Future-Ready Enterprise Architecture: Autonomous Systems and Emerging Technology Integration

from typing import Dict, List, Any, Optional, Tuple, Union
import asyncio
import numpy as np
from dataclasses import dataclass, field
from enum import Enum
import tensorflow as tf
from quantum_computing import QuantumCircuit, QuantumOptimizer
import torch
import pandas as pd
from datetime import datetime, timedelta
import json

class AutonomyLevel(Enum):
    MONITORED = "monitored"           # Human oversight required
    ASSISTED = "assisted"             # Human approval for changes
    SUPERVISED = "supervised"         # Human intervention on exceptions
    AUTONOMOUS = "autonomous"         # Fully autonomous operation
    EMERGENT = "emergent"            # Self-evolving capabilities

@dataclass
class ArchitecturalGenome:
    """Genetic representation of architectural patterns for evolutionary optimization."""
    service_topology: Dict[str, Any]
    communication_patterns: List[Dict[str, Any]]
    resource_allocation: Dict[str, float]
    performance_characteristics: Dict[str, float]
    security_posture: Dict[str, Any]
    compliance_mappings: Dict[str, List[str]]
    business_value_weights: Dict[str, float]
    mutation_rate: float = 0.1
    fitness_score: float = 0.0

class AutonomousArchitectureOrchestrator:
    """Autonomous architecture orchestration with self-optimization capabilities."""

    def __init__(self,
                 enterprise_context: Dict[str, Any],
                 business_objectives: List[Dict[str, Any]],
                 autonomy_level: AutonomyLevel):
        self.enterprise_context = enterprise_context
        self.business_objectives = business_objectives
        self.autonomy_level = autonomy_level

        # Initialize AI/ML components
        self.optimization_engine = self._initialize_optimization_engine()
        self.prediction_models = self._initialize_prediction_models()
        self.decision_engine = self._initialize_decision_engine()

        # Architecture genome population
        self.architecture_population = []
        self.elite_architectures = []

        # Quantum computing integration
        self.quantum_optimizer = QuantumOptimizer()

        # Continuous learning systems
        self.learning_engine = self._initialize_learning_engine()

    async def evolve_architecture_continuously(
        self,
        evolution_parameters: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Continuously evolve architecture using genetic algorithms and reinforcement learning."""

        evolution_generations = evolution_parameters.get('generations', 100)
        population_size = evolution_parameters.get('population_size', 50)
        elite_preservation = evolution_parameters.get('elite_preservation', 0.1)

        # Initialize architecture population
        if not self.architecture_population:
            self.architecture_population = await self._generate_initial_population(
                population_size
            )

        evolution_history = []

        for generation in range(evolution_generations):
            # Evaluate fitness of all architectures
            fitness_evaluations = await self._evaluate_population_fitness(
                self.architecture_population
            )

            # Select elite architectures
            elite_count = int(population_size * elite_preservation)
            elite_architectures = sorted(
                fitness_evaluations,
                key=lambda x: x['fitness_score'],
                reverse=True
            )[:elite_count]

            # Genetic operations: selection, crossover, mutation
            new_generation = await self._genetic_operations(
                fitness_evaluations, population_size, elite_count
            )

            # Quantum-enhanced optimization for top candidates
            quantum_optimized = await self._quantum_optimize_elite(
                elite_architectures[:5]
            )

            # Combine traditional and quantum-optimized architectures
            self.architecture_population = (
                [arch['genome'] for arch in elite_architectures] +
                new_generation +
                quantum_optimized
            )

            # Real-world performance validation
            validation_results = await self._validate_architectures_in_production(
                elite_architectures[:3]
            )

            # Continuous learning from production feedback
            await self._update_learning_models(validation_results)

            generation_summary = {
                'generation': generation,
                'best_fitness': elite_architectures[0]['fitness_score'],
                'average_fitness': np.mean([arch['fitness_score'] for arch in fitness_evaluations]),
                'architectural_diversity': self._calculate_population_diversity(),
                'quantum_optimization_impact': self._analyze_quantum_impact(quantum_optimized),
                'production_validation': validation_results
            }

            evolution_history.append(generation_summary)

            # Adaptive evolution parameters
            evolution_parameters = await self._adapt_evolution_parameters(
                generation_summary, evolution_parameters
            )

        return {
            'evolution_history': evolution_history,
            'optimal_architecture': elite_architectures[0],
            'architecture_portfolio': elite_architectures[:10],
            'learning_insights': await self._extract_learning_insights(),
            'deployment_recommendations': await self._generate_deployment_strategy(
                elite_architectures[0]
            )
        }

    async def implement_autonomous_incident_response(
        self,
        incident_parameters: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Autonomous incident detection, analysis, and resolution system."""

