1447 lines
50 KiB
Markdown
1447 lines
50 KiB
Markdown
# Cloud Cost Optimization
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You are a cloud cost optimization expert specializing in reducing infrastructure expenses while maintaining performance and reliability. Analyze cloud spending, identify savings opportunities, and implement cost-effective architectures across AWS, Azure, and GCP.
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## Context
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The user needs to optimize cloud infrastructure costs without compromising performance or reliability. Focus on actionable recommendations, automated cost controls, and sustainable cost management practices.
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## Requirements
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$ARGUMENTS
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## Instructions
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### 1. Cost Analysis and Visibility
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Implement comprehensive cost analysis:
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**Cost Analysis Framework**
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```python
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import boto3
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import pandas as pd
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from datetime import datetime, timedelta
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from typing import Dict, List, Any
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import json
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class CloudCostAnalyzer:
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def __init__(self, cloud_provider: str):
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self.provider = cloud_provider
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self.client = self._initialize_client()
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self.cost_data = None
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def analyze_costs(self, time_period: int = 30):
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"""Comprehensive cost analysis"""
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analysis = {
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'total_cost': self._get_total_cost(time_period),
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'cost_by_service': self._analyze_by_service(time_period),
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'cost_by_resource': self._analyze_by_resource(time_period),
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'cost_trends': self._analyze_trends(time_period),
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'anomalies': self._detect_anomalies(time_period),
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'waste_analysis': self._identify_waste(),
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'optimization_opportunities': self._find_opportunities()
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}
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return self._generate_report(analysis)
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def _analyze_by_service(self, days: int):
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"""Analyze costs by service"""
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if self.provider == 'aws':
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ce = boto3.client('ce')
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response = ce.get_cost_and_usage(
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TimePeriod={
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'Start': (datetime.now() - timedelta(days=days)).strftime('%Y-%m-%d'),
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'End': datetime.now().strftime('%Y-%m-%d')
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},
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Granularity='DAILY',
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Metrics=['UnblendedCost'],
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GroupBy=[
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{'Type': 'DIMENSION', 'Key': 'SERVICE'}
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]
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)
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# Process response
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service_costs = {}
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for result in response['ResultsByTime']:
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for group in result['Groups']:
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service = group['Keys'][0]
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cost = float(group['Metrics']['UnblendedCost']['Amount'])
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if service not in service_costs:
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service_costs[service] = []
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service_costs[service].append(cost)
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# Calculate totals and trends
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analysis = {}
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for service, costs in service_costs.items():
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analysis[service] = {
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'total': sum(costs),
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'average_daily': sum(costs) / len(costs),
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'trend': self._calculate_trend(costs),
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'percentage': (sum(costs) / self._get_total_cost(days)) * 100
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}
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return analysis
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def _identify_waste(self):
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"""Identify wasted resources"""
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waste_analysis = {
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'unused_resources': self._find_unused_resources(),
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'oversized_resources': self._find_oversized_resources(),
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'unattached_storage': self._find_unattached_storage(),
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'idle_load_balancers': self._find_idle_load_balancers(),
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'old_snapshots': self._find_old_snapshots(),
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'untagged_resources': self._find_untagged_resources()
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}
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total_waste = sum(item['estimated_savings']
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for category in waste_analysis.values()
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for item in category)
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waste_analysis['total_potential_savings'] = total_waste
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return waste_analysis
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def _find_unused_resources(self):
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"""Find resources with no usage"""
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unused = []
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if self.provider == 'aws':
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# Check EC2 instances
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ec2 = boto3.client('ec2')
