The core goal of load balancing is to distribute incoming requests across multiple instances of a service so that no single instance becomes overloaded and so that the overall service is more available and responsive. That matches option D, which is the correct answer.

In Kubernetes, load balancing commonly appears through the Service abstraction. A Service selects a set of Pods using labels and provides stable access via a virtual IP (ClusterIP) and DNS name. Traffic sent to the Service is then forwarded to one of the healthy backend Pods. This spreads load across replicas and provides resilience: if one Pod fails, it is removed from endpoints (or becomes NotReady) and traffic shifts to remaining replicas. The actual traffic distribution mechanism depends on the networking implementation (kube-proxy using iptables/IPVS or an eBPF dataplane), but the intent remains consistent: distribute requests across multiple backends.

Option A describes monitoring/observability, not load balancing. Option B describes progressive delivery patterns like canary or A/B routing; that can be implemented with advanced routing layers (Ingress controllers, service meshes), but it’s not the general definition of load balancing. Option C describes scheduling/placement of instances (Pods) across cluster nodes, which is the role of the scheduler and controllers, not load balancing.

In cloud environments, load balancing may also be implemented by external load balancers (cloud LBs) in front of the cluster, then forwarded to NodePorts or ingress endpoints, and again balanced internally to Pods. At each layer, the objective is the same: spread request traffic across multiple service instances to improve performance and availability.

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