COST07-BP04 - Implement pricing models for variable consumption

import boto3
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple
import json
from scipy import stats
import matplotlib.pyplot as plt

@dataclass
class WorkloadPattern:
    workload_name: str
    usage_pattern: str  # steady, variable, bursty, seasonal
    peak_to_average_ratio: float
    predictability_score: float  # 0-1, where 1 is highly predictable
    fault_tolerance: str  # high, medium, low
    latency_sensitivity: str  # high, medium, low
    current_pricing_model: str

@dataclass
class PricingModelOption:
    model_name: str
    service_type: str
    pricing_structure: str  # fixed, variable, hybrid
    cost_per_unit: float
    minimum_cost: float
    scaling_characteristics: Dict
    suitability_score: float

@dataclass
class VariablePricingRecommendation:
    workload_name: str
    current_model: str
    recommended_model: str
    estimated_savings: float
    implementation_complexity: str
    risk_assessment: str
    rationale: str

class VariableConsumptionOptimizer:
    def __init__(self):
        self.cloudwatch = boto3.client('cloudwatch')
        self.lambda_client = boto3.client('lambda')
        self.ec2 = boto3.client('ec2')
        self.dynamodb = boto3.client('dynamodb')
        self.ce_client = boto3.client('ce')
        
        # Pricing models configuration
        self.pricing_models = {
            'lambda': {
                'requests': 0.0000002,  # $0.20 per 1M requests
                'duration': 0.0000166667,  # $0.0000166667 per GB-second
                'minimum_duration': 0.1  # 100ms minimum
            },
            'fargate': {
                'vcpu_per_second': 0.00001406,  # $0.04048 per vCPU per hour
                'memory_per_second': 0.00000353  # $0.004445 per GB per hour
            },
            'spot_instances': {
                'discount_range': [0.5, 0.9],  # 50-90% discount
                'interruption_rate': 0.05  # 5% average interruption rate
            },
            'dynamodb_on_demand': {
                'read_request': 0.00000025,  # $0.25 per million read requests
                'write_request': 0.00000125  # $1.25 per million write requests
            }
        }
    
    def analyze_variable_consumption_opportunities(self, workloads: List[WorkloadPattern]) -> Dict:
        """Analyze opportunities for variable consumption pricing"""
        
        analysis_results = {
            'analysis_date': datetime.now().isoformat(),
            'workloads_analyzed': len(workloads),
            'workload_analysis': {},
            'pricing_recommendations': [],
            'savings_summary': {},
            'implementation_roadmap': {}
        }
        
        # Analyze each workload
        for workload in workloads:
            workload_analysis = self.analyze_single_workload(workload)
            analysis_results['workload_analysis'][workload.workload_name] = workload_analysis
            
            # Generate recommendations
            recommendations = self.generate_variable_pricing_recommendations(workload, workload_analysis)
            analysis_results['pricing_recommendations'].extend(recommendations)
        
        # Create savings summary
        analysis_results['savings_summary'] = self.calculate_savings_summary(
            analysis_results['pricing_recommendations']
        )
        
        # Create implementation roadmap
        analysis_results['implementation_roadmap'] = self.create_implementation_roadmap(
            analysis_results['pricing_recommendations']
        )
        
        return analysis_results
    
    def analyze_single_workload(self, workload: WorkloadPattern) -> Dict:
        """Analyze a single workload for variable pricing suitability"""
        
        analysis = {
            'workload_name': workload.workload_name,
            'current_pricing_model': workload.current_pricing_model,
            'usage_characteristics': {
                'pattern': workload.usage_pattern,
                'peak_to_average_ratio': workload.peak_to_average_ratio,
                'predictability': workload.predictability_score,
                'variability_score': self.calculate_variability_score(workload)
            },
            'suitability_analysis': {},
            'cost_modeling': {},
            'risk_assessment': {}
        }
        
        # Analyze suitability for different variable pricing models
        analysis['suitability_analysis'] = {
            'serverless': self.analyze_serverless_suitability(workload),
            'spot_instances': self.analyze_spot_suitability(workload),
            'auto_scaling': self.analyze_auto_scaling_suitability(workload),
            'on_demand_database': self.analyze_on_demand_db_suitability(workload)
        }
        
        # Model costs for different pricing approaches
        analysis['cost_modeling'] = self.model_variable_pricing_costs(workload)
        
        # Assess risks
        analysis['risk_assessment'] = self.assess_variable_pricing_risks(workload)
        
        return analysis
    
    def calculate_variability_score(self, workload: WorkloadPattern) -> float:
        """Calculate variability score for a workload"""
        