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cloudwatch = boto3.client('cloudwatch')
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instances = ec2.describe_instances(
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Filters=[{'Name': 'instance-state-name', 'Values': ['running']}]
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)
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for reservation in instances['Reservations']:
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for instance in reservation['Instances']:
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# Check CPU utilization
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metrics = cloudwatch.get_metric_statistics(
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Namespace='AWS/EC2',
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MetricName='CPUUtilization',
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Dimensions=[
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{'Name': 'InstanceId', 'Value': instance['InstanceId']}
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],
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StartTime=datetime.now() - timedelta(days=7),
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EndTime=datetime.now(),
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Period=3600,
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Statistics=['Average']
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)
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if metrics['Datapoints']:
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avg_cpu = sum(d['Average'] for d in metrics['Datapoints']) / len(metrics['Datapoints'])
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if avg_cpu < 5: # Less than 5% CPU usage
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unused.append({
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'resource_type': 'EC2 Instance',
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'resource_id': instance['InstanceId'],
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'reason': f'Average CPU: {avg_cpu:.2f}%',
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'estimated_savings': self._calculate_instance_cost(instance)
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})
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return unused
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```
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### 2. Resource Rightsizing
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Implement intelligent rightsizing:
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**Rightsizing Engine**
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```python
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class ResourceRightsizer:
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def __init__(self):
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self.utilization_thresholds = {
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'cpu_low': 20,
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'cpu_high': 80,
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'memory_low': 30,
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'memory_high': 85,
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'network_low': 10,
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'network_high': 70
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}
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def analyze_rightsizing_opportunities(self):
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"""Find rightsizing opportunities"""
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opportunities = {
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'ec2_instances': self._rightsize_ec2(),
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'rds_instances': self._rightsize_rds(),
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'containers': self._rightsize_containers(),
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'lambda_functions': self._rightsize_lambda(),
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'storage_volumes': self._rightsize_storage()
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}
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return self._prioritize_opportunities(opportunities)
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def _rightsize_ec2(self):
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"""Rightsize EC2 instances"""
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recommendations = []
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instances = self._get_running_instances()
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for instance in instances:
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# Get utilization metrics
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utilization = self._get_instance_utilization(instance['InstanceId'])
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# Determine if oversized or undersized
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current_type = instance['InstanceType']
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recommended_type = self._recommend_instance_type(
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current_type,
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utilization
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)
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if recommended_type != current_type:
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current_cost = self._get_instance_cost(current_type)
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new_cost = self._get_instance_cost(recommended_type)
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recommendations.append({
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'resource_id': instance['InstanceId'],
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'current_type': current_type,
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'recommended_type': recommended_type,
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'reason': self._generate_reason(utilization),
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'current_cost': current_cost,
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'new_cost': new_cost,
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'monthly_savings': (current_cost - new_cost) * 730,
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'effort': 'medium',
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'risk': 'low' if 'downsize' in self._generate_reason(utilization) else 'medium'
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})
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return recommendations
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def _recommend_instance_type(self, current_type: str, utilization: Dict):
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"""Recommend optimal instance type"""
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# Parse current instance family and size
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family, size = self._parse_instance_type(current_type)
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# Calculate required resources
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required_cpu = self._calculate_required_cpu(utilization['cpu'])
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required_memory = self._calculate_required_memory(utilization['memory'])
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# Find best matching instance
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instance_catalog = self._get_instance_catalog()
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candidates = []
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for instance_type, specs in instance_catalog.items():
