COST08-BP02 - Optimize data transfer charges

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 enum import Enum
import gzip
import base64

class OptimizationStrategy(Enum):
    CLOUDFRONT_CDN = "cloudfront_cdn"
    REGIONAL_CACHING = "regional_caching"
    DATA_COMPRESSION = "data_compression"
    API_OPTIMIZATION = "api_optimization"
    DATA_LOCALITY = "data_locality"
    BATCH_PROCESSING = "batch_processing"

@dataclass
class OptimizationOpportunity:
    strategy: OptimizationStrategy
    current_cost: float
    potential_savings: float
    implementation_effort: str
    expected_timeline: str
    risk_level: str
    description: str

@dataclass
class TransferOptimizationPlan:
    plan_id: str
    total_current_cost: float
    total_potential_savings: float
    optimization_opportunities: List[OptimizationOpportunity]
    implementation_phases: List[Dict]
    success_metrics: List[str]

class DataTransferOptimizer:
    def __init__(self):
        self.cloudfront = boto3.client('cloudfront')
        self.elasticache = boto3.client('elasticache')
        self.s3 = boto3.client('s3')
        self.ce_client = boto3.client('ce')
        self.cloudwatch = boto3.client('cloudwatch')
        
        # Optimization parameters
        self.optimization_thresholds = {
            'high_internet_egress': 1000,  # $1000/month threshold
            'high_inter_region': 500,      # $500/month threshold
            'low_cache_hit_rate': 0.7,     # 70% cache hit rate threshold
            'high_api_payload': 1024       # 1KB average payload threshold
        }
        
    def analyze_optimization_opportunities(self, transfer_data: Dict) -> TransferOptimizationPlan:
        """Analyze data transfer patterns and identify optimization opportunities"""
        
        opportunities = []
        total_current_cost = sum(transfer_data.get('cost_breakdown', {}).values())
        
        # CloudFront CDN optimization
        cloudfront_opportunity = self.analyze_cloudfront_opportunity(transfer_data)
        if cloudfront_opportunity:
            opportunities.append(cloudfront_opportunity)
        
        # Regional caching optimization
        caching_opportunity = self.analyze_caching_opportunity(transfer_data)
        if caching_opportunity:
            opportunities.append(caching_opportunity)
        
        # Data compression optimization
        compression_opportunity = self.analyze_compression_opportunity(transfer_data)
        if compression_opportunity:
            opportunities.append(compression_opportunity)
        
        # API optimization
        api_opportunity = self.analyze_api_optimization(transfer_data)
        if api_opportunity:
            opportunities.append(api_opportunity)
        
        # Data locality optimization
        locality_opportunity = self.analyze_data_locality_opportunity(transfer_data)
        if locality_opportunity:
            opportunities.append(locality_opportunity)
        
        # Calculate total potential savings
        total_potential_savings = sum(opp.potential_savings for opp in opportunities)
        
        # Create implementation phases
        implementation_phases = self.create_implementation_phases(opportunities)
        
        # Define success metrics
        success_metrics = [
            'Data transfer cost reduction percentage',
            'CloudFront cache hit rate improvement',
            'Inter-region transfer volume reduction',
            'API payload size reduction',
            'Overall transfer efficiency improvement'
        ]
        
        return TransferOptimizationPlan(
            plan_id=f"TRANSFER_OPT_{datetime.now().strftime('%Y%m%d')}",
            total_current_cost=total_current_cost,
            total_potential_savings=total_potential_savings,
            optimization_opportunities=opportunities,
            implementation_phases=implementation_phases,
            success_metrics=success_metrics
        )
    
    def analyze_cloudfront_opportunity(self, transfer_data: Dict) -> Optional[OptimizationOpportunity]:
        """Analyze CloudFront CDN optimization opportunity"""
        
        internet_egress_cost = transfer_data.get('cost_breakdown', {}).get('internet_egress', 0)
        
