# Advanced API service configuration
[DEFAULT]
enabled_apis = osapi_compute,metadata
osapi_compute_workers = 8
metadata_workers = 4
max_request_body_size = 114688

# Rate limiting configuration
[api]
auth_strategy = keystone
max_limit = 1000
compute_link_prefix = http://controller:8774
glance_link_prefix = http://controller:9292

# Advanced request handling
[wsgi]
api_paste_config = /etc/nova/api-paste.ini
secure_proxy_ssl_header = HTTP_X_FORWARDED_PROTO

# CORS configuration for web applications
[cors]
allowed_origin = https://dashboard.example.com,https://cli.example.com
allow_credentials = true
expose_headers = Content-Type,Cache-Control,Content-Language,Expires,Last-Modified,Pragma

# Advanced scheduler configuration
[scheduler]
driver = filter_scheduler
max_attempts = 10
periodic_task_interval = 60

[filter_scheduler]
# Comprehensive filter chain
enabled_filters = AvailabilityZoneFilter,
                 ComputeFilter,
                 ComputeCapabilitiesFilter,
                 ImagePropertiesFilter,
                 CoreFilter,
                 RamFilter,
                 DiskFilter,
                 NumaTopologyFilter,
                 ServerGroupAntiAffinityFilter,
                 ServerGroupAffinityFilter,
                 PciPassthroughFilter,
                 NUMATopologyFilter,
                 AggregateInstanceExtraSpecFilter

# Weight configuration for optimal placement
weight_classes = nova.scheduler.weights.all_weighers
ram_weight_multiplier = 1.0
cpu_weight_multiplier = 1.0
disk_weight_multiplier = 1.0
io_ops_weight_multiplier = -1.0

# Advanced scheduling options
track_instance_changes = true
placement_aggregate_required_for_tenants = true

# Production conductor configuration
[conductor]
workers = 8
task_log = true
instance_sync_time = 600

# Database connection pooling
[database]
connection = mysql+pymysql://nova:password@controller/nova
max_pool_size = 30
max_overflow = 60
pool_timeout = 30
pool_recycle = 3600
pool_pre_ping = true

# Advanced workflow management
[cells]
call_timeout = 60
capabilities = hypervisor=kvm,cpu_arch=x86_64,virt_type=kvm
reserve_percent = 10.0

# RabbitMQ cluster configuration for high availability
[DEFAULT]
transport_url = rabbit://nova:password@controller1:5672,nova:password@controller2:5672,nova:password@controller3:5672/nova

# Advanced messaging configuration
[oslo_messaging_rabbit]
heartbeat_timeout_threshold = 60
heartbeat_rate = 3
rabbit_retry_interval = 1
rabbit_retry_backoff = 2
rabbit_max_retries = 0
rabbit_ha_queues = true
rabbit_queue_ttl = 0
rabbit_durable_queues = true

# Message notification system
[notifications]
notification_format = versioned
notification_topics = notifications
notify_on_state_change = vm_and_task_state
default_notification_level = INFO

# Master-slave database configuration
[api_database]
connection = mysql+pymysql://nova:password@db-master/nova_api
slave_connection = mysql+pymysql://nova:password@db-slave/nova_api

[database]
connection = mysql+pymysql://nova:password@db-master/nova
slave_connection = mysql+pymysql://nova:password@db-slave/nova

# Connection pool optimization
max_pool_size = 50
max_overflow = 100
pool_timeout = 30
pool_recycle = 3600
pool_pre_ping = true

# Database migration and versioning
[upgrade_levels]
compute = auto

# Resource provider hierarchy example
def create_nested_resource_providers():
    """Create a nested resource provider hierarchy for NUMA topology"""
    
    # Root compute node resource provider
    compute_rp = {
        'uuid': str(uuid.uuid4()),
        'name': 'compute-node-01',
        'parent_provider_uuid': None,
        'root_provider_uuid': None
    }
    
    # NUMA node resource providers
    numa_providers = []
    for numa_id in range(2):  # 2 NUMA nodes
        numa_rp = {
            'uuid': str(uuid.uuid4()),
            'name': f'compute-node-01-numa-{numa_id}',
            'parent_provider_uuid': compute_rp['uuid'],
            'root_provider_uuid': compute_rp['uuid']
        }
        numa_providers.append(numa_rp)
    
    # GPU resource providers under specific NUMA nodes
    gpu_providers = []
    for numa_idx, numa_rp in enumerate(numa_providers):
        for gpu_id in range(2):  # 2 GPUs per NUMA node
            gpu_rp = {
                'uuid': str(uuid.uuid4()),
                'name': f'gpu-{numa_idx}-{gpu_id}',
                'parent_provider_uuid': numa_rp['uuid'],
                'root_provider_uuid': compute_rp['uuid']
            }
            gpu_providers.append(gpu_rp)
    
    return compute_rp, numa_providers, gpu_providers

# Inventory management for nested providers
compute_inventory = {
    'MEMORY_MB': {'total': 262144, 'reserved': 4096, 'allocation_ratio': 1.0},
    'DISK_GB': {'total': 1000, 'reserved': 50, 'allocation_ratio': 1.0}
}

numa_inventory = {
    'VCPU': {'total': 24, 'reserved': 2, 'allocation_ratio': 1.0},
    'MEMORY_MB': {'total': 131072, 'reserved': 2048, 'allocation_ratio': 1.0}
}

gpu_inventory = {
    'VGPU': {'total': 1, 'reserved': 0, 'allocation_ratio': 1.0},
    'VGPU_MEMORY_MB': {'total': 16384, 'reserved': 0, 'allocation_ratio': 1.0}
}

# Custom resource classes for specialized hardware
CUSTOM_RESOURCE_CLASSES = {
    'CUSTOM_FPGA_INTEL_ARRIA10': 'Custom FPGA Intel Arria 10',
    'CUSTOM_NIC_SRIOV_VF': 'SR-IOV Virtual Function',
    'CUSTOM_NVME_SSD': 'NVMe SSD Storage',
    'CUSTOM_PMEM': 'Persistent Memory',
    'CUSTOM_GPU_NVIDIA_V100': 'NVIDIA Tesla V100 GPU'
}

# Traits for hardware capabilities and requirements
CUSTOM_TRAITS = {
    'CUSTOM_CPU_INTEL_SKYLAKE': 'Intel Skylake CPU Architecture',
    'CUSTOM_SECURITY_TRUSTED_BOOT': 'Trusted Boot Support',
    'CUSTOM_STORAGE_ENCRYPTION': 'Hardware Storage Encryption',
    'CUSTOM_NETWORK_RDMA': 'RDMA Network Support',
    'CUSTOM_ACCELERATOR_AI': 'AI Acceleration Capable'
}

# Resource provider configuration with traits
def configure_compute_traits(rp_uuid):
    """Configure traits for a compute resource provider"""
    traits = [
        'HW_CPU_X86_AVX2',
        'HW_CPU_X86_AVX512F',
        'HW_NIC_SRIOV',
        'STORAGE_DISK_SSD',
        'CUSTOM_CPU_INTEL_SKYLAKE',
        'CUSTOM_SECURITY_TRUSTED_BOOT'
    ]
    
