multi_cloud_patterns:
  distributed_application:
    description: "Different components on different clouds"
    example:
      frontend: "AWS CloudFront + S3"
      api_gateway: "Azure API Management"
      compute: "Google Cloud Run"
      database: "AWS RDS"
      analytics: "Google BigQuery"
    benefits:
      - "Best service for each component"
      - "Avoid single point of failure"
      - "Cost optimization"
    challenges:
      - "Complex networking"
      - "Data transfer costs"
      - "Operational overhead"

  active_active:
    description: "Same application running on multiple clouds"
    architecture:
      load_balancer: "Global load balancer"
      regions:
        - aws: "us-east-1"
        - azure: "East US"
        - gcp: "us-east1"
      data_sync: "Multi-master replication"
    benefits:
      - "High availability"
      - "Geographic distribution"
      - "Provider redundancy"
    challenges:
      - "Data consistency"
      - "Complex deployment"
      - "Higher costs"

  disaster_recovery:
    description: "Primary on one cloud, DR on another"
    setup:
      primary: "AWS"
      secondary: "Azure"
      rpo: "15 minutes"
      rto: "1 hour"
      replication: "Continuous"
    benefits:
      - "Provider-level redundancy"
      - "Cost-effective DR"
      - "Compliance adherence"
    challenges:
      - "Testing complexity"
      - "Data replication"
      - "Failover procedures"

  cloud_bursting:
    description: "Overflow to other clouds during peak"
    implementation:
      base_capacity: "Private cloud"
      burst_providers:
        - "AWS Spot Instances"
        - "Azure Spot VMs"
        - "GCP Preemptible"
      triggers:
        - "CPU > 80%"
        - "Queue depth > 1000"
        - "Response time > 2s"
    benefits:
      - "Cost optimization"
      - "Handle peak loads"
      - "Maintain performance"

class MultiCloudArchitecture:
    def __init__(self):
        self.providers = ['aws', 'azure', 'gcp']
        self.components = {}

    def design_reference_architecture(self):
        """Design comprehensive multi-cloud architecture"""

        architecture = {
            'control_plane': {
                'orchestration': {
                    'tool': 'Kubernetes',
                    'deployment': 'Multi-cluster',
                    'management': 'Rancher/Anthos',
                    'federation': 'KubeFed'
                },
                'service_mesh': {
                    'tool': 'Istio',
                    'features': [
                        'Cross-cloud communication',
                        'Traffic management',
                        'Security policies',
                        'Observability'
                    ]
                },
                'ci_cd': {
                    'tool': 'GitLab/GitHub Actions',
                    'deployment_targets': self.providers,
                    'artifact_storage': 'Cloud-agnostic registry'
                }
            },

            'data_plane': {
                'compute': {
                    'aws': {
                        'services': ['EKS', 'Lambda', 'Fargate'],
                        'regions': ['us-east-1', 'eu-west-1']
                    },
                    'azure': {
                        'services': ['AKS', 'Functions', 'Container Instances'],
                        'regions': ['East US', 'West Europe']
                    },
                    'gcp': {
                        'services': ['GKE', 'Cloud Functions', 'Cloud Run'],
                        'regions': ['us-east1', 'europe-west1']
                    }
                },
                'storage': {
                    'object_storage': {
                        'primary': 'AWS S3',
                        'replication': ['Azure Blob', 'GCS'],
                        'sync_tool': 'Rclone/DataSync'
                    },
                    'databases': {
                        'transactional': 'AWS RDS with Azure SQL failover',
                        'nosql': 'Multi-cloud Cassandra',
                        'analytics': 'BigQuery with Synapse integration'
                    }
                },
                'networking': {
                    'backbone': 'SD-WAN or Transit Gateway',
                    'cdn': 'Multi-CDN strategy',
                    'dns': 'Cloud-agnostic DNS'
                }
            },

            'management_plane': {
                'monitoring': {
                    'metrics': 'Prometheus + Thanos',
                    'logging': 'ELK Stack',
                    'tracing': 'Jaeger',
                    'dashboards': 'Grafana'
                },
                'security': {
                    'identity': 'Okta/Auth0',
                    'secrets': 'HashiCorp Vault',
                    'policies': 'Open Policy Agent',
                    'scanning': 'Cloud-agnostic tools'
                },
                'cost': {
                    'tracking': 'CloudHealth/Cloudability',
                    'optimization': 'Spot.io/Cast.ai',
                    'allocation': 'Tag-based'
                }
            }
        }

        return architecture

service_mapping:
  compute:
    virtual_machines:
      aws: "EC2"
      azure: "Virtual Machines"
      gcp: "Compute Engine"
      features_comparison:
        instance_types: "Similar offerings"
        pricing_models: "On-demand, Reserved, Spot"
        auto_scaling: "All support"