        # Multi-dimensional anomaly detection
        anomaly_detection = await self._detect_multi_dimensional_anomalies()

        # Intelligent incident classification
        incident_classification = await self._classify_incidents_intelligently(
            anomaly_detection
        )

        # Predictive impact analysis
        impact_prediction = await self._predict_incident_impact(
            incident_classification
        )

        # Autonomous resolution decision tree
        resolution_decisions = await self._generate_autonomous_resolutions(
            incident_classification, impact_prediction
        )

        autonomous_actions = []

        for incident in incident_classification:
            incident_id = incident['incident_id']
            severity = incident['severity']
            predicted_impact = impact_prediction.get(incident_id, {})

            # Determine autonomy level for this incident
            autonomy_decision = await self._determine_incident_autonomy_level(
                incident, predicted_impact, self.autonomy_level
            )

            if autonomy_decision['can_resolve_autonomously']:
                # Execute autonomous resolution
                resolution_result = await self._execute_autonomous_resolution(
                    incident, resolution_decisions[incident_id]
                )

                autonomous_actions.append({
                    'incident_id': incident_id,
                    'action': 'autonomous_resolution',
                    'resolution_steps': resolution_result['steps'],
                    'outcome': resolution_result['outcome'],
                    'confidence': autonomy_decision['confidence']
                })

                # Continuous learning from resolution outcome
                await self._learn_from_resolution_outcome(
                    incident, resolution_result
                )

            else:
                # Escalate with comprehensive analysis
                escalation_package = await self._prepare_escalation_package(
                    incident, predicted_impact, resolution_decisions[incident_id]
                )

                autonomous_actions.append({
                    'incident_id': incident_id,
                    'action': 'escalation',
                    'escalation_package': escalation_package,
                    'reason': autonomy_decision['escalation_reason']
                })

        return {
            'incidents_detected': len(incident_classification),
            'autonomous_resolutions': len([a for a in autonomous_actions if a['action'] == 'autonomous_resolution']),
            'escalations': len([a for a in autonomous_actions if a['action'] == 'escalation']),
            'autonomous_actions': autonomous_actions,
            'system_health_improvement': await self._measure_health_improvement(),
            'learning_updates': await self._extract_incident_learning()
        }

    async def orchestrate_quantum_hybrid_computing(
        self,
        computational_workloads: List[Dict[str, Any]]
    ) -> Dict[str, Any]:
        """Orchestrate hybrid classical-quantum computing for enterprise workloads."""

        workload_analysis = []

        for workload in computational_workloads:
            # Analyze workload characteristics
            workload_profile = await self._analyze_workload_profile(workload)

            # Quantum advantage assessment
            quantum_advantage = await self._assess_quantum_advantage(workload_profile)

            # Hybrid computing strategy
            if quantum_advantage['advantage_score'] > 0.7:
                # Design quantum circuit
                quantum_circuit = await self._design_quantum_circuit(workload)

                # Hybrid execution plan
                hybrid_plan = await self._design_hybrid_execution_plan(
                    workload, quantum_circuit, workload_profile
                )

                # Execute hybrid computation
                execution_result = await self._execute_hybrid_computation(
                    hybrid_plan
                )

                workload_analysis.append({
                    'workload_id': workload['workload_id'],
                    'execution_strategy': 'quantum_hybrid',
                    'quantum_circuit': quantum_circuit,
                    'hybrid_plan': hybrid_plan,
                    'execution_result': execution_result,
                    'performance_improvement': execution_result['quantum_speedup'],
                    'cost_efficiency': execution_result['cost_comparison']
                })

            else:
                # Classical optimization
                classical_optimization = await self._optimize_classical_execution(
                    workload, workload_profile
                )

                workload_analysis.append({
                    'workload_id': workload['workload_id'],
                    'execution_strategy': 'classical_optimized',
                    'optimization_strategy': classical_optimization,
                    'quantum_readiness_score': quantum_advantage['advantage_score']
                })