    # Set traits for resource provider
    placement_client.set_traits(rp_uuid, traits)
    
    return traits

# Complex allocation request for AI workload
allocation_request = {
    'allocations': {
        'compute-node-01': {
            'resources': {
                'MEMORY_MB': 32768,
                'DISK_GB': 100
            }
        },
        'compute-node-01-numa-0': {
            'resources': {
                'VCPU': 16
            }
        },
        'gpu-0-0': {
            'resources': {
                'VGPU': 1,
                'VGPU_MEMORY_MB': 16384
            }
        }
    },
    'mappings': {
        '1': ['compute-node-01', 'compute-node-01-numa-0', 'gpu-0-0']
    },
    'consumer_uuid': str(uuid.uuid4())
}

# Constraint-based allocation with traits
def create_ai_workload_request():
    """Create allocation request for AI workload with specific requirements"""
    request_spec = {
        'resources': {
            'VCPU': 16,
            'MEMORY_MB': 32768,
            'VGPU': 1
        },
        'required_traits': [
            'CUSTOM_ACCELERATOR_AI',
            'HW_CPU_X86_AVX512F'
        ],
        'forbidden_traits': [
            'CUSTOM_LEGACY_HARDWARE'
        ],
        'member_of': [
            ['aggregate-gpu-cluster', 'aggregate-high-memory']
        ]
    }
    
    return request_spec

# Custom filter for specialized workloads
from nova.scheduler import filters
from nova.scheduler.filters import utils

class GPUAffinityFilter(filters.BaseHostFilter):
    """Filter for GPU affinity requirements"""
    
    def host_passes(self, host_state, spec_obj):
        """Determine if host meets GPU affinity requirements"""
        
        # Extract GPU requirements from flavor extra specs
        gpu_type = spec_obj.flavor.extra_specs.get('gpu:type')
        gpu_count = int(spec_obj.flavor.extra_specs.get('gpu:count', 0))
        
        if not gpu_type or gpu_count == 0:
            return True  # No GPU requirements
        
        # Check available GPUs on host
        available_gpus = self._get_available_gpus(host_state, gpu_type)
        
        if len(available_gpus) < gpu_count:
            return False
        
        # Check NUMA affinity if required
        numa_affinity = spec_obj.flavor.extra_specs.get('gpu:numa_affinity', 'false')
        if numa_affinity.lower() == 'true':
            return self._check_numa_affinity(host_state, available_gpus, spec_obj)
        
        return True
    
    def _get_available_gpus(self, host_state, gpu_type):
        """Get available GPUs of specified type"""
        # Implementation for GPU discovery and availability check
        pass
    
    def _check_numa_affinity(self, host_state, gpus, spec_obj):
        """Check NUMA topology affinity for optimal performance"""
        # Implementation for NUMA-aware GPU scheduling
        pass

# Register custom filter
[filter_scheduler]
enabled_filters = AvailabilityZoneFilter,ComputeFilter,GPUAffinityFilter

# Custom weigher for energy efficiency
class EnergyEfficiencyWeigher(weights.BaseHostWeigher):
    """Weigher that considers power consumption and efficiency"""
    
    def _weigh_object(self, host_state, weight_properties):
        """Calculate weight based on energy efficiency metrics"""
        
        # Get host power consumption metrics
        power_usage = host_state.metrics.get('power_usage_watts', 0)
        cpu_utilization = host_state.cpu_usage_percent
        
        # Calculate efficiency score
        if cpu_utilization > 0:
            efficiency = (cpu_utilization / 100.0) / (power_usage / 1000.0)
        else:
            efficiency = 0
        
        # Normalize efficiency score (0-100)
        normalized_efficiency = min(efficiency * 10, 100)
        
        # Prefer hosts with higher efficiency
        return normalized_efficiency

# Production-ready weigher configuration
class ProductionWeigher(weights.BaseHostWeigher):
    """Comprehensive weigher for production workloads"""
    
    def _weigh_object(self, host_state, weight_properties):
        """Multi-factor weighing for optimal placement"""
        
        # Resource availability weights
        ram_ratio = host_state.free_ram_mb / host_state.total_usable_ram_mb
        cpu_ratio = (host_state.vcpus_total - host_state.vcpus_used) / host_state.vcpus_total
        disk_ratio = host_state.free_disk_mb / host_state.total_usable_disk_gb / 1024
        
        # Performance indicators
        io_ops_ratio = 1.0 - (host_state.num_io_ops / 100.0)  # Lower is better
        
        # Reliability factors
        host_uptime = host_state.metrics.get('uptime_hours', 0)
        failure_rate = host_state.metrics.get('failure_rate', 0)
        
        # Calculate composite score
        resource_score = (ram_ratio * 0.3 + cpu_ratio * 0.3 + disk_ratio * 0.2) * 40
        performance_score = io_ops_ratio * 20
        reliability_score = min(host_uptime / 24, 1.0) * 20 * (1.0 - failure_rate)
        
        total_score = resource_score + performance_score + reliability_score
        
        return total_score

# High availability server group with strict anti-affinity
def create_ha_server_group():
    """Create server group for high availability deployment"""
    
    server_group_spec = {
        'name': 'web-tier-ha',
        'policies': ['anti-affinity'],
        'rules': {
            'max_server_per_host': 1
        },
        'metadata': {
            'description': 'Web tier with strict host separation',
            'availability_requirement': 'high',
            'placement_strategy': 'distribute'
        }
    }
    
    return server_group_spec

# Performance-optimized server group with affinity
def create_performance_server_group():
    """Create server group for performance-critical applications"""
    
    server_group_spec = {
        'name': 'database-cluster',
        'policies': ['affinity'],
        'rules': {
            'max_server_per_host': 3
        },
        'metadata': {
            'description': 'Database cluster with optimized locality',
            'performance_requirement': 'low-latency',
            'placement_strategy': 'consolidate'
        }
    }
    
    return server_group_spec

# Soft policies for balanced placement
def create_balanced_server_group():
    """Create server group with flexible placement policies"""
    
    server_group_spec = {
        'name': 'microservices-tier',
        'policies': ['soft-anti-affinity'],
        'rules': {
            'max_server_per_host': 2
        },
        'metadata': {
            'description': 'Microservices with balanced placement',
            'availability_requirement': 'medium',
            'placement_strategy': 'balanced'
        }
    }
    
    return server_group_spec

# NUMA topology configuration
[libvirt]
cpu_dedicated_set = 2-23,26-47  # Dedicated CPU cores
cpu_shared_set = 0-1,24-25      # Shared CPU cores for OS

# NUMA topology detection and reporting
def detect_numa_topology():
    """Detect and report NUMA topology to placement service"""
    
    topology = {
        'nodes': [],
        'distances': [],
        'cpu_topology': {}
    }
    
    # Discover NUMA nodes
    for node_id in range(numa.get_max_node() + 1):
        if numa.node_exists(node_id):
            node_info = {
                'id': node_id,
                'memory_mb': numa.node_meminfo(node_id)['MemTotal'] // 1024,
                'cpus': numa.node_cpus(node_id),
                'distances': numa.get_node_distances(node_id)
            }
            topology['nodes'].append(node_info)
    
    return topology

# NUMA-aware flavor configuration
flavor_extra_specs = {
    'hw:numa_nodes': '2',           # Request 2 NUMA nodes
    'hw:numa_cpus.0': '0,1,2,3',   # CPUs for NUMA node 0
    'hw:numa_cpus.1': '4,5,6,7',   # CPUs for NUMA node 1
    'hw:numa_mem.0': '4096',       # Memory for NUMA node 0 (MB)
    'hw:numa_mem.1': '4096',       # Memory for NUMA node 1 (MB)
    'hw:cpu_policy': 'dedicated',   # Dedicated CPU cores
    'hw:cpu_thread_policy': 'prefer' # CPU threading preference
}

