multi-cloud-architecture
用決策框架跨 AWS、Azure、GCP、OCI 設計 multi-cloud 架構
中文翻譯
---
name: multi-cloud-architecture
description: 用決策框架設計 multi-cloud 架構，在 AWS、Azure、GCP 和 OCI 之間選擇並整合服務。在建構 multi-cloud 系統、避免 vendor lock-in，或善用多家供應商各自最強的服務時使用。
---
# Multi-Cloud Architecture
跨 AWS、Azure、GCP 和 OCI 設計應用程式架構的決策框架與模式。
## Purpose
設計不綁定特定雲端的架構，並針對跨雲端供應商的服務選擇做出有根據的決定。
## When to Use
- 設計 multi-cloud 策略
- 在雲端供應商之間遷移
- 為特定 workload 選擇雲端服務
- 實作不綁定特定雲端的架構
- 跨供應商優化成本
## Cloud Service Comparison
### Compute Services
| AWS | Azure | GCP | OCI | Use Case |
| ------- | ------------------- | --------------- | ------------------- | ------------------ |
| EC2 | Virtual Machines | Compute Engine | Compute | IaaS VMs |
| ECS | Container Instances | Cloud Run | Container Instances | Containers |
| EKS | AKS | GKE | OKE | Kubernetes |
| Lambda | Functions | Cloud Functions | Functions | Serverless |
| Fargate | Container Apps | Cloud Run | Container Instances | Managed containers |
### Storage Services
| AWS | Azure | GCP | OCI | Use Case |
| ------- | --------------- | --------------- | -------------- | -------------- |
| S3 | Blob Storage | Cloud Storage | Object Storage | Object storage |
| EBS | Managed Disks | Persistent Disk | Block Volumes | Block storage |
| EFS | Azure Files | Filestore | File Storage | File storage |
| Glacier | Archive Storage | Archive Storage | Archive Storage | Cold storage |
### Database Services
| AWS | Azure | GCP | OCI | Use Case |
| ----------- | ---------------- | ------------- | ------------------- | --------------- |
| RDS | SQL Database | Cloud SQL | MySQL HeatWave | Managed SQL |
| DynamoDB | Cosmos DB | Firestore | NoSQL Database | NoSQL |
| Aurora | PostgreSQL/MySQL | Cloud Spanner | Autonomous Database | Distributed SQL |
| ElastiCache | Cache for Redis | Memorystore | OCI Cache | Caching |
**參考：** 完整比較請見 `references/service-comparison.md`
## Multi-Cloud Patterns
### Pattern 1: Single Provider with DR
- 主要 workload 放在一個雲端
- Disaster recovery 放在另一個雲端
- 跨雲端的資料庫複寫
- 自動 failover
### Pattern 2: Best-of-Breed
- 每家供應商都挑最好的服務來用
- AI/ML 放 GCP
- 企業應用放 Azure
- 受監管的資料平台放 OCI
- 一般運算放 AWS
### Pattern 3: Geographic Distribution
- 從最近的雲端區域服務使用者
- 符合資料主權要求
- 全球 load balancing
- 區域性 failover
### Pattern 4: Cloud-Agnostic Abstraction
- 運算用 Kubernetes
- 資料庫用 PostgreSQL
- 相容 S3 的儲存（MinIO）
- 開源工具
## Cloud-Agnostic Architecture
### Use Cloud-Native Alternatives
- **運算：** Kubernetes（EKS/AKS/GKE/OKE）
- **資料庫：** PostgreSQL/MySQL（RDS/SQL Database/Cloud SQL/MySQL HeatWave）
- **訊息佇列：** Apache Kafka 或託管的 streaming 服務（MSK/Event Hubs/Confluent/OCI Streaming）
- **快取：** Redis（ElastiCache/Azure Cache/Memorystore/OCI Cache）
- **物件儲存：** 相容 S3 的 API
- **監控：** Prometheus/Grafana
- **Service Mesh：** Istio/Linkerd
### Abstraction Layers
```
Application Layer
↓
Infrastructure Abstraction (Terraform)
↓
Cloud Provider APIs
↓
AWS / Azure / GCP / OCI
```
## Cost Comparison
### Compute Pricing Factors
- **AWS：** On-demand、Reserved、Spot、Savings Plans
- **Azure：** Pay-as-you-go、Reserved、Spot
- **GCP：** On-demand、Committed use、Preemptible