        if internet_egress_cost > self.optimization_thresholds['high_internet_egress']:
            # Estimate CloudFront savings (typically 30-50% for static content)
            estimated_savings = internet_egress_cost * 0.4  # 40% average savings
            
            return OptimizationOpportunity(
                strategy=OptimizationStrategy.CLOUDFRONT_CDN,
                current_cost=internet_egress_cost,
                potential_savings=estimated_savings,
                implementation_effort='Medium',
                expected_timeline='4-6 weeks',
                risk_level='Low',
                description=f'Implement CloudFront CDN to reduce ${internet_egress_cost:.2f}/month internet egress costs'
            )
        
        return None
    
    def analyze_caching_opportunity(self, transfer_data: Dict) -> Optional[OptimizationOpportunity]:
        """Analyze regional caching optimization opportunity"""
        
        # Look for repeated data access patterns that could benefit from caching
        total_transfer_cost = sum(transfer_data.get('cost_breakdown', {}).values())
        
        # Estimate caching opportunity based on transfer patterns
        if total_transfer_cost > 2000:  # Significant transfer costs
            # Assume 20-30% of transfers could be cached
            cacheable_cost = total_transfer_cost * 0.25
            estimated_savings = cacheable_cost * 0.6  # 60% reduction for cached content
            
            return OptimizationOpportunity(
                strategy=OptimizationStrategy.REGIONAL_CACHING,
                current_cost=cacheable_cost,
                potential_savings=estimated_savings,
                implementation_effort='Medium',
                expected_timeline='3-4 weeks',
                risk_level='Low',
                description=f'Implement regional caching to reduce repeated data transfers'
            )
        
        return None
    
    def analyze_compression_opportunity(self, transfer_data: Dict) -> Optional[OptimizationOpportunity]:
        """Analyze data compression optimization opportunity"""
        
        total_volume = sum(transfer_data.get('volume_breakdown', {}).values())
        total_cost = sum(transfer_data.get('cost_breakdown', {}).values())
        
        if total_volume > 50000:  # 50GB threshold
            # Estimate compression savings (typically 30-70% size reduction)
            compression_ratio = 0.5  # 50% size reduction
            estimated_savings = total_cost * compression_ratio * 0.8  # 80% of transfers compressible
            
            return OptimizationOpportunity(
                strategy=OptimizationStrategy.DATA_COMPRESSION,
                current_cost=total_cost,
                potential_savings=estimated_savings,
                implementation_effort='Low',
                expected_timeline='2-3 weeks',
                risk_level='Low',
                description=f'Implement data compression to reduce transfer volumes by ~50%'
            )
        
        return None
    
    def analyze_api_optimization(self, transfer_data: Dict) -> Optional[OptimizationOpportunity]:
        """Analyze API optimization opportunity"""
        
        # Look for service-to-service transfer costs that could indicate inefficient APIs
        service_transfer_cost = transfer_data.get('cost_breakdown', {}).get('service_specific', 0)
        
        if service_transfer_cost > 300:  # Threshold for API optimization
            # Estimate savings from API optimization (reducing payload sizes, batching, etc.)
            estimated_savings = service_transfer_cost * 0.3  # 30% reduction
            
            return OptimizationOpportunity(
                strategy=OptimizationStrategy.API_OPTIMIZATION,
                current_cost=service_transfer_cost,
                potential_savings=estimated_savings,
                implementation_effort='High',
                expected_timeline='6-8 weeks',
                risk_level='Medium',
                description=f'Optimize API design to reduce data transfer between services'
            )
        
        return None
    
    def analyze_data_locality_opportunity(self, transfer_data: Dict) -> Optional[OptimizationOpportunity]:
        """Analyze data locality optimization opportunity"""
        
        inter_region_cost = transfer_data.get('cost_breakdown', {}).get('inter_region', 0)
        
        if inter_region_cost > self.optimization_thresholds['high_inter_region']:
            # Estimate savings from improved data locality
            estimated_savings = inter_region_cost * 0.6  # 60% reduction through better placement
            
