When architecting an AWS-hosted web application that needs to handle sudden and massive spikes in concurrent users, it's crucial to leverage AWS services and configurations that allow for rapid, automatic scaling. Here are some key considerations and technical details:

1. Elastic Load Balancing (ELB):

   • Use Application Load Balancer (ALB) or Network Load Balancer (NLB) to distribute incoming traffic across multiple instances.
   • Configure the load balancer to scale automatically based on metrics like request count or CPU utilization.
   • Enable cross-zone load balancing to ensure even distribution of traffic across all available instances.
   • Use Amazon Route 53 for DNS management and configure it to route traffic to the load balancer.

2. Auto Scaling:

   • Create an Auto Scaling group to automatically adjust the number of EC2 instances based on demand.
   • Define scaling policies based on metrics like CPU utilization, request count, or custom metrics.
   • Set appropriate minimum and maximum instance counts to handle the expected traffic spikes.
   • Configure the Auto Scaling group to use a launch template or launch configuration that specifies the instance type, AMI, and other necessary settings.
   • Consider using Spot Instances in the Auto Scaling group to optimize costs during non-peak periods.

3. Amazon EC2:

   • Choose instance types with high network bandwidth and CPU capacity to handle the increased load.
   • Utilize Elastic Network Interfaces (ENIs) to enable high-performance networking.
   • Implement stateless application design to allow instances to be added or removed seamlessly.
   • Use Amazon Machine Images (AMIs) with pre-configured software and dependencies to speed up instance provisioning.

4. Amazon Aurora or Amazon DynamoDB:

   • For the database layer, consider using Amazon Aurora, a highly scalable and durable relational database service.
   • Aurora automatically scales read replicas based on load and provides features like auto-scaling storage and multi-region replication.
   • Alternatively, for highly scalable and low-latency NoSQL workloads, consider using Amazon DynamoDB.
   • DynamoDB offers automatic scaling of throughput capacity and can handle massive read/write loads with single-digit millisecond latency.

5. Amazon ElastiCache:

   • Use Amazon ElastiCache (Redis or Memcached) to cache frequently accessed data and reduce the load on the database.
   • ElastiCache provides in-memory caching and can handle sudden spikes in read traffic.
   • Configure ElastiCache clusters with automatic scaling based on memory usage or other metrics.

6. Content Delivery Network (CDN):

   • Implement Amazon CloudFront, a global CDN service, to cache and serve static content from edge locations closer to users.
   • CloudFront reduces the load on the origin servers and improves the overall performance and scalability of the application.

7. Serverless Architecture:

   • Consider using AWS Lambda for serverless computing, allowing you to run code without provisioning or managing servers.
   • Lambda automatically scales based on the number of incoming requests and can handle sudden spikes in traffic.
   • Use Amazon API Gateway to expose Lambda functions as REST APIs and handle request routing and throttling.

8. Monitoring and Logging:

   • Implement robust monitoring and logging solutions to gain visibility into the application's performance and identify bottlenecks.
   • Use Amazon CloudWatch to monitor metrics, set alarms, and trigger auto-scaling actions based on predefined thresholds.
   • Enable detailed logging using AWS CloudTrail and Amazon CloudWatch Logs to track API calls and troubleshoot issues.

9. Cost Optimization:

   • Continuously monitor and optimize costs by utilizing reserved instances, spot instances, and auto-scaling.
   • Set appropriate scaling policies and thresholds to avoid over-provisioning resources during non-peak periods.
   • Implement cost-saving measures like using serverless architecture, caching, and content delivery networks.

10. Load Testing and Performance Optimization:

    • Conduct thorough load testing to identify performance bottlenecks and optimize the application's scalability.
    • Use tools like Apache JMeter or Locust to simulate high-concurrency scenarios and measure response times.
    • Optimize database queries, caching strategies, and application code to handle increased load efficiently.

When architecting applications with highly variable usage patterns, it's essential to design for scalability, fault tolerance, and cost-efficiency from the ground up. AWS provides a wide range of services and configuration options to support such requirements. However, it's crucial to carefully evaluate the specific needs of the application, conduct thorough testing, and continuously monitor and optimize the infrastructure to ensure optimal performance and cost-effectiveness during sudden usage spikes.