> For the complete documentation index, see [llms.txt](https://atomoh.gitbook.io/kubernetes/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://atomoh.gitbook.io/kubernetes/jp/amazon-eks/eks-auto-mode/08-workload-optimization.md).

# ワークロードの最適化

> **対応バージョン**: EKS 1.29+, EKS Auto Mode GA **最終更新**: February 19, 2026

このガイドでは、web services、batch processing、GPU workloads、AI/ML training など、さまざまなワークロードタイプに合わせて EKS Auto Mode configurations を最適化する方法を説明します。

***

## Web Services（可用性優先）

```yaml
# web-service-optimized.yaml
apiVersion: karpenter.sh/v1
kind: NodePool
metadata:
  name: web-tier
spec:
  template:
    metadata:
      labels:
        tier: web
    spec:
      requirements:
        # General-purpose instances
        - key: karpenter.k8s.aws/instance-category
          operator: In
          values: ["m"]
        - key: karpenter.k8s.aws/instance-size
          operator: In
          values: ["large", "xlarge", "2xlarge"]
        # Use only On-Demand (availability first)
        - key: karpenter.sh/capacity-type
          operator: In
          values: ["on-demand"]
      taints:
        - key: tier
          value: web
          effect: NoSchedule
      nodeClassRef:
        group: eks.amazonaws.com
        kind: NodeClass
        name: default
  disruption:
    consolidationPolicy: WhenEmptyOrUnderutilized
    consolidateAfter: 5m
    budgets:
      - nodes: "10%"
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: web-frontend
spec:
  replicas: 10
  selector:
    matchLabels:
      app: web-frontend
  template:
    metadata:
      labels:
        app: web-frontend
    spec:
      tolerations:
        - key: tier
          value: web
          effect: NoSchedule
      nodeSelector:
        tier: web
      affinity:
        podAntiAffinity:
          preferredDuringSchedulingIgnoredDuringExecution:
            - weight: 100
              podAffinityTerm:
                labelSelector:
                  matchLabels:
                    app: web-frontend
                topologyKey: kubernetes.io/hostname
      containers:
        - name: web
          image: my-web-app:latest
          resources:
            requests:
              cpu: 500m
              memory: 512Mi
            limits:
              cpu: 1000m
              memory: 1Gi
          readinessProbe:
            httpGet:
              path: /health
              port: 8080
            initialDelaySeconds: 5
            periodSeconds: 10
          livenessProbe:
            httpGet:
              path: /health
              port: 8080
            initialDelaySeconds: 15
            periodSeconds: 20
```

### Web Services 最適化サマリー

| 観点              | 推奨事項                      | 理由               |
| --------------- | ------------------------- | ---------------- |
| Capacity type   | On-Demand                 | 高可用性の要件          |
| Instance family | M-series（general purpose） | CPU/メモリのバランス     |
| Anti-affinity   | ホスト名ごと                    | nodes 全体に分散      |
| Health checks   | readiness と liveness の両方  | 障害をすばやく検出        |
| PDB             | minAvailable: N-1         | 更新中も service を維持 |

***

## Batch Processing（コスト優先、Spot）

```yaml
# batch-processing-optimized.yaml
apiVersion: karpenter.sh/v1
kind: NodePool
metadata:
  name: batch-tier
spec:
  template:
    metadata:
      labels:
        tier: batch
    spec:
      requirements:
        # Compute-optimized
        - key: karpenter.k8s.aws/instance-category
          operator: In
          values: ["c"]
        - key: karpenter.k8s.aws/instance-size
          operator: In
          values: ["xlarge", "2xlarge", "4xlarge"]
        # Use only Spot (cost first)
        - key: karpenter.sh/capacity-type
          operator: In
          values: ["spot"]
        # Various instance types for better Spot availability
        - key: karpenter.k8s.aws/instance-generation
          operator: In
          values: ["5", "6", "7"]
      taints:
        - key: tier
          value: batch
          effect: NoSchedule
      nodeClassRef:
        group: eks.amazonaws.com
        kind: NodeClass
        name: default
  disruption:
    consolidationPolicy: WhenEmpty
    consolidateAfter: 30s
---
apiVersion: batch/v1
kind: Job
metadata:
  name: data-processing
spec:
  parallelism: 20
  completions: 100
  backoffLimit: 10
  template:
    spec:
      tolerations:
        - key: tier
          value: batch
          effect: NoSchedule
      nodeSelector:
        tier: batch
      restartPolicy: OnFailure
      terminationGracePeriodSeconds: 30
      containers:
        - name: processor
          image: my-batch-processor:latest
          resources:
            requests:
              cpu: 2000m
              memory: 4Gi
            limits:
              cpu: 4000m
              memory: 8Gi
          env:
            - name: SPOT_AWARE
              value: "true"
```