            return OptimizationOpportunity(
                strategy=OptimizationStrategy.DATA_LOCALITY,
                current_cost=inter_region_cost,
                potential_savings=estimated_savings,
                implementation_effort='High',
                expected_timeline='8-12 weeks',
                risk_level='Medium',
                description=f'Optimize data placement to reduce ${inter_region_cost:.2f}/month inter-region transfers'
            )
        
        return None
    
    def implement_cloudfront_optimization(self, domain_name: str, origin_config: Dict) -> Dict:
        """Implement CloudFront CDN optimization"""
        
        cloudfront_config = {
            'distribution_config': {
                'caller_reference': f'optimization-{datetime.now().strftime("%Y%m%d%H%M%S")}',
                'comment': 'Data transfer cost optimization distribution',
                'default_cache_behavior': {
                    'target_origin_id': 'primary-origin',
                    'viewer_protocol_policy': 'redirect-to-https',
                    'allowed_methods': {
                        'quantity': 7,
                        'items': ['GET', 'HEAD', 'OPTIONS', 'PUT', 'POST', 'PATCH', 'DELETE'],
                        'cached_methods': {
                            'quantity': 2,
                            'items': ['GET', 'HEAD']
                        }
                    },
                    'forwarded_values': {
                        'query_string': False,
                        'cookies': {'forward': 'none'},
                        'headers': {
                            'quantity': 1,
                            'items': ['Host']
                        }
                    },
                    'trusted_signers': {
                        'enabled': False,
                        'quantity': 0
                    },
                    'min_ttl': 0,
                    'default_ttl': 86400,  # 24 hours
                    'max_ttl': 31536000,   # 1 year
                    'compress': True  # Enable compression
                },
                'origins': {
                    'quantity': 1,
                    'items': [
                        {
                            'id': 'primary-origin',
                            'domain_name': origin_config['domain_name'],
                            'custom_origin_config': {
                                'http_port': 80,
                                'https_port': 443,
                                'origin_protocol_policy': 'https-only',
                                'origin_ssl_protocols': {
                                    'quantity': 1,
                                    'items': ['TLSv1.2']
                                }
                            }
                        }
                    ]
                },
                'enabled': True,
                'price_class': 'PriceClass_100',  # Use only US, Canada, Europe edge locations
                'http_version': 'http2'
            },
            'optimization_settings': {
                'compression_enabled': True,
                'cache_behaviors': self.create_optimized_cache_behaviors(),
                'monitoring': self.setup_cloudfront_monitoring()
            }
        }
        
        return cloudfront_config
    
    def create_optimized_cache_behaviors(self) -> List[Dict]:
        """Create optimized cache behaviors for different content types"""
        
        cache_behaviors = [
            {
                'path_pattern': '/api/*',
                'target_origin_id': 'primary-origin',
                'viewer_protocol_policy': 'https-only',
                'allowed_methods': ['GET', 'HEAD', 'OPTIONS', 'PUT', 'POST', 'PATCH', 'DELETE'],
                'cached_methods': ['GET', 'HEAD'],
                'forwarded_values': {
                    'query_string': True,
                    'cookies': {'forward': 'all'},
                    'headers': ['Authorization', 'Content-Type']
                },
                'min_ttl': 0,
                'default_ttl': 0,  # No caching for API calls
                'max_ttl': 0,
                'compress': True
            },
            {
                'path_pattern': '/static/*',
                'target_origin_id': 'primary-origin',
                'viewer_protocol_policy': 'https-only',
                'allowed_methods': ['GET', 'HEAD'],
                'cached_methods': ['GET', 'HEAD'],
                'forwarded_values': {
                    'query_string': False,
                    'cookies': {'forward': 'none'}
                },
                'min_ttl': 86400,     # 24 hours
                'default_ttl': 604800, # 7 days
                'max_ttl': 31536000,   # 1 year
                'compress': True
            },
            {
                'path_pattern': '/images/*',
                'target_origin_id': 'primary-origin',
                'viewer_protocol_policy': 'https-only',
                'allowed_methods': ['GET', 'HEAD'],
                'cached_methods': ['GET', 'HEAD'],
                'forwarded_values': {
                    'query_string': False,
                    'cookies': {'forward': 'none'}
                },
                'min_ttl': 604800,    # 7 days
                'default_ttl': 2592000, # 30 days
                'max_ttl': 31536000,   # 1 year
                'compress': False  # Images are already compressed
            }
        ]
        