    containers:
      managed_kubernetes:
        aws: "EKS"
        azure: "AKS"
        gcp: "GKE"
        differentiators:
          eks: "Broad ecosystem"
          aks: "Windows containers"
          gke: "Autopilot mode"

      serverless_containers:
        aws: "Fargate"
        azure: "Container Instances"
        gcp: "Cloud Run"

    serverless:
      functions:
        aws: "Lambda"
        azure: "Functions"
        gcp: "Cloud Functions"
        comparison:
          cold_start: "GCP fastest"
          language_support: "Azure most extensive"
          integration: "AWS deepest"

  storage:
    object_storage:
      aws: "S3"
      azure: "Blob Storage"
      gcp: "Cloud Storage"
      features:
        storage_classes: "All offer tiering"
        lifecycle_policies: "Supported"
        versioning: "Available"

    block_storage:
      aws: "EBS"
      azure: "Managed Disks"
      gcp: "Persistent Disks"
      performance_tiers:
        ssd: "All providers"
        throughput_optimized: "Varies"
        iops_optimized: "Available"

    file_storage:
      aws: "EFS/FSx"
      azure: "Files/NetApp Files"
      gcp: "Filestore"

  database:
    relational:
      managed_service:
        aws: "RDS/Aurora"
        azure: "SQL Database/PostgreSQL"
        gcp: "Cloud SQL/Spanner"

      serverless:
        aws: "Aurora Serverless"
        azure: "SQL Database Serverless"
        gcp: "Not available"

    nosql:
      document:
        aws: "DocumentDB/DynamoDB"
        azure: "Cosmos DB"
        gcp: "Firestore"

      key_value:
        aws: "ElastiCache"
        azure: "Cache for Redis"
        gcp: "Memorystore"

  networking:
    virtual_network:
      aws: "VPC"
      azure: "VNet"
      gcp: "VPC"

    load_balancer:
      aws: "ELB/ALB/NLB"
      azure: "Load Balancer/Application Gateway"
      gcp: "Cloud Load Balancing"

    cdn:
      aws: "CloudFront"
      azure: "CDN"
      gcp: "Cloud CDN"

class CloudProviderAnalysis:
    def __init__(self):
        self.providers = {
            'aws': {
                'strengths': [
                    'Largest service portfolio',
                    'Mature ecosystem',
                    'Global infrastructure',
                    'Enterprise adoption'
                ],
                'best_for': [
                    'General purpose workloads',
                    'Startup to enterprise',
                    'Complex architectures',
                    'Third-party integrations'
                ],
                'considerations': [
                    'Can be complex',
                    'Pricing complexity',
                    'Steep learning curve'
                ]
            },
            'azure': {
                'strengths': [
                    'Microsoft integration',
                    'Hybrid cloud focus',
                    'Enterprise features',
                    'Compliance certifications'
                ],
                'best_for': [
                    'Windows workloads',
                    'Microsoft shops',
                    'Hybrid scenarios',
                    'Enterprise compliance'
                ],
                'considerations': [
                    'UI/UX inconsistency',
                    'Regional limitations',
                    'Support challenges'
                ]
            },
            'gcp': {
                'strengths': [
                    'Data analytics',
                    'Machine learning',
                    'Kubernetes (GKE)',
                    'Developer experience'
                ],
                'best_for': [
                    'Big data workloads',
                    'AI/ML projects',
                    'Container workloads',
                    'Modern applications'
                ],
                'considerations': [
                    'Smaller service portfolio',
                    'Enterprise features gaps',
                    'Market share concerns'
                ]
            }
        }

    def recommend_provider_for_workload(self, workload_type):
        """Recommend best provider for specific workload"""

        recommendations = {
            'web_application': {
                'primary': 'aws',
                'reason': 'Comprehensive services and global reach',
                'alternative': 'azure',
                'multi_cloud_strategy': 'Use AWS for compute, CloudFlare for CDN'
            },
            'windows_enterprise': {
                'primary': 'azure',
                'reason': 'Native Windows integration and licensing',
                'alternative': 'aws',
                'multi_cloud_strategy': 'Azure for Windows, AWS for Linux workloads'
            },
            'big_data_analytics': {
                'primary': 'gcp',
                'reason': 'BigQuery and Dataflow excellence',
                'alternative': 'aws',
                'multi_cloud_strategy': 'GCP for analytics, AWS for data lake'
            },
            'machine_learning': {
                'primary': 'gcp',
                'reason': 'TensorFlow integration and AI Platform',
                'alternative': 'aws',
                'multi_cloud_strategy': 'GCP for training, AWS for inference'
            },
            'hybrid_cloud': {
                'primary': 'azure',
                'reason': 'Azure Arc and Stack capabilities',
                'alternative': 'aws',
                'multi_cloud_strategy': 'Azure for hybrid, AWS for pure cloud'
            }
        }

        return recommendations.get(workload_type, {
            'primary': 'aws',
            'reason': 'Most versatile platform',
            'multi_cloud_strategy': 'Evaluate based on specific requirements'
        })

workload_placement:
  decision_factors:
    technical:
      - performance_requirements:
          latency: "Critical factor"
          throughput: "Bandwidth needs"
          compute_intensity: "CPU/GPU requirements"

