AWS Certified Solutions Architect – ProfessionalContinuously Improve Existing SolutionsEasy
A global e-commerce company experiences seasonal traffic spikes, with order processing rates fluctuating significantly. Their current order processing system uses a fixed cluster of Amazon EC2 instances, leading to over-provisioning during off-peak times and under-provisioning during peak sales, resulting in high costs and poor customer experience. The company wants to improve this solution by making the order processing highly scalable and cost-efficient. Which approach should they take?
- AMigrate the order processing to a larger, fixed cluster of EC2 instances with higher capacity.
- BDeploy the application on Amazon Lightsail instances with manual scaling adjustments during peak times.
- CUse AWS Fargate for containerized order processing with a fixed number of tasks.
- DImplement AWS Auto Scaling Groups for EC2 instances behind an Application Load Balancer and use Amazon SQS for decoupling.
Show answer & explanationAnswer & explanation
Correct answer: D. Implement AWS Auto Scaling Groups for EC2 instances behind an Application Load Balancer and use Amazon SQS for decoupling.
AWS Auto Scaling Groups dynamically adjust the number of EC2 instances based on demand, ensuring optimal capacity and cost efficiency. Decoupling with Amazon SQS handles fluctuating message volumes and prevents backlogs, making the system resilient and scalable.
Why the other options are wrong
- A. Migrating to a larger fixed cluster would only exacerbate over-provisioning during off-peak and might still under-provision during extreme peaks, without solving the core scalability issue.
- B. Amazon Lightsail is designed for simpler, predictable workloads and manual scaling is inefficient and prone to human error for highly fluctuating traffic.
- C. Using a fixed number of Fargate tasks does not address the elastic scalability requirement for fluctuating traffic.
Elastic Scalability
The ability of a system to automatically scale its resources up or down in response to changes in demand, ensuring optimal performance and cost efficiency.
- Matches capacity to demand.
- Prevents over-provisioning and under-provisioning.
- Often uses services like Auto Scaling Groups, SQS, and serverless compute.
Memory trick: Elasticity stretches and shrinks like a rubber band with demand.