Descubrimiento basado en cliente y basado en el servidor Equilibrio de carga en el lado del cliente y en el lado del servidor Equilibrio de carga dentro de Kubernetes: mecanismos integrados como ClusterIP, NodePort, y LoadBalancer. Usar Ingress Controllers o Service Meshes como Istio. Combinando Descubrimiento y Equilibrio Service Mesh como solución avanzada Mejores prácticas generales Client-based discovery and Server-based Client-side and Server-side load balancing Load balancing within Kubernetes: built-in mechanisms like ClusterIP, NodePort, and LoadBalancer. Using Ingress Controllers or Service Meshes like Istio. Combining Discovery and Balancing Service Mesh as an advanced solution General best practices

For managing a complex microservice system, it is often a good idea to use a platform, such as kubernetes, to abstract away complexities of service discovery and load balancing. This way the platform takes care of the required functionalities and developers don't have to concern themselves with them. Platforms often offer ways to modify the functionalities so administrators can tailor them to system needs without concerning developers with details. It is also a good idea to use a load Aware load balancing system instead of round robin or similar load balancers. Instead of blindly distributing load between instances, you can distribute them based on CPU usage or queue length or similar metrics to gain better performance from your system.

To tackle service discovery and load balancing in a complex microservices system, I would implement a service registry like Consul or Eureka for dynamic service discovery, ensuring services can locate each other seamlessly. For load balancing, I would use a combination of client-side tools like Ribbon or server-side proxies such as NGINX or Envoy, enabling efficient traffic distribution. Additionally, I would integrate health checks and circuit breakers to enhance system resilience and maintain performance under varying loads.

Kubernetes makes service discovery straightforward by providing DNS-based resolution for all services, allowing them to communicate dynamically. e.g., K8 Services like ClusterIP or LoadBalancer provide stable endpoints, while headless services allow more direct communication between Pods when needed. On LB side, k8 can handle traffic internally wth kube-proxy & externally with Ingress controllers like NGINX or cloud solutions like AWS ALB. Advanced needs, tools like Istio or Linkerd (service meshes) come in handy, for features like intelligent traffic routing, retries, and failover. Horizontal Pod Autoscaling also helps scale services based on demand, while global load balancers can manage multi-region traffic.

For micro service discovery and management, i use several alternatives: zookeeper, or a message bus that eliminates the need to "locate" the service - it just registers for messages, thus it is just 'there' to serve. Scale and health are achieved through k8s or it's managed version like CloudRun or similar. In general - a good architecture takes care of itself.