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Difficulty: MediumPerformance and Scalability Optimization

A digital learning platform offers synchronized online examination sessions for universities worldwide. During the first 55 minutes of a scheduled exam, traffic spikes instantaneously from 100100 requests per second (RPS) to over 60,00060,000 RPS as thousands of students log in and fetch the exam question database. The architecture utilizes an Application Load Balancer (ALB) and an Auto Scaling group of Amazon EC2 instances, backed by a single Amazon RDS PostgreSQL database instance configured with Multi-AZ. During the initial minutes of the exam, the database experiences high CPU utilization, and the ALB returns HTTP 502 Bad Gateway and 503 Service Unavailable errors due to dropped requests.

Which two architectural improvements should a Solutions Architect implement to address these performance and scalability issues? (Select TWO.)

  1. Submit a support ticket to AWS to pre-warm the Application Load Balancer (ALB) with the expected traffic rate and volume before the exam begins.Answer
  2. Migrate the database to Amazon Aurora PostgreSQL and configure Aurora Auto Scaling to dynamically scale reader instances to handle the spikes in query traffic.Answer
  3. C
    Configure the web application to direct read-only query traffic to the standby replica of the Amazon RDS Multi-AZ deployment to offload the primary database.
  4. D
    Rely on the Application Load Balancer's (ALB) default automatic scaling mechanisms to dynamically scale up its capacity in response to the sudden login surge.
  5. E
    Configure the Auto Scaling group to monitor the RDS Multi-AZ secondary instance's CPU utilization to determine when to launch additional EC2 instances.

Answer

The correct architecture requires pre-warming the Application Load Balancer to handle the sudden surge in connection volume, and migrating the backend database to Amazon Aurora to utilize Aurora Replicas for horizontal read scaling under load.
Pre-warming the Application Load Balancer is critical for handling flash traffic spikes that exceed the rate at which the load balancer can automatically scale out. Without pre-warming, the load balancer will drop connections and return errors. Additionally, migrating from a single RDS instance to Amazon Aurora enables horizontal scaling of read capacity through Aurora Auto Scaling for reader replicas, resolving the database CPU bottleneck.

Step-by-Step Solution

1
Analyze the load balancer performance characteristics under flash traffic conditions.
Identify that the Application Load Balancer cannot scale fast enough automatically to handle an instantaneous spike to 60,00060,000 RPS without dropping connections.
Application Load Balancer auto-scaling is designed for gradual traffic changes; immediate spikes require pre-warming by AWS Support.
2
Analyze database bottleneck constraints in standard Amazon RDS Multi-AZ deployments.
Recognize that the standby database instance in Amazon RDS Multi-AZ is strictly passive and cannot be utilized to offload read-heavy query spikes.
To scale read queries, the database tier must support active replicas, which is not possible with passive standby RDS instances.
3
Select performance and scaling optimization strategies.
Choose to pre-warm the Application Load Balancer to prevent load balancer drops, and migrate the database to Amazon Aurora to dynamically scale read operations via Aurora Replicas.
Aurora Replicas are active read endpoints and can auto-scale horizontally to resolve CPU constraints during query surges.

Key Concept

Handling massive, instantaneous flash traffic requires pre-warming the load balancer and scaling the database tier horizontally using active read replicas, as standard RDS standby instances are passive.
Estimated Time:3m 0s
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Topics

Performance and Scalability Optimization

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