# Huge pages configuration for performance
hugepage_flavor_specs = {
    'hw:mem_page_size': '1GB',     # Use 1GB huge pages
    'hw:numa_nodes': '1',          # Single NUMA node
    'hw:cpu_policy': 'dedicated'    # Dedicated CPUs required
}

# Geographic cell configuration
CELL_MAPPINGS = {
    'cell-us-east-1': {
        'region': 'us-east-1',
        'availability_zones': ['us-east-1a', 'us-east-1b', 'us-east-1c'],
        'database_url': 'mysql+pymysql://nova:password@db-us-east/nova_cell1',
        'transport_url': 'rabbit://nova:password@mq-us-east-1,mq-us-east-2,mq-us-east-3/nova',
        'capacity': {
            'max_instances': 50000,
            'max_compute_nodes': 1000
        },
        'policies': {
            'data_residency': 'us',
            'compliance': ['soc2', 'hipaa']
        }
    },
    'cell-eu-west-1': {
        'region': 'eu-west-1',
        'availability_zones': ['eu-west-1a', 'eu-west-1b', 'eu-west-1c'],
        'database_url': 'mysql+pymysql://nova:password@db-eu-west/nova_cell2',
        'transport_url': 'rabbit://nova:password@mq-eu-west-1,mq-eu-west-2,mq-eu-west-3/nova',
        'capacity': {
            'max_instances': 30000,
            'max_compute_nodes': 600
        },
        'policies': {
            'data_residency': 'eu',
            'compliance': ['gdpr', 'iso27001']
        }
    }
}

# Cell selection algorithm
class GeographicCellSelector:
    """Select optimal cell based on geographic and policy requirements"""
    
    def select_cell(self, instance_request):
        """Select cell for instance based on requirements"""
        
        # Extract requirements from request
        preferred_region = instance_request.get('region')
        data_residency = instance_request.get('data_residency')
        compliance_requirements = instance_request.get('compliance', [])
        
        # Filter cells by requirements
        eligible_cells = []
        for cell_name, cell_config in CELL_MAPPINGS.items():
            # Check region preference
            if preferred_region and cell_config['region'] != preferred_region:
                continue
                
            # Check data residency
            if data_residency and cell_config['policies']['data_residency'] != data_residency:
                continue
                
            # Check compliance requirements
            if not all(req in cell_config['policies']['compliance'] for req in compliance_requirements):
                continue
                
            # Check capacity
            if self._check_capacity(cell_name, cell_config):
                eligible_cells.append((cell_name, cell_config))
        
        # Select optimal cell
        return self._select_optimal_cell(eligible_cells, instance_request)
    
    def _check_capacity(self, cell_name, cell_config):
        """Check if cell has available capacity"""
        current_instances = self._get_current_instance_count(cell_name)
        return current_instances < cell_config['capacity']['max_instances']
    
    def _select_optimal_cell(self, eligible_cells, instance_request):
        """Select the best cell from eligible options"""
        if not eligible_cells:
            raise Exception("No eligible cells found for request")
        
        # Implement load balancing logic
        # For simplicity, select cell with lowest utilization
        best_cell = min(eligible_cells, 
                       key=lambda x: self._get_utilization_ratio(x[0]))
        
        return best_cell[0]

# Cross-cell migration implementation
class CrossCellMigrator:
    """Handle instance migration between cells"""
    
    def __init__(self, source_cell, destination_cell):
        self.source_cell = source_cell
        self.destination_cell = destination_cell
        
    def migrate_instance(self, instance_uuid, migration_options=None):
        """Migrate instance between cells"""
        
        migration_options = migration_options or {}
        
        # Phase 1: Preparation
        migration_id = self._prepare_migration(instance_uuid, migration_options)
        
        try:
            # Phase 2: Create destination instance
            dest_instance = self._create_destination_instance(instance_uuid, migration_id)
            
            # Phase 3: Data synchronization
            self._synchronize_data(instance_uuid, dest_instance, migration_id)
            
            # Phase 4: Network reconfiguration
            self._reconfigure_network(instance_uuid, dest_instance, migration_id)
            
            # Phase 5: Cutover
            self._perform_cutover(instance_uuid, dest_instance, migration_id)
            
            # Phase 6: Cleanup
            self._cleanup_source(instance_uuid, migration_id)
            
            return dest_instance
            
        except Exception as e:
            # Rollback on failure
            self._rollback_migration(instance_uuid, migration_id, str(e))
            raise
    
    def _prepare_migration(self, instance_uuid, options):
        """Prepare migration process"""
        migration_id = str(uuid.uuid4())
        
        # Create migration record
        migration_record = {
            'id': migration_id,
            'instance_uuid': instance_uuid,
            'source_cell': self.source_cell,
            'destination_cell': self.destination_cell,
            'status': 'preparing',
            'options': options,
            'created_at': datetime.utcnow()
        }
        
        # Store in global database for tracking
        self._store_migration_record(migration_record)
        
        return migration_id
    
    def _synchronize_data(self, source_instance, dest_instance, migration_id):
        """Synchronize instance data between cells"""
        
        # Volume synchronization
        volumes = self._get_instance_volumes(source_instance)
        for volume in volumes:
            self._replicate_volume(volume, dest_instance, migration_id)
        
        # Metadata synchronization
        metadata = self._get_instance_metadata(source_instance)
        self._apply_metadata(dest_instance, metadata, migration_id)
        
        # Configuration synchronization
        config = self._get_instance_configuration(source_instance)
        self._apply_configuration(dest_instance, config, migration_id)

# Advanced GPU configuration
[pci]
# GPU passthrough configuration
passthrough_whitelist = [
    {
        "vendor_id": "10de",    # NVIDIA
        "product_id": "1db4",   # Tesla V100
        "physical_network": null
    },
    {
        "vendor_id": "1002",    # AMD
        "product_id": "66a0",   # Radeon Instinct MI25
        "physical_network": null
    }
]

# GPU resource tracking
alias = {
    "nvidia-v100": {
        "vendor_id": "10de",
        "product_id": "1db4",
        "device_type": "type-VF"
    }
}

[devices]
enabled_vgpu_types = nvidia-11,nvidia-12,nvidia-13

# Virtual GPU configuration
class VGPUManager:
    """Manage virtual GPU resources and allocation"""
    
    def __init__(self):
        self.vgpu_types = self._discover_vgpu_types()
        self.available_gpus = self._inventory_gpus()
    
    def create_vgpu_instance(self, instance_uuid, vgpu_type, gpu_uuid):
        """Create virtual GPU instance"""
        
        vgpu_config = {
            'instance_uuid': instance_uuid,
            'vgpu_type': vgpu_type,
            'parent_gpu_uuid': gpu_uuid,
            'memory_mb': self.vgpu_types[vgpu_type]['memory_mb'],
            'virtual_display_heads': self.vgpu_types[vgpu_type]['display_heads'],
            'max_resolution': self.vgpu_types[vgpu_type]['max_resolution']
        }
        