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if (specs['vcpu'] >= required_cpu and
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specs['memory'] >= required_memory):
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candidates.append({
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'type': instance_type,
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'cost': specs['cost'],
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'vcpu': specs['vcpu'],
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'memory': specs['memory'],
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'efficiency_score': self._calculate_efficiency_score(
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specs, required_cpu, required_memory
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)
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})
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# Select best candidate
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if candidates:
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best = sorted(candidates,
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key=lambda x: (x['efficiency_score'], x['cost']))[0]
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return best['type']
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return current_type
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def create_rightsizing_automation(self):
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"""Automated rightsizing implementation"""
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return '''
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import boto3
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from datetime import datetime
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import logging
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class AutomatedRightsizer:
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def __init__(self):
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self.ec2 = boto3.client('ec2')
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self.cloudwatch = boto3.client('cloudwatch')
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self.logger = logging.getLogger(__name__)
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def execute_rightsizing(self, recommendations: List[Dict], dry_run: bool = True):
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"""Execute rightsizing recommendations"""
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results = []
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for recommendation in recommendations:
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try:
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if recommendation['risk'] == 'low' or self._get_approval(recommendation):
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result = self._resize_instance(
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recommendation['resource_id'],
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recommendation['recommended_type'],
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dry_run=dry_run
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)
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results.append(result)
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except Exception as e:
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self.logger.error(f"Failed to resize {recommendation['resource_id']}: {e}")
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return results
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def _resize_instance(self, instance_id: str, new_type: str, dry_run: bool):
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"""Resize an EC2 instance"""
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# Create snapshot for rollback
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snapshot_id = self._create_snapshot(instance_id)
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try:
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# Stop instance
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if not dry_run:
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self.ec2.stop_instances(InstanceIds=[instance_id])
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self._wait_for_state(instance_id, 'stopped')
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# Change instance type
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self.ec2.modify_instance_attribute(
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InstanceId=instance_id,
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InstanceType={'Value': new_type},
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DryRun=dry_run
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)
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# Start instance
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if not dry_run:
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self.ec2.start_instances(InstanceIds=[instance_id])
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self._wait_for_state(instance_id, 'running')
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return {
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'instance_id': instance_id,
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'status': 'success',
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'new_type': new_type,
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'snapshot_id': snapshot_id
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}
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except Exception as e:
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# Rollback on failure
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if not dry_run:
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self._rollback_instance(instance_id, snapshot_id)
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raise
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'''
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```
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### 3. Reserved Instances and Savings Plans
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Optimize commitment-based discounts:
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**Reservation Optimizer**
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```python
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class ReservationOptimizer:
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def __init__(self):
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self.usage_history = None
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self.existing_reservations = None
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def analyze_reservation_opportunities(self):
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"""Analyze opportunities for reservations"""
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analysis = {
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'current_coverage': self._analyze_current_coverage(),
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'usage_patterns': self._analyze_usage_patterns(),
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'recommendations': self._generate_recommendations(),
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'roi_analysis': self._calculate_roi(),
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'risk_assessment': self._assess_commitment_risk()
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}
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return analysis
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def _analyze_usage_patterns(self):
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"""Analyze historical usage patterns"""
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# Get 12 months of usage data
