High-Performance AWS Deployment for Large-Scale Conference Application
This project involved the end-to-end design, deployment, and optimization of a scalable and fault-tolerant web application that needed to support tens of thousands of concurrent users during a global hybrid conference event. The core engineering challenge was ensuring the system could dynamically handle sudden spikes in traffic, sustain high availability, and deliver low-latency performance to users distributed across multiple continents.
The event hosted over 10,000 attendees both in person and online. A key event activity involved a real-time raffle registration for cloud certification vouchers, where thousands of users attempted to submit their email addresses simultaneously. The intense traffic spike during registration served as the main stress test for the system’s scalability and resilience. As the principal engineer leading this initiative, my responsibility encompassed architecture design, implementation, AWS infrastructure configuration, load testing, and operational monitoring during the live deployment.
Architecture Design and AWS Service Integration
The system architecture was built around a fully managed AWS stack adhering to cloud-native and high-availability principles. The primary services included Amazon Elastic Beanstalk for compute orchestration, Amazon DynamoDB for data persistence, and Amazon CloudFront for content delivery and caching. Additional services such as CloudWatch, IAM, API Gateway, and AWS Certificate Manager supported observability, governance, and security layers.
1. Compute Environment: AWS Elastic Beanstalk
Elastic Beanstalk served as the main platform for deploying and orchestrating the backend application. The environment was configured to run Node.js on Amazon EC2 instances within an Auto Scaling group.
- Environment Type: A load-balanced, autoscaling environment was selected to ensure horizontal scaling based on real-time traffic metrics.
- Instance Type: t3.medium instances were used during development, later upgraded to m5.large in production to handle compute-intensive workloads during spikes.
- Scaling Policy: Scaling triggers were defined via CloudWatch alarms monitoring CPU utilization thresholds at 60% for scale-out and 30% for scale-in.
- Load Balancer: An Application Load Balancer (ALB) was configured with health checks monitoring a
/healthendpoint to ensure traffic was only routed to healthy nodes. - Deployment Strategy: Elastic Beanstalk handled rolling deployments with an immutable strategy for production. Blue/green deployments were used for version testing without downtime.
For continuous delivery, AWS CodePipeline and CodeBuild automated the integration and deployment flow, pulling from a GitHub repository, building packages, and pushing new versions to Elastic Beanstalk across multiple environments (staging and production).
2. Data Layer: Amazon DynamoDB
Amazon DynamoDB was chosen as the data store for recording user registrations. The key requirements were high write throughput, zero-downtime scalability, and millisecond-level latency.
- Table Structure: The primary table used an email attribute as the partition key to ensure unique user entries. A timestamp attribute provided sorting for analytics.
- Capacity Mode: On-demand capacity was selected to automatically adjust to bursts of write traffic without manual intervention.
- Read/Write Throughput: Benchmark tests demonstrated consistent latency below 10ms at over 2,000 writes per second during load simulations.
- DAX Caching: DynamoDB Accelerator (DAX) was integrated for high-speed read caching, reducing database latency for repeated queries.
- Encryption and Access Control: Data was encrypted at rest using AWS KMS. IAM policies restricted database access to application services only, following the principle of least privilege.
3. Content Delivery and Caching: Amazon CloudFront
To achieve global low latency and efficient content delivery, CloudFront was implemented as the CDN layer in front of the web application and static assets.
- Origin Configuration: Two origins were defined—Elastic Beanstalk for dynamic API content and an S3 bucket for static assets such as HTML, CSS, and images.
- Cache Behavior: Dynamic paths were cached with short TTLs to ensure freshness, while static content had longer expiration policies.
- Edge Locations: With over 400 global edge nodes, CloudFront served nearby users efficiently, reducing first-byte latency by 60–70%.
- Security: HTTPS-only communication was enforced using certificates provisioned via AWS Certificate Manager. Integration with AWS WAF allowed filtering of malicious requests and rate-limiting to prevent abuse.
