Design a geo-distributed Load Balancing System

Question

Design a geo-distributed load balancing system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.

Codemia · Accepted Answer

### Functional Requirements
1. **Load Balancing**: Distribute incoming traffic across multiple servers in different geographic regions to optimize resource utilization and minimize latency.
2. **Health Checks**: Continuously monitor the health of servers and reroute traffic in case of failures.
3. **Geo-routing**: Direct requests to the nearest server based on user location.
4. **Rate Limiting**: Implement API rate limiting to prevent abuse and ensure fair usage across clients.
5. **User Authentication**: Secure the system through user authentication and authorization mechanisms.

### Non-Functional Requirements
1. **Scalability**: The system should handle millions of requests with the ability to scale horizontally.
2. **Availability**: Aim for high availability (99.99%) to ensure minimal downtime.
3. **Latency**: Maintain low latency (<100ms) for end-users regardless of their geographic location.
4. **Consistency**: Provide eventual consistency for user data across different regions.

Assuming Morgan Stanley expects to handle 10 million requests per day, we can break it down:
- **Average Requests per Second**: 10 million requests / 86,400 seconds = ~115 requests/second.
- **Peak Load**: Assuming peak traffic is 10x the average, we can anticipate ~1,150 requests/second.
- **Server Capacity**: If each server can handle 100 requests/second, we will need a minimum of 12 servers (1,150 / 100) to handle peak loads efficiently.
- **Geographic Distribution**: Assuming operations in 3 regions (US, EU, APAC), we could allocate 4 servers per region for redundancy and load balancing.

### Component Diagram
- **Client**: End-users accessing the load balancing system via web or mobile applications.
- **API Gateway**: Use **AWS API Gateway** to route requests, enforce rate limiting, and handle authentication.
- **Load Balancer**: Implement **AWS Elastic Load Balancer (ELB)** for distributing incoming requests to the backend servers.
- **Backend Servers**: Use **Docker containers** orchestrated by **Kubernetes** for deploying application instances across data centers.
- **Database**: Utilize **Amazon DynamoDB** for a highly available and scalable NoSQL database capable of handling geo-distributed data.
- **Monitoring**: Implement **Prometheus** and **Grafana** for monitoring, alerting, and performance tracking.

### Schema Design
1. **User Table**: 
   - `user_id (PK)`: String
   - `username`: String
   - `password_hash`: String
   - `email`: String
   - `region`: String

2. **Request Logs Table**: 
   - `log_id (PK)`: String
   - `user_id (FK)`: String
   - `timestamp`: DateTime
   - `request_type`: String
   - `response_time`: Number

### Access Patterns
- **User Authentication**: Query `User Table` based on `username`.
- **Logging Requests**: Insert records into `Request Logs Table` for monitoring and auditing purposes.

1. **Consistency vs. Availability**: Opting for eventual consistency in user data allows the system to remain highly available and responsive. This is crucial for a financial institution like Morgan Stanley, where latency is a critical factor.
2. **Rate Limiting Strategies**: Implementing strict rate limiting could potentially lead to throttling legitimate users during peak times, impacting availability. Therefore, a dynamic rate limiting approach based on user behavior can mitigate this issue.
3. **Geographic Redundancy Costs**: Hosting servers in multiple geographic regions increases operational costs, yet this trade-off ensures low latency and high availability for users worldwide. The decision to allocate resources across regions should be balanced against budget constraints.
4. **Monitoring Complexity**: While implementing robust monitoring increases operational overhead, it is essential for quick incident response and ensuring system reliability.

Design a geo-distributed Load Balancing System

Morgan Stanley

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