        return {
            'workload_analysis': workload_analysis,
            'quantum_utilization_ratio': len([w for w in workload_analysis if w['execution_strategy'] == 'quantum_hybrid']) / len(workload_analysis),
            'aggregate_performance_improvement': await self._calculate_aggregate_performance_improvement(workload_analysis),
            'quantum_infrastructure_recommendations': await self._generate_quantum_infrastructure_recommendations(workload_analysis)
        }

from typing import Dict, List, Any, Optional
import asyncio
from dataclasses import dataclass
from enum import Enum
import networkx as nx
import geopy.distance
import kubernetes
from federated_learning import FederatedLearningManager

class EdgeNodeType(Enum):
    MICRO_EDGE = "micro_edge"           # IoT devices, sensors
    EDGE_GATEWAY = "edge_gateway"       # Local processing hubs
    REGIONAL_EDGE = "regional_edge"     # Regional data centres
    CLOUD_EDGE = "cloud_edge"          # Cloud provider edge locations

@dataclass
class EdgeNode:
    node_id: str
    node_type: EdgeNodeType
    geographical_location: Tuple[float, float]  # latitude, longitude
    computational_capacity: Dict[str, float]
    connectivity_profile: Dict[str, Any]
    security_capabilities: List[str]
    compliance_certifications: List[str]
    business_services: List[str]

class EdgeNativeArchitectureOrchestrator:
    """Orchestrator for edge-native enterprise architecture."""

    def __init__(self, edge_topology: Dict[str, Any]):
        self.edge_topology = edge_topology
        self.edge_nodes = {}
        self.service_mesh = {}
        self.federated_learning_manager = FederatedLearningManager()

        # Initialize edge network graph
        self.edge_network = nx.Graph()
        self._build_edge_network_topology()

    async def optimize_edge_service_placement(
        self,
        services: List[Dict[str, Any]],
        optimization_objectives: Dict[str, float]
    ) -> Dict[str, Any]:
        """Optimize service placement across edge topology for multiple objectives."""

        # Analyze service requirements
        service_requirements = await self._analyze_service_requirements(services)

        # Model edge resource capacity
        edge_capacity_model = await self._model_edge_capacity()

        # Network topology analysis
        network_analysis = await self._analyze_network_topology()

        # Multi-objective optimization
        placement_optimization = await self._solve_multi_objective_placement(
            service_requirements,
            edge_capacity_model,
            network_analysis,
            optimization_objectives
        )

        # Validate placement feasibility
        feasibility_validation = await self._validate_placement_feasibility(
            placement_optimization
        )

        if not feasibility_validation['feasible']:
            # Generate alternative placements
            alternative_placements = await self._generate_alternative_placements(
                service_requirements, feasibility_validation['constraints']
            )
            placement_optimization = alternative_placements[0]

        # Execute service deployment
        deployment_results = await self._deploy_services_to_edge(
            placement_optimization
        )

        # Monitor placement performance
        performance_monitoring = await self._setup_placement_monitoring(
            deployment_results
        )

        return {
            'optimized_placement': placement_optimization,
            'deployment_results': deployment_results,
            'performance_monitoring': performance_monitoring,
            'optimization_metrics': {
                'latency_improvement': placement_optimization.get('latency_improvement', 0),
                'bandwidth_utilization': placement_optimization.get('bandwidth_efficiency', 0),
                'cost_optimization': placement_optimization.get('cost_reduction', 0),
                'reliability_score': placement_optimization.get('reliability_score', 0)
            }
        }

    async def implement_federated_machine_learning(
        self,
        learning_objectives: List[Dict[str, Any]],
        privacy_requirements: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Implement federated learning across edge infrastructure."""

        federated_learning_results = {}

        for objective in learning_objectives:
            objective_id = objective['objective_id']
            learning_task = objective['learning_task']

            # Select participating edge nodes
            participant_nodes = await self._select_federated_participants(
                objective, privacy_requirements
            )

            # Design federated learning architecture
            federated_architecture = await self._design_federated_architecture(
                learning_task, participant_nodes, privacy_requirements
            )

            # Initialize federated learning
            federated_session = await self.federated_learning_manager.initialize_session(
                federated_architecture
            )