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usage_data = self._get_historical_usage(months=12)
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patterns = {
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'stable_workloads': [],
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'variable_workloads': [],
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'seasonal_patterns': [],
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'growth_trends': []
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}
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# Analyze each instance family
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for family in self._get_instance_families(usage_data):
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family_usage = self._filter_by_family(usage_data, family)
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# Calculate stability metrics
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stability = self._calculate_stability(family_usage)
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if stability['coefficient_of_variation'] < 0.1:
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patterns['stable_workloads'].append({
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'family': family,
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'average_usage': stability['mean'],
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'min_usage': stability['min'],
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'recommendation': 'reserved_instance',
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'term': '3_year',
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'payment': 'all_upfront'
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})
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elif stability['coefficient_of_variation'] < 0.3:
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patterns['variable_workloads'].append({
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'family': family,
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'average_usage': stability['mean'],
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'baseline': stability['percentile_25'],
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'recommendation': 'savings_plan',
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'commitment': stability['percentile_25']
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})
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# Check for seasonal patterns
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if self._has_seasonal_pattern(family_usage):
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patterns['seasonal_patterns'].append({
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'family': family,
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'pattern': self._identify_seasonal_pattern(family_usage),
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'recommendation': 'spot_with_savings_plan_baseline'
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})
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return patterns
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def _generate_recommendations(self):
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"""Generate reservation recommendations"""
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recommendations = []
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patterns = self._analyze_usage_patterns()
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current_costs = self._calculate_current_costs()
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# Reserved Instance recommendations
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for workload in patterns['stable_workloads']:
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ri_options = self._calculate_ri_options(workload)
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for option in ri_options:
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savings = current_costs[workload['family']] - option['total_cost']
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if savings > 0:
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recommendations.append({
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'type': 'reserved_instance',
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'family': workload['family'],
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'quantity': option['quantity'],
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'term': option['term'],
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'payment': option['payment_option'],
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'upfront_cost': option['upfront_cost'],
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'monthly_cost': option['monthly_cost'],
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'total_savings': savings,
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'break_even_months': option['upfront_cost'] / (savings / 36),
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'confidence': 'high'
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})
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# Savings Plan recommendations
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for workload in patterns['variable_workloads']:
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sp_options = self._calculate_savings_plan_options(workload)
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for option in sp_options:
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recommendations.append({
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'type': 'savings_plan',
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'commitment_type': option['type'],
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'hourly_commitment': option['commitment'],
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'term': option['term'],
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'estimated_savings': option['savings'],
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'flexibility': option['flexibility_score'],
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'confidence': 'medium'
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})
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return sorted(recommendations, key=lambda x: x.get('total_savings', 0), reverse=True)
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def create_reservation_dashboard(self):
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"""Create reservation tracking dashboard"""
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return '''
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<!DOCTYPE html>
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<html>
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<head>
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<title>Reservation & Savings Dashboard</title>
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<script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
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</head>
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<body>
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<div class="dashboard">
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<div class="summary-cards">
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<div class="card">
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<h3>Current Coverage</h3>
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<div class="metric">{coverage_percentage}%</div>
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<div class="sub-metric">On-Demand: ${on_demand_cost}</div>
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<div class="sub-metric">Reserved: ${reserved_cost}</div>
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</div>