- **OCI：** Pay-as-you-go、年度承諾、burstable/flexible shape、preemptible instance
### Cost Optimization Strategies
1. 使用預留/承諾容量（省 30% 到 70%）
2. 善用 spot/preemptible instance
3. 調整資源到適當大小
4. 對變動的 workload 使用 serverless
5. 優化資料傳輸成本
6. 實作 lifecycle policy
7. 使用成本分攤標籤
8. 用雲端成本工具監控
**參考：** 見 `references/multi-cloud-patterns.md`
## Migration Strategy
### Phase 1: Assessment
- 盤點現有基礎設施
- 找出相依關係
- 評估雲端相容性
- 估算成本
### Phase 2: Pilot
- 挑選試點 workload
- 在目標雲端實作
- 徹底測試
- 記錄學到的經驗
### Phase 3: Migration
- 逐步遷移 workload
- 維持一段雙邊並行期
- 監控效能
- 驗證功能
### Phase 4: Optimization
- 調整資源到適當大小
- 採用雲端原生服務
- 優化成本
- 強化安全性
## Best Practices
1. **使用 infrastructure as code**（Terraform/OpenTofu）
2. **為部署建立 CI/CD pipeline**
3. **跨雲端為失敗做設計**
4. **盡可能使用託管服務**
5. **實作完整的監控**
6. **讓成本優化自動化**
7. **遵循安全最佳實務**
8. **記錄各雲端專屬的設定**
9. **測試 disaster recovery** 程序
10. **訓練團隊**熟悉多個雲端
## Related Skills
- `terraform-module-library` - 用於 IaC 實作
- `cost-optimization` - 用於成本管理
- `hybrid-cloud-networking` - 用於連線英文原文
---
name: multi-cloud-architecture
description: Design multi-cloud architectures using a decision framework to select and integrate services across AWS, Azure, GCP, and OCI. Use when building multi-cloud systems, avoiding vendor lock-in, or leveraging best-of-breed services from multiple providers.
---
# Multi-Cloud Architecture
Decision framework and patterns for architecting applications across AWS, Azure, GCP, and OCI.
## Purpose
Design cloud-agnostic architectures and make informed decisions about service selection across cloud providers.
## When to Use
- Design multi-cloud strategies
- Migrate between cloud providers
- Select cloud services for specific workloads
- Implement cloud-agnostic architectures
- Optimize costs across providers
## Cloud Service Comparison
### Compute Services
| AWS | Azure | GCP | OCI | Use Case |
| ------- | ------------------- | --------------- | ------------------- | ------------------ |
| EC2 | Virtual Machines | Compute Engine | Compute | IaaS VMs |
| ECS | Container Instances | Cloud Run | Container Instances | Containers |
| EKS | AKS | GKE | OKE | Kubernetes |
| Lambda | Functions | Cloud Functions | Functions | Serverless |
| Fargate | Container Apps | Cloud Run | Container Instances | Managed containers |
### Storage Services
| AWS | Azure | GCP | OCI | Use Case |
| ------- | --------------- | --------------- | -------------- | -------------- |
| S3 | Blob Storage | Cloud Storage | Object Storage | Object storage |
| EBS | Managed Disks | Persistent Disk | Block Volumes | Block storage |
| EFS | Azure Files | Filestore | File Storage | File storage |
| Glacier | Archive Storage | Archive Storage | Archive Storage | Cold storage |
### Database Services
| AWS | Azure | GCP | OCI | Use Case |
| ----------- | ---------------- | ------------- | ------------------- | --------------- |
| RDS | SQL Database | Cloud SQL | MySQL HeatWave | Managed SQL |
| DynamoDB | Cosmos DB | Firestore | NoSQL Database | NoSQL |
| Aurora | PostgreSQL/MySQL | Cloud Spanner | Autonomous Database | Distributed SQL |
| ElastiCache | Cache for Redis | Memorystore | OCI Cache | Caching |
