COST05-BP06 - Implement processes to regularly evaluate new services

import boto3
import json
import requests
from datetime import datetime, timedelta
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple
from enum import Enum

class EvaluationStatus(Enum):
    IDENTIFIED = "identified"
    UNDER_REVIEW = "under_review"
    PILOT_APPROVED = "pilot_approved"
    PILOT_IN_PROGRESS = "pilot_in_progress"
    PILOT_COMPLETE = "pilot_complete"
    APPROVED = "approved"
    REJECTED = "rejected"
    DEFERRED = "deferred"

class ServiceCategory(Enum):
    COMPUTE = "compute"
    STORAGE = "storage"
    DATABASE = "database"
    NETWORKING = "networking"
    ANALYTICS = "analytics"
    MACHINE_LEARNING = "machine_learning"
    SECURITY = "security"
    MANAGEMENT = "management"

@dataclass
class ServiceEvaluation:
    service_name: str
    category: ServiceCategory
    announcement_date: str
    evaluation_status: EvaluationStatus
    potential_use_cases: List[str]
    cost_impact_estimate: str
    business_value_score: float
    technical_feasibility_score: float
    migration_complexity_score: float
    pilot_results: Optional[Dict]
    final_recommendation: Optional[str]
    evaluation_notes: List[str]

class NewServiceEvaluationManager:
    def __init__(self):
        self.ce_client = boto3.client('ce')
        self.pricing = boto3.client('pricing', region_name='us-east-1')
        self.organizations = boto3.client('organizations')
        
        # Service evaluation database (in practice, this would be a real database)
        self.evaluations = {}
        
        # Evaluation criteria weights
        self.evaluation_weights = {
            'cost_impact': 0.3,
            'business_value': 0.25,
            'technical_feasibility': 0.2,
            'strategic_alignment': 0.15,
            'migration_complexity': 0.1
        }
    
    def monitor_new_services(self) -> List[Dict]:
        """Monitor for new AWS service announcements"""
        
        # In practice, this would integrate with AWS APIs, RSS feeds, or other sources
        # For demonstration, returning sample new services
        
        new_services = [
            {
                'service_name': 'Amazon Aurora Serverless v2',
                'category': ServiceCategory.DATABASE,
                'announcement_date': '2024-01-15',
                'description': 'Serverless database with instant scaling',
                'potential_benefits': ['Cost optimization for variable workloads', 'Reduced management overhead'],
                'initial_assessment': 'High potential for cost savings on development databases'
            },
            {
                'service_name': 'AWS Graviton3 Instances',
                'category': ServiceCategory.COMPUTE,
                'announcement_date': '2024-01-10',
                'description': 'Next-generation ARM-based instances',
                'potential_benefits': ['Up to 25% better price-performance', 'Lower energy consumption'],
                'initial_assessment': 'Significant cost optimization potential for compatible workloads'
            },
            {
                'service_name': 'Amazon S3 Express One Zone',
                'category': ServiceCategory.STORAGE,
                'announcement_date': '2024-01-05',
                'description': 'High-performance storage class',
                'potential_benefits': ['10x faster performance', 'Optimized for frequent access'],
                'initial_assessment': 'Potential cost optimization for high-performance storage needs'
            }
        ]
        
        return new_services
    
    def evaluate_new_service(self, service_info: Dict) -> ServiceEvaluation:
        """Perform comprehensive evaluation of a new service"""
        
        evaluation = ServiceEvaluation(
            service_name=service_info['service_name'],
            category=service_info['category'],
            announcement_date=service_info['announcement_date'],
            evaluation_status=EvaluationStatus.UNDER_REVIEW,
            potential_use_cases=[],
            cost_impact_estimate="",
            business_value_score=0.0,
            technical_feasibility_score=0.0,
            migration_complexity_score=0.0,
            pilot_results=None,
            final_recommendation=None,
            evaluation_notes=[]
        )
        
        # Identify potential use cases
        evaluation.potential_use_cases = self.identify_use_cases(service_info)
        
        # Estimate cost impact
        evaluation.cost_impact_estimate = self.estimate_cost_impact(service_info)
        
        # Score different evaluation dimensions
        evaluation.business_value_score = self.score_business_value(service_info)
        evaluation.technical_feasibility_score = self.score_technical_feasibility(service_info)
        evaluation.migration_complexity_score = self.score_migration_complexity(service_info)
        
        # Add initial evaluation notes
        evaluation.evaluation_notes.append(
            f"Initial evaluation completed on {datetime.now().strftime('%Y-%m-%d')}"
        )
        
        return evaluation
    
    def identify_use_cases(self, service_info: Dict) -> List[str]:
        """Identify potential use cases for the new service"""
        
        use_cases = []
        service_name = service_info['service_name']
        category = service_info['category']
        