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<div class="card">
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<h3>Potential Savings</h3>
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<div class="metric">${potential_savings}/month</div>
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<div class="sub-metric">{recommendations_count} opportunities</div>
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</div>
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<div class="card">
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<h3>Expiring Soon</h3>
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<div class="metric">{expiring_count} RIs</div>
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<div class="sub-metric">Next 30 days</div>
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</div>
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</div>
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<div class="charts">
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<canvas id="coverageChart"></canvas>
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<canvas id="savingsChart"></canvas>
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</div>
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<div class="recommendations-table">
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<h3>Top Recommendations</h3>
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<table>
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<tr>
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<th>Type</th>
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<th>Resource</th>
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<th>Term</th>
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<th>Upfront</th>
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<th>Monthly Savings</th>
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<th>ROI</th>
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<th>Action</th>
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</tr>
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{recommendation_rows}
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</table>
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</div>
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</div>
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</body>
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</html>
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'''
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```
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### 4. Spot Instance Optimization
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Leverage spot instances effectively:
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**Spot Instance Manager**
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```python
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class SpotInstanceOptimizer:
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def __init__(self):
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self.spot_advisor = self._init_spot_advisor()
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self.interruption_handler = None
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def identify_spot_opportunities(self):
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"""Identify workloads suitable for spot"""
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workloads = self._analyze_workloads()
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spot_candidates = {
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'batch_processing': [],
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'dev_test': [],
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'stateless_apps': [],
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'ci_cd': [],
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'data_processing': []
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}
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for workload in workloads:
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suitability = self._assess_spot_suitability(workload)
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if suitability['score'] > 0.7:
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spot_candidates[workload['type']].append({
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'workload': workload['name'],
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'current_cost': workload['cost'],
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'spot_savings': workload['cost'] * 0.7, # ~70% savings
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'interruption_tolerance': suitability['interruption_tolerance'],
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'recommended_strategy': self._recommend_spot_strategy(workload)
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})
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return spot_candidates
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def _recommend_spot_strategy(self, workload):
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"""Recommend spot instance strategy"""
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if workload['interruption_tolerance'] == 'high':
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return {
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'strategy': 'spot_fleet_diverse',
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'instance_pools': 10,
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'allocation_strategy': 'capacity-optimized',
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'on_demand_base': 0,
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'spot_percentage': 100
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}
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elif workload['interruption_tolerance'] == 'medium':
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return {
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'strategy': 'mixed_instances',
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'on_demand_base': 25,
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'spot_percentage': 75,
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'spot_allocation': 'lowest-price'
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}
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else:
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return {
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'strategy': 'spot_with_fallback',
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'primary': 'spot',
|
|
'fallback': 'on-demand',
|
|
'checkpointing': True
|
|
}
|
|
|
|
def create_spot_configuration(self):
|
|
"""Create spot instance configuration"""
|
|
return '''
|
|
# Terraform configuration for Spot instances
|
|
resource "aws_spot_fleet_request" "processing_fleet" {
|
|
iam_fleet_role = aws_iam_role.spot_fleet.arn
|
|
|
|
allocation_strategy = "diversified"
|
|
target_capacity = 100
|
|
valid_until = timeadd(timestamp(), "168h")
|
|
|
|
# Define multiple launch specifications for diversity
|
|
dynamic "launch_specification" {
|
|
for_each = var.spot_instance_types
|
|
|
|
content {
|
|
instance_type = launch_specification.value
|
|
ami = var.ami_id
|
|
key_name = var.key_name
|
|
subnet_id = var.subnet_ids[launch_specification.key % length(var.subnet_ids)]
|
|
|
|
weighted_capacity = var.instance_weights[launch_specification.value]
|
|
spot_price = var.max_spot_prices[launch_specification.value]
|
|
|
|
user_data = base64encode(templatefile("${path.module}/spot-init.sh", {
|
|
interruption_handler = true
|
|
checkpoint_s3_bucket = var.checkpoint_bucket
|
|
}))
|
|
|
|
tags = {
|
|
Name = "spot-processing-${launch_specification.key}"
|
|
Type = "spot"
|
|
}
|
|
}
|
|
}
|
|
|
|
# Interruption handling
|
|
lifecycle {