        # Create vGPU through hypervisor driver
        vgpu_uuid = self._create_vgpu_device(vgpu_config)
        
        # Update resource allocation in placement
        self._update_gpu_allocation(gpu_uuid, vgpu_type, vgpu_uuid)
        
        return vgpu_uuid
    
    def _discover_vgpu_types(self):
        """Discover available vGPU types from hardware"""
        vgpu_types = {}
        
        for gpu in self._get_physical_gpus():
            supported_types = self._query_vgpu_types(gpu)
            for vtype in supported_types:
                vgpu_types[vtype['name']] = {
                    'memory_mb': vtype['framebuffer_mb'],
                    'display_heads': vtype['max_heads'],
                    'max_resolution': vtype['max_resolution'],
                    'instances_per_gpu': vtype['max_instances']
                }
        
        return vgpu_types

# FPGA resource provider configuration
class FPGAResourceProvider:
    """Manage FPGA resources and bitstream deployment"""
    
    def __init__(self):
        self.fpga_devices = self._discover_fpga_devices()
        self.bitstream_library = self._load_bitstream_library()
    
    def provision_fpga_instance(self, instance_uuid, bitstream_id, fpga_device_id):
        """Provision FPGA instance with specific bitstream"""
        
        # Validate bitstream compatibility
        fpga_device = self.fpga_devices[fpga_device_id]
        bitstream = self.bitstream_library[bitstream_id]
        
        if not self._is_compatible(fpga_device, bitstream):
            raise ValueError(f"Bitstream {bitstream_id} incompatible with FPGA {fpga_device_id}")
        
        # Program FPGA with bitstream
        programming_result = self._program_fpga(fpga_device_id, bitstream)
        
        if not programming_result['success']:
            raise RuntimeError(f"FPGA programming failed: {programming_result['error']}")
        
        # Create virtual function for instance
        vf_config = {
            'instance_uuid': instance_uuid,
            'fpga_device_id': fpga_device_id,
            'bitstream_id': bitstream_id,
            'virtual_functions': bitstream['virtual_functions'],
            'memory_regions': bitstream['memory_layout']
        }
        
        return self._create_fpga_vf(vf_config)
    
    def _discover_fpga_devices(self):
        """Discover and inventory FPGA devices"""
        devices = {}
        
        # Use vendor-specific discovery mechanisms
        intel_fpgas = self._discover_intel_fpgas()
        xilinx_fpgas = self._discover_xilinx_fpgas()
        
        devices.update(intel_fpgas)
        devices.update(xilinx_fpgas)
        
        return devices
    
    def _load_bitstream_library(self):
        """Load available bitstream configurations"""
        library = {
            'crypto_accelerator_v1': {
                'vendor': 'intel',
                'family': 'arria10',
                'functions': ['aes256', 'rsa2048', 'ecdsa'],
                'virtual_functions': 4,
                'memory_layout': {
                    'ddr_channels': 2,
                    'on_chip_memory': '20MB'
                }
            },
            'ai_inference_v2': {
                'vendor': 'xilinx',
                'family': 'versal',
                'functions': ['cnn_inference', 'rnn_processing'],
                'virtual_functions': 8,
                'memory_layout': {
                    'hbm_channels': 4,
                    'ultra_ram': '50MB'
                }
            }
        }
        
        return library

# High-performance storage configuration
[libvirt]
# Storage optimization
disk_cachemodes = file=directsync,block=none
images_rbd_pool = vms-ssd
images_rbd_ceph_conf = /etc/ceph/ceph.conf
rbd_user = nova
rbd_secret_uuid = 7b52bd92-7db2-4d62-a1be-70fc7b1b7516

# NVMe optimization
use_multipath = true
volume_use_multipath = true
iscsi_use_multipath = true

# Live migration storage optimization
live_migration_tunnelled = false
live_migration_with_native_luks = false
live_migration_completion_timeout = 1800
live_migration_progress_timeout = 300

# Storage QoS integration
class StorageQoSManager:
    """Manage storage QoS policies for instances"""
    
    def __init__(self):
        self.qos_policies = self._load_qos_policies()
        self.storage_backends = self._discover_storage_backends()
    
    def apply_storage_qos(self, instance_uuid, qos_policy_id):
        """Apply QoS policy to instance storage"""
        
        policy = self.qos_policies[qos_policy_id]
        instance_volumes = self._get_instance_volumes(instance_uuid)
        
        for volume in instance_volumes:
            # Apply QoS limits at block device level
            self._apply_blkio_limits(volume, policy)
            
            # Apply QoS at storage backend if supported
            backend = self._get_volume_backend(volume)
            if backend['qos_support']:
                self._apply_backend_qos(volume, policy, backend)
    
    def _apply_blkio_limits(self, volume, policy):
        """Apply block I/O limits using cgroups"""
        limits = {
            'read_iops_sec': policy.get('read_iops', 0),
            'write_iops_sec': policy.get('write_iops', 0),
            'read_bytes_sec': policy.get('read_bandwidth', 0),
            'write_bytes_sec': policy.get('write_bandwidth', 0)
        }
        
        # Apply limits to volume device
        for limit_type, value in limits.items():
            if value > 0:
                self._set_blkio_limit(volume['device_path'], limit_type, value)
    
    def _load_qos_policies(self):
        """Load storage QoS policies"""
        return {
            'bronze': {
                'read_iops': 1000,
                'write_iops': 1000,
                'read_bandwidth': 50 * 1024 * 1024,   # 50 MB/s
                'write_bandwidth': 50 * 1024 * 1024   # 50 MB/s
            },
            'silver': {
                'read_iops': 5000,
                'write_iops': 5000,
                'read_bandwidth': 200 * 1024 * 1024,  # 200 MB/s
                'write_bandwidth': 200 * 1024 * 1024  # 200 MB/s
            },
            'gold': {
                'read_iops': 20000,
                'write_iops': 20000,
                'read_bandwidth': 1000 * 1024 * 1024, # 1 GB/s
                'write_bandwidth': 1000 * 1024 * 1024 # 1 GB/s
            }
        }

# SR-IOV network configuration
[pci]
# SR-IOV NIC configuration
passthrough_whitelist = [
    {
        "vendor_id": "8086",    # Intel
        "product_id": "154d",   # X520 SR-IOV VF
        "physical_network": "physnet1"
    },
    {
        "vendor_id": "15b3",    # Mellanox
        "product_id": "1016",   # ConnectX-4 VF
        "physical_network": "physnet2"
    }
]

# Network optimization settings
[libvirt]
use_virtio_for_bridges = true
vif_plugging_timeout = 300
vif_plugging_is_fatal = false

# Advanced networking class
class HighPerformanceNetworking:
    """Manage high-performance networking features"""
    
    def __init__(self):
        self.sriov_devices = self._discover_sriov_devices()
        self.dpdk_interfaces = self._discover_dpdk_interfaces()
    
    def create_sriov_port(self, instance_uuid, physical_network, vnic_type='direct'):
        """Create SR-IOV port for high-performance networking"""
        
        # Find available VF on specified physical network
        available_vf = self._find_available_vf(physical_network)
        if not available_vf:
            raise Exception(f"No available VFs on physical network {physical_network}")
        