        return cache_behaviors
    
    def implement_regional_caching(self, cache_config: Dict) -> Dict:
        """Implement regional caching with ElastiCache"""
        
        elasticache_config = {
            'cache_cluster_id': f'transfer-opt-{datetime.now().strftime("%Y%m%d")}',
            'engine': 'redis',
            'cache_node_type': 'cache.r6g.large',
            'num_cache_nodes': 2,
            'parameter_group_name': 'default.redis7',
            'port': 6379,
            'preferred_availability_zones': cache_config.get('availability_zones', ['us-east-1a', 'us-east-1b']),
            'security_group_ids': cache_config.get('security_group_ids', []),
            'subnet_group_name': cache_config.get('subnet_group_name'),
            'tags': [
                {'Key': 'Purpose', 'Value': 'DataTransferOptimization'},
                {'Key': 'Environment', 'Value': cache_config.get('environment', 'production')}
            ],
            'optimization_settings': {
                'cache_strategies': self.define_cache_strategies(),
                'ttl_configurations': self.define_ttl_configurations(),
                'monitoring': self.setup_cache_monitoring()
            }
        }
        
        return elasticache_config
    
    def define_cache_strategies(self) -> Dict:
        """Define caching strategies for different data types"""
        
        strategies = {
            'database_queries': {
                'strategy': 'write_through',
                'ttl': 3600,  # 1 hour
                'key_pattern': 'db:query:{hash}',
                'compression': True
            },
            'api_responses': {
                'strategy': 'cache_aside',
                'ttl': 1800,  # 30 minutes
                'key_pattern': 'api:{endpoint}:{params_hash}',
                'compression': True
            },
            'static_content': {
                'strategy': 'write_through',
                'ttl': 86400,  # 24 hours
                'key_pattern': 'static:{path_hash}',
                'compression': False  # Already compressed
            },
            'user_sessions': {
                'strategy': 'write_through',
                'ttl': 7200,  # 2 hours
                'key_pattern': 'session:{user_id}',
                'compression': True
            }
        }
        
        return strategies
    
    def implement_data_compression(self, compression_config: Dict) -> Dict:
        """Implement data compression optimization"""
        
        compression_settings = {
            'algorithms': {
                'text_data': 'gzip',
                'json_data': 'gzip',
                'binary_data': 'lz4',
                'images': 'webp',  # For image optimization
                'videos': 'h264'   # For video optimization
            },
            'compression_levels': {
                'real_time': 1,    # Fast compression for real-time data
                'standard': 6,     # Balanced compression
                'archival': 9      # Maximum compression for archival
            },
            'implementation': {
                'api_gateway': self.configure_api_gateway_compression(),
                'application_level': self.configure_application_compression(),
                'storage_level': self.configure_storage_compression()
            }
        }
        
        return compression_settings
    
    def configure_api_gateway_compression(self) -> Dict:
        """Configure API Gateway compression settings"""
        
        return {
            'minimum_compression_size': 1024,  # 1KB minimum
            'content_types': [
                'application/json',
                'application/xml',
                'text/plain',
                'text/html',
                'text/css',
                'application/javascript'
            ],
            'compression_level': 6
        }
    
    def optimize_data_locality(self, locality_config: Dict) -> Dict:
        """Implement data locality optimization"""
        