|
|
create_before_destroy = true
|
|
}
|
|
}
|
|
|
|
# Spot interruption handler
|
|
resource "aws_lambda_function" "spot_interruption_handler" {
|
|
filename = "spot-handler.zip"
|
|
function_name = "spot-interruption-handler"
|
|
role = aws_iam_role.lambda_role.arn
|
|
handler = "handler.main"
|
|
runtime = "python3.9"
|
|
|
|
environment {
|
|
variables = {
|
|
CHECKPOINT_BUCKET = var.checkpoint_bucket
|
|
SNS_TOPIC_ARN = aws_sns_topic.spot_interruptions.arn
|
|
}
|
|
}
|
|
}
|
|
'''
|
|
```
|
|
|
|
### 5. Storage Optimization
|
|
|
|
Optimize storage costs:
|
|
|
|
**Storage Optimizer**
|
|
```python
|
|
class StorageOptimizer:
|
|
def analyze_storage_costs(self):
|
|
"""Comprehensive storage analysis"""
|
|
analysis = {
|
|
'ebs_volumes': self._analyze_ebs_volumes(),
|
|
's3_buckets': self._analyze_s3_buckets(),
|
|
'snapshots': self._analyze_snapshots(),
|
|
'lifecycle_opportunities': self._find_lifecycle_opportunities(),
|
|
'compression_opportunities': self._find_compression_opportunities()
|
|
}
|
|
|
|
return analysis
|
|
|
|
def _analyze_s3_buckets(self):
|
|
"""Analyze S3 bucket costs and optimization"""
|
|
s3 = boto3.client('s3')
|
|
cloudwatch = boto3.client('cloudwatch')
|
|
|
|
buckets = s3.list_buckets()['Buckets']
|
|
bucket_analysis = []
|
|
|
|
for bucket in buckets:
|
|
bucket_name = bucket['Name']
|
|
|
|
# Get storage metrics
|
|
metrics = self._get_s3_metrics(bucket_name)
|
|
|
|
# Analyze storage classes
|
|
storage_class_distribution = self._get_storage_class_distribution(bucket_name)
|
|
|
|
# Calculate optimization potential
|
|
optimization = self._calculate_s3_optimization(
|
|
bucket_name,
|
|
metrics,
|
|
storage_class_distribution
|
|
)
|
|
|
|
bucket_analysis.append({
|
|
'bucket_name': bucket_name,
|
|
'total_size_gb': metrics['size_gb'],
|
|
'total_objects': metrics['object_count'],
|
|
'current_cost': metrics['monthly_cost'],
|
|
'storage_classes': storage_class_distribution,
|
|
'optimization_recommendations': optimization['recommendations'],
|
|
'potential_savings': optimization['savings']
|
|
})
|
|
|
|
return bucket_analysis
|
|
|
|
def create_lifecycle_policies(self):
|
|
"""Create S3 lifecycle policies"""
|
|
return '''
|
|
import boto3
|
|
from datetime import datetime
|
|
|
|
class S3LifecycleManager:
|
|
def __init__(self):
|
|
self.s3 = boto3.client('s3')
|
|
|
|
def create_intelligent_lifecycle(self, bucket_name: str, access_patterns: Dict):
|
|
"""Create lifecycle policy based on access patterns"""
|
|
|
|
rules = []
|
|
|
|
# Intelligent tiering for unknown access patterns
|
|
if access_patterns.get('unpredictable'):
|
|
rules.append({
|
|
'ID': 'intelligent-tiering',
|
|
'Status': 'Enabled',
|
|
'Transitions': [{
|
|
'Days': 1,
|
|
'StorageClass': 'INTELLIGENT_TIERING'
|
|
}]
|
|
})
|
|
|
|
# Standard lifecycle for predictable patterns
|
|
if access_patterns.get('predictable'):
|
|
rules.append({
|
|
'ID': 'standard-lifecycle',
|
|
'Status': 'Enabled',
|
|
'Transitions': [
|
|
{
|
|
'Days': 30,
|
|
'StorageClass': 'STANDARD_IA'
|
|
},
|
|
{
|
|
'Days': 90,
|
|
'StorageClass': 'GLACIER'
|
|
},
|
|
{
|
|
'Days': 180,
|
|
'StorageClass': 'DEEP_ARCHIVE'
|
|
}
|
|
]
|
|
})
|
|
|
|
# Delete old versions
|
|
rules.append({
|
|
'ID': 'delete-old-versions',
|
|
'Status': 'Enabled',
|
|
'NoncurrentVersionTransitions': [
|
|
{
|
|
'NoncurrentDays': 30,
|
|
'StorageClass': 'GLACIER'
|
|
}
|
|
],
|
|
'NoncurrentVersionExpiration': {
|
|
'NoncurrentDays': 90
|
|
}
|
|
})
|
|
|
|
# Apply lifecycle configuration
|
|
self.s3.put_bucket_lifecycle_configuration(
|
|
Bucket=bucket_name,
|
|
LifecycleConfiguration={'Rules': rules}
|
|
)
|
|
|
|
return rules
|
|
|
|
def optimize_ebs_volumes(self):
|
|
"""Optimize EBS volume types and sizes"""
|
|
ec2 = boto3.client('ec2')
|
|
|
|
volumes = ec2.describe_volumes()['Volumes']
|
|
optimizations = []
|
|
|
|
for volume in volumes:
|
|
# Analyze volume metrics
|
|
iops_usage = self._get_volume_iops_usage(volume['VolumeId'])
|
|
throughput_usage = self._get_volume_throughput_usage(volume['VolumeId'])
|
|
|
|
current_type = volume['VolumeType']
|
|
recommended_type = self._recommend_volume_type(
|
|
iops_usage,
|
|
throughput_usage,
|
|
volume['Size']
|
|
)
|
|
|
|
if recommended_type != current_type:
|
|
optimizations.append({
|
|
'volume_id': volume['VolumeId'],
|
|
'current_type': current_type,
|
|
'recommended_type': recommended_type,
|
|
'reason': self._get_optimization_reason(
|
|
current_type,
|
|
recommended_type,
|
|
iops_usage,
|
|
throughput_usage
|
|
),
|
|
'monthly_savings': self._calculate_volume_savings(
|
|
volume,
|
|
recommended_type
|
|
)
|
|
})
|
|
|
|
return optimizations
|
|
'''
|
|
```
|
|
|
|
### 6. Network Cost Optimization
|
|
|
|
Reduce network transfer costs:
|
|
|
|
**Network Cost Optimizer**
|
|
```python
|
|
class NetworkCostOptimizer:
|
|
def analyze_network_costs(self):
|
|
"""Analyze network transfer costs"""
|
|
analysis = {
|
|
'data_transfer_costs': self._analyze_data_transfer(),
|
|
'nat_gateway_costs': self._analyze_nat_gateways(),
|
|
'load_balancer_costs': self._analyze_load_balancers(),
|
|
'vpc_endpoint_opportunities': self._find_vpc_endpoint_opportunities(),
|
|
'cdn_optimization': self._analyze_cdn_usage()
|
|
}
|
|
|
|
return analysis
|
|
|
|
def _analyze_data_transfer(self):
|
|
"""Analyze data transfer patterns and costs"""
|
|
transfers = {
|
|
'inter_region': self._get_inter_region_transfers(),
|
|
'internet_egress': self._get_internet_egress(),
|
|
'inter_az': self._get_inter_az_transfers(),
|
|
'vpc_peering': self._get_vpc_peering_transfers()
|
|
}
|
|
|
|
recommendations = []
|
|
|
|
# Analyze inter-region transfers
|
|
if transfers['inter_region']['monthly_gb'] > 1000:
|
|
recommendations.append({
|
|
'type': 'region_consolidation',
|
|
'description': 'Consider consolidating resources in fewer regions',
|
|
'current_cost': transfers['inter_region']['monthly_cost'],
|
|
'potential_savings': transfers['inter_region']['monthly_cost'] * 0.8
|
|
})
|
|
|
|
# Analyze internet egress
|
|
if transfers['internet_egress']['monthly_gb'] > 10000:
|
|
recommendations.append({
|
|
'type': 'cdn_implementation',
|
|
'description': 'Implement CDN to reduce origin egress',
|
|
'current_cost': transfers['internet_egress']['monthly_cost'],
|
|
'potential_savings': transfers['internet_egress']['monthly_cost'] * 0.6
|
|
})
|
|
|
|
return {
|
|
'current_costs': transfers,
|
|
'recommendations': recommendations
|
|
}
|
|
|
|
def create_network_optimization_script(self):
|
|
"""Script to implement network optimizations"""
|
|
return '''
|
|
#!/usr/bin/env python3
|
|
import boto3
|
|
from collections import defaultdict
|
|
|
|
class NetworkOptimizer:
|
|
def __init__(self):
|
|
self.ec2 = boto3.client('ec2')
|
|
self.cloudwatch = boto3.client('cloudwatch')
|
|
|
|
def optimize_nat_gateways(self):
|
|
"""Consolidate and optimize NAT gateways"""
|
|
# Get all NAT gateways
|
|
nat_gateways = self.ec2.describe_nat_gateways()['NatGateways']
|
|
|
|
# Group by VPC
|
|
vpc_nat_gateways = defaultdict(list)
|
|
for nat in nat_gateways:
|
|
if nat['State'] == 'available':
|
|
vpc_nat_gateways[nat['VpcId']].append(nat)
|
|
|
|
optimizations = []
|
|
|
|
for vpc_id, nats in vpc_nat_gateways.items():
|
|
if len(nats) > 1:
|
|
# Check if consolidation is possible
|
|
traffic_analysis = self._analyze_nat_traffic(nats)
|
|
|
|
if traffic_analysis['can_consolidate']:
|
|
optimizations.append({
|
|
'vpc_id': vpc_id,
|
|
'action': 'consolidate_nat',
|
|
'current_count': len(nats),
|
|
'recommended_count': traffic_analysis['recommended_count'],
|
|
'monthly_savings': (len(nats) - traffic_analysis['recommended_count']) * 45
|
|
})
|
|
|
|
return optimizations
|
|
|
|
def implement_vpc_endpoints(self):
|
|
"""Implement VPC endpoints for AWS services"""
|
|
services_to_check = ['s3', 'dynamodb', 'ec2', 'sns', 'sqs']
|
|