        # Configure VF for instance
        vf_config = {
            'instance_uuid': instance_uuid,
            'pci_device': available_vf['pci_address'],
            'physical_network': physical_network,
            'vnic_type': vnic_type,
            'mac_address': self._generate_mac_address(),
            'vlan_id': None  # Set by network service
        }
        
        # Reserve VF in resource tracking
        self._reserve_vf(available_vf['pci_address'], instance_uuid)
        
        return vf_config
    
    def configure_dpdk_interface(self, instance_uuid, numa_node=None):
        """Configure DPDK interface for packet processing acceleration"""
        
        # Select optimal DPDK interface
        dpdk_interface = self._select_dpdk_interface(numa_node)
        
        if not dpdk_interface:
            raise Exception("No available DPDK interfaces")
        
        # Configure huge pages for DPDK
        hugepage_config = self._configure_hugepages(instance_uuid, dpdk_interface)
        
        # Configure CPU isolation for DPDK
        cpu_config = self._configure_dpdk_cpus(instance_uuid, numa_node)
        
        # Create DPDK-enabled port
        dpdk_port = {
            'instance_uuid': instance_uuid,
            'interface': dpdk_interface['name'],
            'numa_node': numa_node,
            'hugepage_config': hugepage_config,
            'cpu_config': cpu_config,
            'driver': 'vfio-pci'
        }
        
        return dpdk_port
    
    def _configure_hugepages(self, instance_uuid, dpdk_interface):
        """Configure huge pages for DPDK instance"""
        
        # Calculate required huge pages
        required_memory = self._get_instance_memory(instance_uuid)
        hugepage_size = 1024 * 1024 * 1024  # 1GB huge pages
        hugepage_count = (required_memory + hugepage_size - 1) // hugepage_size
        
        # Allocate huge pages on appropriate NUMA node
        numa_node = dpdk_interface['numa_node']
        self._allocate_hugepages(numa_node, hugepage_count)
        
        return {
            'size': hugepage_size,
            'count': hugepage_count,
            'numa_node': numa_node
        }

# Advanced security configuration
[DEFAULT]
# Instance security
allow_resize_to_same_host = false
secure_proxy_ssl_header = HTTP_X_FORWARDED_PROTO
secure_proxy_ssl_header_value = https

# Compute security
compute_monitors = cpu.virt_driver
compute_manager = nova.compute.manager.ComputeManager

[libvirt]
# Security hardening
uid = 107  # libvirt-qemu user
gid = 107  # libvirt-qemu group

# Security features
sysinfo_serial = hardware
hw_machine_type = q35  # More secure machine type

# Memory protection
mem_stats_period_seconds = 0  # Disable for security
remove_unused_base_images = true
remove_unused_original_minimum_age_seconds = 86400

class TenantIsolationManager:
    """Manage tenant isolation and security boundaries"""
    
    def __init__(self):
        self.security_domains = self._initialize_security_domains()
        self.isolation_policies = self._load_isolation_policies()
    
    def create_isolated_instance(self, instance_spec, tenant_id):
        """Create instance with appropriate isolation measures"""
        
        # Apply tenant-specific security policies
        security_policy = self._get_tenant_security_policy(tenant_id)
        
        # Configure SELinux/AppArmor labels
        security_labels = self._generate_security_labels(tenant_id, instance_spec)
        
        # Set up network isolation
        network_isolation = self._configure_network_isolation(tenant_id, instance_spec)
        
        # Configure storage isolation
        storage_isolation = self._configure_storage_isolation(tenant_id, instance_spec)
        
        # Create instance with security configuration
        instance_config = {
            'tenant_id': tenant_id,
            'security_labels': security_labels,
            'network_isolation': network_isolation,
            'storage_isolation': storage_isolation,
            'security_policy': security_policy
        }
        
        return self._launch_secure_instance(instance_config)
    
    def _generate_security_labels(self, tenant_id, instance_spec):
        """Generate SELinux/AppArmor security labels"""
        
        # Create unique security context for tenant
        security_context = f"system_u:system_r:nova_tenant_{tenant_id}_t:s0"
        
        # Configure MAC (Mandatory Access Control)
        mac_labels = {
            'selinux': {
                'context': security_context,
                'type': f'nova_tenant_{tenant_id}_exec_t',
                'level': 's0'
            },
            'apparmor': {
                'profile': f'nova-instance-{tenant_id}',
                'mode': 'enforce'
            }
        }
        
        return mac_labels
    
    def _configure_network_isolation(self, tenant_id, instance_spec):
        """Configure network-level isolation"""
        
        isolation_config = {
            'private_vlan': f'vlan-{tenant_id}',
            'security_groups': [f'sg-default-{tenant_id}'],
            'network_namespace': f'netns-{tenant_id}',
            'firewall_rules': self._generate_tenant_firewall_rules(tenant_id)
        }
        
        return isolation_config

# Trusted computing implementation
class TrustedComputeManager:
    """Manage trusted computing and attestation for instances"""
    
    def __init__(self):
        self.attestation_service = self._initialize_attestation_service()
        self.trusted_hosts = self._discover_trusted_hosts()
    
    def create_trusted_instance(self, instance_spec, trust_requirements):
        """Create instance with trusted computing requirements"""
        
        # Validate trust requirements
        self._validate_trust_requirements(trust_requirements)
        
        # Find trusted compute host
        trusted_host = self._select_trusted_host(trust_requirements)
        
        # Configure trusted boot
        trusted_boot_config = self._configure_trusted_boot(instance_spec, trust_requirements)
        
        # Set up attestation monitoring
        attestation_config = self._setup_attestation_monitoring(instance_spec, trusted_host)
        
        # Launch instance with trust configuration
        instance_config = {
            'host': trusted_host,
            'trusted_boot': trusted_boot_config,
            'attestation': attestation_config,
            'trust_requirements': trust_requirements
        }
        
        return self._launch_trusted_instance(instance_config)
    
    def _configure_trusted_boot(self, instance_spec, trust_requirements):
        """Configure trusted boot with TPM and measured boot"""
        
        trusted_boot = {
            'enable_tpm': True,
            'tpm_version': '2.0',
            'measured_boot': True,
            'secure_boot': trust_requirements.get('secure_boot', False),
            'boot_attestation': True,
            'pcr_banks': ['sha1', 'sha256']
        }
        
        # Configure TPM device for instance
        if trusted_boot['enable_tpm']:
            trusted_boot['tpm_device'] = {
                'type': 'emulator',
                'model': 'tpm-tis',
                'backend': {
                    'type': 'passthrough',
                    'device': '/dev/tpm0'
                }
            }
        
        return trusted_boot
    
    def verify_instance_trust(self, instance_uuid):
        """Verify current trust status of instance"""
        
        # Get instance attestation data
        attestation_data = self._get_instance_attestation(instance_uuid)
        
        # Verify TPM measurements
        tpm_verification = self._verify_tpm_measurements(attestation_data)
        
        # Check runtime integrity
        runtime_integrity = self._check_runtime_integrity(instance_uuid)
        
        # Compile trust status
        trust_status = {
            'instance_uuid': instance_uuid,
            'trusted': tpm_verification['valid'] and runtime_integrity['valid'],
            'last_verification': datetime.utcnow(),
            'tpm_status': tpm_verification,
            'runtime_status': runtime_integrity,
            'trust_score': self._calculate_trust_score(tpm_verification, runtime_integrity)
        }
        
        return trust_status

# Compliance audit system
class ComplianceAuditManager:
    """Manage compliance auditing and reporting for Nova operations"""
    
    def __init__(self):
        self.audit_backends = self._initialize_audit_backends()
        self.compliance_frameworks = self._load_compliance_frameworks()
    
    def audit_compute_operation(self, operation, context, resource_info):
        """Audit compute operations for compliance"""
        