        optimization_plan = {
            'data_placement_strategy': {
                'user_data': 'region_based_on_user_location',
                'application_data': 'co_locate_with_compute',
                'shared_data': 'replicate_to_high_usage_regions',
                'archival_data': 'single_region_lowest_cost'
            },
            'replication_strategy': {
                'critical_data': {
                    'replication_type': 'synchronous',
                    'target_regions': locality_config.get('critical_regions', ['us-east-1', 'us-west-2']),
                    'consistency': 'strong'
                },
                'non_critical_data': {
                    'replication_type': 'asynchronous',
                    'target_regions': locality_config.get('backup_regions', ['us-east-1']),
                    'consistency': 'eventual'
                }
            },
            'access_patterns': {
                'read_heavy_workloads': 'read_replicas_in_user_regions',
                'write_heavy_workloads': 'single_region_with_caching',
                'mixed_workloads': 'regional_primary_with_read_replicas'
            },
            'migration_plan': self.create_data_migration_plan(locality_config)
        }
        
        return optimization_plan
    
    def create_implementation_phases(self, opportunities: List[OptimizationOpportunity]) -> List[Dict]:
        """Create phased implementation plan for optimizations"""
        
        # Sort opportunities by effort and impact
        low_effort_high_impact = [opp for opp in opportunities if opp.implementation_effort == 'Low']
        medium_effort = [opp for opp in opportunities if opp.implementation_effort == 'Medium']
        high_effort = [opp for opp in opportunities if opp.implementation_effort == 'High']
        
        phases = []
        
        # Phase 1: Quick wins (low effort, high impact)
        if low_effort_high_impact:
            phases.append({
                'phase': 1,
                'name': 'Quick Wins',
                'duration': '2-4 weeks',
                'opportunities': low_effort_high_impact,
                'expected_savings': sum(opp.potential_savings for opp in low_effort_high_impact),
                'success_criteria': [
                    'Implementation completed within timeline',
                    'Cost reduction achieved as projected',
                    'No performance degradation'
                ]
            })
        
        # Phase 2: Medium effort optimizations
        if medium_effort:
            phases.append({
                'phase': 2,
                'name': 'Medium Impact Optimizations',
                'duration': '4-8 weeks',
                'opportunities': medium_effort,
                'expected_savings': sum(opp.potential_savings for opp in medium_effort),
                'success_criteria': [
                    'Successful implementation with minimal disruption',
                    'Performance metrics maintained or improved',
                    'Cost targets achieved'
                ]
            })
        
        # Phase 3: High effort, strategic optimizations
        if high_effort:
            phases.append({
                'phase': 3,
                'name': 'Strategic Optimizations',
                'duration': '8-16 weeks',
                'opportunities': high_effort,
                'expected_savings': sum(opp.potential_savings for opp in high_effort),
                'success_criteria': [
                    'Successful architectural changes implemented',
                    'Long-term cost reduction achieved',
                    'Improved system efficiency and performance'
                ]
            })
        
        return phases
    
    def monitor_optimization_effectiveness(self, baseline_metrics: Dict) -> Dict:
        """Monitor the effectiveness of data transfer optimizations"""
        
        monitoring_framework = {
            'cost_metrics': {
                'total_transfer_cost_reduction': self.calculate_cost_reduction(baseline_metrics),
                'cost_per_gb_improvement': self.calculate_efficiency_improvement(baseline_metrics),
                'roi_calculation': self.calculate_optimization_roi(baseline_metrics)
            },
            'performance_metrics': {
                'cache_hit_rates': self.monitor_cache_performance(),
                'transfer_latency': self.monitor_transfer_latency(),
                'user_experience_impact': self.monitor_user_experience()
            },
            'operational_metrics': {
                'optimization_coverage': self.calculate_optimization_coverage(),
                'automation_effectiveness': self.monitor_automation_effectiveness(),
                'incident_impact': self.monitor_incident_impact()
            }
        }
        
