AWS Global Accelerator 実装ガイド - グローバル高速化の設計と運用 ​

import boto3
import json
from datetime import datetime, timedelta

def adjust_traffic_weights():
    """
    パフォーマンスメトリクスに基づく動的ウェイト調整
    """
    ga_client = boto3.client('globalaccelerator')
    cloudwatch = boto3.client('cloudwatch')
    
    # CloudWatchからレスポンス時間取得
    us_east_latency = get_average_latency('us-east-1')
    eu_west_latency = get_average_latency('eu-west-1')
    
    # レスポンス時間に基づくウェイト計算
    total_inverse = (1/us_east_latency) + (1/eu_west_latency)
    us_weight = int((1/us_east_latency) / total_inverse * 100)
    eu_weight = 100 - us_weight
    
    # Endpoint Group更新
    ga_client.update_endpoint_group(
        EndpointGroupArn='arn:aws:globalaccelerator::123456789012:accelerator/abcd1234/listener/1234/endpoint-group/5678',
        EndpointConfigurations=[
            {
                'EndpointId': 'arn:aws:elasticloadbalancing:us-east-1:123456789012:loadbalancer/app/us-east-alb',
                'Weight': us_weight
            },
            {
                'EndpointId': 'arn:aws:elasticloadbalancing:eu-west-1:123456789012:loadbalancer/app/eu-west-alb', 
                'Weight': eu_weight
            }
        ]
    )

def get_average_latency(region):
    """指定リージョンの平均レスポンス時間を取得"""
    cloudwatch = boto3.client('cloudwatch', region_name=region)
    
    response = cloudwatch.get_metric_statistics(
        Namespace='AWS/ApplicationELB',
        MetricName='TargetResponseTime',
        StartTime=datetime.now() - timedelta(minutes=5),
        EndTime=datetime.now(),
        Period=60,
        Statistics=['Average']
    )
    
    if response['Datapoints']:
        return sum(point['Average'] for point in response['Datapoints']) / len(response['Datapoints'])
    return 1.0  # デフォルト値

import boto3
import pandas as pd
from datetime import datetime, timedelta

def analyze_flow_logs():
    """
    Flow Logsを分析してトラフィックパターンを把握
    """
    s3_client = boto3.client('s3')
    
    # S3からFlow Logs取得
    response = s3_client.list_objects_v2(
        Bucket='ga-flow-logs-bucket',
        Prefix='accelerator-logs/'
    )
    
    traffic_patterns = {}
    
    for obj in response.get('Contents', []):
        # ログファイル読み取り
        log_data = s3_client.get_object(
            Bucket='ga-flow-logs-bucket', 
            Key=obj['Key']
        )
        
        # CSV解析（簡略版）
        df = pd.read_csv(log_data['Body'], sep=' ')
        
        # 地理的分布分析
        for _, row in df.iterrows():
            source_ip = row['sourceAddress']
            endpoint = row['globalAcceleratorEndpoint']
            
            if source_ip not in traffic_patterns:
                traffic_patterns[source_ip] = {
                    'total_bytes': 0,
                    'connection_count': 0,
                    'primary_endpoint': {}
                }
            
            traffic_patterns[source_ip]['total_bytes'] += row['numBytes']
            traffic_patterns[source_ip]['connection_count'] += 1
            
            if endpoint not in traffic_patterns[source_ip]['primary_endpoint']:
                traffic_patterns[source_ip]['primary_endpoint'][endpoint] = 0
            traffic_patterns[source_ip]['primary_endpoint'][endpoint] += 1
    
    return traffic_patterns

import boto3
import time

def monitor_error_rates():
    # エラー率を監視するロジックを実装
    return True # 正常時はTrueを返す

def rollback_deployment(blue_endpoint_arn, green_endpoint_arn):
    # ロールバック処理を実装
    print("Deployment failed, rolling back.")