        # Create audit record
        audit_record = {
            'timestamp': datetime.utcnow(),
            'operation': operation,
            'user_id': context.user_id,
            'project_id': context.project_id,
            'resource_type': 'compute_instance',
            'resource_id': resource_info.get('instance_uuid'),
            'source_ip': context.remote_address,
            'user_agent': context.user_agent,
            'outcome': 'pending'
        }
        
        # Add operation-specific details
        if operation == 'instance.create':
            audit_record.update({
                'flavor_id': resource_info.get('flavor_id'),
                'image_id': resource_info.get('image_id'),
                'availability_zone': resource_info.get('availability_zone'),
                'security_groups': resource_info.get('security_groups', [])
            })
        elif operation == 'instance.delete':
            audit_record.update({
                'deletion_reason': resource_info.get('reason'),
                'force_delete': resource_info.get('force', False)
            })
        
        # Store audit record
        self._store_audit_record(audit_record)
        
        # Check compliance requirements
        self._check_compliance_violations(audit_record)
        
        return audit_record['id']
    
    def generate_compliance_report(self, framework, start_date, end_date):
        """Generate compliance report for specified framework"""
        
        if framework not in self.compliance_frameworks:
            raise ValueError(f"Unknown compliance framework: {framework}")
        
        framework_config = self.compliance_frameworks[framework]
        
        # Gather audit data for time period
        audit_data = self._query_audit_data(start_date, end_date)
        
        # Apply framework-specific analysis
        compliance_analysis = self._analyze_compliance(audit_data, framework_config)
        
        # Generate report
        report = {
            'framework': framework,
            'period': {
                'start': start_date,
                'end': end_date
            },
            'summary': compliance_analysis['summary'],
            'violations': compliance_analysis['violations'],
            'recommendations': compliance_analysis['recommendations'],
            'evidence': compliance_analysis['evidence']
        }
        
        return report
    
    def _load_compliance_frameworks(self):
        """Load compliance framework definitions"""
        return {
            'soc2': {
                'name': 'SOC 2 Type II',
                'controls': {
                    'access_control': {
                        'description': 'Logical and physical access controls',
                        'requirements': [
                            'multi_factor_authentication',
                            'privileged_access_management',
                            'access_reviews'
                        ]
                    },
                    'change_management': {
                        'description': 'System change management',
                        'requirements': [
                            'change_authorization',
                            'change_testing',
                            'change_documentation'
                        ]
                    }
                }
            },
            'hipaa': {
                'name': 'HIPAA Security Rule',
                'controls': {
                    'access_control': {
                        'description': 'Information access management',
                        'requirements': [
                            'unique_user_identification',
                            'emergency_access_procedures',
                            'automatic_logoff',
                            'encryption_decryption'
                        ]
                    },
                    'audit_controls': {
                        'description': 'Audit controls implementation',
                        'requirements': [
                            'audit_log_retention',
                            'audit_log_protection',
                            'audit_review_procedures'
                        ]
                    }
                }
            }
        }

# Comprehensive monitoring configuration
class NovaMonitoringSystem:
    """Advanced monitoring system for Nova infrastructure"""
    
    def __init__(self):
        self.metrics_collectors = self._initialize_collectors()
        self.alerting_engine = self._initialize_alerting()
        self.observability_stack = self._setup_observability()
    
    def collect_comprehensive_metrics(self):
        """Collect metrics across all Nova components"""
        
        metrics = {
            'timestamp': datetime.utcnow(),
            'infrastructure': self._collect_infrastructure_metrics(),
            'services': self._collect_service_metrics(),
            'instances': self._collect_instance_metrics(),
            'performance': self._collect_performance_metrics(),
            'security': self._collect_security_metrics()
        }
        
        # Process and store metrics
        self._process_metrics(metrics)
        
        # Check alerting thresholds
        self._evaluate_alerts(metrics)
        
        return metrics
    
    def _collect_infrastructure_metrics(self):
        """Collect infrastructure-level metrics"""
        
        infrastructure_metrics = {}
        
        # Hypervisor metrics
        hypervisors = self._get_hypervisor_list()
        for hypervisor in hypervisors:
            hv_metrics = {
                'cpu_utilization': self._get_cpu_utilization(hypervisor),
                'memory_utilization': self._get_memory_utilization(hypervisor),
                'disk_utilization': self._get_disk_utilization(hypervisor),
                'network_throughput': self._get_network_throughput(hypervisor),
                'instance_count': self._get_instance_count(hypervisor),
                'uptime': self._get_uptime(hypervisor)
            }
            infrastructure_metrics[hypervisor['name']] = hv_metrics
        
        return infrastructure_metrics
    
    def _collect_service_metrics(self):
        """Collect Nova service metrics"""
        
        service_metrics = {}
        
        # API service metrics
        api_metrics = {
            'request_rate': self._get_api_request_rate(),
            'response_time_p95': self._get_api_response_time_percentile(95),
            'response_time_p99': self._get_api_response_time_percentile(99),
            'error_rate': self._get_api_error_rate(),
            'concurrent_requests': self._get_concurrent_requests(),
            'queue_depth': self._get_api_queue_depth()
        }
        service_metrics['api'] = api_metrics
        
        # Scheduler metrics
        scheduler_metrics = {
            'scheduling_time_avg': self._get_avg_scheduling_time(),
            'scheduling_failures': self._get_scheduling_failures(),
            'filter_execution_time': self._get_filter_execution_metrics(),
            'weigher_execution_time': self._get_weigher_execution_metrics(),
            'placement_requests': self._get_placement_request_metrics()
        }
        service_metrics['scheduler'] = scheduler_metrics
        
        # Conductor metrics
        conductor_metrics = {
            'task_queue_depth': self._get_conductor_queue_depth(),
            'task_execution_time': self._get_task_execution_metrics(),
            'database_connection_pool': self._get_db_pool_metrics(),
            'rpc_call_latency': self._get_rpc_latency_metrics()
        }
        service_metrics['conductor'] = conductor_metrics
        
        return service_metrics
    
    def _collect_performance_metrics(self):
        """Collect performance-related metrics"""
        
        performance_metrics = {
            'instance_boot_time': self._get_instance_boot_metrics(),
            'migration_performance': self._get_migration_metrics(),
            'snapshot_performance': self._get_snapshot_metrics(),
            'resize_performance': self._get_resize_metrics(),
            'volume_attach_time': self._get_volume_attach_metrics()
        }
        
        return performance_metrics

# Advanced alerting system
class IntelligentAlertingEngine:
    """Machine learning-enhanced alerting for Nova"""
    
    def __init__(self):
        self.alert_rules = self._load_alert_rules()
        self.ml_models = self._load_ml_models()
        self.notification_channels = self._setup_notifications()
    
    def evaluate_anomaly_alerts(self, metrics):
        """Use ML models to detect anomalies and generate alerts"""
        
        anomalies = []
        
