Contents
Roadmap info from roadmap website
Load Balancing
Load balancing in distributes network traffic across multiple pods or nodes using a Service object. A Service provides a stable network endpoint for a set of pods, allowing other pods or external clients to access them through a single IP address and DNS name. Kubernetes offers three types of load balancing algorithms for Services, which distribute traffic based on round-robin, least connections, or IP hash. Load balancing is an essential part of Kubernetes networking, providing efficient and reliable traffic distribution across a cluster.
Learn more from the following resources:
- @official@Load Balancing - Documentation
- @video@Tutorial | Load Balancing Service in Kubernetes
- @article@Ingress Controllers: The Swiss Army Knife of Kubernetes
| Algorithm | Round-Robin | Least Connections | IP Hash |
|---|---|---|---|
| Function | Distributes requests sequentially to each server in order. | Routes requests to the server with the fewest active connections. | Distributes requests based on the hash of the client’s IP address. |
| Use Case | Best for evenly distributing load across servers with similar performance. | Ideal when server load can vary or some servers handle connections faster. | Useful when session persistence is needed (sticky sessions). |
| Balancing Method | Cycles through servers in a fixed order, regardless of their current load. | Prioritizes servers that have less load by connection count. | Maps requests from the same client IP to the same server. |
| Advantages | Simple and efficient in environments with similar servers. | Ensures better load distribution when servers have unequal workloads. | Guarantees the same client is always routed to the same server. |
| Disadvantages | Doesn’t account for varying server loads or connection speeds. | Can lead to imbalanced routing in case of long-lived connections. | Less effective when traffic is unevenly distributed by IP or if one server gets disproportionate requests. |
| Scalability | Scalable but less optimal for dynamic environments. | More scalable for environments with fluctuating workloads. | Scalable for environments that need session persistence. |
| Complexity | Very simple to implement. | Slightly more complex, requiring tracking of active connections. | Requires hash calculation and IP tracking for consistency. |