      - service_dependencies:
          native_services: "Provider-specific features"
          integration_needs: "Third-party services"
          api_availability: "Regional presence"

      - data_gravity:
          data_location: "Where data resides"
          transfer_costs: "Egress charges"
          compliance: "Data residency"

    business:
      - cost_optimization:
          pricing_models: "Compare TCO"
          commitment_discounts: "Reserved capacity"
          spot_availability: "Preemptible options"

      - vendor_relationships:
          enterprise_agreements: "Existing contracts"
          support_quality: "SLA requirements"
          credits_available: "Committed spend"

      - compliance_requirements:
          certifications: "Required standards"
          data_sovereignty: "Regional laws"
          audit_requirements: "Compliance needs"

    operational:
      - team_expertise:
          existing_skills: "Current knowledge"
          training_requirements: "Learning curve"
          operational_tooling: "Management tools"

      - integration_complexity:
          api_compatibility: "Cross-cloud APIs"
          networking_requirements: "Connectivity needs"
          monitoring_capabilities: "Observability"

  placement_strategies:
    performance_optimized:
      principle: "Place workloads where they perform best"
      examples:
        - "AI/ML workloads → GCP"
        - "Windows workloads → Azure"
        - "Serverless → AWS Lambda"

    cost_optimized:
      principle: "Minimize total cost of ownership"
      examples:
        - "Spot workloads → Cheapest provider"
        - "Storage-heavy → Best storage pricing"
        - "Committed workloads → Best discounts"

    resilience_optimized:
      principle: "Maximize availability and DR"
      examples:
        - "Critical apps → Multi-cloud active-active"
        - "Data → Replicated across clouds"
        - "Services → Provider redundancy"

class WorkloadMigrationPlanner:
    def __init__(self):
        self.workloads = []
        self.providers = ['aws', 'azure', 'gcp']

    def analyze_workload_fit(self, workload):
        """Analyze best cloud fit for workload"""

        scoring_matrix = {
            'criteria': {
                'performance': {
                    'weight': 0.3,
                    'scores': self.score_performance(workload)
                },
                'cost': {
                    'weight': 0.25,
                    'scores': self.score_cost(workload)
                },
                'features': {
                    'weight': 0.2,
                    'scores': self.score_features(workload)
                },
                'compliance': {
                    'weight': 0.15,
                    'scores': self.score_compliance(workload)
                },
                'operations': {
                    'weight': 0.1,
                    'scores': self.score_operations(workload)
                }
            }
        }

        # Calculate weighted scores
        final_scores = {}
        for provider in self.providers:
            score = 0
            for criterion, data in scoring_matrix['criteria'].items():
                score += data['weight'] * data['scores'][provider]
            final_scores[provider] = score

        # Generate recommendation
        recommended = max(final_scores, key=final_scores.get)

        return {
            'workload': workload['name'],
            'recommendation': recommended,
            'scores': final_scores,
            'reasoning': self.generate_reasoning(workload, recommended),
            'migration_complexity': self.assess_migration_complexity(workload),
            'multi_cloud_option': self.suggest_multi_cloud_split(workload)
        }

    def create_migration_waves(self, workloads):
        """Create phased migration plan"""

        waves = {
            'wave_1': {
                'name': 'Quick Wins',
                'duration': '3 months',
                'criteria': [
                    'Low complexity',
                    'Stateless applications',
                    'Dev/test environments'
                ],
                'workloads': self.filter_workloads(workloads, 'quick_wins')
            },
            'wave_2': {
                'name': 'Core Applications',
                'duration': '6 months',
                'criteria': [
                    'Medium complexity',
                    'Business applications',
                    'Some state management'
                ],
                'workloads': self.filter_workloads(workloads, 'core_apps')
            },
            'wave_3': {
                'name': 'Complex Systems',
                'duration': '6-12 months',
                'criteria': [
                    'High complexity',
                    'Legacy systems',
                    'Significant refactoring'
                ],
                'workloads': self.filter_workloads(workloads, 'complex')
            }
        }

        return waves

multi_cloud_networking:
  connectivity_options:
    cloud_interconnect:
      description: "Direct private connections between clouds"
      implementations:
        aws_azure:
          - method: "ExpressRoute + Direct Connect via partner"
          - bandwidth: "1-100 Gbps"
          - latency: "<5ms typical"