        # Use case identification based on service category and current infrastructure
        if category == ServiceCategory.DATABASE:
            current_databases = self.get_current_database_usage()
            if 'variable_workloads' in current_databases:
                use_cases.append("Replace over-provisioned RDS instances for development environments")
            if 'high_availability_required' in current_databases:
                use_cases.append("Improve availability while reducing costs for production databases")
        
        elif category == ServiceCategory.COMPUTE:
            current_compute = self.get_current_compute_usage()
            if 'arm_compatible_workloads' in current_compute:
                use_cases.append("Migrate ARM-compatible workloads for better price-performance")
            if 'batch_processing' in current_compute:
                use_cases.append("Optimize batch processing workloads with better price-performance")
        
        elif category == ServiceCategory.STORAGE:
            current_storage = self.get_current_storage_usage()
            if 'high_iops_required' in current_storage:
                use_cases.append("Replace expensive high-IOPS storage with more cost-effective solution")
            if 'frequent_access_patterns' in current_storage:
                use_cases.append("Optimize storage costs for frequently accessed data")
        
        return use_cases
    
    def estimate_cost_impact(self, service_info: Dict) -> str:
        """Estimate the potential cost impact of adopting the new service"""
        
        service_name = service_info['service_name']
        category = service_info['category']
        
        # Get current spending in the relevant category
        current_spending = self.get_current_category_spending(category)
        
        # Estimate potential savings based on service characteristics
        if 'serverless' in service_name.lower():
            estimated_savings = current_spending * 0.3  # 30% savings estimate
            impact = f"Estimated savings: ${estimated_savings:,.2f}/month (30% reduction in {category.value} costs)"
        
        elif 'graviton' in service_name.lower():
            estimated_savings = current_spending * 0.25  # 25% savings estimate
            impact = f"Estimated savings: ${estimated_savings:,.2f}/month (25% price-performance improvement)"
        
        elif 'express' in service_name.lower():
            # May increase costs but provide value through performance
            estimated_increase = current_spending * 0.1  # 10% increase
            impact = f"Estimated cost increase: ${estimated_increase:,.2f}/month (10% increase for performance benefits)"
        
        else:
            impact = "Cost impact requires detailed analysis"
        
        return impact
    
    def score_business_value(self, service_info: Dict) -> float:
        """Score the business value of the new service (0-1 scale)"""
        
        score = 0.5  # Base score
        
        # Increase score based on potential benefits
        benefits = service_info.get('potential_benefits', [])
        
        for benefit in benefits:
            if 'cost' in benefit.lower():
                score += 0.2
            if 'performance' in benefit.lower():
                score += 0.15
            if 'management' in benefit.lower():
                score += 0.1
            if 'scalability' in benefit.lower():
                score += 0.1
        
        return min(1.0, score)
    
    def score_technical_feasibility(self, service_info: Dict) -> float:
        """Score the technical feasibility of adopting the service (0-1 scale)"""
        
        score = 0.7  # Base score assuming most AWS services are technically feasible
        
        service_name = service_info['service_name']
        category = service_info['category']
        
        # Adjust score based on complexity factors
        if 'serverless' in service_name.lower():
            score += 0.1  # Serverless is generally easier to adopt
        
        if category == ServiceCategory.MACHINE_LEARNING:
            score -= 0.2  # ML services may require more expertise
        
        if 'graviton' in service_name.lower():
            score -= 0.1  # ARM architecture may require application changes
        
        return max(0.0, min(1.0, score))
    
    def score_migration_complexity(self, service_info: Dict) -> float:
        """Score the migration complexity (0-1 scale, where 1 is low complexity)"""
        
        score = 0.6  # Base score
        
        service_name = service_info['service_name']
        category = service_info['category']
        
        # Adjust score based on migration factors
        if category == ServiceCategory.DATABASE:
            score -= 0.2  # Database migrations are typically complex
        
        if 'serverless' in service_name.lower():
            score += 0.2  # Serverless often requires architectural changes but less infrastructure management
        
        if category == ServiceCategory.STORAGE:
            score += 0.1  # Storage migrations are often straightforward
        
        return max(0.0, min(1.0, score))
    
    def create_pilot_program(self, evaluation: ServiceEvaluation) -> Dict:
        """Create a pilot program for evaluating the new service"""
        