def blue_green_deployment(green_endpoint_arn, blue_endpoint_arn):
    """
    Blue-Green デプロイメントの段階的実行
    """
    ga_client = boto3.client('globalaccelerator')
    
    # 段階的ウェイト調整
    weight_transitions = [
        (90, 10),   # Blue: 90%, Green: 10%
        (70, 30),   # Blue: 70%, Green: 30%
        (50, 50),   # Blue: 50%, Green: 50%
        (30, 70),   # Blue: 30%, Green: 70%
        (0, 100)    # Blue: 0%, Green: 100%
    ]
    
    for blue_weight, green_weight in weight_transitions:
        # エンドポイント設定更新
        ga_client.update_endpoint_group(
            EndpointGroupArn='arn:aws:globalaccelerator::123456789012:accelerator/abcd1234/listener/1234/endpoint-group/5678',
            EndpointConfigurations=[
                {
                    'EndpointId': blue_endpoint_arn,
                    'Weight': blue_weight
                },
                {
                    'EndpointId': green_endpoint_arn,
                    'Weight': green_weight
                }
            ]
        )
        
        # エラー率監視
        if not monitor_error_rates():
            # ロールバック
            rollback_deployment(blue_endpoint_arn, green_endpoint_arn)
            return False
        
        # 次の段階まで待機
        time.sleep(300)  # 5分間隔
    
    return True

def check_all_endpoints_health():
    # エンドポイントのヘルス状態を確認するロジックを実装
    return {}

def activate_disaster_recovery(endpoint_group_arn):
    # DR環境の有効化ロジックを実装
    print(f"Activating DR for {endpoint_group_arn}")

def redistribute_traffic(endpoint_group_arn, healthy_endpoints):
    # トラフィック再分散ロジックを実装
    print(f"Redistributing traffic for {endpoint_group_arn}")

def automated_failover():
    """
    ヘルスチェック結果に基づく自動フェイルオーバー
    """
    ga_client = boto3.client('globalaccelerator')
    
    # 全エンドポイントのヘルス状態確認
    endpoint_health = check_all_endpoints_health()
    
    for endpoint_group_arn, endpoints in endpoint_health.items():
        healthy_endpoints = [ep for ep in endpoints if ep['healthy']]
        
        if len(healthy_endpoints) == 0:
            # すべてのエンドポイントが異常な場合、DR環境有効化
            activate_disaster_recovery(endpoint_group_arn)
        elif len(healthy_endpoints) < len(endpoints):
            # 一部エンドポイント異常の場合、トラフィック再分散
            redistribute_traffic(endpoint_group_arn, healthy_endpoints)

def calculate_ga_cost(total_bytes):
    # Global Acceleratorのコスト計算ロジックを実装
    return total_bytes * 0.015  # サンプル料金

def calculate_cloudfront_cost(total_bytes):
    # CloudFrontのコスト計算ロジックを実装
    return total_bytes * 0.085  # サンプル料金

def optimize_ga_costs():
    """
    使用パターン分析によるコスト最適化提案
    """
    cloudwatch = boto3.client('cloudwatch')
    
    # データ処理量の分析
    data_processing_metrics = cloudwatch.get_metric_statistics(
        Namespace='AWS/GlobalAccelerator',
        MetricName='ProcessedBytesIn',
        StartTime=datetime.now() - timedelta(days=30),
        EndTime=datetime.now(),
        Period=86400,
        Statistics=['Sum']
    )
    
    total_bytes = sum(point['Sum'] for point in data_processing_metrics['Datapoints'])
    estimated_cost = calculate_ga_cost(total_bytes)
    
    # CloudFront + ALBとの比較
    cloudfront_cost = calculate_cloudfront_cost(total_bytes)
    
    if cloudfront_cost < estimated_cost:
        return {
            'recommendation': 'Consider migrating to CloudFront for cost optimization',
            'savings': estimated_cost - cloudfront_cost
        }
    else:
        return {
            'recommendation': 'Global Accelerator is cost-effective for your use case',
            'current_cost': estimated_cost
        }