        aws_gcp:
          - method: "Partner Interconnect"
          - providers: ["Megaport", "Equinix", "PacketFabric"]

        azure_gcp:
          - method: "ExpressRoute + Cloud Interconnect"
          - configuration: "Cross-connect at edge location"

    vpn_mesh:
      description: "Site-to-site VPNs between clouds"
      topology: "Full mesh or hub-spoke"
      configuration:
        ipsec_parameters:
          encryption: "AES-256"
          integrity: "SHA-256"
          dh_group: "14 or higher"
          pfs: "enabled"

        routing: "BGP preferred"
        redundancy: "Multiple tunnels"

    sd_wan:
      description: "Software-defined WAN overlay"
      benefits:
        - "Simplified management"
        - "Dynamic path selection"
        - "Application-aware routing"

      vendors:
        - "Cisco Viptela"
        - "VMware VeloCloud"
        - "Silver Peak"

    transit_architecture:
      description: "Centralized transit network"
      components:
        transit_vpc:
          aws: "Transit Gateway"
          azure: "Virtual WAN"
          gcp: "Not native - use appliances"

        routing:
          protocol: "BGP"
          as_path_prepending: "For path preference"
          communities: "For policy control"

class MultiCloudNetworking:
    def __init__(self):
        self.providers = {
            'aws': AWSNetworking(),
            'azure': AzureNetworking(),
            'gcp': GCPNetworking()
        }

    def design_multi_cloud_network(self):
        """Design comprehensive multi-cloud network"""

        network_design = {
            'architecture': 'hub-and-spoke',
            'hubs': {
                'primary': {
                    'location': 'aws-us-east-1',
                    'services': ['Transit Gateway', 'Direct Connect'],
                    'ip_range': '10.0.0.0/16'
                },
                'secondary': {
                    'location': 'azure-eastus',
                    'services': ['Virtual WAN', 'ExpressRoute'],
                    'ip_range': '10.1.0.0/16'
                }
            },
            'spokes': {
                'aws_workloads': {
                    'vpcs': ['10.10.0.0/16', '10.11.0.0/16'],
                    'connection': 'Transit Gateway attachment'
                },
                'azure_workloads': {
                    'vnets': ['10.20.0.0/16', '10.21.0.0/16'],
                    'connection': 'Virtual WAN connection'
                },
                'gcp_workloads': {
                    'vpcs': ['10.30.0.0/16', '10.31.0.0/16'],
                    'connection': 'VPN to transit hubs'
                }
            },
            'interconnects': [
                {
                    'type': 'Direct peering',
                    'between': ['AWS Transit Gateway', 'Azure Virtual WAN'],
                    'bandwidth': '10 Gbps',
                    'redundancy': 'Active-Active'
                }
            ],
            'security': {
                'firewalls': 'Centralized in hubs',
                'segmentation': 'Microsegmentation policies',
                'inspection': 'East-West traffic inspection'
            }
        }

        return network_design

    def implement_network_automation(self):
        """Automate multi-cloud network operations"""

        automation = {
            'infrastructure_as_code': {
                'tool': 'Terraform',
                'modules': [
                    'aws-transit-gateway',
                    'azure-virtual-wan',
                    'gcp-vpn',
                    'multi-cloud-peering'
                ],
                'state_management': 'Remote backend with locking'
            },

            'configuration_management': {
                'routing_updates': '''
# Automated BGP configuration
def update_bgp_routes(provider, routes):
    if provider == 'aws':
        # Update Transit Gateway route tables
        tgw_client.create_route(
            DestinationCidrBlock=route['cidr'],
            TransitGatewayAttachmentId=route['attachment']
        )
    elif provider == 'azure':
        # Update Virtual WAN routes
        vwan_client.routes.create_or_update(
            route_table_name='default',
            route_name=route['name'],
            address_prefixes=[route['cidr']],
            next_hop_ip_address=route['next_hop']
        )
                ''',

                'policy_sync': 'Automated security policy distribution'
            },

            'monitoring': {
                'flow_logs': 'Centralized collection',
                'latency_monitoring': 'Cross-cloud probes',
                'bandwidth_tracking': 'Per-connection metrics'
            }
        }

        return automation

data_management_strategy:
  data_architecture:
    patterns:
      distributed_data:
        description: "Data spread across multiple clouds"
        use_cases:
          - "Geographic distribution"
          - "Compliance requirements"
          - "Performance optimization"

        implementation:
          partitioning: "By region or customer"
          consistency: "Eventual consistency"
          conflict_resolution: "Last-write-wins or CRDT"

      replicated_data:
        description: "Same data in multiple clouds"
        use_cases:
          - "High availability"
          - "Disaster recovery"
          - "Read performance"

        implementation:
          replication: "Multi-master or master-slave"
          sync_frequency: "Real-time or batch"
          conflict_handling: "Application-specific"

