Building Robust AI Clusters: Kubernetes Best Practices#

Local Environment Setup#

Setting up a local Kubernetes environment is great for learning and small-scale experiments. Tools like Minikube or Kind (Kubernetes in Docker) let you run a single-node cluster on your machine.

1. Install Minikube:
   • On macOS (using Homebrew):

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     brew update
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     brew install minikube
     

   • On Linux (Debian/Ubuntu):

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     curl -LO https://storage.googleapis.com/minikube/releases/latest/minikube-linux-amd64
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     sudo install minikube-linux-amd64 /usr/local/bin/minikube
     

2. Start Minikube:

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   minikube start
   

3. Verify the installation:

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   kubectl get nodes
   

Cloud-Based Setup#

For production-level AI workloads, you likely need a multi-node cluster in the cloud. Popular managed Kubernetes services include:

1. Amazon Elastic Kubernetes Service (EKS)
2. Google Kubernetes Engine (GKE)
3. Microsoft Azure Kubernetes Service (AKS)

Managed Kubernetes services simplify tasks like control-plane updates, node provisioning, auto-scaling, and integrations with cloud services for logging, security, and networking.

Docker Basics#

Most AI workloads can be containerized using Docker. The key is to ensure your Docker image has:

• Base OS layer: Typically Debian, Ubuntu, or Alpine for a minimal footprint.
• Frameworks and Libraries: TensorFlow, PyTorch, scikit-learn, or other specialized libraries.
• Entry Point: The command or script your container runs when started.

Best Practices for Dockerfiles#

When building Docker images for AI, consider:

1. Caching: Use multi-stage builds to keep images small and leverage Docker’s layer caching.
2. Dependency Pinning: Pin dependencies to specific versions to ensure reproducibility (e.g., pip install tensorflow==2.5.0).
3. Non-Root User: Avoid running your container as root for better security.
4. Layer Minimization: Combine RUN statements to reduce the number of image layers.

Example Dockerfile snippet for a PyTorch-based application:

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FROM pytorch/pytorch:1.12.1-cuda11.3-cudnn8-runtime
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# Install system dependencies
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RUN apt-get update && \
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    apt-get install -y --no-install-recommends \
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    curl \
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    && apt-get clean && \
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    rm -rf /var/lib/apt/lists/*
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# Install Python dependencies
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COPY requirements.txt /app/
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WORKDIR /app
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RUN pip install --no-cache-dir -r requirements.txt
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# Copy code and set entry point
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COPY . /app
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ENTRYPOINT ["python", "main.py"]

Defining a Deployment and Service#

Once you have your container image, you can define a Kubernetes Deployment and Service to run it. An example YAML manifest might look like this:

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apiVersion: apps/v1
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kind: Deployment
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metadata:
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  name: ai-inference-deployment
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spec:
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  replicas: 3
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  selector:
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    matchLabels:
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      app: ai-inference
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  template:
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    metadata:
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      labels:
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        app: ai-inference
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    spec:
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      containers:
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      - name: ai-inference-container
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        image: your-repo/ai-inference:latest
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        ports:
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        - containerPort: 8080
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        resources:
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          requests:
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            cpu: "500m"
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            memory: "1Gi"
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          limits:
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            cpu: "1"
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            memory: "2Gi"
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---
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apiVersion: v1
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kind: Service
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metadata:
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  name: ai-inference-service
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spec:
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  selector:
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    app: ai-inference
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  ports:
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    - protocol: TCP
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      port: 80
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      targetPort: 8080
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  type: ClusterIP

In this example:

• replicas: Ensures 3 pods of your AI service are running.
• resources: Requests and limits for CPU and memory ensure each pod has the necessary resources to run AI inference efficiently.
• Service: Exposes your Deployment internally on port 80 (mapped to 8080 on the container).

Load Balancing and Scaling#

Kubernetes automatically distributes traffic across the pods behind a Service. For external access, you can use an Ingress controller or set the Service type to LoadBalancer on cloud providers:

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type: LoadBalancer

Kubernetes can also do horizontal pod autoscaling (HPA) based on CPU usage or custom metrics:

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apiVersion: autoscaling/v2
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kind: HorizontalPodAutoscaler
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metadata:
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  name: ai-inference-hpa
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spec:
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  scaleTargetRef:
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    apiVersion: apps/v1
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    kind: Deployment
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    name: ai-inference-deployment
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  minReplicas: 3
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  maxReplicas: 10
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  metrics:
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  - type: Resource
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    resource:
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      name: cpu
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      target:
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        type: Utilization
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        averageUtilization: 70

Monitoring and Logging#

Tools like Prometheus, Grafana, and ELK Stack (Elasticsearch, Logstash, Kibana) can help you monitor resource usage, set alerts, and access logs. Many AI frameworks (e.g., TensorFlow) emit logs you can collect with Kubernetes logging drivers.