        return monitoring_framework

CloudFront_Optimization_Config:
  distribution_name: "data-transfer-optimization"
  optimization_objective: "Reduce internet egress costs by 40%"
  
  origin_configuration:
    primary_origin:
      domain_name: "api.example.com"
      protocol_policy: "https-only"
      custom_headers:
        - name: "X-Forwarded-Proto"
          value: "https"
          
  cache_behaviors:
    default_behavior:
      viewer_protocol_policy: "redirect-to-https"
      compress: true
      ttl:
        min: 0
        default: 86400  # 24 hours
        max: 31536000   # 1 year
        
    api_endpoints:
      path_pattern: "/api/*"
      cache_policy: "no-cache"
      origin_request_policy: "forward-all"
      compress: true
      
    static_assets:
      path_pattern: "/static/*"
      cache_policy: "long-term-cache"
      ttl:
        min: 86400      # 24 hours
        default: 604800 # 7 days
        max: 31536000   # 1 year
      compress: true
      
    images:
      path_pattern: "/images/*"
      cache_policy: "image-optimization"
      ttl:
        min: 604800     # 7 days
        default: 2592000 # 30 days
        max: 31536000   # 1 year
      compress: false
      
  optimization_features:
    compression:
      enabled: true
      minimum_size: 1024  # 1KB
      content_types:
        - "application/json"
        - "text/html"
        - "text/css"
        - "application/javascript"
        
    http2_support: true
    ipv6_support: true
    
  monitoring:
    cache_hit_rate_target: 85
    origin_latency_target: 200  # milliseconds
    cost_reduction_target: 40   # percent
    
  estimated_savings:
    monthly_internet_egress_reduction: 1200.00
    cloudfront_costs: 800.00
    net_monthly_savings: 400.00
    annual_savings: 4800.00

def create_regional_caching_strategy():
    """Create comprehensive regional caching strategy"""
    
    strategy = {
        'cache_tiers': {
            'edge_cache': {
                'technology': 'CloudFront',
                'ttl_range': '1 hour - 1 year',
                'content_types': ['static assets', 'images', 'videos'],
                'cache_hit_target': 90
            },
            'regional_cache': {
                'technology': 'ElastiCache Redis',
                'ttl_range': '5 minutes - 24 hours',
                'content_types': ['API responses', 'database queries', 'computed results'],
                'cache_hit_target': 80
            },
            'application_cache': {
                'technology': 'In-memory caching',
                'ttl_range': '1 minute - 1 hour',
                'content_types': ['session data', 'user preferences', 'temporary results'],
                'cache_hit_target': 95
            }
        },
        
        'cache_invalidation': {
            'strategies': {
                'time_based': 'TTL expiration for predictable content',
                'event_based': 'Immediate invalidation for critical updates',
                'version_based': 'Version tags for controlled updates'
            },
            'invalidation_patterns': {
                'wildcard_patterns': ['/api/v1/*', '/user/*/profile'],
                'tag_based_invalidation': ['user-data', 'product-catalog'],
                'selective_invalidation': 'Target specific cache keys'
            }
        },
        
        'cache_warming': {
            'strategies': {
                'predictive_warming': 'Pre-load cache based on usage patterns',
                'scheduled_warming': 'Regular cache refresh for critical data',
                'on_demand_warming': 'Load cache when first requested'
            },
            'warming_triggers': [
                'Application deployment',
                'Cache invalidation events',
                'Scheduled maintenance windows'
            ]
        },
        
        'monitoring_and_optimization': {
            'key_metrics': [
                'Cache hit ratio by content type',
                'Cache miss penalty (latency impact)',
                'Cache storage utilization',
                'Invalidation frequency and impact'
            ],
            'optimization_triggers': [
                'Cache hit ratio below 80%',
                'High cache miss latency',
                'Frequent cache invalidations',
                'Storage utilization above 85%'
            ]
        }
    }
    
    return strategy