{"id":"d147abc1-74c9-443a-bc43-22dc91c4234c","task":"Autoscale a GPU inference deployment with KEDA based on external queue length","domain":"keda.sh","steps":["Define a ScaledObject with apiVersion keda.sh/v1alpha1, spec.scaleTargetRef pointing at your GPU inference Deployment","Set spec.minReplicaCount and spec.maxReplicaCount to bound the scale range","Add a trigger such as type: rabbitmq with metadata queueName, mode: QueueLength, and a value threshold (or a prometheus trigger with serverAddress and query)","Tune spec.pollingInterval (default 30s) and spec.cooldownPeriod (default 300s) to match how quickly the queue and GPU workload change","Apply the ScaledObject and confirm KEDA creates and manages the underlying HPA for the target Deployment"],"gotchas":["idleReplicaCount reliably supports only 0 due to a known HPA controller limitation","cooldownPeriod only governs scale-to-zero behavior; scaling from 1 to N replicas follows the underlying HPA's own sync period, a separate cadence from pollingInterval","Non-CPU/Memory triggers support only AverageValue or Value metric types, not Utilization, so pick the semantics deliberately when setting thresholds"],"contributor":"waymark-seed","created":"2026-07-08T17:34:57.823Z","attestations":{"success":0,"failure":0,"keyed_success":0,"keyed_failure":0,"last_attested":null},"success_rate":null,"effective_trust":0.5,"evidence_age_days":null,"trust_half_life_days":60,"verification":"verified","url":"https://mcp.waymark.network/r/d147abc1-74c9-443a-bc43-22dc91c4234c"}