Auto-scaling solution of AWS project with terraform for a Major E-Commerce Platform
Project Description
A major e-commerce platform, experiencing rapid growth and traffic spikes during promotional events, required a solution to manage its AWS infrastructure efficiently. The client sought continuous monitoring and real-time adjustments of their resources to ensure optimal performance and cost management.
Client's Main Requests:
- Implement a 24/7 monitoring and response system for AWS resources to handle server resource exhaustion swiftly.
- Enable real-time adjustments of AWS service parameters such as instance types for RDS (MySQL) and EC2 instances, based on performance metrics.
Key Metrics
- Dynamic scaling of over 100 AWS instances.
- Sub-10-minute response time for resource adjustments after alert triggers.
- 46% reduction of service downtime
- Up to 30% reduced operational costs.
Project Goals
- Review existing Infrastructure-as-a-code approaches. Enhance the terraform implementation in infrastructure definition.
- Ensure that the cloud infrastructure is seamlessly scaled according to the load and specific metrics to be monitored.
Key Challenges and Results
Terraform for Infrastructure as Code (IaC):
- Terraform was used to codify the entire AWS infrastructure, allowing for version-controlled, consistent deployment and management of resources like EC2, RDS, and Auto Scaling configurations.
- This setup enabled predictable deployments and simplified the process of replicating environments across different regions or recovery scenarios.
- AWS CloudWatch was configured to monitor vital metrics and log streams from all relevant AWS services. Custom alarms were set to trigger AWS Lambda functions for specific conditions indicative of performance bottlenecks or potential failures.
- Lambda functions, defined using Terraform, were scripted to execute changes within AWS services, such as modifying EC2 instance types or scaling RDS capacity in real-time.
- Auto Scaling policies were defined using Terraform to automatically adjust the number of EC2 instances in response to varying load patterns, ensuring efficient handling of incoming traffic without manual intervention.
- RDS scaling was managed through Terraform to adjust database instance sizes and configurations dynamically, based on the alerts from CloudWatch, thereby maintaining database performance and cost efficiency.
- The implementation of a fully automated, Terraform-managed infrastructure resulted in significant improvements in response times to performance deviations, reducing downtime and latency issues.
- Cost efficiency was maximized by using Terraform to fine-tune resource allocation based on real-time demand, avoiding over-provisioning and underutilization.
- The platform successfully handled peak loads during high-traffic events, maintaining high availability and customer satisfaction due to the robust, scalable infrastructure.
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