      federated_data:
        description: "Virtual integration without movement"
        use_cases:
          - "Analytics across clouds"
          - "Minimize data transfer"
          - "Maintain data sovereignty"

        implementation:
          query_federation: "Presto, Trino, or similar"
          catalog: "Centralized metadata"
          caching: "Strategic caching layer"

  data_services:
    storage:
      object_storage:
        primary: "Choose based on features/cost"
        replication_tools:
          - "Rclone"
          - "AWS DataSync"
          - "Azure Data Factory"
          - "GCP Transfer Service"

      databases:
        multi_cloud_options:
          - "CockroachDB": "Distributed SQL"
          - "Cassandra": "Multi-DC NoSQL"
          - "MongoDB Atlas": "Managed multi-cloud"
          - "Snowflake": "Multi-cloud data warehouse"

    data_movement:
      strategies:
        batch_transfer:
          tools: ["Apache Airflow", "Talend", "Informatica"]
          frequency: "Scheduled"
          use_case: "Large volume, non-critical"

        streaming:
          tools: ["Kafka", "Pulsar", "Kinesis Data Streams"]
          latency: "Near real-time"
          use_case: "Event data, IoT"

        change_data_capture:
          tools: ["Debezium", "AWS DMS", "Striim"]
          latency: "Minutes"
          use_case: "Database sync"

class MultiCloudDataSync:
    def __init__(self):
        self.sync_engines = {}

    def implement_data_sync_strategy(self):
        """Implement multi-cloud data synchronization"""

        sync_strategy = {
            'object_storage_sync': {
                'tool': 'Rclone',
                'configuration': '''
# Rclone configuration for multi-cloud sync
[aws]
type = s3
provider = AWS
access_key_id = ${AWS_ACCESS_KEY}
secret_access_key = ${AWS_SECRET_KEY}
region = us-east-1

[azure]
type = azureblob
account = ${AZURE_ACCOUNT}
key = ${AZURE_KEY}

[gcp]
type = google cloud storage
service_account_file = ${GCP_SA_FILE}
project_number = ${GCP_PROJECT}

# Sync script
rclone sync aws:source-bucket azure:dest-container --transfers 32
rclone sync aws:source-bucket gcp:dest-bucket --transfers 32
                ''',
                'scheduling': 'Cron or Airflow',
                'monitoring': 'Custom metrics + alerts'
            },

            'database_replication': {
                'pattern': 'Multi-master',
                'implementation': '''
class MultiCloudDatabaseReplication:
    def setup_cockroachdb_cluster(self):
        """Setup CockroachDB across multiple clouds"""

        cluster_config = {
            'nodes': [
                {
                    'cloud': 'aws',
                    'region': 'us-east-1',
                    'count': 3,
                    'instance_type': 'm5.xlarge'
                },
                {
                    'cloud': 'azure',
                    'region': 'eastus',
                    'count': 3,
                    'instance_type': 'Standard_D4s_v3'
                },
                {
                    'cloud': 'gcp',
                    'region': 'us-east1',
                    'count': 3,
                    'instance_type': 'n1-standard-4'
                }
            ],
            'replication_factor': 3,
            'locality': 'cloud,region,zone',
            'survival_goals': {
                'region_failure': 'survive',
                'cloud_failure': 'survive'
            }
        }

        return cluster_config
                '''
            },

            'streaming_data': {
                'architecture': 'Multi-cloud Kafka',
                'setup': {
                    'clusters': ['AWS MSK', 'Confluent Cloud', 'Self-managed'],
                    'mirroring': 'MirrorMaker 2.0',
                    'schema_registry': 'Centralized',
                    'monitoring': 'Unified dashboard'
                }
            }
        }

        return sync_strategy

security_architecture:
  identity_and_access:
    strategy: "Centralized identity, federated access"
    implementation:
      identity_provider:
        primary: "Okta/Azure AD"
        protocols: ["SAML", "OIDC", "OAuth"]

      cloud_integration:
        aws:
          method: "SAML federation"
          roles: "Assumed via STS"

        azure:
          method: "Azure AD integration"
          roles: "RBAC assignments"

        gcp:
          method: "Workload identity federation"
          roles: "IAM bindings"

      access_patterns:
        human_users:
          authentication: "SSO + MFA"
          authorization: "Role-based"
          session: "Time-limited"

        service_accounts:
          authentication: "Workload identity"
          authorization: "Least privilege"
          rotation: "Automated"

  data_security:
    encryption:
      at_rest:
        strategy: "Cloud-native + BYOK"
        key_management: "Centralized HSM"

      in_transit:
        internal: "mTLS everywhere"
        external: "TLS 1.3 minimum"

