Logo
Tutorials Articles Code Playground
Sign In Get Started
Module: Load Balancing

Traffic Routing

System Design Fundamentals: Load Balancing - Traffic Routing

Load balancing is a critical component of scalable and highly available systems. It distributes incoming network traffic across multiple servers to ensure no single server is overwhelmed. This document focuses on the traffic routing aspect of load balancing, detailing different algorithms and considerations.

I. Why Load Balancing?

  • Scalability: Handles increased traffic by distributing it across more servers.
  • High Availability: If one server fails, traffic is automatically routed to healthy servers, minimizing downtime.
  • Improved Performance: Reduces response times by preventing server overload.
  • Resource Optimization: Ensures efficient utilization of server resources.
  • Maintenance: Allows for server maintenance (updates, patching) without service interruption.

II. Load Balancing Architectures

There are two main architectural approaches:

  • Hardware Load Balancers: Dedicated physical appliances (e.g., F5 BIG-IP, Citrix ADC). Offer high performance and advanced features but are expensive and less flexible.
  • Software Load Balancers: Software running on standard servers (e.g., HAProxy, Nginx, AWS ELB, Google Cloud Load Balancing). More flexible, cost-effective, and easier to scale. Often used in cloud environments.

Placement of Load Balancers:

  • Layer 4 Load Balancers: Operate at the Transport Layer (TCP/UDP). Fast and efficient, but limited in routing decisions. Typically used for simple traffic distribution.
  • Layer 7 Load Balancers: Operate at the Application Layer (HTTP/HTTPS). Can make routing decisions based on application-specific data (e.g., URL, cookies, headers). More flexible but require more processing power.

III. Traffic Routing Algorithms

These algorithms determine how the load balancer distributes traffic to backend servers.

1. Round Robin:

  • Description: Distributes requests sequentially to each server in the pool.
  • Pros: Simple to implement, fair distribution if servers are identical.
  • Cons: Doesn't consider server load or health. Can lead to uneven load if servers have different capacities or processing times.
  • Use Cases: Simple applications with identical servers.

2. Weighted Round Robin:

  • Description: Assigns weights to each server, indicating its capacity. Servers with higher weights receive more requests.
  • Pros: Allows for utilizing servers with different capacities effectively.
  • Cons: Requires manual configuration of weights. Doesn't dynamically adjust to changing server load.
  • Use Cases: Heterogeneous server pools where servers have varying processing power.

3. Least Connections:

  • Description: Routes requests to the server with the fewest active connections.
  • Pros: Dynamically adjusts to server load. Good for long-lived connections (e.g., WebSockets).
  • Cons: Can be less effective if connections vary significantly in duration.
  • Use Cases: Applications with varying connection lengths, like streaming services or real-time applications.

4. Weighted Least Connections:

  • Description: Combines Least Connections with weights. Prioritizes servers with fewer connections and higher weights.
  • Pros: Balances load and utilizes server capacity effectively.
  • Cons: More complex to configure.
  • Use Cases: Heterogeneous server pools with varying connection lengths.

5. IP Hash (Source IP Hash):

  • Description: Calculates a hash based on the client's IP address and routes requests to the same server consistently.
  • Pros: Ensures session persistence (sticky sessions) without requiring cookies.
  • Cons: Can lead to uneven load if clients are concentrated in a few IP addresses. Doesn't work well with NAT (Network Address Translation).
  • Use Cases: Applications requiring session persistence where cookies are undesirable.

6. URL Hash:

  • Description: Calculates a hash based on the requested URL and routes requests to the same server consistently.
  • Pros: Useful for caching scenarios where specific URLs should be served from the same server.
  • Cons: Can lead to uneven load if certain URLs are more popular than others.
  • Use Cases: Caching layers, content delivery networks (CDNs).

7. Least Response Time:

  • Description: Routes requests to the server with the lowest average response time.
  • Pros: Dynamically adjusts to server performance. Provides the best user experience.
  • Cons: Requires continuous monitoring of server response times. More complex to implement.
  • Use Cases: Performance-critical applications.

8. Content-Aware Routing (Layer 7):

  • Description: Routes requests based on the content of the request (e.g., HTTP headers, cookies, URL path).
  • Pros: Highly flexible. Allows for routing based on application logic. Can be used for A/B testing, canary deployments, and feature flags.
  • Cons: Requires more processing power. More complex to configure.
  • Use Cases: Complex applications with specific routing requirements.

IV. Health Checks

Load balancers regularly perform health checks on backend servers to ensure they are healthy and responsive.

  • Types of Health Checks:
    • TCP Connection Check: Attempts to establish a TCP connection to the server.
    • HTTP/HTTPS Check: Sends an HTTP/HTTPS request to the server and verifies the response code.
    • Custom Health Check: Executes a custom script or program to verify server health.
  • Actions on Failure: If a health check fails, the load balancer automatically stops sending traffic to that server.

V. Session Persistence (Sticky Sessions)

Ensuring that requests from the same client are consistently routed to the same server.

  • Methods:
    • Cookies: The load balancer inserts a cookie into the client's browser, which identifies the server.
    • IP Hash: As described above.
    • Source IP Affinity: Similar to IP Hash.
    • URL Parameters: Appending a server identifier to the URL.

VI. Considerations for Choosing a Load Balancing Solution

  • Traffic Volume: The expected amount of traffic.
  • Application Complexity: The complexity of the application and its routing requirements.
  • Scalability Requirements: The need to scale the system horizontally.
  • Cost: The cost of the load balancing solution.
  • Maintenance Overhead: The effort required to maintain the load balancing solution.
  • Cloud Provider Integration: If using a cloud provider, consider their native load balancing services.

This document provides a foundational understanding of load balancing and traffic routing. The best approach will depend on the specific requirements of your application and infrastructure.