        # CPU utilization anomaly detection
        cpu_anomaly = self._detect_cpu_anomaly(metrics['infrastructure'])
        if cpu_anomaly:
            anomalies.append(cpu_anomaly)
        
        # API response time anomaly detection
        api_anomaly = self._detect_api_anomaly(metrics['services']['api'])
        if api_anomaly:
            anomalies.append(api_anomaly)
        
        # Instance creation pattern anomaly
        creation_anomaly = self._detect_creation_pattern_anomaly(metrics['instances'])
        if creation_anomaly:
            anomalies.append(creation_anomaly)
        
        # Process identified anomalies
        for anomaly in anomalies:
            self._process_anomaly_alert(anomaly)
        
        return anomalies
    
    def _detect_cpu_anomaly(self, infrastructure_metrics):
        """Detect CPU utilization anomalies using time series analysis"""
        
        # Collect CPU utilization data points
        cpu_data = []
        for host, metrics in infrastructure_metrics.items():
            cpu_data.append({
                'host': host,
                'utilization': metrics['cpu_utilization'],
                'timestamp': datetime.utcnow()
            })
        
        # Apply anomaly detection model
        model = self.ml_models['cpu_anomaly_detector']
        anomaly_scores = model.predict(cpu_data)
        
        # Identify anomalies above threshold
        threshold = 0.8
        anomalies = []
        
        for i, score in enumerate(anomaly_scores):
            if score > threshold:
                anomaly = {
                    'type': 'cpu_utilization_anomaly',
                    'host': cpu_data[i]['host'],
                    'severity': self._calculate_severity(score),
                    'utilization': cpu_data[i]['utilization'],
                    'anomaly_score': score,
                    'timestamp': cpu_data[i]['timestamp']
                }
                anomalies.append(anomaly)
        
        return anomalies[0] if anomalies else None
    
    def create_intelligent_alert(self, alert_type, context, severity='medium'):
        """Create context-aware alert with intelligent routing"""
        
        alert = {
            'id': str(uuid.uuid4()),
            'type': alert_type,
            'severity': severity,
            'context': context,
            'created_at': datetime.utcnow(),
            'status': 'active'
        }
        
        # Add predictive information
        alert['prediction'] = self._generate_alert_prediction(alert_type, context)
        
        # Determine notification strategy
        notification_strategy = self._determine_notification_strategy(alert)
        
        # Route alert appropriately
        self._route_alert(alert, notification_strategy)
        
        return alert
    
    def _generate_alert_prediction(self, alert_type, context):
        """Generate predictive insights for alert"""
        
        prediction = {
            'likely_cause': self._predict_root_cause(alert_type, context),
            'estimated_impact': self._estimate_impact(alert_type, context),
            'suggested_actions': self._suggest_remediation_actions(alert_type, context),
            'similar_incidents': self._find_similar_incidents(alert_type, context)
        }
        
        return prediction

# OpenTelemetry integration for Nova
from opentelemetry import trace
from opentelemetry.exporter.jaeger.thrift import JaegerExporter
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor

class NovaTracing:
    """Distributed tracing implementation for Nova"""
    
    def __init__(self):
        self.tracer_provider = self._setup_tracer_provider()
        self.tracer = trace.get_tracer(__name__)
    
    def _setup_tracer_provider(self):
        """Configure OpenTelemetry tracer provider"""
        
        # Set up tracer provider
        trace.set_tracer_provider(TracerProvider())
        tracer_provider = trace.get_tracer_provider()
        
        # Configure Jaeger exporter
        jaeger_exporter = JaegerExporter(
            agent_host_name="jaeger-agent",
            agent_port=6831,
            collector_endpoint="http://jaeger-collector:14268/api/traces"
        )
        
        # Add span processor
        span_processor = BatchSpanProcessor(jaeger_exporter)
        tracer_provider.add_span_processor(span_processor)
        
        return tracer_provider
    
    def trace_instance_lifecycle(self, instance_uuid, operation):
        """Trace complete instance lifecycle operations"""
        
        with self.tracer.start_as_current_span(f"instance.{operation}") as span:
            # Add instance context
            span.set_attribute("instance.uuid", instance_uuid)
            span.set_attribute("operation.type", operation)
            
            # Trace API request handling
            with self.tracer.start_as_current_span("api.request_processing"):
                self._trace_api_processing(instance_uuid, operation)
            
            # Trace scheduler decision
            if operation in ['create', 'migrate']:
                with self.tracer.start_as_current_span("scheduler.host_selection"):
                    self._trace_scheduling_decision(instance_uuid)
            
            # Trace compute node operations
            with self.tracer.start_as_current_span("compute.instance_operation"):
                self._trace_compute_operations(instance_uuid, operation)
            
            # Trace external service interactions
            self._trace_external_service_calls(instance_uuid, operation)
    
    def _trace_scheduling_decision(self, instance_uuid):
        """Trace scheduler decision process"""
        
        with self.tracer.start_as_current_span("scheduler.filter_phase") as filter_span:
            # Trace filter execution
            filters_applied = self._get_applied_filters(instance_uuid)
            filter_span.set_attribute("filters.count", len(filters_applied))
            filter_span.set_attribute("filters.list", ",".join(filters_applied))
            
            # Trace each filter
            for filter_name in filters_applied:
                with self.tracer.start_as_current_span(f"filter.{filter_name}"):
                    self._trace_filter_execution(filter_name, instance_uuid)
        
        with self.tracer.start_as_current_span("scheduler.weighing_phase") as weigh_span:
            # Trace weighing process
            weighers_applied = self._get_applied_weighers(instance_uuid)
            weigh_span.set_attribute("weighers.count", len(weighers_applied))
            weigh_span.set_attribute("weighers.list", ",".join(weighers_applied))
            
            # Trace each weigher
            for weigher_name in weighers_applied:
                with self.tracer.start_as_current_span(f"weigher.{weigher_name}"):
                    self._trace_weigher_execution(weigher_name, instance_uuid)

# Multi-region deployment configuration
class MultiRegionNovaManager:
    """Manage Nova deployments across multiple regions"""
    
    def __init__(self):
        self.regions = self._load_region_configurations()
        self.global_services = self._setup_global_services()
        self.region_coordinators = self._initialize_region_coordinators()
    
    def _load_region_configurations(self):
        """Load configuration for all regions"""
        