4. Monitoring, Logging, and Security Framework
Operational visibility and performance control were delivered through a well-instrumented observability framework:
- CloudWatch Metrics: Custom dashboards tracked CPU usage, request latency, DynamoDB read/write units, and scaling events.
- CloudWatch Logs: Application and system logs were aggregated and stored for real-time troubleshooting and post-event forensics.
- Alarms: Automatic alerts were configured for error rates above 1%, latency exceeding 200ms, and DynamoDB throttling events.
- AWS X-Ray: Used for end-to-end tracing of user requests to detect latency bottlenecks between application components.
- Security Management: All network access was controlled via Amazon VPC security groups and IAM roles. IAM roles were tightly scoped to prevent privilege escalation. AWS Shield Standard protected against potential DDoS activity.
Development Workflow and Load Testing
The development lifecycle adopted a continuous integration and deployment (CI/CD) pipeline integrated with AWS developer tools. CodePipeline coordinated build and deployment steps, CodeBuild handled automated testing, and Elastic Beanstalk performed rolling updates.
Load testing formed a critical phase before production rollout. Tools like Apache JMeter and Locust were used to simulate concurrent user sessions. Testing scenarios replicated expected peak usage patterns, including 10,000 simultaneous form submissions within a 30-second window.
Test metrics confirmed that:
- API response times averaged 180ms even under peak load.
- The system maintained a 99.99% success rate for email submissions.
- Auto Scaling triggered within 45 seconds of CPU thresholds, doubling compute capacity.
- DynamoDB auto-scaled write capacity seamlessly, demonstrating no throttling.
Stress tests were executed under multiple geographic profiles by generating traffic from regions including North America, Europe, and Asia, validating global edge cache performance.
Event Day Operation and Real-Time Scaling
On the day of deployment, traffic to the raffle application spiked sharply as the host announced registration open. Within 15 seconds, the system processed thousands of concurrent API calls. Elastic Beanstalk scaling policies successfully launched new EC2 instances, doubling fleet size within minutes to maintain steady performance.
CloudFront handled over 80% of total requests through cached responses, which dramatically reduced origin load. DynamoDB processed more than 50,000 write operations in under five minutes without latency degradation.
Monitoring dashboards showed CPU utilization peaking at 72%, network I/O surges within acceptable thresholds, and sub-second p99 latency across all regions. No downtime or degradation was observed during the live event, validating the system’s load resilience.
Post-Event Analysis and Optimization
After the event, a detailed post-mortem and analytics phase was conducted. AWS X-Ray traces highlighted a few minor latency outliers linked to API Gateway cold starts, later mitigated by enabling provisioned concurrency for Lambda functions handling auxiliary tasks (such as confirmation emails). DynamoDB partition metrics were analyzed to optimize table design, improving future write distribution.
CloudWatch reports confirmed near-perfect uptime during the 3-hour conference window. The data collected was archived in Amazon S3 and queried using AWS Athena for performance insights.
Further improvements implemented post-event included:
- Deployment of AWS Global Accelerator to enhance TCP routing efficiency.
- Fine-tuning ALB target response time thresholds.
- Adding a Redis cache layer using Amazon ElastiCache to offload frequent session lookups.
Key Technical Takeaways
- Managed AWS services like Elastic Beanstalk and DynamoDB simplify scalability while maintaining DevOps flexibility.
- On-demand capacity and distributed caching significantly reduce manual database tuning.
- Multi-layer caching (Edge + DAX) is essential for maintaining high global performance.
- Auto-scaling and monitoring automation ensure predictable infrastructure response under burst load.
- Observability tools like CloudWatch and X-Ray provide essential diagnostics for maintaining subsecond latency at scale.
This project demonstrated the effectiveness of a well-architected serverless and managed-service-based cloud deployment. The infrastructure dynamically scaled to meet unpredictable global demand while maintaining security, compliance, and cost efficiency. Overall, the application’s performance validated the combination of AWS Elastic Beanstalk, DynamoDB, and CloudFront as an ideal architecture pattern for real-time, high-volume web applications.