            # Execute federated training
            training_results = await self._execute_federated_training(
                federated_session, objective
            )

            # Model aggregation with privacy preservation
            aggregated_model = await self._aggregate_models_privately(
                training_results, privacy_requirements
            )

            # Validate federated model performance
            model_validation = await self._validate_federated_model(
                aggregated_model, objective
            )

            # Deploy federated model across edge
            deployment_strategy = await self._deploy_federated_model(
                aggregated_model, participant_nodes
            )

            federated_learning_results[objective_id] = {
                'learning_task': learning_task,
                'participant_nodes': participant_nodes,
                'training_results': training_results,
                'aggregated_model': aggregated_model,
                'model_validation': model_validation,
                'deployment_strategy': deployment_strategy,
                'privacy_preservation_score': self._calculate_privacy_score(
                    privacy_requirements, training_results
                )
            }

        return {
            'federated_learning_results': federated_learning_results,
            'overall_learning_performance': await self._calculate_overall_learning_performance(
                federated_learning_results
            ),
            'privacy_compliance_assessment': await self._assess_privacy_compliance(
                federated_learning_results, privacy_requirements
            ),
            'edge_utilization_efficiency': await self._calculate_edge_utilization_efficiency(
                federated_learning_results
            )
        }

from typing import Dict, List, Any, Optional, Tuple
import asyncio
from dataclasses import dataclass
from enum import Enum
import hashlib
import secrets
from cryptography.hazmat.primitives import hashes, serialization
from cryptography.hazmat.primitives.asymmetric import rsa, padding
import numpy as np
from quantum_cryptography import QuantumKeyDistribution, LatticeBasedCrypto

class QuantumThreatLevel(Enum):
    LOW = "low"                    # Current classical threat landscape
    EMERGING = "emerging"          # Early quantum computers
    MODERATE = "moderate"          # NISQ-era quantum computers
    HIGH = "high"                  # Fault-tolerant quantum computers
    CRITICAL = "critical"          # Large-scale quantum computers

@dataclass
class CryptographicProfile:
    algorithm_family: str
    key_size: int
    security_level: int
    quantum_resistance: bool
    performance_characteristics: Dict[str, float]
    compliance_certifications: List[str]
    threat_resistance: Dict[QuantumThreatLevel, float]

class QuantumResistantSecurityArchitecture:
    """Quantum-resistant security architecture for enterprise systems."""

    def __init__(self, security_requirements: Dict[str, Any]):
        self.security_requirements = security_requirements
        self.cryptographic_agility_framework = {}
        self.quantum_key_distribution = QuantumKeyDistribution()
        self.lattice_crypto = LatticeBasedCrypto()

        # Initialize post-quantum cryptographic algorithms
        self.pqc_algorithms = self._initialize_pqc_algorithms()

        # Hybrid cryptography management
        self.hybrid_crypto_manager = self._initialize_hybrid_crypto_manager()

    async def implement_cryptographic_agility(
        self,
        systems: List[Dict[str, Any]],
        migration_timeline: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Implement cryptographic agility for seamless algorithm transitions."""

        agility_implementation = {}

        for system in systems:
            system_id = system['system_id']
            current_crypto = system['current_cryptography']

            # Assess quantum vulnerability
            vulnerability_assessment = await self._assess_quantum_vulnerability(
                current_crypto
            )

            # Design migration strategy
            migration_strategy = await self._design_crypto_migration_strategy(
                system, vulnerability_assessment, migration_timeline
            )

            # Implement hybrid cryptography
            hybrid_implementation = await self._implement_hybrid_cryptography(
                system, migration_strategy
            )

            # Setup algorithm negotiation
            algorithm_negotiation = await self._setup_algorithm_negotiation(
                system, hybrid_implementation
            )

            # Performance monitoring
            performance_monitoring = await self._setup_crypto_performance_monitoring(
                system, hybrid_implementation
            )

            agility_implementation[system_id] = {
                'vulnerability_assessment': vulnerability_assessment,
                'migration_strategy': migration_strategy,
                'hybrid_implementation': hybrid_implementation,
                'algorithm_negotiation': algorithm_negotiation,
                'performance_monitoring': performance_monitoring,
                'quantum_readiness_score': self._calculate_quantum_readiness_score(
                    vulnerability_assessment, hybrid_implementation
                )
            }

        return {
            'agility_implementation': agility_implementation,
            'overall_quantum_readiness': await self._calculate_overall_quantum_readiness(
                agility_implementation
            ),
            'migration_timeline_adherence': await self._assess_timeline_adherence(
                agility_implementation, migration_timeline
            ),
            'performance_impact_analysis': await self._analyze_performance_impact(
                agility_implementation
            )
        }

    async def deploy_quantum_key_distribution_network(
        self,
        network_topology: Dict[str, Any],
        security_requirements: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Deploy quantum key distribution network for ultimate security."""