        regions = {
            'us-east-1': {
                'name': 'US East (Virginia)',
                'endpoint': 'https://nova-us-east-1.example.com',
                'cells': ['cell-us-east-1a', 'cell-us-east-1b', 'cell-us-east-1c'],
                'capacity': {
                    'max_instances': 100000,
                    'max_compute_nodes': 2000
                },
                'policies': {
                    'data_residency': 'us',
                    'availability_zones': ['us-east-1a', 'us-east-1b', 'us-east-1c'],
                    'disaster_recovery': 'us-west-1'
                },
                'compliance': ['soc2', 'fedramp'],
                'network': {
                    'availability_zones': 3,
                    'edge_locations': 15,
                    'bandwidth_gbps': 100
                }
            },
            'eu-central-1': {
                'name': 'EU Central (Frankfurt)',
                'endpoint': 'https://nova-eu-central-1.example.com',
                'cells': ['cell-eu-central-1a', 'cell-eu-central-1b'],
                'capacity': {
                    'max_instances': 50000,
                    'max_compute_nodes': 1000
                },
                'policies': {
                    'data_residency': 'eu',
                    'availability_zones': ['eu-central-1a', 'eu-central-1b'],
                    'disaster_recovery': 'eu-west-1'
                },
                'compliance': ['gdpr', 'iso27001'],
                'network': {
                    'availability_zones': 2,
                    'edge_locations': 8,
                    'bandwidth_gbps': 50
                }
            },
            'ap-southeast-1': {
                'name': 'Asia Pacific (Singapore)',
                'endpoint': 'https://nova-ap-southeast-1.example.com',
                'cells': ['cell-ap-southeast-1a', 'cell-ap-southeast-1b'],
                'capacity': {
                    'max_instances': 30000,
                    'max_compute_nodes': 600
                },
                'policies': {
                    'data_residency': 'apac',
                    'availability_zones': ['ap-southeast-1a', 'ap-southeast-1b'],
                    'disaster_recovery': 'ap-northeast-1'
                },
                'compliance': ['iso27001'],
                'network': {
                    'availability_zones': 2,
                    'edge_locations': 5,
                    'bandwidth_gbps': 25
                }
            }
        }
        
        return regions
    
    def orchestrate_global_deployment(self, deployment_spec):
        """Orchestrate deployment across multiple regions"""
        
        deployment_plan = self._create_global_deployment_plan(deployment_spec)
        
        # Execute regional deployments in parallel
        deployment_results = {}
        
        for region_name, region_deployment in deployment_plan.items():
            # Regional deployment with local coordination
            result = self._execute_regional_deployment(region_name, region_deployment)
            deployment_results[region_name] = result
        
        # Verify global consistency
        consistency_check = self._verify_global_consistency(deployment_results)
        
        if not consistency_check['success']:
            # Rollback on consistency failure
            self._rollback_global_deployment(deployment_results)
            raise Exception(f"Global deployment consistency check failed: {consistency_check['errors']}")
        
        return deployment_results
    
    def _create_global_deployment_plan(self, deployment_spec):
        """Create deployment plan for multiple regions"""
        
        deployment_plan = {}
        
        # Determine target regions
        target_regions = deployment_spec.get('regions', list(self.regions.keys()))
        
        for region_name in target_regions:
            region_config = self.regions[region_name]
            
            # Create region-specific deployment
            region_deployment = {
                'region': region_name,
                'instances': self._calculate_regional_instances(deployment_spec, region_config),
                'scheduling_policy': self._determine_regional_scheduling(deployment_spec, region_config),
                'compliance_requirements': region_config['compliance'],
                'data_residency': region_config['policies']['data_residency']
            }
            
            deployment_plan[region_name] = region_deployment
        
        return deployment_plan

# High availability and disaster recovery system
class NovaHADRManager:
    """Manage high availability and disaster recovery for Nova"""
    
    def __init__(self):
        self.ha_policies = self._load_ha_policies()
        self.dr_strategies = self._load_dr_strategies()
        self.backup_managers = self._initialize_backup_managers()
    
    def implement_ha_architecture(self, deployment_config):
        """Implement comprehensive HA architecture"""
        
        ha_architecture = {
            'control_plane_ha': self._setup_control_plane_ha(),
            'data_plane_ha': self._setup_data_plane_ha(),
            'database_ha': self._setup_database_ha(),
            'message_queue_ha': self._setup_message_queue_ha(),
            'storage_ha': self._setup_storage_ha(),
            'network_ha': self._setup_network_ha()
        }
        
        # Configure automated failover
        failover_config = self._configure_automated_failover(ha_architecture)
        
        # Set up health monitoring
        health_monitoring = self._setup_ha_health_monitoring(ha_architecture)
        
        return {
            'architecture': ha_architecture,
            'failover': failover_config,
            'monitoring': health_monitoring
        }
    
    def _setup_control_plane_ha(self):
        """Configure control plane high availability"""
        
        control_plane_ha = {
            'api_servers': {
                'deployment_mode': 'active-active',
                'load_balancer': {
                    'type': 'haproxy',
                    'algorithm': 'leastconn',
                    'health_check': '/healthcheck',
                    'nodes': [
                        {'host': 'nova-api-1', 'port': 8774, 'weight': 100},
                        {'host': 'nova-api-2', 'port': 8774, 'weight': 100},
                        {'host': 'nova-api-3', 'port': 8774, 'weight': 100}
                    ]
                },
                'session_affinity': False,
                'auto_scaling': {
                    'min_replicas': 3,
                    'max_replicas': 10,
                    'target_cpu_utilization': 70
                }
            },
            'schedulers': {
                'deployment_mode': 'active-active',
                'leader_election': True,
                'work_distribution': 'hash_ring',
                'nodes': [
                    {'host': 'nova-scheduler-1', 'weight': 1.0},
                    {'host': 'nova-scheduler-2', 'weight': 1.0},
                    {'host': 'nova-scheduler-3', 'weight': 1.0}
                ]
            },
            'conductors': {
                'deployment_mode': 'active-active',
                'worker_distribution': 'round_robin',
                'nodes': [
                    {'host': 'nova-conductor-1', 'workers': 8},
                    {'host': 'nova-conductor-2', 'workers': 8},
                    {'host': 'nova-conductor-3', 'workers': 8}
                ]
            }
        }
        
        return control_plane_ha
    
    def implement_disaster_recovery(self, dr_requirements):
        """Implement comprehensive disaster recovery strategy"""
        
        dr_strategy = {
            'rpo_target': dr_requirements.get('rpo_minutes', 15),  # Recovery Point Objective
            'rto_target': dr_requirements.get('rto_minutes', 60),  # Recovery Time Objective
            'backup_strategy': self._design_backup_strategy(dr_requirements),
            'replication_strategy': self._design_replication_strategy(dr_requirements),
            'failover_procedures': self._create_failover_procedures(dr_requirements),
            'testing_schedule': self._create_dr_testing_schedule(dr_requirements)
        }
        
        # Implement automated backup
        backup_config = self._implement_automated_backup(dr_strategy)
        
        # Set up cross-region replication
        replication_config = self._setup_cross_region_replication(dr_strategy)
        
        # Configure automated DR orchestration
        dr_orchestration = self._setup_dr_orchestration(dr_strategy)
        
        return {
            'strategy': dr_strategy,
            'backup': backup_config,
            'replication': replication_config,
            'orchestration': dr_orchestration
        }
    
    def _design_backup_strategy(self, dr_requirements):
        """Design comprehensive backup strategy"""
        
        backup_strategy = {
            'database_backup': {
                'frequency': 'hourly',
                'retention': {
                    'hourly': '24 hours',
                    'daily': '30 days',
                    'weekly': '12 weeks',
                    'monthly': '12 months'
                },
                'compression': True,
                'encryption': True,
                'validation': True
            },
            'configuration_backup': {
                'frequency': 'daily',
                'retention': '90 days',
                'version_control': True,
                'automated_deployment': True
            },
            'instance_backup': {
                'policy': 'user_defined',
                'snapshot_consistency': 'application_consistent',
                'cross_region_copy': True,
                'lifecycle_management': True
            }
        }
        
        return backup_strategy