        pilot_program = {
            'pilot_id': f"PILOT-{evaluation.service_name.replace(' ', '-')}-{datetime.now().strftime('%Y%m%d')}",
            'service_name': evaluation.service_name,
            'pilot_objectives': self.define_pilot_objectives(evaluation),
            'success_criteria': self.define_success_criteria(evaluation),
            'pilot_timeline': self.create_pilot_timeline(evaluation),
            'resource_requirements': self.estimate_pilot_resources(evaluation),
            'risk_assessment': self.assess_pilot_risks(evaluation),
            'monitoring_plan': self.create_monitoring_plan(evaluation)
        }
        
        return pilot_program
    
    def define_pilot_objectives(self, evaluation: ServiceEvaluation) -> List[str]:
        """Define objectives for the pilot program"""
        
        objectives = [
            f"Validate cost savings potential for {evaluation.service_name}",
            "Assess technical feasibility and integration requirements",
            "Evaluate performance characteristics and operational impact",
            "Identify migration complexity and resource requirements"
        ]
        
        # Add service-specific objectives
        if evaluation.category == ServiceCategory.DATABASE:
            objectives.append("Test database performance and compatibility")
            objectives.append("Validate backup and recovery procedures")
        
        elif evaluation.category == ServiceCategory.COMPUTE:
            objectives.append("Benchmark application performance")
            objectives.append("Test auto-scaling and load handling")
        
        return objectives
    
    def define_success_criteria(self, evaluation: ServiceEvaluation) -> List[str]:
        """Define success criteria for the pilot program"""
        
        criteria = [
            "Cost reduction of at least 15% compared to current solution",
            "Performance meets or exceeds current solution",
            "No significant operational complexity increase",
            "Migration effort within acceptable limits"
        ]
        
        # Add service-specific criteria
        if evaluation.business_value_score > 0.8:
            criteria.append("Demonstrates clear business value beyond cost savings")
        
        if evaluation.technical_feasibility_score < 0.7:
            criteria.append("Technical challenges are manageable and documented")
        
        return criteria
    
    def generate_evaluation_report(self, evaluation: ServiceEvaluation) -> Dict:
        """Generate comprehensive evaluation report"""
        
        # Calculate overall score
        overall_score = (
            evaluation.business_value_score * self.evaluation_weights['business_value'] +
            evaluation.technical_feasibility_score * self.evaluation_weights['technical_feasibility'] +
            (1 - evaluation.migration_complexity_score) * self.evaluation_weights['migration_complexity'] +
            0.8 * self.evaluation_weights['cost_impact']  # Assume positive cost impact
        )
        
        # Generate recommendation
        if overall_score >= 0.8:
            recommendation = "Strongly Recommended - Proceed with pilot program"
        elif overall_score >= 0.6:
            recommendation = "Recommended - Proceed with careful evaluation"
        elif overall_score >= 0.4:
            recommendation = "Conditional - Consider for specific use cases"
        else:
            recommendation = "Not Recommended - Defer evaluation"
        
        report = {
            'service_name': evaluation.service_name,
            'evaluation_date': datetime.now().strftime('%Y-%m-%d'),
            'overall_score': overall_score,
            'recommendation': recommendation,
            'cost_impact': evaluation.cost_impact_estimate,
            'business_value_score': evaluation.business_value_score,
            'technical_feasibility_score': evaluation.technical_feasibility_score,
            'migration_complexity_score': evaluation.migration_complexity_score,
            'potential_use_cases': evaluation.potential_use_cases,
            'next_steps': self.generate_next_steps(evaluation, overall_score),
            'evaluation_notes': evaluation.evaluation_notes
        }
        
        return report
    
    def generate_next_steps(self, evaluation: ServiceEvaluation, overall_score: float) -> List[str]:
        """Generate recommended next steps based on evaluation results"""
        
        next_steps = []
        
        if overall_score >= 0.8:
            next_steps.extend([
                "Create detailed pilot program plan",
                "Allocate resources for pilot implementation",
                "Set up monitoring and measurement framework",
                "Begin pilot program within 30 days"
            ])
        
        elif overall_score >= 0.6:
            next_steps.extend([
                "Conduct deeper technical analysis",
                "Engage with AWS solutions architects",
                "Create proof-of-concept implementation",
                "Reassess after additional analysis"
            ])
        
        elif overall_score >= 0.4:
            next_steps.extend([
                "Monitor service evolution and improvements",
                "Identify specific use cases for future evaluation",
                "Schedule re-evaluation in 6 months",
                "Consider for future architecture decisions"
            ])
        
        else:
            next_steps.extend([
                "Document evaluation rationale",
                "Monitor for significant service updates",
                "Schedule re-evaluation in 12 months",
                "Focus on higher-priority service evaluations"
            ])
        
        return next_steps
    
    def get_current_category_spending(self, category: ServiceCategory) -> float:
        """Get current spending for a service category"""
        
