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Difficulty: MediumOptimizing Compute and Storage Performance

A company hosts a high-frequency flash sale application on a fleet of Amazon EC2 instances in an Auto Scaling group behind an Application Load Balancer (ALB). The application data is stored in an Amazon RDS for MySQL Multi-AZ DB instance. During scheduled weekly flash sales, traffic surges instantly by 300%300\% within two minutes. During these peaks, users experience HTTP 503 Service Unavailable errors, and database read response times increase significantly. A Solutions Architect needs to optimize both compute and storage performance to handle the next flash sale without service degradation.

Which combination of actions should the Solutions Architect take to address these performance issues? (Select TWO.)

  1. Migrate the database to Amazon Aurora MySQL, deploy Aurora Replicas, and configure Aurora Auto Scaling to dynamically adjust the number of replicas.Answer
  2. Request AWS Support to pre-warm the Application Load Balancer to the expected capacity prior to the scheduled flash sales.Answer
  3. C
    Configure the application to route read queries to the standby replica instance of the Amazon RDS MySQL Multi-AZ deployment during peak traffic.
  4. D
    Rely on the Application Load Balancer's automatic scaling metrics to dynamically provision capacity during the flash sale traffic spike.
  5. E
    Deploy an Amazon ElastiCache for Memcached cluster to act as a persistent database replication target and cache transactional data.

Answer

Migrate the database to Amazon Aurora MySQL with Aurora Auto Scaling and request AWS Support to pre-warm the Application Load Balancer before the sale.
Pre-warming the Application Load Balancer handles sudden, massive traffic spikes that exceed standard automatic scaling rates. Migrating to Amazon Aurora MySQL allows read workloads to be distributed across multiple Aurora Replicas that scale dynamically with Auto Scaling, resolving the storage bottleneck.

Step-by-Step Solution

1
Analyze the load balancer scaling limitation.
The Application Load Balancer cannot scale quickly enough to handle the sudden 300%300\% increase in traffic within two minutes, which triggers HTTP 503 errors.
Determine the need to pre-warm the ALB to ensure capacity is available prior to the scheduled flash sale.
2
Analyze the database read performance constraint.
The primary database experiences high read latency, and the RDS Multi-AZ standby cannot be used for read scaling.
Identify the need to migrate to Amazon Aurora MySQL to use active Aurora Replicas and leverage auto-scaling to distribute read loads.
3
Combine the compute and storage tier optimizations.
Pre-warming the ALB resolves the ingress routing limit, and migrating to Aurora with replica scaling resolves the read performance limit.
Ensure a comprehensive architecture that prevents bottlenecking at both the load balancing and storage layers.

Key Concept

Optimizing compute scaling limits using ALB pre-warming and database read capacity scaling using Amazon Aurora Replicas.
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Topics

Optimizing Compute and Storage Performance