Why GPUs for AI?#

GPUs significantly accelerate math-intensive operations like matrix multiplication and convolution, making them essential for training and, in many cases, inference of deep learning models. As AI models scale up in size, GPU-based acceleration becomes almost a requirement.

NVIDIA GPU Support#

For Kubernetes to recognize and schedule GPU resources, you generally need:

1. NVIDIA Drivers on each node.
2. NVIDIA Container Runtime to run GPU-enabled containers.
3. NVIDIA Kubernetes Device Plugin to advertise GPU resources to the Kubernetes scheduler.

On each node, install the driver and container runtime, then deploy the device plugin:

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kubectl apply -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/v0.11.0/nvidia-device-plugin.yml

After deployment, you can request GPU resources in your Pod specification:

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resources:
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  limits:
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    nvidia.com/gpu: 1

GPU Scheduling Best Practices#

• Use selectors and taints to schedule GPU pods only on GPU-equipped nodes.
• Namespace Quotas to prevent a single project from monopolizing all GPUs.
• Resource Limits to avoid overallocating memory, which can degrade performance or lead to crashes.

Local and Network Storage Options#

AI workloads often require large datasets or need to store intermediate results. Kubernetes provides different storage backends:

1. HostPath: Directly mounts a path from the host filesystem to a Pod.
2. NFS or Network File Systems: Standard choice for shared storage across multiple Pods.
3. Block Storage: Cloud providers offer block storage (e.g., AWS EBS, GCE Persistent Disk) for high-performance needs.

Persistent Volume Configuration#

A PersistentVolume (PV) represents the actual storage resource (e.g., an NFS share or an EBS volume), while a PersistentVolumeClaim (PVC) is a request for storage by a user.

Example YAML for an NFS-based PersistentVolume and PersistentVolumeClaim:

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apiVersion: v1
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kind: PersistentVolume
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metadata:
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  name: nfs-pv
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spec:
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  capacity:
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    storage: 100Gi
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  accessModes:
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    - ReadWriteMany
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  nfs:
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    path: /mnt/data
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    server: 192.168.1.100
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---
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apiVersion: v1
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kind: PersistentVolumeClaim
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metadata:
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  name: nfs-pvc
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spec:
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  accessModes:
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    - ReadWriteMany
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  resources:
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    requests:
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      storage: 50Gi

In your Pod spec, reference the PVC:

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volumes:
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- name: dataset-volume
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  persistentVolumeClaim:
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    claimName: nfs-pvc

Autoscaling Strategies#

Horizontal autoscaling scales the number of Pods, while vertical autoscaling adjusts the resource requests and limits of existing Pods. For AI, sometimes vertical scaling with GPUs is more relevant, but it’s essential to combine both approaches effectively. Tools like the Vertical Pod Autoscaler (VPA) automatically adjust container resources based on past usage.

Multi-Cluster Deployments#

As your AI workloads grow, you might need multiple clusters to:

• Isolate Environments: Separate dev/test from production.
• Distribute Globally: Run clusters in multiple geographic regions.
• Reduce Blast Radius: Limit the impact of cluster-level failures.

You can manage multiple clusters with solutions like Rancher, Google Anthos, or Red Hat Advanced Cluster Management, which centralize policy, security, and workload orchestration.

Service Mesh Integration#

A service mesh like Istio or Linkerd adds observability, traffic management, security, and policy layers to your microservices:

• Traffic Splitting: Gradually roll out new AI models or redirect a percentage of traffic for canary testing.
• mTLS Encryption: Secure Pod-to-Pod communications in zero-trust environments.
• Tracing: Gain insights into latencies across multiple services in an AI pipeline.

Role-Based Access Control (RBAC)#

RBAC grants fine-grained access to Kubernetes resources. Assign roles and cluster roles to specific users or service accounts, ensuring only designated engineers or services can modify high-impact resources (e.g., GPU nodes, production deployments).