**Reference:** See `references/service-comparison.md` for complete comparison
## Multi-Cloud Patterns
### Pattern 1: Single Provider with DR
- Primary workload in one cloud
- Disaster recovery in another
- Database replication across clouds
- Automated failover
### Pattern 2: Best-of-Breed
- Use best service from each provider
- AI/ML on GCP
- Enterprise apps on Azure
- Regulated data platforms on OCI
- General compute on AWS
### Pattern 3: Geographic Distribution
- Serve users from nearest cloud region
- Data sovereignty compliance
- Global load balancing
- Regional failover
### Pattern 4: Cloud-Agnostic Abstraction
- Kubernetes for compute
- PostgreSQL for database
- S3-compatible storage (MinIO)
- Open source tools
## Cloud-Agnostic Architecture
### Use Cloud-Native Alternatives
- **Compute:** Kubernetes (EKS/AKS/GKE/OKE)
- **Database:** PostgreSQL/MySQL (RDS/SQL Database/Cloud SQL/MySQL HeatWave)
- **Message Queue:** Apache Kafka or managed streaming (MSK/Event Hubs/Confluent/OCI Streaming)
- **Cache:** Redis (ElastiCache/Azure Cache/Memorystore/OCI Cache)
- **Object Storage:** S3-compatible API
- **Monitoring:** Prometheus/Grafana
- **Service Mesh:** Istio/Linkerd
### Abstraction Layers
```
Application Layer
↓
Infrastructure Abstraction (Terraform)
↓
Cloud Provider APIs
↓
AWS / Azure / GCP / OCI
```
## Cost Comparison
### Compute Pricing Factors
- **AWS:** On-demand, Reserved, Spot, Savings Plans
- **Azure:** Pay-as-you-go, Reserved, Spot
- **GCP:** On-demand, Committed use, Preemptible
- **OCI:** Pay-as-you-go, annual commitments, burstable/flexible shapes, preemptible instances
### Cost Optimization Strategies
1. Use reserved/committed capacity (30-70% savings)
2. Leverage spot/preemptible instances
3. Right-size resources
4. Use serverless for variable workloads
5. Optimize data transfer costs
6. Implement lifecycle policies
7. Use cost allocation tags
8. Monitor with cloud cost tools
**Reference:** See `references/multi-cloud-patterns.md`
## Migration Strategy
### Phase 1: Assessment
- Inventory current infrastructure
- Identify dependencies
- Assess cloud compatibility
- Estimate costs
### Phase 2: Pilot
- Select pilot workload
- Implement in target cloud
- Test thoroughly
- Document learnings
### Phase 3: Migration
- Migrate workloads incrementally
- Maintain dual-run period
- Monitor performance
- Validate functionality
### Phase 4: Optimization
- Right-size resources
- Implement cloud-native services
- Optimize costs
- Enhance security
## Best Practices
1. **Use infrastructure as code** (Terraform/OpenTofu)
2. **Implement CI/CD pipelines** for deployments
3. **Design for failure** across clouds
4. **Use managed services** when possible
5. **Implement comprehensive monitoring**
6. **Automate cost optimization**
7. **Follow security best practices**
8. **Document cloud-specific configurations**
9. **Test disaster recovery** procedures
10. **Train teams** on multiple clouds
## Related Skills
- `terraform-module-library` - For IaC implementation
- `cost-optimization` - For cost management
- `hybrid-cloud-networking` - For connectivity