Autoscaling Strategies for Inference Workloads

Diffusion model inference workloads are often characterized by high resource demands (particularly GPU compute and memory) and potentially variable traffic patterns. Provisioning infrastructure statically for peak load can lead to significant underutilization and unnecessary costs, while under-provisioning results in poor performance and failed requests during surges. Autoscaling provides a mechanism to dynamically adjust the number of compute resources (specifically, the pods running your inference service) based on real-time demand, optimizing both cost and performance.

For workloads deployed on Kubernetes, the primary tool for managing application scaling is the Horizontal Pod Autoscaler (HPA). The HPA automatically adjusts the number of replicas in a Deployment, ReplicaSet, or StatefulSet based on observed metrics.

Limitations of Standard Metrics for Diffusion Workloads

By default, HPA often relies on basic metrics like CPU utilization or memory consumption. While useful for many applications, these metrics are frequently inadequate for scaling diffusion model inference services effectively:

1. GPU Bottlenecks: Diffusion models are typically GPU-bound. A pod might show low CPU utilization while its GPU is fully saturated, processing inference steps. Scaling based only on CPU would fail to add capacity when needed.
2. Memory Patterns: While GPU memory is critical, total pod RAM usage might not directly correlate with the need for more concurrent processing units, especially if models are loaded once per pod.
3. Asynchronous Processing: Many diffusion services use asynchronous request processing via queues. The load isn't always immediately reflected in the CPU/memory of the worker pods; it often manifests as a backlog in the queue.

Therefore, effective autoscaling for diffusion models almost always requires leveraging custom or external metrics that better reflect the actual workload and bottlenecks.

Scaling Based on GPU Utilization

One of the most direct ways to scale GPU-bound workloads is by monitoring the utilization of the GPU accelerators themselves. If your pods' GPUs are consistently running hot, it's a clear signal that more processing capacity is needed.

To implement this, you need:

1. GPU Metric Exposition: A mechanism to expose GPU utilization metrics from each node or pod. The NVIDIA Data Center GPU Manager (DCGM) exporter (dcgm-exporter) is a common tool for this, scraping metrics like DCGM_FI_DEV_GPU_UTIL (GPU utilization) and making them available to Prometheus.
2. Custom Metrics API: Kubernetes needs a way to query these custom metrics. This is often achieved by installing a custom metrics adapter, such as the prometheus-adapter, which translates Prometheus queries into a format the HPA understands.
3. HPA Configuration: Define an HPA object that targets the GPU utilization metric.

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: diffusion-worker-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: diffusion-worker
  minReplicas: 1
  maxReplicas: 10
  metrics:
  - type: Pods # Using Pods metric type for per-pod GPU utilization
    pods:
      metric:
        name: dcgm_gpu_utilization # Metric name exposed via adapter
      target:
        type: AverageValue
        averageValue: 75 # Target average utilization (e.g., 75%)

In this example, the HPA attempts to maintain an average GPU utilization of 75% across all diffusion-worker pods. If the average exceeds 75%, it scales up; if it drops significantly below, it scales down (respecting cooldown periods).

Be cautious: High GPU utilization doesn't always mean the GPU is the only bottleneck. Ensure I/O, model loading, or pre/post-processing aren't limiting factors. Also, utilization can sometimes be misleading if the GPU is waiting for data or CPU tasks.

Scaling Based on Request Queue Length

For asynchronous inference APIs, where requests are placed onto a message queue (like RabbitMQ, Kafka, AWS SQS, or Google Pub/Sub) before being picked up by worker pods, the length of this queue is an excellent indicator of load. A growing queue shows that the current number of workers cannot keep up with the incoming request rate.

This approach requires integrating metrics from the queuing system into the Kubernetes scaling mechanism. While possible with custom metrics adapters querying the queue API, KEDA (Kubernetes Event-driven Autoscaling) significantly simplifies this.

KEDA extends Kubernetes with custom resources (ScaledObject, TriggerAuthentication, etc.) and controllers specifically designed for event-driven scaling. It includes built-in "scalers" for numerous event sources, including message queues, databases, and monitoring systems.