### Batch Processing 最適化サマリー

| 観点                 | 推奨事項                        | 理由                  |
| ------------------ | --------------------------- | ------------------- |
| Capacity type      | Spot のみ                     | 最大限のコスト削減           |
| Instance family    | C-series（compute optimized） | CPU 集約型ワークロード       |
| Instance diversity | 複数世代                        | Spot の可用性を向上        |
| Restart policy     | OnFailure                   | Spot interrupts に対応 |
| Consolidation      | 積極的（30s）                    | jobs 後のすばやいクリーンアップ  |

***

## GPU Workloads（p5、g5）

```yaml
# gpu-workload-optimized.yaml
apiVersion: karpenter.sh/v1
kind: NodePool
metadata:
  name: gpu-tier
spec:
  template:
    metadata:
      labels:
        tier: gpu
        accelerator: nvidia
    spec:
      requirements:
        # GPU instances
        - key: karpenter.k8s.aws/instance-category
          operator: In
          values: ["g", "p"]
        - key: karpenter.k8s.aws/instance-gpu-manufacturer
          operator: In
          values: ["nvidia"]
        # Specific GPU instance types
        - key: node.kubernetes.io/instance-type
          operator: In
          values: ["g5.xlarge", "g5.2xlarge", "g5.4xlarge", "p5.48xlarge"]
        # On-Demand (GPU Spot availability is low)
        - key: karpenter.sh/capacity-type
          operator: In
          values: ["on-demand"]
      taints:
        - key: nvidia.com/gpu
          value: "true"
          effect: NoSchedule
      nodeClassRef:
        group: eks.amazonaws.com
        kind: NodeClass
        name: gpu-nodeclass
  limits:
    nvidia.com/gpu: 16
  disruption:
    consolidationPolicy: WhenEmpty
    consolidateAfter: 10m  # GPU takes longer to start
---
apiVersion: eks.amazonaws.com/v1
kind: NodeClass
metadata:
  name: gpu-nodeclass
spec:
  amiFamily: AL2023
  blockDeviceMappings:
    - deviceName: /dev/xvda
      ebs:
        volumeSize: 200Gi  # Large volume for model caching
        volumeType: gp3
        iops: 6000
        throughput: 250
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: ml-inference
spec:
  replicas: 2
  selector:
    matchLabels:
      app: ml-inference
  template:
    metadata:
      labels:
        app: ml-inference
    spec:
      tolerations:
        - key: nvidia.com/gpu
          value: "true"
          effect: NoSchedule
      nodeSelector:
        tier: gpu
      containers:
        - name: inference
          image: my-ml-model:latest
          resources:
            limits:
              nvidia.com/gpu: 1
            requests:
              cpu: 4000m
              memory: 16Gi
```

### GPU Instance 選択ガイド

| Instance    | GPUs    | GPU Memory | Use Case       |
| ----------- | ------- | ---------- | -------------- |
| g5.xlarge   | 1x A10G | 24GB       | 小規模 inference  |
| g5.2xlarge  | 1x A10G | 24GB       | 中規模 inference  |
| g5.4xlarge  | 1x A10G | 24GB       | 大規模 inference  |
| g5.12xlarge | 4x A10G | 96GB       | マルチモデル serving |
| p5.48xlarge | 8x H100 | 640GB      | 大規模 training   |

### GPU 最適化サマリー

| 観点            | 推奨事項                     | 理由                      |
| ------------- | ------------------------ | ----------------------- |
| Capacity type | On-Demand                | GPU Spot の可用性は限定的       |
| Storage       | 200GB+ gp3               | モデル caching、checkpoints |
| Consolidation | 緩やか（10m）                 | GPU startup は遅い         |
| Limits        | nvidia.com/gpu limit を設定 | GPU コストの runaway を防止    |