    data_loss_prevention:
      scanning: "Cloud-agnostic DLP"
      policies: "Unified across clouds"
      enforcement: "Automated remediation"

  network_security:
    perimeter:
      waf: "Multi-cloud WAF strategy"
      ddos: "Cloud-native protection"

    microsegmentation:
      east_west: "Zero trust networking"
      policies: "Centralized management"

    monitoring:
      flow_logs: "Aggregated analysis"
      threat_detection: "SIEM integration"

  compliance:
    framework:
      standards: ["SOC2", "ISO27001", "HIPAA", "GDPR"]

    controls:
      mapping: "Control to cloud service mapping"
      evidence: "Automated collection"
      reporting: "Unified dashboards"

    continuous_compliance:
      scanning: "Daily configuration checks"
      remediation: "Automated where possible"
      exceptions: "Tracked and reviewed"

class MultiCloudSecurity:
    def __init__(self):
        self.security_tools = {
            'cspm': 'Prisma Cloud',
            'siem': 'Splunk',
            'secrets': 'HashiCorp Vault'
        }

    def implement_zero_trust_architecture(self):
        """Implement zero trust across multiple clouds"""

        zero_trust_components = {
            'identity_verification': {
                'implementation': '''
# Multi-cloud identity verification
class MultiCloudIdentityBroker:
    def __init__(self):
        self.providers = {
            'aws': AWSSTSClient(),
            'azure': AzureADClient(),
            'gcp': GCPIAMClient()
        }

    def authenticate_user(self, token):
        # Verify with central IdP
        user = self.idp.verify_token(token)

        # Generate cloud-specific credentials
        credentials = {}
        for cloud, client in self.providers.items():
            if user.has_access(cloud):
                creds = client.assume_role_with_saml(
                    user.saml_assertion,
                    role=user.cloud_roles[cloud],
                    duration=3600
                )
                credentials[cloud] = creds

        return credentials
                ''',
                'session_management': 'Time-bound with continuous verification'
            },

            'device_trust': {
                'verification': 'Certificate-based',
                'compliance_check': 'OS patches, antivirus, encryption',
                'continuous_assessment': 'Every 30 minutes'
            },

            'application_security': {
                'api_gateway': 'Centralized with cloud backends',
                'authentication': 'mTLS between services',
                'authorization': 'OPA policies',
                'secrets_management': '''
# Centralized secrets management
vault_config = {
    'backend': 'Consul',
    'seal': {
        'type': 'awskms',
        'key_id': 'alias/vault-seal'
    },
    'auth_methods': {
        'kubernetes': {
            'aws_eks': True,
            'azure_aks': True,
            'gcp_gke': True
        },
        'cloud_iam': {
            'aws': True,
            'azure': True,
            'gcp': True
        }
    },
    'secrets_engines': {
        'aws': 'Dynamic credentials',
        'azure': 'Dynamic credentials',
        'gcp': 'Dynamic credentials',
        'database': 'Dynamic credentials',
        'kv': 'Static secrets'
    }
}
                '''
            },

            'network_security': {
                'segmentation': 'Micro-segmentation with Istio',
                'encryption': 'Automatic mTLS',
                'policies': 'Centralized with local enforcement'
            }
        }

        return zero_trust_components

cost_management:
  visibility:
    tools:
      primary: "CloudHealth/Cloudability"
      features:
        - "Unified billing view"
        - "Cost allocation"
        - "Anomaly detection"
        - "Optimization recommendations"

    tagging_strategy:
      mandatory_tags:
        - "Environment"
        - "Application"
        - "Owner"
        - "CostCenter"
        - "Project"

      enforcement: "Cloud policies"
      compliance: "Weekly reports"

  optimization_strategies:
    compute:
      right_sizing:
        analysis: "Cross-cloud performance metrics"
        recommendations: "Weekly review"
        automation: "Auto-scaling policies"

      commitment_optimization:
        strategy: "Portfolio approach"
        allocation:
          steady_state: "3-year commitments"
          variable: "1-year or on-demand"
          burst: "Spot/preemptible"

      spot_arbitrage:
        tools: ["Spot.io", "Cast.ai"]
        strategy: "Cross-cloud spot pricing"

    storage:
      tiering:
        automation: "Lifecycle policies"
        cross_cloud: "Cost-based migration"

      deduplication:
        scope: "Within and across clouds"
        tools: "Cloud-native or third-party"

    network:
      optimization:
        - "Minimize cross-cloud transfer"
        - "Use private connectivity"
        - "CDN for static content"
        - "Compress data in transit"

    data_transfer:
      strategies:
        - "Process data where it lives"
        - "Strategic caching"
        - "Batch transfers during off-peak"

class MultiCloudCostManagement:
    def __init__(self):
        self.providers = ['aws', 'azure', 'gcp']
        self.cost_data = {}

    def implement_cost_allocation(self):
        """Implement cost allocation across clouds"""

        allocation_model = {
            'data_collection': {
                'aws': {
                    'source': 'Cost and Usage Report',
                    'frequency': 'Hourly',
                    'storage': 'S3'
                },
                'azure': {
                    'source': 'Cost Management API',
                    'frequency': 'Daily',
                    'storage': 'Blob Storage'
                },
                'gcp': {
                    'source': 'BigQuery Billing Export',
                    'frequency': 'Real-time',
                    'storage': 'BigQuery'
                }
            },

