Functions state machine to run parallel AWS Lambda functions for pre-warming the database before a sale event. Implement an Amazon EventBridge rule to activate the state machine.
D
Employ a scheduled scaling policy for the EC2 instances. Manage the database on an Amazon Aurora PostgreSQL Multi-AZ DB cluster. Set up an Amazon EventBridge rule to trigger an AWS Lambda function for creating a larger Aurora Replica before a sale event, and failover to this replica. Establish another EventBridge rule to invoke a Lambda function to scale down the Aurora Replica after the event.