***

## AI/ML Training Workloads

```yaml
# ml-training-optimized.yaml
apiVersion: karpenter.sh/v1
kind: NodePool
metadata:
  name: ml-training
spec:
  template:
    metadata:
      labels:
        tier: ml-training
    spec:
      requirements:
        # Large-scale GPU instances
        - key: node.kubernetes.io/instance-type
          operator: In
          values: ["p5.48xlarge", "p4d.24xlarge"]
        - key: karpenter.sh/capacity-type
          operator: In
          values: ["on-demand"]
      taints:
        - key: ml-training
          value: "true"
          effect: NoSchedule
      nodeClassRef:
        group: eks.amazonaws.com
        kind: NodeClass
        name: ml-training-nodeclass
  limits:
    nvidia.com/gpu: 64
---
apiVersion: eks.amazonaws.com/v1
kind: NodeClass
metadata:
  name: ml-training-nodeclass
spec:
  amiFamily: AL2023
  # Enable EFA networking
  blockDeviceMappings:
    - deviceName: /dev/xvda
      ebs:
        volumeSize: 500Gi
        volumeType: gp3
        iops: 16000
        throughput: 1000
    # Additional volume for training data
    - deviceName: /dev/xvdb
      ebs:
        volumeSize: 2000Gi
        volumeType: gp3
---
apiVersion: kubeflow.org/v1
kind: PyTorchJob
metadata:
  name: distributed-training
spec:
  pytorchReplicaSpecs:
    Master:
      replicas: 1
      template:
        spec:
          tolerations:
            - key: ml-training
              value: "true"
              effect: NoSchedule
          nodeSelector:
            tier: ml-training
          containers:
            - name: pytorch
              image: my-training-image:latest
              resources:
                limits:
                  nvidia.com/gpu: 8
    Worker:
      replicas: 3
      template:
        spec:
          tolerations:
            - key: ml-training
              value: "true"
              effect: NoSchedule
          nodeSelector:
            tier: ml-training
          containers:
            - name: pytorch
              image: my-training-image:latest
              resources:
                limits:
                  nvidia.com/gpu: 8
```

### ML Training 最適化サマリー

| 観点            | 推奨事項                     | 理由                         |
| ------------- | ------------------------ | -------------------------- |
| Instance type | p5.48xlarge、p4d.24xlarge | 最大 GPU capacity            |
| Storage       | 500GB+ root、2TB+ data    | 大規模 datasets、checkpoints   |
| IOPS          | 16000+                   | checkpoint writes を高速化     |
| Networking    | EFA-enabled              | distributed training       |
| Framework     | PyTorchJob、TFJob         | native distributed support |

***

## ワークロードタイプ早見表

| ワークロード            | NodePool Strategy  | Instance Types | Capacity       | Consolidation |
| ----------------- | ------------------ | -------------- | -------------- | ------------- |
| Web services      | Availability-first | m-series       | On-Demand      | 中程度（5m）       |
| API backend       | Mixed              | m/c-series     | Mixed          | 中程度（5m）       |
| Batch processing  | Cost-first         | c-series       | Spot only      | 積極的（30s）      |
| CI/CD             | Cost-first         | c/m-series     | Spot preferred | 積極的（1m）       |
| Databases         | Stability-first    | r-series       | On-Demand      | 保守的（10m）      |
| GPU inference     | Availability-first | g5-series      | On-Demand      | 緩やか（10m）      |
| ML training       | Performance-first  | p5/p4d         | On-Demand      | 緩やか（15m）      |
| Stream processing | Balanced           | m/c-series     | Mixed          | 中程度（5m）       |

***

## Pod Resource ガイドライン

### CPU-Bound ワークロード

```yaml
resources:
  requests:
    cpu: 2000m      # Request what you need
    memory: 2Gi
  limits:
    cpu: 4000m      # Allow some burst
    memory: 4Gi
```

### Memory-Bound ワークロード

```yaml
resources:
  requests:
    cpu: 500m
    memory: 8Gi     # Request what you need
  limits:
    cpu: 1000m
    memory: 8Gi     # Limit = request (no overcommit)
```

### GPU Workloads

```yaml
resources:
  requests:
    cpu: 4000m
    memory: 16Gi
  limits:
    nvidia.com/gpu: 1   # GPU limits are always required
    cpu: 8000m
    memory: 32Gi
```

***

< [前へ: Node Lifecycle](/kubernetes/jp/amazon-eks/eks-auto-mode/07-node-lifecycle.md) | [目次](/kubernetes/jp/amazon-eks/eks-auto-mode.md) | [次へ: Migration Guide](/kubernetes/jp/amazon-eks/eks-auto-mode/09-migration-guide.md) >