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Difficulty: HardPerformance and Scalability Optimization

An IoT energy company is launching a smart grid monitoring platform to ingest and analyze electricity consumption data from over 1212 million smart meters. During extreme weather events, the platform experiences sudden, massive surges in telemetry queries as utility providers run diagnostic reports. Read traffic is expected to spike from 500500 queries per second (QPS) to over 200,000200,000 QPS within less than 33 minutes. The backend application runs on Amazon ECS on AWS Fargate behind an Application Load Balancer (ALB), accessing data stored in an Amazon Aurora PostgreSQL database cluster. Which two actions should a solutions architect recommend to optimize the performance and scalability of the architecture to handle these traffic spikes? (Select TWO.)

  1. Contact AWS Support to pre-warm the Application Load Balancer (ALB) with the expected traffic profile and ramp-up rate.Answer
  2. Configure Amazon Aurora Auto Scaling to dynamically scale reader instances based on CPU utilization, and configure the application to utilize the Aurora reader endpoint for read-only reporting queries.Answer
  3. C
    Rely on standard ALB scaling and configure aggressive step-scaling policies for the ECS tasks to handle the sudden flash traffic.
  4. D
    Configure Amazon RDS Multi-AZ replication and route reporting read queries to the standby database instance to scale read throughput during peak spikes.
  5. E
    Deploy an Amazon ElastiCache for Memcached cluster with multi-AZ replication enabled to cache database query results and handle the read traffic spikes.

Answer

To optimize performance and scalability for flash traffic, the solutions architect must contact AWS Support to pre-warm the Application Load Balancer, and configure Amazon Aurora Auto Scaling to scale reader replicas while using the reader endpoint for reporting queries.
Pre-warming the ALB ensures that the load balancer has enough capacity already provisioned to absorb the 400400-fold traffic spike without dropping connections. Scaling the database tier horizontally using Aurora Auto Scaling and routing the read-only reporting queries to the reader endpoint offloads CPU utilization from the primary instance and allows the read capacity to dynamically adjust to the spike.

Step-by-Step Solution

1
Analyze the load balancer scaling characteristics under extreme flash traffic conditions.
The traffic scales from 500500 QPS to 200,000200,000 QPS in less than 33 minutes (a 400400-fold increase). Standard Application Load Balancer automatic scaling cannot cope with such rapid increases, necessitating pre-warming.
Requesting AWS Support to pre-warm the ALB ensures the load balancer is provisioned with sufficient capacity beforehand to prevent dropped connections.
2
Analyze database scaling options for the read-heavy reporting queries.
Amazon Aurora allows horizontal scaling of reads via Aurora Replicas. Configuring Aurora Auto Scaling allows the cluster to automatically add reader instances based on CPU utilization metrics.
Routing read traffic to the Aurora reader endpoint ensures the load is distributed across the dynamically scaled reader replicas, preventing database CPU exhaustion on the primary instance.
3
Evaluate the architectural limits of alternative options.
Multi-AZ standby database instances cannot serve read traffic, and ElastiCache for Memcached does not support multi-AZ replication.
Eliminating these invalid patterns ensures the design complies with high availability and operational requirements.

Key Concept

Handling massive, instantaneous traffic spikes requires pre-warming the load balancing layer and utilizing horizontal read scaling with Aurora reader endpoints and auto-scaling replicas.
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Topics

Performance and Scalability Optimization

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