vpc_list = self.ec2.describe_vpcs()['Vpcs']
|
|
|
|
implementations = []
|
|
|
|
for vpc in vpc_list:
|
|
vpc_id = vpc['VpcId']
|
|
|
|
# Check existing endpoints
|
|
existing = self._get_existing_endpoints(vpc_id)
|
|
|
|
for service in services_to_check:
|
|
if service not in existing:
|
|
# Check if service is being used
|
|
if self._is_service_used(vpc_id, service):
|
|
# Create VPC endpoint
|
|
endpoint = self._create_vpc_endpoint(vpc_id, service)
|
|
|
|
implementations.append({
|
|
'vpc_id': vpc_id,
|
|
'service': service,
|
|
'endpoint_id': endpoint['VpcEndpointId'],
|
|
'estimated_savings': self._estimate_endpoint_savings(vpc_id, service)
|
|
})
|
|
|
|
return implementations
|
|
|
|
def optimize_cloudfront_distribution(self):
|
|
"""Optimize CloudFront for cost reduction"""
|
|
cloudfront = boto3.client('cloudfront')
|
|
|
|
distributions = cloudfront.list_distributions()
|
|
optimizations = []
|
|
|
|
for dist in distributions.get('DistributionList', {}).get('Items', []):
|
|
# Analyze distribution patterns
|
|
analysis = self._analyze_distribution(dist['Id'])
|
|
|
|
if analysis['optimization_potential']:
|
|
optimizations.append({
|
|
'distribution_id': dist['Id'],
|
|
'recommendations': [
|
|
{
|
|
'action': 'adjust_price_class',
|
|
'current': dist['PriceClass'],
|
|
'recommended': analysis['recommended_price_class'],
|
|
'savings': analysis['price_class_savings']
|
|
},
|
|
{
|
|
'action': 'optimize_cache_behaviors',
|
|
'cache_improvements': analysis['cache_improvements'],
|
|
'savings': analysis['cache_savings']
|
|
}
|
|
]
|
|
})
|
|
|
|
return optimizations
|
|
'''
|
|
```
|
|
|
|
### 7. Container Cost Optimization
|
|
|
|
Optimize container workloads:
|
|
|
|
**Container Cost Optimizer**
|
|
```python
|
|
class ContainerCostOptimizer:
|
|
def optimize_ecs_costs(self):
|
|
"""Optimize ECS/Fargate costs"""
|
|
return {
|
|
'cluster_optimization': self._optimize_clusters(),
|
|
'task_rightsizing': self._rightsize_tasks(),
|
|
'scheduling_optimization': self._optimize_scheduling(),
|
|
'fargate_spot': self._implement_fargate_spot()
|
|
}
|
|
|
|
def _rightsize_tasks(self):
|
|
"""Rightsize ECS tasks"""
|
|
ecs = boto3.client('ecs')
|
|
cloudwatch = boto3.client('cloudwatch')
|
|
|
|
clusters = ecs.list_clusters()['clusterArns']
|
|
recommendations = []
|
|
|
|
for cluster in clusters:
|
|
# Get services
|
|
services = ecs.list_services(cluster=cluster)['serviceArns']
|
|
|
|
for service in services:
|
|
# Get task definition
|
|
service_detail = ecs.describe_services(
|
|
cluster=cluster,
|
|
services=[service]
|
|
)['services'][0]
|
|
|
|
task_def = service_detail['taskDefinition']
|
|
|
|
# Analyze resource utilization
|
|
utilization = self._analyze_task_utilization(cluster, service)
|
|
|
|
# Generate recommendations
|
|
if utilization['cpu']['average'] < 30 or utilization['memory']['average'] < 40:
|
|
recommendations.append({
|
|
'cluster': cluster,
|
|
'service': service,
|
|
'current_cpu': service_detail['cpu'],
|
|
'current_memory': service_detail['memory'],
|
|
'recommended_cpu': int(service_detail['cpu'] * 0.7),
|
|
'recommended_memory': int(service_detail['memory'] * 0.8),
|
|
'monthly_savings': self._calculate_task_savings(
|
|
service_detail,
|
|
utilization
|
|
)
|
|
})
|
|
|
|
return recommendations
|
|
|
|
def create_k8s_cost_optimization(self):
|
|
"""Kubernetes cost optimization"""
|
|
return '''
|
|
apiVersion: v1
|
|
kind: ConfigMap
|
|
metadata:
|
|
name: cost-optimization-config
|
|
data:
|
|
vertical-pod-autoscaler.yaml: |
|
|
apiVersion: autoscaling.k8s.io/v1
|
|
kind: VerticalPodAutoscaler
|
|
metadata:
|
|
name: app-vpa
|
|
spec:
|
|
targetRef:
|
|
apiVersion: apps/v1
|
|
kind: Deployment
|
|
name: app-deployment
|
|
updatePolicy:
|
|
updateMode: "Auto"
|
|
resourcePolicy:
|
|
containerPolicies:
|
|
- containerName: app
|
|
minAllowed:
|
|
cpu: 100m
|
|
memory: 128Mi
|
|
maxAllowed:
|
|
cpu: 2
|
|
memory: 2Gi
|
|
|
|
cluster-autoscaler-config.yaml: |
|
|
apiVersion: apps/v1
|
|
kind: Deployment
|
|
metadata:
|
|
name: cluster-autoscaler
|
|
spec:
|
|
template:
|
|
spec:
|
|
containers:
|
|
- image: k8s.gcr.io/autoscaling/cluster-autoscaler:v1.21.0
|
|
name: cluster-autoscaler
|
|
command:
|
|
- ./cluster-autoscaler
|
|
- --v=4
|
|
- --stderrthreshold=info
|
|
- --cloud-provider=aws
|
|
- --skip-nodes-with-local-storage=false
|
|
- --expander=priority
|
|
- --node-group-auto-discovery=asg:tag=k8s.io/cluster-autoscaler/enabled,k8s.io/cluster-autoscaler/cluster-name
|
|
- --scale-down-enabled=true
|
|
- --scale-down-unneeded-time=10m
|
|
- --scale-down-utilization-threshold=0.5
|
|
|
|
spot-instance-handler.yaml: |
|
|
apiVersion: apps/v1
|
|
kind: DaemonSet
|
|
metadata:
|
|
name: aws-node-termination-handler
|
|
spec:
|
|
selector:
|
|
matchLabels:
|
|
app: aws-node-termination-handler
|
|
template:
|
|
spec:
|
|
containers:
|
|
- name: aws-node-termination-handler
|
|
image: amazon/aws-node-termination-handler:v1.13.0
|
|
env:
|
|
- name: NODE_NAME
|
|
valueFrom:
|
|
fieldRef:
|
|
fieldPath: spec.nodeName
|
|
- name: ENABLE_SPOT_INTERRUPTION_DRAINING
|
|
value: "true"
|
|
- name: ENABLE_SCHEDULED_EVENT_DRAINING
|
|
value: "true"
|
|
'''
|
|
```
|
|
|
|
### 8. Serverless Cost Optimization
|
|
|
|
Optimize serverless workloads:
|
|
|
|
**Serverless Optimizer**
|
|
```python
|
|
class ServerlessOptimizer:
|
|
def optimize_lambda_costs(self):
|
|
"""Optimize Lambda function costs"""
|
|
lambda_client = boto3.client('lambda')
|
|
cloudwatch = boto3.client('cloudwatch')
|
|
|
|
functions = lambda_client.list_functions()['Functions']
|
|
optimizations = []
|
|
|
|
for function in functions:
|
|
# Analyze function performance
|
|
analysis = self._analyze_lambda_function(function)
|
|
|
|
# Memory optimization
|
|
if analysis['memory_optimization_possible']:
|
|
optimizations.append({
|
|
'function_name': function['FunctionName'],
|
|
'type': 'memory_optimization',
|
|
'current_memory': function['MemorySize'],
|
|
'recommended_memory': analysis['optimal_memory'],
|
|
'estimated_savings': analysis['memory_savings']
|
|
})
|
|
|
|
# Timeout optimization
|
|
if analysis['timeout_optimization_possible']:
|
|
optimizations.append({
|
|
'function_name': function['FunctionName'],
|
|
'type': 'timeout_optimization',
|
|
'current_timeout': function['Timeout'],
|
|
'recommended_timeout': analysis['optimal_timeout'],
|
|
'risk_reduction': 'prevents unnecessary charges from hanging functions'
|
|
})
|
|
|
|
return optimizations
|
|
|
|
def implement_lambda_cost_controls(self):
|
|
"""Implement Lambda cost controls"""
|
|
return '''
|
|
import json
|
|
import boto3
|
|
from datetime import datetime
|
|
|
|
def lambda_cost_controller(event, context):
|
|
"""Lambda function to monitor and control Lambda costs"""
|
|
|
|
cloudwatch = boto3.client('cloudwatch')
|
|
lambda_client = boto3.client('lambda')
|
|
|
|
# Get current month costs
|
|
costs = get_current_month_lambda_costs()
|
|
|
|
# Check against budget
|
|
budget_limit = float(os.environ.get('MONTHLY_BUDGET', '1000'))
|
|
|
|
if costs > budget_limit * 0.8: # 80% of budget
|
|
# Implement cost controls
|
|
high_cost_functions = identify_high_cost_functions()
|
|
|
|
for func in high_cost_functions:
|
|
# Reduce concurrency
|
|
lambda_client.put_function_concurrency(
|
|
FunctionName=func['FunctionName'],
|
|
ReservedConcurrentExecutions=max(
|
|
1,
|
|
int(func['CurrentConcurrency'] * 0.5)
|
|
)
|
|
)
|
|
|
|
# Alert
|
|
send_cost_alert(func, costs, budget_limit)
|
|
|
|
# Implement provisioned concurrency optimization
|
|
optimize_provisioned_concurrency()
|
|
|
|
return {
|
|
'statusCode': 200,
|
|
'body': json.dumps({
|
|
'current_costs': costs,
|
|
'budget_limit': budget_limit,
|
|
'actions_taken': len(high_cost_functions)
|
|
})
|
|
}
|
|
|
|
def optimize_provisioned_concurrency():
|
|
"""Optimize provisioned concurrency based on usage patterns"""
|
|