            'normalization': '''
# Normalize cost data across clouds
def normalize_costs(raw_costs):
    normalized = []

    for provider, costs in raw_costs.items():
        for item in costs:
            normalized.append({
                'provider': provider,
                'service': map_service_name(provider, item['service']),
                'cost': item['cost'],
                'usage': normalize_usage_metrics(provider, item),
                'tags': item.get('tags', {}),
                'timestamp': item['timestamp']
            })

    return normalized
            ''',

            'allocation_rules': {
                'direct_costs': 'Tag-based allocation',
                'shared_costs': {
                    'method': 'Usage-based split',
                    'metrics': ['compute_hours', 'storage_gb', 'requests']
                },
                'overhead': {
                    'method': 'Proportional distribution',
                    'includes': ['Support', 'Licensing', 'Tools']
                }
            },

            'reporting': {
                'dashboards': {
                    'executive': 'High-level spend and trends',
                    'department': 'Detailed breakdown by team',
                    'technical': 'Resource-level optimization'
                },
                'alerts': {
                    'budget_threshold': 80,
                    'anomaly_detection': 'ML-based',
                    'forecast_breach': '30 days ahead'
                }
            }
        }

        return allocation_model

operations_framework:
  monitoring_and_observability:
    architecture: "Centralized monitoring, distributed collection"

    components:
      metrics:
        collection: "Prometheus + cloud-native"
        storage: "Thanos for long-term"
        visualization: "Grafana"

      logging:
        collection: "Fluentd/Fluent Bit"
        processing: "Logstash"
        storage: "Elasticsearch"
        analysis: "Kibana"

      tracing:
        instrumentation: "OpenTelemetry"
        collection: "Jaeger/Zipkin"
        storage: "Cassandra/Elasticsearch"

      alerting:
        rules: "Prometheus AlertManager"
        routing: "PagerDuty/Opsgenie"
        escalation: "Automated"

  automation:
    infrastructure:
      tool: "Terraform"
      structure:
        - "modules/aws"
        - "modules/azure" 
        - "modules/gcp"
        - "modules/multi-cloud"

      state: "Remote with locking"

    configuration:
      tool: "Ansible/Chef/Puppet"
      inventory: "Dynamic from cloud APIs"

    orchestration:
      workflows: "Apache Airflow"
      tasks:
        - "Provisioning"
        - "Deployment"
        - "Scaling"
        - "Maintenance"

  disaster_recovery:
    strategy: "Active-passive with automated failover"

    components:
      data_backup:
        frequency: "Continuous"
        locations: "Cross-cloud replication"
        retention: "30 days hot, 1 year cold"

      application_dr:
        rpo: "15 minutes"
        rto: "1 hour"
        testing: "Monthly drills"

      runbooks:
        storage: "Version controlled"
        automation: "Where possible"
        training: "Quarterly"

class MultiCloudOperations:
    def __init__(self):
        self.automation_tools = {
            'terraform': TerraformClient(),
            'ansible': AnsibleClient(),
            'airflow': AirflowClient()
        }

    def implement_automated_operations(self):
        """Implement automated multi-cloud operations"""

        automation_framework = {
            'deployment_pipeline': {
                'stages': [
                    {
                        'name': 'Validate',
                        'actions': [
                            'Terraform validate',
                            'Policy checks',
                            'Cost estimation'
                        ]
                    },
                    {
                        'name': 'Plan',
                        'actions': [
                            'Terraform plan',
                            'Change review',
                            'Approval gates'
                        ]
                    },
                    {
                        'name': 'Deploy',
                        'actions': [
                            'Terraform apply',
                            'Configuration management',
                            'Smoke tests'
                        ]
                    },
                    {
                        'name': 'Verify',
                        'actions': [
                            'Health checks',
                            'Performance tests',
                            'Security scans'
                        ]
                    }
                ],