        # This would integrate with AWS Cost Explorer API
        # For demonstration, returning sample values
        
        category_spending = {
            ServiceCategory.COMPUTE: 5000,
            ServiceCategory.DATABASE: 3000,
            ServiceCategory.STORAGE: 2000,
            ServiceCategory.NETWORKING: 1000,
            ServiceCategory.ANALYTICS: 1500,
            ServiceCategory.MACHINE_LEARNING: 800,
            ServiceCategory.SECURITY: 600,
            ServiceCategory.MANAGEMENT: 400
        }
        
        return category_spending.get(category, 1000)

New_Service_Evaluation:
  service_name: "Amazon Aurora Serverless v2"
  evaluation_id: "EVAL-2024-001"
  evaluation_date: "2024-01-15"
  evaluator: "Cloud Architecture Team"
  
  service_details:
    category: "Database"
    announcement_date: "2024-01-10"
    general_availability: "2024-02-01"
    description: "Serverless relational database with instant scaling"
    key_features:
      - "Instant scaling from 0.5 to 128 ACUs"
      - "Pay only for capacity used"
      - "Compatible with MySQL and PostgreSQL"
      - "Automatic pause and resume"
      
  current_state_analysis:
    relevant_workloads:
      - "Development databases (15 RDS instances)"
      - "Staging environments (8 RDS instances)"
      - "Analytics workloads (variable usage)"
    current_monthly_cost: 4500
    pain_points:
      - "Over-provisioned development databases"
      - "Manual scaling for variable workloads"
      - "High costs for infrequently used environments"
      
  evaluation_scores:
    business_value: 0.85
    technical_feasibility: 0.90
    cost_impact: 0.80
    strategic_alignment: 0.75
    migration_complexity: 0.70
    overall_score: 0.80
    
  cost_analysis:
    estimated_monthly_savings: 1350  # 30% reduction
    migration_cost: 5000
    payback_period_months: 4
    three_year_savings: 43200
    
  pilot_program:
    scope: "Migrate 3 development databases"
    duration: "8 weeks"
    success_criteria:
      - "Cost reduction >= 25%"
      - "Performance equivalent or better"
      - "No application compatibility issues"
      - "Operational complexity acceptable"
    resources_required:
      - "1 database engineer (50% time)"
      - "1 application developer (25% time)"
      - "AWS support engagement"
      
  recommendation: "Proceed with pilot program"
  rationale: |
    Aurora Serverless v2 shows strong potential for cost optimization
    in our development and variable workload scenarios. High scores
    across all evaluation criteria and clear cost savings justify
    proceeding with a structured pilot program.
    
  next_steps:
    - "Create detailed pilot implementation plan"
    - "Set up monitoring and cost tracking"
    - "Begin pilot with lowest-risk development database"
    - "Schedule weekly progress reviews"
    
  risks_and_mitigations:
    - risk: "Application compatibility issues"
      mitigation: "Thorough testing in isolated environment"
    - risk: "Performance degradation"
      mitigation: "Comprehensive benchmarking and monitoring"
    - risk: "Operational complexity"
      mitigation: "Training and documentation development"

def create_service_evaluation_dashboard():
    """Create dashboard for tracking service evaluations"""
    
    dashboard_config = {
        'dashboard_name': 'New Service Evaluation Pipeline',
        'widgets': [
            {
                'type': 'metric',
                'title': 'Services Under Evaluation',
                'metric': 'custom.service_evaluation.under_review',
                'period': 86400
            },
            {
                'type': 'metric',
                'title': 'Pilot Programs Active',
                'metric': 'custom.service_evaluation.pilots_active',
                'period': 86400
            },
            {
                'type': 'metric',
                'title': 'Services Approved This Quarter',
                'metric': 'custom.service_evaluation.approved_quarterly',
                'period': 86400
            },
            {
                'type': 'table',
                'title': 'Evaluation Pipeline Status',
                'columns': ['Service Name', 'Category', 'Status', 'Overall Score', 'Next Review'],
                'data_source': 'evaluation_pipeline_table'
            },
            {
                'type': 'pie_chart',
                'title': 'Evaluations by Category',
                'metric': 'custom.service_evaluation.by_category',
                'period': 86400
            },
            {
                'type': 'line_chart',
                'title': 'Cost Savings from New Services',
                'metric': 'custom.cost_optimization.new_service_savings',
                'period': 2592000  # 30 days
            }
        ],
        'refresh_interval': 3600
    }
    
    return dashboard_config