To scale based on an SQS queue using KEDA:

1. Install KEDA: Add the KEDA components to your cluster.
2. Define ScaledObject: Create a ScaledObject resource targeting your deployment and specifying the queue trigger.

apiVersion: keda.sh/v1alpha1
kind: ScaledObject
metadata:
  name: diffusion-worker-scaler
  namespace: default
spec:
  scaleTargetRef:
    name: diffusion-worker # Name of the Deployment to scale
  pollingInterval: 15  # How often to check the queue (seconds)
  cooldownPeriod:  120 # Wait period before scaling down (seconds)
  minReplicaCount: 1
  maxReplicaCount: 15
  triggers:
  - type: aws-sqs-queue
    metadata:
      # Required: queueURL, awsRegion
      queueURL: "https://sqs.us-east-1.amazonaws.com/123456789012/diffusion-requests"
      awsRegion: "us-east-1"
      # Target value for scaling: average number of messages per replica
      queueLength: "5"
      # Optional: Authentication details (e.g., using IRSA)
    # authenticationRef:
    #   name: keda-trigger-auth-aws-credentials

KEDA monitors the specified SQS queue. If the number of visible messages (ApproximateNumberOfMessagesVisible in SQS terms) divided by the current number of replicas exceeds the target queueLength (here, 5 messages per pod), KEDA will instruct the HPA (which KEDA manages internally for Deployment scaling) to scale up the diffusion-worker deployment.

Diagram illustrating KEDA scaling based on queue length. KEDA monitors the queue, exposes a metric, which the HPA uses to adjust the deployment's replica count.

Queue-based scaling is particularly well-suited for diffusion models because:

• It directly measures pending work.
• It decouples the request ingestion rate from the processing rate.
• It handles bursty traffic gracefully by allowing requests to buffer.

Other Custom Metrics

While GPU utilization and queue length are common, other metrics can also be valuable:

• Average Inference Latency: Monitor the time taken per inference request (e.g., P95 or P99 latency). If latency exceeds a threshold, scale up. This directly targets user experience but can be reactive. Requires careful implementation to avoid scaling based on transient spikes.
• Requests Per Second (RPS) per Pod: Target a specific throughput per pod. If the incoming RPS divided by the number of pods exceeds the target, scale up.

Coordinating Pod and Node Scaling

Horizontal Pod Autoscaling adjusts the number of pods. However, if you need more pods than can fit on the existing cluster nodes (especially nodes with GPUs), you also need to scale the underlying infrastructure. This is the job of the Cluster Autoscaler.

The Cluster Autoscaler watches for pods that are unschedulable due to resource constraints (like lack of available GPUs). If it detects such pods, and if scaling up is possible within configured limits and available node pool types, it interacts with the cloud provider API (AWS, GCP, Azure) to provision new nodes (e.g., GPU instances). Conversely, if nodes are underutilized for a period and their pods can be rescheduled elsewhere, it terminates them to save costs.

Effective autoscaling often involves tuning both the HPA (for pods) and the Cluster Autoscaler (for nodes) to work together harmoniously. Ensure your GPU node pools are correctly configured for the Cluster Autoscaler to manage.

Tuning and Best Practices

• Set Realistic Limits: Define sensible minReplicas and maxReplicas for your HPA/ScaledObject to control costs and prevent runaway scaling.
• Configure Cooldowns: Use cooldownPeriod (KEDA) or HPA's stabilization window settings (behavior.scaleDown.stabilizationWindowSeconds) to prevent rapid fluctuations (thrashing) where the system scales up and down too quickly.
• Choose the Right Metric(s): Select metrics that genuinely reflect the bottleneck in your system. For diffusion models, this is often GPU utilization or queue length. Sometimes, combining multiple metrics (e.g., scaling up aggressively on queue length but scaling down based on sustained low GPU utilization) offers the best balance.
• Load Test: Simulate realistic and peak load scenarios to observe how your autoscaling configuration behaves. Tune thresholds, target values, and cooldown periods based on these tests.
• Monitor Autoscaler Events: Keep an eye on HPA and Cluster Autoscaler events in Kubernetes to understand why scaling decisions are being made and troubleshoot issues.

By implementing thoughtful autoscaling strategies based on relevant metrics like GPU utilization or queue length, you can build diffusion model deployment infrastructure that is both cost-effective and responsive to varying user demand, ensuring resources are available when needed without paying for idle capacity during quiet periods.