The kube-scheduler is unevenly assigning workloads across nodes is the correct option.

An uneven placement by the kube-scheduler can concentrate many pods on a small set of nodes and cause CPU and memory contention as well as CPU throttling and higher disk wait and cache eviction. When nodes are overloaded the pods running on them will exhibit increased response times even though networking and application code are fine. The scheduler decides placement using resource requests and limits node labels taints tolerations and affinity rules and misconfigured requests or skewed affinity policies can lead to an unbalanced distribution that produces the latency you observe.

Some pods are stuck in the Pending state is unlikely because pending pods are not scheduled and the typical symptom is missing or reduced capacity rather than running pods becoming slower. Pending state usually produces unavailable replicas or failed requests instead of a subset of running services showing degraded latency.

Cloud Load Balancer misconfiguration would generally affect routing or external ingress and cause connection failures or broad latency across traffic through that balancer. Since network congestion was ruled out and the problem appears tied to node-local performance it is a less likely explanation.

A StatefulSet was deployed without an associated Service impacts stable network identity and service discovery and would more likely make those pods unreachable or harder to address rather than cause slower responses for pods that are running on overloaded nodes.

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