        # Higher variability score indicates better suitability for variable pricing
        variability_factors = {
            'peak_to_average_ratio': min(workload.peak_to_average_ratio / 5.0, 1.0),  # Normalize to 1.0
            'unpredictability': 1.0 - workload.predictability_score,
            'usage_pattern_factor': {
                'steady': 0.2,
                'variable': 0.7,
                'bursty': 0.9,
                'seasonal': 0.6
            }.get(workload.usage_pattern, 0.5)
        }
        
        # Weighted average of variability factors
        weights = {'peak_to_average_ratio': 0.4, 'unpredictability': 0.3, 'usage_pattern_factor': 0.3}
        
        variability_score = sum(
            variability_factors[factor] * weight 
            for factor, weight in weights.items()
        )
        
        return min(1.0, variability_score)
    
    def analyze_serverless_suitability(self, workload: WorkloadPattern) -> Dict:
        """Analyze suitability for serverless pricing models"""
        
        suitability = {
            'overall_score': 0.0,
            'factors': {},
            'recommended_services': [],
            'considerations': []
        }
        
        # Analyze factors
        factors = {
            'usage_variability': self.calculate_variability_score(workload),
            'fault_tolerance': {
                'high': 0.9, 'medium': 0.6, 'low': 0.3
            }.get(workload.fault_tolerance, 0.5),
            'latency_tolerance': {
                'high': 0.3, 'medium': 0.7, 'low': 0.9
            }.get(workload.latency_sensitivity, 0.5),
            'event_driven_nature': {
                'bursty': 0.9, 'variable': 0.7, 'seasonal': 0.6, 'steady': 0.3
            }.get(workload.usage_pattern, 0.5)
        }
        
        suitability['factors'] = factors
        
        # Calculate overall score
        weights = {
            'usage_variability': 0.3,
            'fault_tolerance': 0.2,
            'latency_tolerance': 0.2,
            'event_driven_nature': 0.3
        }
        
        suitability['overall_score'] = sum(
            factors[factor] * weight for factor, weight in weights.items()
        )
        
        # Recommend specific serverless services
        if suitability['overall_score'] > 0.7:
            suitability['recommended_services'] = ['AWS Lambda', 'AWS Fargate']
            if workload.usage_pattern in ['bursty', 'variable']:
                suitability['recommended_services'].append('DynamoDB On-Demand')
        
        # Add considerations
        if workload.latency_sensitivity == 'high':
            suitability['considerations'].append('Consider cold start latency impact')
        if workload.fault_tolerance == 'low':
            suitability['considerations'].append('Implement robust error handling and retries')
        
        return suitability
    
    def analyze_spot_suitability(self, workload: WorkloadPattern) -> Dict:
        """Analyze suitability for Spot Instance pricing"""
        
        suitability = {
            'overall_score': 0.0,
            'factors': {},
            'estimated_savings': 0.0,
            'interruption_tolerance': 'unknown',
            'considerations': []
        }
        
        # Analyze factors
        factors = {
            'fault_tolerance': {
                'high': 0.9, 'medium': 0.5, 'low': 0.1
            }.get(workload.fault_tolerance, 0.3),
            'time_flexibility': {
                'bursty': 0.8, 'variable': 0.7, 'seasonal': 0.6, 'steady': 0.4
            }.get(workload.usage_pattern, 0.5),
            'stateless_nature': 0.8,  # Assume most workloads can be made stateless
            'checkpoint_capability': 0.7  # Assume reasonable checkpoint capability
        }
        
        suitability['factors'] = factors
        
        # Calculate overall score
        weights = {
            'fault_tolerance': 0.4,
            'time_flexibility': 0.3,
            'stateless_nature': 0.2,
            'checkpoint_capability': 0.1
        }
        
        suitability['overall_score'] = sum(
            factors[factor] * weight for factor, weight in weights.items()
        )
        
        # Estimate savings
        if suitability['overall_score'] > 0.6:
            suitability['estimated_savings'] = 0.7  # 70% average savings
        elif suitability['overall_score'] > 0.4:
            suitability['estimated_savings'] = 0.5  # 50% savings with mixed usage
        
        # Assess interruption tolerance
        if workload.fault_tolerance == 'high':
            suitability['interruption_tolerance'] = 'high'
        elif workload.fault_tolerance == 'medium':
            suitability['interruption_tolerance'] = 'medium'
        else:
            suitability['interruption_tolerance'] = 'low'
        
