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Difficulty: Very hardPerformance and Scalability Optimization

A media broadcasting company is planning a global live-streaming event that expects a massive flash crowd of 15,000,00015,000,000 concurrent viewers to authenticate, fetch metadata, and establish session states within a 55-minute window. The current architecture consists of an Application Load Balancer (ALB) routing requests to a containerized microservices fleet on Amazon Elastic Container Service (ECS) on AWS Fargate, and Amazon Aurora PostgreSQL for the database layer. Video metadata changes infrequently, but session states require sub-millisecond updates and must survive an Availability Zone outage. Which combination of architectural optimizations should the Solutions Architect implement to ensure the platform scales seamlessly and maintains performance during the initial traffic spike? (Select THREE.)

  1. Request AWS Support to pre-warm the Application Load Balancer to the expected peak request rate and throughput before the event begins.Answer
  2. Implement an Amazon ElastiCache for Redis cluster with Multi-AZ replication to cache video metadata and store user session states.Answer
  3. Deploy Aurora Replicas in the database cluster and configure Aurora Auto Scaling to dynamically scale read operations.Answer
  4. D
    Configure read-heavy metadata queries to target the passive standby instance in an Amazon RDS Multi-AZ deployment to offload the primary database instance.
  5. E
    Deploy an Amazon ElastiCache for Memcached cluster to store persistent session state and enable replication groups for multi-AZ failover.
  6. F
    Rely on the Application Load Balancer's built-in automatic scaling to dynamically provision additional capacity as soon as the traffic spike starts.

Answer

To optimize performance and scalability for the flash traffic event, the Solutions Architect must request AWS Support to pre-warm the Application Load Balancer, deploy Amazon ElastiCache for Redis in a Multi-AZ configuration to manage caching and persistent session state, and implement Aurora Replicas with Aurora Auto Scaling to scale read queries horizontally.
Pre-warming the Application Load Balancer ensures the proxy instances are scaled out before the traffic arrives. Amazon ElastiCache for Redis provides the required sub-millisecond latencies for metadata caching and session lookup, and its Multi-AZ replication ensures durability. Using Aurora Replicas with Aurora Auto Scaling provides the necessary read capacity scaling, offloading the database writer instance.

Step-by-Step Solution

1
Address the load balancing layer scalability limitations for flash crowds.
Identify that automatic scaling of ALBs is reactive and slow for extreme spikes, requiring pre-warming by AWS Support to pre-allocate capacity.
Prevents immediate HTTP 502/504 errors when millions of requests hit the load balancer within a 5-minute window.
2
Select the correct caching engine for persistent, highly available session data.
Choose Amazon ElastiCache for Redis over Memcached because Redis supports replication, data structures, and multi-AZ configurations for high availability.
Ensures that user sessions survive node or AZ failures without requiring users to re-authenticate.
3
Implement database horizontal read scaling.
Configure Aurora Replicas and target them using the cluster reader endpoint, enabling Aurora Auto Scaling to adjust instance counts.
Enables the database to scale read operations dynamically, avoiding overload on the primary writer node.

Key Concept

Handling extreme flash traffic by pre-warming load balancers, selecting replication-capable in-memory data stores, and scaling database reads horizontally using active replica scaling.
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Topics

Performance and Scalability Optimization

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