functions = get_functions_with_provisioned_concurrency()
|
|
|
|
for func in functions:
|
|
# Analyze invocation patterns
|
|
patterns = analyze_invocation_patterns(func['FunctionName'])
|
|
|
|
if patterns['predictable']:
|
|
# Schedule provisioned concurrency
|
|
create_scheduled_scaling(
|
|
func['FunctionName'],
|
|
patterns['peak_hours'],
|
|
patterns['peak_concurrency']
|
|
)
|
|
else:
|
|
# Consider removing provisioned concurrency
|
|
if patterns['avg_cold_starts'] < 10: # per minute
|
|
remove_provisioned_concurrency(func['FunctionName'])
|
|
'''
|
|
```
|
|
|
|
### 9. Cost Allocation and Tagging
|
|
|
|
Implement cost allocation strategies:
|
|
|
|
**Cost Allocation Manager**
|
|
```python
|
|
class CostAllocationManager:
|
|
def implement_tagging_strategy(self):
|
|
"""Implement comprehensive tagging strategy"""
|
|
return {
|
|
'required_tags': [
|
|
{'key': 'Environment', 'values': ['prod', 'staging', 'dev', 'test']},
|
|
{'key': 'CostCenter', 'values': 'dynamic'},
|
|
{'key': 'Project', 'values': 'dynamic'},
|
|
{'key': 'Owner', 'values': 'dynamic'},
|
|
{'key': 'Department', 'values': 'dynamic'}
|
|
],
|
|
'automation': self._create_tagging_automation(),
|
|
'enforcement': self._create_tag_enforcement(),
|
|
'reporting': self._create_cost_allocation_reports()
|
|
}
|
|
|
|
def _create_tagging_automation(self):
|
|
"""Automate resource tagging"""
|
|
return '''
|
|
import boto3
|
|
from datetime import datetime
|
|
|
|
class AutoTagger:
|
|
def __init__(self):
|
|
self.tag_policies = self.load_tag_policies()
|
|
|
|
def auto_tag_resources(self, event, context):
|
|
"""Auto-tag resources on creation"""
|
|
|
|
# Parse CloudTrail event
|
|
detail = event['detail']
|
|
event_name = detail['eventName']
|
|
|
|
# Map events to resource types
|
|
if event_name.startswith('Create'):
|
|
resource_arn = self.extract_resource_arn(detail)
|
|
|
|
if resource_arn:
|
|
# Determine tags
|
|
tags = self.determine_tags(detail)
|
|
|
|
# Apply tags
|
|
self.apply_tags(resource_arn, tags)
|
|
|
|
# Log tagging action
|
|
self.log_tagging(resource_arn, tags)
|
|
|
|
def determine_tags(self, event_detail):
|
|
"""Determine tags based on context"""
|
|
tags = []
|
|
|
|
# User-based tags
|
|
user_identity = event_detail.get('userIdentity', {})
|
|
if 'userName' in user_identity:
|
|
tags.append({
|
|
'Key': 'Creator',
|
|
'Value': user_identity['userName']
|
|
})
|
|
|
|
# Time-based tags
|
|
tags.append({
|
|
'Key': 'CreatedDate',
|
|
'Value': datetime.now().strftime('%Y-%m-%d')
|
|
})
|
|
|
|
# Environment inference
|
|
if 'prod' in event_detail.get('sourceIPAddress', ''):
|
|
env = 'prod'
|
|
elif 'dev' in event_detail.get('sourceIPAddress', ''):
|
|
env = 'dev'
|
|
else:
|
|
env = 'unknown'
|
|
|
|
tags.append({
|
|
'Key': 'Environment',
|
|
'Value': env
|
|
})
|
|
|
|
return tags
|
|
|
|
def create_cost_allocation_dashboard(self):
|
|
"""Create cost allocation dashboard"""
|
|
return """
|
|
SELECT
|
|
tags.environment,
|
|
tags.department,
|
|
tags.project,
|
|
SUM(costs.amount) as total_cost,
|
|
SUM(costs.amount) / SUM(SUM(costs.amount)) OVER () * 100 as percentage
|
|
FROM
|
|
aws_costs costs
|
|
JOIN
|
|
resource_tags tags ON costs.resource_id = tags.resource_id
|
|
WHERE
|
|
costs.date >= DATE_TRUNC('month', CURRENT_DATE)
|
|
GROUP BY
|
|
tags.environment,
|
|
tags.department,
|
|
tags.project
|
|
ORDER BY
|
|
total_cost DESC
|
|
"""
|
|
'''
|
|
```
|
|
|
|
### 10. Cost Monitoring and Alerts
|
|
|
|
Implement proactive cost monitoring:
|
|
|
|
**Cost Monitoring System**
|
|
```python
|
|
class CostMonitoringSystem:
|
|
def setup_cost_alerts(self):
|
|
"""Setup comprehensive cost alerting"""
|
|
alerts = []
|
|
|
|
# Budget alerts
|
|
alerts.extend(self._create_budget_alerts())
|
|
|
|
# Anomaly detection
|
|
alerts.extend(self._create_anomaly_alerts())
|
|
|
|
# Threshold alerts
|
|
alerts.extend(self._create_threshold_alerts())
|
|
|
|
# Forecast alerts
|
|
alerts.extend(self._create_forecast_alerts())
|
|
|
|
return alerts
|
|
|
|
def _create_anomaly_alerts(self):
|
|
"""Create anomaly detection alerts"""
|
|
ce = boto3.client('ce')
|
|
|
|
# Create anomaly monitor
|
|
monitor = ce.create_anomaly_monitor(
|
|
AnomalyMonitor={
|
|
'MonitorName': 'ServiceCostMonitor',
|
|
'MonitorType': 'DIMENSIONAL',
|
|
'MonitorDimension': 'SERVICE'
|
|
}
|
|
)
|
|
|
|
# Create anomaly subscription
|
|
subscription = ce.create_anomaly_subscription(
|
|
AnomalySubscription={
|
|
'SubscriptionName': 'CostAnomalyAlerts',
|
|
'Threshold': 100.0, # Alert on anomalies > $100
|
|
'Frequency': 'DAILY',
|
|
'MonitorArnList': [monitor['MonitorArn']],
|
|
'Subscribers': [
|
|
{
|
|
'Type': 'EMAIL',
|
|
'Address': 'team@company.com'
|
|
},
|
|
{
|
|
'Type': 'SNS',
|
|
'Address': 'arn:aws:sns:us-east-1:123456789012:cost-alerts'
|
|
}
|
|
]
|
|
}
|
|
)
|
|
|
|
return [monitor, subscription]
|
|
|
|
def create_cost_dashboard(self):
|
|
"""Create executive cost dashboard"""
|
|
return '''
|
|
<!DOCTYPE html>
|
|
<html>
|
|
<head>
|
|
<title>Cloud Cost Dashboard</title>
|
|
<script src="https://d3js.org/d3.v7.min.js"></script>
|
|
<style>
|
|
.metric-card {
|
|
background: #f5f5f5;
|
|
padding: 20px;
|
|
margin: 10px;
|
|
border-radius: 8px;
|
|
box-shadow: 0 2px 4px rgba(0,0,0,0.1);
|
|
}
|
|
.alert { color: #d32f2f; }
|
|
.warning { color: #f57c00; }
|
|
.success { color: #388e3c; }
|
|
</style>
|
|
</head>
|
|
<body>
|
|
<div id="dashboard">
|
|
<h1>Cloud Cost Optimization Dashboard</h1>
|
|
|
|
<div class="summary-row">
|
|
<div class="metric-card">
|
|
<h3>Current Month Spend</h3>
|
|
<div class="metric">${current_spend}</div>
|
|
<div class="trend ${spend_trend_class}">${spend_trend}% vs last month</div>
|
|
</div>
|
|
|
|
<div class="metric-card">
|
|
<h3>Projected Month End</h3>
|
|
<div class="metric">${projected_spend}</div>
|
|
<div class="budget-status">Budget: ${budget}</div>
|
|
</div>
|
|
|
|
<div class="metric-card">
|
|
<h3>Optimization Opportunities</h3>
|
|
<div class="metric">${total_savings_identified}</div>
|
|
<div class="count">{opportunity_count} recommendations</div>
|
|
</div>
|
|
|
|
<div class="metric-card">
|
|
<h3>Realized Savings</h3>
|
|
<div class="metric">${realized_savings_mtd}</div>
|
|
<div class="count">YTD: ${realized_savings_ytd}</div>
|
|
</div>
|
|
</div>
|
|
|
|
<div class="charts-row">
|
|
<div id="spend-trend-chart"></div>
|
|
<div id="service-breakdown-chart"></div>
|
|
<div id="optimization-progress-chart"></div>
|
|
</div>
|
|
|
|
<div class="recommendations-section">
|
|
<h2>Top Optimization Recommendations</h2>
|
|
<table id="recommendations-table">
|
|
<thead>
|
|
<tr>
|
|
<th>Priority</th>
|
|
<th>Service</th>
|
|
<th>Recommendation</th>
|
|
<th>Monthly Savings</th>
|
|
<th>Effort</th>
|
|
<th>Action</th>
|
|
</tr>
|
|
</thead>
|
|
<tbody>
|
|
${recommendation_rows}
|
|
</tbody>
|
|
</table>
|
|
</div>
|
|
</div>
|
|
|
|
<script>
|
|
// Real-time updates
|
|
setInterval(updateDashboard, 60000);
|
|
|
|
// Initialize charts
|
|
initializeCharts();
|
|
</script>
|
|
</body>
|
|
</html>
|
|
'''
|
|
```
|
|
|
|
## Output Format
|
|
|
|
1. **Cost Analysis Report**: Comprehensive breakdown of current cloud costs
|
|
2. **Optimization Recommendations**: Prioritized list of cost-saving opportunities
|
|
3. **Implementation Scripts**: Automated scripts for implementing optimizations
|
|
4. **Monitoring Dashboards**: Real-time cost tracking and alerting
|
|
5. **ROI Calculations**: Detailed savings projections and payback periods
|
|
6. **Risk Assessment**: Analysis of risks associated with each optimization
|
|
7. **Implementation Roadmap**: Phased approach to cost optimization
|
|
8. **Best Practices Guide**: Long-term cost management strategies
|
|
|
|
Focus on delivering immediate cost savings while establishing sustainable cost optimization practices that maintain performance and reliability standards. |