                'rollback': 'Automated on failure'
            },

            'auto_remediation': {
                'health_checks': '''
# Multi-cloud health monitoring
health_checks = {
    'aws': {
        'endpoint': 'https://app.aws.example.com/health',
        'expected_status': 200,
        'timeout': 10
    },
    'azure': {
        'endpoint': 'https://app.azure.example.com/health',
        'expected_status': 200,
        'timeout': 10
    },
    'gcp': {
        'endpoint': 'https://app.gcp.example.com/health',
        'expected_status': 200,
        'timeout': 10
    }
}

def auto_remediate(cloud, issue):
    if issue == 'unhealthy_instance':
        # Replace instance
        terraform.apply(target=f'{cloud}_asg')
    elif issue == 'high_error_rate':
        # Scale up
        increase_capacity(cloud, 20)
    elif issue == 'disk_full':
        # Clean up logs
        run_cleanup_job(cloud)
                ''',

                'incident_response': 'Automated runbooks'
            },

            'chaos_engineering': {
                'tool': 'Litmus/Gremlin',
                'experiments': [
                    'Cloud provider failure',
                    'Region failure',
                    'Service degradation',
                    'Network partition'
                ],
                'frequency': 'Weekly in non-prod'
            }
        }

        return automation_framework

implementation_roadmap:
  phase_1_foundation:
    duration: "3 months"
    objectives:
      - "Establish governance framework"
      - "Setup connectivity"
      - "Implement security baseline"
      - "Create automation foundation"

    deliverables:
      - governance:
          - "Cloud decision framework"
          - "Policies and standards"
          - "Team structure"

      - technical:
          - "Network connectivity"
          - "Identity federation"
          - "Basic monitoring"

      - process:
          - "Change management"
          - "Cost tracking"
          - "Support model"

  phase_2_pilot:
    duration: "3 months"
    objectives:
      - "Migrate pilot workloads"
      - "Validate architecture"
      - "Refine operations"

    workloads:
      - "Dev/test environments"
      - "Stateless applications"
      - "New greenfield projects"

    success_criteria:
      - "Successful migration"
      - "Cost targets met"
      - "Performance validated"
      - "Team confidence"

  phase_3_scale:
    duration: "6-12 months"
    objectives:
      - "Migrate production workloads"
      - "Implement advanced features"
      - "Optimize operations"

    capabilities:
      - "Multi-cloud data platform"
      - "Advanced security"
      - "Full automation"
      - "Disaster recovery"

  phase_4_optimize:
    duration: "Ongoing"
    objectives:
      - "Continuous optimization"
      - "Innovation adoption"
      - "Strategic evolution"

    focus_areas:
      - "Cost optimization"
      - "Performance tuning"
      - "New service adoption"
      - "Competitive advantage"

def define_multi_cloud_metrics():
    """Define success metrics for multi-cloud adoption"""

    metrics = {
        'technical_metrics': {
            'availability': {
                'target': '99.99%',
                'measurement': 'Synthetic monitoring across clouds'
            },
            'performance': {
                'latency': '<100ms p99',
                'throughput': '>10K TPS',
                'multi_cloud_overhead': '<10%'
            },
            'resilience': {
                'rto': '<1 hour',
                'rpo': '<15 minutes',
                'cloud_failure_recovery': 'Automated'
            }
        },

        'business_metrics': {
            'cost': {
                'savings': '20-30% through optimization',
                'predictability': '±5% monthly variance',
                'unit_economics': 'Improved by 25%'
            },
            'agility': {
                'deployment_speed': '50% faster',
                'service_adoption': 'Best-of-breed',
                'innovation_velocity': 'Increased'
            },
            'risk': {
                'vendor_dependency': '<40% any provider',
                'compliance_coverage': '100%',
                'negotiation_leverage': 'Improved'
            }
        },

        'operational_metrics': {
            'automation': {
                'infrastructure': '>90% automated',
                'deployments': '100% CI/CD',
                'remediation': '>80% auto-healed'
            },
            'efficiency': {
                'tool_consolidation': '50% reduction',
                'operational_overhead': '<20% increase',
                'skill_utilization': 'Cross-cloud expertise'
            }
        }
    }

    return metrics

best_practices:
  architecture:
    - "Design for portability from the start"
    - "Use cloud-agnostic tools where possible"
    - "Implement abstraction layers"
    - "Standardize on containers and Kubernetes"
    - "Document cloud-specific dependencies"

  operations:
    - "Centralize monitoring and logging"
    - "Automate everything possible"
    - "Implement consistent security policies"
    - "Regular disaster recovery testing"
    - "Continuous cost optimization"

  development:
    - "Cloud-agnostic application design"
    - "Use standard APIs and protocols"
    - "Implement feature flags for cloud-specific features"
    - "Comprehensive testing across clouds"
    - "Performance benchmarking"

  governance:
    - "Clear cloud selection criteria"
    - "Consistent tagging strategy"
    - "Regular architecture reviews"
    - "Vendor management strategy"
    - "Skills development program"

  common_pitfalls:
    - "Underestimating complexity"
    - "Ignoring data transfer costs"
    - "Inconsistent security policies"
    - "Tool proliferation"
    - "Inadequate automation"