Design Load Balancing Infrastructure for microservices

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. **Geo-distributed Load Balancing**: The system should intelligently distribute incoming requests across multiple microservices located in different geographical regions.
2. **Health Checks**: Regular health checks must be performed on all services to ensure they are responsive and can handle requests.
3. **Request Routing**: The ability to route requests based on latency, service availability, and user location.
4. **Monitoring and Logging**: Capture metrics on request counts, latencies, and error rates for analysis.
5. **Dynamic Configuration**: Ability to update load balancing rules without downtime.

### Non-Functional Requirements
1. **Scalability**: Should handle millions of requests per second with low latency.
2. **Availability**: The system must have at least 99.99% uptime.
3. **Consistency**: Eventual consistency for service data across geo-regions.
4. **Performance**: Average latency should be under 50ms for user requests.

Assuming DE Shaw anticipates handling 1 million requests per second globally, with an average request size of 1 KB:
1. **Requests per Year**: 1 million requests/second * 60 seconds/minute * 60 minutes/hour * 24 hours/day * 365 days/year = 31.536 trillion requests/year.
2. **Data Storage**: If we log 100 bytes per request for monitoring, that's 3.1536 trillion bytes (or about 3.15 TB) just for logging.
3. **Bandwidth**: 1 million requests * 1 KB/request = 1 GB/second, translating to approximately 31.536 PB/year.
4. **Scaling**: Plan for 3x peak load, targeting 3 million requests per second to accommodate traffic spikes.

### Component Diagram
- **Load Balancer (HAProxy)**: Distributes requests based on geographical location and service health.
- **Service Registry (Consul)**: Maintains an updated list of available services and their health status.
- **API Gateway (Kong)**: Acts as an entry point for clients, handling authentication and routing to microservices.
- **Metrics Store (Prometheus)**: Collects metrics from services for monitoring.
- **Analysis and Reporting (Grafana)**: Visualizes metrics and logs for performance monitoring.
- **Feature Store (Feast)**: Manages features for ML model input.
- **ML Model (TensorFlow Serving)**: Hosts the load balancing model for intelligent routing decisions.
- **Database (Cassandra)**: A NoSQL database for storing service states and logs, ensuring high availability and scalability.

### Schema Design
1. **ServiceState Table**: 
   - `ServiceID`: UUID (Primary Key)
   - `Region`: String
   - `HealthStatus`: Boolean
   - `Latency`: Float
   - `LastChecked`: Timestamp

2. **RequestLog Table**:
   - `RequestID`: UUID (Primary Key)
   - `Timestamp`: Timestamp
   - `ServiceID`: UUID
   - `Latency`: Float
   - `StatusCode`: Integer

3. **FeatureStore Table**:
   - `FeatureID`: UUID (Primary Key)
   - `ServiceID`: UUID
   - `FeatureVector`: Array of Float
   - `LastUpdated`: Timestamp

This schema supports efficient querying for service health checks and allows for rapid feature retrieval for ML model inference.

### Architectural Decisions
1. **Consistency vs. Availability**: Chose eventual consistency for service state updates to improve availability and performance, especially across geo-distributed regions.
2. **Load Balancing Algorithm**: Opted for a weighted round-robin approach to balance load while considering service latency, trading off slightly on complexity for better traffic distribution.
3. **Data Store Choice**: Selected Cassandra for its ability to handle high write and read throughput, accepting eventual consistency to ensure availability.
4. **Model Freshness**: Implemented canary deployments for ML models to ensure freshness and mitigate risks from model drift, balancing the need for exploration and exploitation in routing decisions.
5. **Monitoring and Alerts**: Built in comprehensive monitoring but accepted a potential delay in detecting model degradation, as real-time monitoring can lead to increased costs and complexity.

Design Load Balancing Infrastructure for microservices

DE Shaw

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