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Difficulty: MediumPerformance and Scalability Optimization

A digital advertising company is designing a real-time bidding (RTB) platform that processes ad auction requests from global ad exchanges. The system must handle a baseline of 100,000100,000 requests per second (RPS) and scale to support sudden spikes of up to 800,000800,000 RPS with sub-1515 millisecond response times. The current design proposes using an Application Load Balancer (ALB) routing traffic to Amazon ECS tasks running on AWS Fargate, with Amazon Aurora PostgreSQL as the persistent database.

Which two actions should the solutions architect take to meet the performance and latency requirements under peak traffic loads? (Select two.)

  1. Request AWS Support to pre-warm the ALB to the expected peak traffic capacity of 800,000800,000 requests per second.Answer
  2. Configure Aurora Replicas in the database cluster and set up Auto Scaling to dynamically add replicas based on read load.Answer
  3. C
    Rely on the ALB's default automatic scaling behavior to handle the surge from the baseline of 100,000100,000 to 800,000800,000 requests per second.
  4. D
    Direct a portion of the read queries to the Multi-AZ standby instance of the Aurora PostgreSQL database during peak traffic.
  5. E
    Deploy Amazon ElastiCache for Memcached to store user sessions and configure multi-AZ replication to ensure persistence during a failover.

Answer

The correct actions are to request AWS Support to pre-warm the Application Load Balancer (ALB) to the expected peak traffic capacity of 800,000800,000 requests per second, and to configure Aurora Replicas in the database cluster with Auto Scaling to dynamically handle the read load.
Pre-warming the Application Load Balancer (ALB) ensures that it has sufficient capacity provisioned beforehand to handle the massive, instantaneous 8-fold traffic spike without dropping connections. Utilizing Aurora Replicas with Auto Scaling allows the database layer to horizontally scale read capacity to handle the increased query volume, preserving sub-15ms response times.

Step-by-Step Solution

1
Analyze traffic characteristics and scaling limitations of the load balancing tier.
The application faces an immediate 8-fold traffic surge from 100,000100,000 to 800,000800,000 requests per second. Under default behavior, the ALB will fail to scale quickly enough, resulting in dropped packets and increased latency.
Requesting pre-warming for the ALB is necessary to prepare the balancer's capacity ahead of time for the peak spike.
2
Evaluate the database read-scaling capabilities of Amazon Aurora.
Dynamic read-scaling is achieved by adding Aurora Replicas to the cluster and configuring Auto Scaling based on metrics such as CPU utilization.
This offloads read operations from the primary writer node to the horizontally scaled reader nodes.
3
Eliminate incorrect configurations for databases and caches.
Multi-AZ standby instances cannot receive read queries. Amazon ElastiCache for Memcached does not offer replication or persistent failover features.
Identifying invalid database replication architectures and cache engine features helps refine the solution to comply with best practices.

Key Concept

Handling sudden massive spikes requires pre-provisioning capacity on the entry layer (ELB pre-warming) and utilizing scalable read replicas at the database tier.
Estimated Time:2m 0s
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Topics

Performance and Scalability Optimization

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