To dynamically adjust the replica count of an active AI inference service in Kubernetes based on fluctuations in query traffic or hardware resource consumption, which component should be configured?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Imagine your AI model goes viral, and suddenly you've got ten thousand queries hitting your cluster every second. If you don't scale up fast, your system is going to crash. That's where the Horizontal Pod Autoscaler, or HPA, comes in. Think of the HPA like a thermostat: it constantly monitors metrics like CPU usage, memory, or custom traffic rates, and automatically spins up more pods when things get hot, then scales them back down when the rush is over to save you money. What about the other components? The Kubernetes Scheduler just assigns new pods to nodes. Triton is the server that actually runs your model, and DCGM gathers GPU telemetry. Remember, it's the HPA that does the actual scaling.
Full explanation below image
Full Explanation
The Horizontal Pod Autoscaler (HPA) is a standard Kubernetes controller that dynamically scales the number of replica pods in a deployment, replication controller, or stateful set. The scaling decisions are based on user-defined thresholds for system metrics like CPU utilization or custom metrics (e.g., HTTP request rates, GPU duty cycles via Prometheus). This is critical for AI inference workloads where traffic patterns are volatile. The Kubernetes Scheduler (Choice A) is responsible for choosing the best node for a pod to run on based on resource availability, but it does not create or destroy replicas. NVIDIA Triton Inference Server (Choice B) is an open-source inference serving software that manages model execution but relies on orchestrators like Kubernetes for scaling. NVIDIA DCGM (Choice D) is a suite of tools for managing and monitoring NVIDIA GPUs in cluster environments; it provides telemetry data but does not perform scaling actions itself.