An electric vehicle (EV) charging network provider is designing a new global charging sess… — AWS Certified Solutions Architect - Professional

An electric vehicle (EV) charging network provider is designing a new global charging session management system. The system must process real-time telemetry and session initialization requests from $200,000$ charging stations. At the start of peak operational hours, session initialization requests spike from $2,000$ requests per second to $60,000$ requests per second within a $1$ -minute window. The system must process these requests with sub-100ms latency, validate the user's account status, verify charger availability, and persist session records. The read-to-write ratio is $4:1$ .

The architecture uses Amazon Route 53, an Application Load Balancer (ALB), and Amazon ECS on AWS Fargate for the API layer. The database layer is Amazon Aurora PostgreSQL.

Which two actions should the Solutions Architect recommend to ensure the platform scales efficiently and meets the performance requirements during peak traffic? (Select TWO.)

Request AWS Support to pre-warm the Application Load Balancer (ALB) to the expected peak throughput before the scheduled start of peak hours, and configure Amazon ECS Scheduled Scaling to scale out the Fargate tasks to the anticipated capacity prior to the spike.Answer
Implement Amazon ElastiCache for Redis to cache charger availability and user account status, and configure Amazon Aurora Auto Scaling to scale read replicas horizontally while maintaining a minimum replica count sufficient to absorb the initial burst.Answer
C
Configure the ECS Fargate tasks to scale out using dynamic target tracking based on CPU utilization, and configure the Amazon Aurora Multi-AZ standby instance in the secondary Availability Zone to serve read traffic during peak periods.
D
Implement Amazon ElastiCache for Memcached to replicate session states and cache database queries, and set the Auto Scaling cooldown period for the Fargate tasks to 10 seconds to allow rapid scale-out.
E
Rely on the Application Load Balancer's default automatic scaling to absorb the spike, and configure Amazon Aurora Auto Scaling to dynamically scale the primary writer instance to a larger DB instance class during the event.

Answer

Request AWS Support to pre-warm the Application Load Balancer and configure ECS Scheduled Scaling before the event, and implement Amazon ElastiCache for Redis along with a pre-configured minimum replica count for Aurora Read Replicas.

To support a sudden 30-fold increase in traffic within a single minute, reactive scaling mechanisms are insufficient. Pre-warming the Application Load Balancer (ALB) ensures that AWS scales the load balancer's capacity ahead of time, preventing dropped connections at the start of the peak. Similarly, configuring Amazon ECS Scheduled Scaling ensures that Fargate tasks are fully provisioned and ready to accept traffic before the spike starts. To optimize the database layer, implementing Amazon ElastiCache for Redis offloads the frequent read operations (charger availability and account status) from the database. For the remaining reads, setting a high minimum count for Aurora Read Replicas ensures that read capacity is already in place to absorb the initial query spike.

Step-by-Step Solution

Analyze the workload characteristics and scaling limits.

The traffic spikes 30-fold from

2,000\text{ rps}

60,000\text{ rps}

within

1

minute. Standard reactive scaling mechanisms (ALB auto-scaling, ECS target tracking, and Aurora Auto Scaling) have latency in spawning resources and will fail to absorb the sudden burst.

This establishes that proactive scaling strategies are required at the entry and compute layers to prevent performance degradation.

Address the load balancing and compute provisioning layers.

Pre-warming the ALB configures the load balancer for the target peak throughput beforehand. ECS Scheduled Scaling pre-allocates Fargate tasks to match the target capacity right before the peak hours begin, eliminating startup delay.

This guarantees that incoming connections are not dropped at the ingress point and that sufficient backend container capacity exists immediately.

Optimize the database layer for read and write loads.

Amazon ElastiCache for Redis caches the read-heavy transactions (charger availability and account status). Setting a high minimum reader count for Aurora Read Replicas pre-warms the database scaling pool for any uncached reads.

This isolates the primary database instance from write starvation and read latency spikes, adhering to AWS database best practices.

Key Concept

Proactive scaling and caching strategies are required to handle rapid, massive traffic spikes that exceed the response times of reactive auto-scaling policies.

Estimated Time:3m 0s

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