Queue-Depth-Driven GPU Autoscaling

Queue-Depth-Driven GPU Autoscaling (Kubernetes KEDA, Karpenter, Prometheus Custom Metrics) governs dynamic compute elasticity across heavy generative workloads, harmonizing strict queue wait SLAs with aggressive multi-million-dollar cloud GPU cost optimizations; standard CPU/GPU-utilization HPAs fail because a GPU running a single task reports 100% load, blind to 5,000 pending backlog requests; KEDA continuously monitors message queue backlog depth ($L$) and target queue latency ($T_{\text{target}}$) to derive required GPU replicas: $R = \lceil \frac{L}{C \times T_{\text{target}}} \rceil$ ($C$ is single-GPU throughput), leveraging pre-warmed container pools and Spot GPU instances for elastic cluster scaling.

AI image/video generation bursts during viral trends, automatic overnight scale-down, and hybrid Spot/On-Demand fleet cost governance.

Statically provisioning peak GPU capacity leaves 70%+ of hardware idle during off-peak hours (burning millions); utilization-based scaling triggers severe latency collapses; queue-depth metric scaling scales directly with user demand.

Derive the mathematical formula for target replica counts based on queue backlog depth, worker capacity, and target latency?

How to accelerate multi-gigabyte GPU container cold starts from minutes to seconds using Stargz lazy-pulling and pre-warmed daemon nodes?

Design a hybrid Spot and On-Demand GPU fleet architecture that exploits 70% Spot discounts while hedging against node reclamation?

How does Kubernetes Pod Disruption Budget and preStop hook drain active long-running diffusion tasks before terminating GPU instances?

Explain the design of stabilization cooldown windows preventing metric flapping and thrashing during transient load dips?