        # Add considerations
        if suitability['overall_score'] > 0.5:
            suitability['considerations'].extend([
                'Implement graceful interruption handling',
                'Use Spot Fleet for diversification',
                'Consider mixed instance types and AZs'
            ])
        
        return suitability
    
    def model_variable_pricing_costs(self, workload: WorkloadPattern) -> Dict:
        """Model costs for different variable pricing approaches"""
        
        cost_models = {
            'current_model': self.estimate_current_costs(workload),
            'serverless_model': self.estimate_serverless_costs(workload),
            'spot_model': self.estimate_spot_costs(workload),
            'auto_scaling_model': self.estimate_auto_scaling_costs(workload),
            'hybrid_model': self.estimate_hybrid_costs(workload)
        }
        
        # Calculate comparative analysis
        current_cost = cost_models['current_model']['monthly_cost']
        
        for model_name, model_data in cost_models.items():
            if model_name != 'current_model':
                model_data['savings_vs_current'] = current_cost - model_data['monthly_cost']
                model_data['savings_percentage'] = (
                    (current_cost - model_data['monthly_cost']) / current_cost * 100
                    if current_cost > 0 else 0
                )
        
        return cost_models
    
    def generate_variable_pricing_recommendations(self, workload: WorkloadPattern, 
                                                analysis: Dict) -> List[VariablePricingRecommendation]:
        """Generate variable pricing recommendations for a workload"""
        
        recommendations = []
        
        # Serverless recommendation
        serverless_suitability = analysis['suitability_analysis']['serverless']
        if serverless_suitability['overall_score'] > 0.7:
            serverless_savings = analysis['cost_modeling']['serverless_model']['savings_vs_current']
            if serverless_savings > 0:
                recommendations.append(VariablePricingRecommendation(
                    workload_name=workload.workload_name,
                    current_model=workload.current_pricing_model,
                    recommended_model='serverless',
                    estimated_savings=serverless_savings,
                    implementation_complexity='medium',
                    risk_assessment='low-medium',
                    rationale=f'High serverless suitability score ({serverless_suitability["overall_score"]:.2f}) with ${serverless_savings:.2f}/month savings'
                ))
        
        # Spot instance recommendation
        spot_suitability = analysis['suitability_analysis']['spot_instances']
        if spot_suitability['overall_score'] > 0.6:
            spot_savings = analysis['cost_modeling']['spot_model']['savings_vs_current']
            if spot_savings > 0:
                recommendations.append(VariablePricingRecommendation(
                    workload_name=workload.workload_name,
                    current_model=workload.current_pricing_model,
                    recommended_model='spot_instances',
                    estimated_savings=spot_savings,
                    implementation_complexity='medium-high',
                    risk_assessment='medium',
                    rationale=f'Good spot suitability with {spot_suitability["estimated_savings"]:.0%} potential savings'
                ))
        
        # Auto-scaling recommendation
        auto_scaling_suitability = analysis['suitability_analysis']['auto_scaling']
        if auto_scaling_suitability['overall_score'] > 0.6:
            auto_scaling_savings = analysis['cost_modeling']['auto_scaling_model']['savings_vs_current']
            if auto_scaling_savings > 0:
                recommendations.append(VariablePricingRecommendation(
                    workload_name=workload.workload_name,
                    current_model=workload.current_pricing_model,
                    recommended_model='auto_scaling',
                    estimated_savings=auto_scaling_savings,
                    implementation_complexity='low-medium',
                    risk_assessment='low',
                    rationale=f'Variable usage pattern benefits from auto-scaling with ${auto_scaling_savings:.2f}/month savings'
                ))
        
        return recommendations
    
    def implement_serverless_cost_optimization(self, workload_name: str) -> Dict:
        """Implement serverless cost optimization strategies"""
        
        optimization_strategies = {
            'lambda_optimization': {
                'memory_optimization': self.optimize_lambda_memory(workload_name),
                'timeout_optimization': self.optimize_lambda_timeout(workload_name),
                'provisioned_concurrency': self.analyze_provisioned_concurrency_needs(workload_name),
                'cold_start_optimization': self.optimize_cold_starts(workload_name)
            },
            'fargate_optimization': {
                'right_sizing': self.optimize_fargate_sizing(workload_name),
                'spot_fargate': self.analyze_fargate_spot_opportunities(workload_name),
                'scheduling_optimization': self.optimize_fargate_scheduling(workload_name)
            },
            'database_optimization': {
                'on_demand_vs_provisioned': self.analyze_dynamodb_pricing_models(workload_name),
                'auto_scaling_configuration': self.optimize_dynamodb_auto_scaling(workload_name)
            }
        }
        