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kind: Role
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apiVersion: rbac.authorization.k8s.io/v1
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metadata:
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  name: data-engineer-role
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  namespace: ai
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rules:
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- apiGroups: [""]
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  resources: ["pods", "services"]
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  verbs: ["get", "list", "watch"]
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---
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kind: RoleBinding
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apiVersion: rbac.authorization.k8s.io/v1
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metadata:
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  name: data-engineer-rolebinding
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  namespace: ai
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subjects:
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- kind: User
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  name: data-engineer
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roleRef:
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  kind: Role
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  name: data-engineer-role
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  apiGroup: rbac.authorization.k8s.io

Scanning for Vulnerabilities#

Use container security scanners like Trivy, Aqua, or Anchore to catch known vulnerabilities in your Docker images. Set up a pipeline to automatically scan images before pushing them to your container registry.

Secrets Management#

Instead of embedding credentials in code or images, store them as Kubernetes Secrets. Combine with HashiCorp Vault or AWS Secrets Manager for enterprise-level secret rotation and auditing.

Kubeflow Overview#

Kubeflow is a toolkit for running ML pipelines on Kubernetes. It includes:

• Notebooks: Jupyter-based environments for data exploration and model prototyping.
• Pipelines: A directed acyclic graph (DAG) system to define and execute workflows.
• Hyperparameter Tuning: Automatic hyperparameter optimization using Katib.
• Serving: Model serving via KFServing or real-time inference frameworks.

Its modular approach lets you deploy only the components you need. Kubeflow pipelines can be integrated with custom steps for data processing, model training, or validation.

Continuous Integration/Continuous Deployment (CI/CD)#

In practice, your code, data transformations, and model binaries change continuously. Automating your builds, tests, and deployments:

• Source Code: Trigger container builds on commits.
• Infrastructure as Code: Use Helm or Kustomize to define environment-specific configurations.
• Model Registry: Version models in an artifact repository before pushing them to a production environment.

Tools like Argo CD, Tekton, or GitLab CI/CD integrate well with Kubernetes-based AI pipelines.

Metrics and Alerts#

Performance metrics for AI workloads might include CPU/GPU utilization, memory usage, disk I/O, network throughput, and custom metrics like inference latency. Combine:

• Prometheus: Scrapes metrics from nodes and containers.
• Grafana: Visualizes metrics with customizable dashboards.
• Alertmanager: Sends alerts when thresholds are exceeded or anomalies detected.

Troubleshooting Common Pitfalls#

1. High I/O Latency: Check if your storage backend is the bottleneck. Switch to a faster block storage option or scale horizontally.
2. GPU Utilization: Ensure you’re making efficient use of GPU cycles. Underutilized GPUs increase cost and add overhead.
3. OOMKilled Pods: If your pods are consistently being restarted due to out-of-memory errors, raise the memory limits or optimize your model’s memory footprint.
4. Networking Issues: Misconfigured service or ingress settings can lead to traffic never reaching your application. Validate service definitions and DNS configurations.

Hybrid and Multi-Cloud Environments#

Run Kubernetes clusters on-prem for sensitive workloads while bursting to the cloud for large-scale training jobs:

• Federated Clusters: Use the Kubernetes Federation APIs or other multi-cluster orchestration tools to manage workloads across clusters.
• Consistency: Ensure consistent security policies, resource configurations, and tooling across on-prem and cloud environments.
• Networking: Set up secure VPN or dedicated connections to link on-prem data centers to cloud VPCs.

High Availability and Disaster Recovery#

For mission-critical AI applications:

• Multi-Zone Clusters: Distribute nodes across different availability zones to mitigate the effect of zone outages.
• Backup and Restore: Tools like Velero can back up cluster state (including persistent volumes) for disaster recovery.
• Redundant Control Planes: Managed Kubernetes services automatically replicate the master components for high availability.

Performance Testing and Optimization#

Consider the following when optimizing an AI cluster:

1. Bottleneck Analysis: Identify if CPU, GPU, memory, or I/O is limiting performance.
2. Node Sizing: Overly large nodes can lead to resource fragmentation, while too-small nodes can lead to oversubscription.
3. Network Overheads: Move large data sets closer to your compute cluster (e.g., leveraging local SSD caches or fast network storage).
4. Cluster Autoscaler Tuning: Adjust scale-up and scale-down parameters to maintain performance without incurring unnecessary costs.

Below is a summarizing table of performance optimization considerations: