Modern containerized applications often span multiple Kubernetes clusters to distribute workloads geographically or to achieve high availability. Load balancing plays a crucial role in ensuring optimal resource allocation and maximizing application performance across these distributed clusters. This case study aims to evaluate and compare different approaches for multi-cluster load balancing in Kubernetes, highlighting their respective pros and cons.
The study examines three prominent load balancing approaches: DNS-based load balancing, Ingress controllers, and Service Mesh. Each approach is assessed based on key criteria such as scalability, flexibility, ease of configuration, performance, and integration with existing Kubernetes infrastructure.
Geographic Distribution: GSLB is well-suited for applications with a geographically distributed architecture. It enables traffic to be directed to the nearest cluster or region based on the client's geographic location. This approach ensures low latency and optimal performance by minimizing the distance that data needs to travel.
External Client Traffic: GSLB primarily focuses on load balancing traffic from external clients to the appropriate clusters or regions. It is effective for applications where most of the traffic originates from external sources, such as end-users accessing a web application.
Failover and Disaster Recovery: GSLB can play a crucial role in disaster recovery scenarios. In the event of a failure in one cluster or region, GSLB can redirect traffic to backup clusters or regions, ensuring high availability and minimal disruption to users.
Microservices Architecture: Service Mesh is well-suited for applications built on a microservices architecture. It allows for efficient load balancing between microservices across multiple clusters, providing granular control over traffic routing, load balancing, and service discovery.
East-West Traffic: Service Mesh excels at handling east-west traffic, which involves communication between services within the same cluster or across clusters. Load balancing can be applied at the service level, ensuring even distribution of traffic and optimizing performance within and across clusters.
Advanced Traffic Management: Service Mesh offers advanced traffic management capabilities such as traffic splitting, circuit breaking, retries, and fault tolerance. These features allow for fine-grained control over traffic patterns, enabling intelligent load balancing decisions based on service health, latency, or other metrics.
https://linkerd.io/2.13/features/multicluster/ https://istio.io/latest/docs/setup/install/multicluster/
Centralized Load Balancing: CNI-based load balancing typically involves a centralized load balancer or ingress controller deployed within each cluster. It is effective for load balancing traffic from external clients to the appropriate cluster and distributing traffic among services within that cluster.
North-South Traffic: CNI-based load balancing is well-suited for managing north-south traffic, which involves communication between external clients and the services within a cluster. Load balancing algorithms are applied at the cluster level to distribute traffic across the available nodes, ensuring efficient resource utilization and optimal performance for incoming requests.
Ingress Controllers and External Traffic Management: CNI-based load balancing often relies on ingress controllers to handle external traffic and manage routing rules. Ingress controllers provide features such as SSL termination, URL-based routing, and load balancing algorithms. They enable flexible management of traffic patterns and can handle complex traffic routing scenarios across multiple clusters.
https://cilium.io/blog/2022/04/12/cilium-multi-cluster-networking/
In summary, GSLB is suitable for geographically distributed architectures and focuses on load balancing traffic from external clients. Service Mesh-based load balancing is well-suited for microservices architectures, handling east-west traffic, and offering advanced traffic management capabilities. CNI-based load balancing is effective for centralized load balancing and managing north-south traffic, relying on ingress controllers for external traffic management. The choice of load balancing approach depends on the specific architecture, traffic patterns, and requirements of the application.