        return optimization_strategies
    
    def create_variable_pricing_monitoring(self) -> Dict:
        """Create monitoring framework for variable pricing models"""
        
        monitoring_framework = {
            'cost_monitoring': {
                'real_time_dashboards': self.create_variable_cost_dashboards(),
                'cost_anomaly_detection': self.setup_cost_anomaly_detection(),
                'budget_alerts': self.create_variable_pricing_budgets(),
                'cost_attribution': self.setup_variable_cost_attribution()
            },
            'performance_monitoring': {
                'serverless_metrics': self.create_serverless_monitoring(),
                'spot_instance_monitoring': self.create_spot_monitoring(),
                'auto_scaling_monitoring': self.create_auto_scaling_monitoring()
            },
            'optimization_automation': {
                'automated_rightsizing': self.setup_automated_rightsizing(),
                'cost_optimization_triggers': self.create_optimization_triggers(),
                'performance_based_scaling': self.setup_performance_scaling()
            }
        }
        
        return monitoring_framework
    
    def create_variable_cost_dashboards(self) -> List[Dict]:
        """Create CloudWatch dashboards for variable cost monitoring"""
        
        dashboards = [
            {
                'dashboard_name': 'Variable Pricing Cost Overview',
                'widgets': [
                    {
                        'type': 'metric',
                        'title': 'Lambda Costs by Function',
                        'metrics': [
                            ['AWS/Lambda', 'Duration', 'FunctionName', 'function-name'],
                            ['AWS/Lambda', 'Invocations', 'FunctionName', 'function-name']
                        ],
                        'period': 3600
                    },
                    {
                        'type': 'metric',
                        'title': 'Spot Instance Savings',
                        'metrics': [
                            ['AWS/EC2', 'SpotInstanceSavings']
                        ],
                        'period': 3600
                    },
                    {
                        'type': 'metric',
                        'title': 'DynamoDB On-Demand Costs',
                        'metrics': [
                            ['AWS/DynamoDB', 'ConsumedReadCapacityUnits'],
                            ['AWS/DynamoDB', 'ConsumedWriteCapacityUnits']
                        ],
                        'period': 3600
                    }
                ]
            },
            {
                'dashboard_name': 'Variable Pricing Performance',
                'widgets': [
                    {
                        'type': 'metric',
                        'title': 'Lambda Cold Start Rate',
                        'expression': 'cold_starts / total_invocations * 100',
                        'metrics': [
                            ['AWS/Lambda', 'Duration', 'FunctionName', 'function-name', {'id': 'duration'}],
                            ['AWS/Lambda', 'Invocations', 'FunctionName', 'function-name', {'id': 'invocations'}]
                        ]
                    },
                    {
                        'type': 'metric',
                        'title': 'Spot Instance Interruption Rate',
                        'metrics': [
                            ['AWS/EC2', 'SpotInstanceInterruptions']
                        ]
                    }
                ]
            }
        ]
        
        return dashboards
    
    def estimate_current_costs(self, workload: WorkloadPattern) -> Dict:
        """Estimate current costs for a workload"""
        
        # This would integrate with actual cost data
        # For demonstration, using sample calculations
        
        base_monthly_cost = 1000  # Sample base cost
        
        return {
            'monthly_cost': base_monthly_cost,
            'cost_breakdown': {
                'compute': base_monthly_cost * 0.7,
                'storage': base_monthly_cost * 0.2,
                'network': base_monthly_cost * 0.1
            },
            'utilization_efficiency': 0.6  # 60% average utilization
        }
    
    def estimate_serverless_costs(self, workload: WorkloadPattern) -> Dict:
        """Estimate costs for serverless implementation"""
        
        # Sample serverless cost calculation
        estimated_requests_per_month = 1000000
        avg_duration_ms = 200
        memory_mb = 512
        
        # Lambda pricing
        request_cost = estimated_requests_per_month * self.pricing_models['lambda']['requests']
        duration_cost = (estimated_requests_per_month * avg_duration_ms / 1000 * 
                        memory_mb / 1024 * self.pricing_models['lambda']['duration'])
        
        total_cost = request_cost + duration_cost
        
        # Apply variability factor
        if workload.usage_pattern in ['variable', 'bursty']:
            total_cost *= 0.7  # 30% savings due to no idle time
        
        return {
            'monthly_cost': total_cost,
            'cost_breakdown': {
                'requests': request_cost,
                'duration': duration_cost
            },
            'scaling_efficiency': 1.0  # Perfect scaling with usage
        }