        # Analyze network topology for QKD deployment
        topology_analysis = await self._analyze_topology_for_qkd(network_topology)

        # Design QKD network architecture
        qkd_network_design = await self._design_qkd_network(
            topology_analysis, security_requirements
        )

        # Implement QKD infrastructure
        qkd_infrastructure = await self._implement_qkd_infrastructure(
            qkd_network_design
        )

        # Setup quantum key management
        quantum_key_management = await self._setup_quantum_key_management(
            qkd_infrastructure
        )

        # Integrate with classical cryptography
        hybrid_integration = await self._integrate_qkd_with_classical_crypto(
            quantum_key_management
        )

        # Monitor QKD network health
        network_monitoring = await self._setup_qkd_network_monitoring(
            qkd_infrastructure
        )

        return {
            'qkd_network_design': qkd_network_design,
            'infrastructure_deployment': qkd_infrastructure,
            'key_management': quantum_key_management,
            'hybrid_integration': hybrid_integration,
            'network_monitoring': network_monitoring,
            'security_assurance': {
                'information_theoretic_security': True,
                'quantum_hacking_resistance': qkd_infrastructure.get('hacking_resistance', 0),
                'key_generation_rate': qkd_infrastructure.get('key_rate', 0),
                'network_coverage': qkd_network_design.get('coverage_percentage', 0)
            }
        }

from typing import Dict, List, Any, Optional
import asyncio
from dataclasses import dataclass
from datetime import datetime, timedelta
import pandas as pd
import numpy as np

@dataclass
class CarbonFootprintProfile:
    energy_consumption_kwh: float
    carbon_intensity_gco2_kwh: float
    renewable_energy_percentage: float
    geographic_location: str
    time_of_use_patterns: Dict[str, float]
    efficiency_metrics: Dict[str, float]

class SustainableArchitectureOrchestrator:
    """Orchestrator for sustainable, carbon-aware enterprise architecture."""

    def __init__(self, sustainability_targets: Dict[str, Any]):
        self.sustainability_targets = sustainability_targets
        self.carbon_monitoring = {}
        self.energy_optimization_models = {}

    async def implement_carbon_aware_scheduling(
        self,
        workloads: List[Dict[str, Any]],
        carbon_constraints: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Implement carbon-aware workload scheduling across global infrastructure."""

        # Analyze workload carbon requirements
        workload_carbon_analysis = await self._analyze_workload_carbon_requirements(
            workloads
        )

        # Monitor real-time carbon intensity
        carbon_intensity_data = await self._monitor_global_carbon_intensity()

        # Predict carbon intensity trends
        carbon_predictions = await self._predict_carbon_intensity_trends(
            carbon_intensity_data
        )

        # Optimize scheduling for carbon efficiency
        carbon_optimized_schedule = await self._optimize_carbon_aware_schedule(
            workload_carbon_analysis,
            carbon_predictions,
            carbon_constraints
        )

        # Execute carbon-aware scheduling
        scheduling_results = await self._execute_carbon_aware_scheduling(
            carbon_optimized_schedule
        )

        # Monitor carbon impact
        carbon_impact_monitoring = await self._monitor_carbon_impact(
            scheduling_results
        )

        return {
            'optimized_schedule': carbon_optimized_schedule,
            'scheduling_results': scheduling_results,
            'carbon_impact': carbon_impact_monitoring,
            'sustainability_metrics': {
                'carbon_reduction_percentage': carbon_impact_monitoring.get('reduction_percentage', 0),
                'renewable_energy_utilization': carbon_impact_monitoring.get('renewable_utilization', 0),
                'energy_efficiency_improvement': carbon_impact_monitoring.get('efficiency_improvement', 0)
            }
        }