Serverless_Cost_Optimization:
  workload: "image-processing-service"
  current_architecture: "EC2 with Auto Scaling"
  target_architecture: "Lambda + S3 + DynamoDB"
  
  cost_analysis:
    current_monthly_cost: 2400.00
    projected_serverless_cost: 1680.00
    estimated_savings: 720.00
    savings_percentage: 30
    
  serverless_components:
    lambda_functions:
      - name: "image-processor"
        memory_mb: 1024
        avg_duration_ms: 3000
        monthly_invocations: 500000
        monthly_cost: 850.00
        
      - name: "thumbnail-generator"
        memory_mb: 512
        avg_duration_ms: 800
        monthly_invocations: 500000
        monthly_cost: 280.00
        
    dynamodb_tables:
      - name: "image-metadata"
        pricing_model: "on-demand"
        read_requests_per_month: 2000000
        write_requests_per_month: 500000
        monthly_cost: 550.00
        
  optimization_strategies:
    lambda_optimization:
      - strategy: "Memory optimization"
        description: "Right-size memory allocation based on profiling"
        potential_savings: 15
        
      - strategy: "Timeout optimization"
        description: "Reduce timeout values to minimize costs"
        potential_savings: 5
        
    cost_controls:
      - control: "Concurrency limits"
        value: 100
        purpose: "Prevent cost spikes"
        
      - control: "Dead letter queues"
        purpose: "Handle failures efficiently"
        
  monitoring_setup:
    cost_alerts:
      - threshold: 2000.00
        period: "monthly"
        action: "notify_team"
        
    performance_monitoring:
      - metric: "Duration"
        threshold: 5000
        action: "investigate_performance"
        
      - metric: "Error rate"
        threshold: 1
        action: "alert_on_call"
        
  implementation_phases:
    phase_1:
      duration: "2 weeks"
      scope: "Core Lambda functions"
      expected_savings: 400.00
      
    phase_2:
      duration: "2 weeks"
      scope: "DynamoDB migration"
      expected_savings: 200.00
      
    phase_3:
      duration: "1 week"
      scope: "Optimization and monitoring"
      expected_savings: 120.00

def create_spot_instance_strategy():
    """Create comprehensive Spot Instance implementation strategy"""
    
    strategy = {
        'workload_assessment': {
            'fault_tolerance_requirements': {
                'high': 'Suitable for Spot with minimal changes',
                'medium': 'Requires checkpointing and state management',
                'low': 'Not recommended for Spot instances'
            },
            'time_sensitivity': {
                'flexible': 'Ideal for Spot instances',
                'moderate': 'Can use Spot with On-Demand backup',
                'critical': 'Use mixed instance types'
            }
        },
        
        'implementation_patterns': {
            'batch_processing': {
                'spot_percentage': 90,
                'interruption_handling': 'Checkpoint and resume',
                'instance_diversification': 'Multiple instance types and AZs'
            },
            'web_applications': {
                'spot_percentage': 70,
                'interruption_handling': 'Graceful draining',
                'backup_capacity': 'On-Demand instances for baseline'
            },
            'development_environments': {
                'spot_percentage': 100,
                'interruption_handling': 'Accept interruptions',
                'data_persistence': 'External storage for important data'
            }
        },
        
        'cost_optimization_techniques': {
            'spot_fleet_configuration': {
                'diversification': 'Use multiple instance types and AZs',
                'allocation_strategy': 'diversified or lowest-price',
                'target_capacity': 'Set based on workload requirements'
            },
            'mixed_instance_types': {
                'on_demand_base': 'Minimum stable capacity',
                'on_demand_percentage': '10-30% above base',
                'spot_allocation': 'Remaining capacity'
            },
            'interruption_handling': {
                'spot_instance_interruption_notice': '2-minute warning',
                'graceful_shutdown': 'Save state and drain connections',
                'automatic_replacement': 'Launch replacement instances'
            }
        },
        
        'monitoring_and_optimization': {
            'cost_tracking': 'Monitor Spot savings vs On-Demand',
            'interruption_monitoring': 'Track interruption rates and impact',
            'performance_monitoring': 'Ensure SLA compliance',
            'automated_optimization': 'Adjust Spot strategies based on patterns'
        }
    }
    
    return strategy