COST10-BP02: Review and analyze this workload regularly

*This page contains guidance for implementing this best practice from the AWS Well-Architected Framework.*

Implement regular review cycles to analyze workloads against new AWS services, features, and best practices to identify optimization opportunities and ensure continued cost efficiency. Regular workload analysis ensures you stay current with AWS innovations and continuously optimize your architecture for cost and performance.

Overview

Regular workload analysis is essential for maintaining cost-optimized architectures in the rapidly evolving AWS ecosystem. This involves establishing systematic review schedules, implementing comprehensive analysis frameworks, and creating actionable optimization roadmaps based on new service capabilities and changing business requirements.

Key components of regular workload analysis include:

• Scheduled Review Cycles: Establishing regular intervals for workload assessment
• Comprehensive Analysis Framework: Systematic evaluation of architecture, costs, and performance
• New Service Integration: Evaluating how new AWS services can improve existing workloads
• Optimization Tracking: Monitoring and measuring the impact of implemented changes
• Continuous Improvement: Using review insights to refine processes and strategies

Implementation

Workload Analysis Framework

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Let me continue with the rest of the implementation:

def identify_optimization_opportunities(self, workload_id: str, metrics: WorkloadMetrics) -> List[OptimizationOpportunity]:
    """Identify optimization opportunities based on workload analysis"""
    
    opportunities = []
    
    # Cost optimization opportunities
    cost_opportunities = self.identify_cost_opportunities(workload_id, metrics)
    opportunities.extend(cost_opportunities)
    
    # Performance optimization opportunities
    performance_opportunities = self.identify_performance_opportunities(workload_id, metrics)
    opportunities.extend(performance_opportunities)
    
    # Utilization optimization opportunities
    utilization_opportunities = self.identify_utilization_opportunities(workload_id, metrics)
    opportunities.extend(utilization_opportunities)
    
    # Security optimization opportunities
    security_opportunities = self.identify_security_opportunities(workload_id, metrics)
    opportunities.extend(security_opportunities)
    
    # New service adoption opportunities
    new_service_opportunities = self.identify_new_service_opportunities(workload_id, metrics)
    opportunities.extend(new_service_opportunities)
    
    return opportunities

def identify_cost_opportunities(self, workload_id: str, metrics: WorkloadMetrics) -> List[OptimizationOpportunity]:
    """Identify cost optimization opportunities"""
    
    opportunities = []
    
    # High cost alert
    if metrics.cost_metrics.get('total_monthly_cost', 0) > self.analysis_config['cost_thresholds']['high_cost_resource']:
        opportunities.append(OptimizationOpportunity(
            opportunity_id=f"COST_{workload_id}_{datetime.now().strftime('%Y%m%d')}_001",
            workload_id=workload_id,
            category=AnalysisCategory.COST_OPTIMIZATION,
            description="High monthly cost detected - review for optimization opportunities",
            current_state=f"Monthly cost: ${metrics.cost_metrics['total_monthly_cost']:,.2f}",
            proposed_solution="Conduct detailed cost analysis and right-sizing review",
            estimated_savings=metrics.cost_metrics['total_monthly_cost'] * 0.15,  # Estimate 15% savings
            implementation_effort="Medium",
            risk_level="Low",
            priority="High",
            status=OptimizationStatus.IDENTIFIED,
            identified_date=datetime.now()
        ))
    
    # Cost trend alert
    cost_trend = metrics.cost_metrics.get('cost_trend', 0)
    if cost_trend > self.analysis_config['cost_thresholds']['cost_increase_alert']:
        opportunities.append(OptimizationOpportunity(
            opportunity_id=f"COST_{workload_id}_{datetime.now().strftime('%Y%m%d')}_002",
            workload_id=workload_id,
            category=AnalysisCategory.COST_OPTIMIZATION,
            description=f"Cost increasing trend detected: {cost_trend:.1%} increase",
            current_state=f"Cost trend: {cost_trend:.1%} increase over last week",
            proposed_solution="Investigate cost drivers and implement cost controls",
            estimated_savings=metrics.cost_metrics['average_daily_cost'] * 30 * abs(cost_trend),
            implementation_effort="Low",
            risk_level="Low",
            priority="Medium",
            status=OptimizationStatus.IDENTIFIED,
            identified_date=datetime.now()
        ))
    
    # Service-specific cost opportunities
    highest_cost_service = metrics.cost_metrics.get('highest_cost_service', '')
    highest_cost_amount = metrics.cost_metrics.get('highest_cost_service_amount', 0)
    
    if highest_cost_amount > 500:  # If highest cost service > $500/month
        opportunities.append(OptimizationOpportunity(
            opportunity_id=f"COST_{workload_id}_{datetime.now().strftime('%Y%m%d')}_003",
            workload_id=workload_id,
            category=AnalysisCategory.COST_OPTIMIZATION,
            description=f"High cost service optimization: {highest_cost_service}",
            current_state=f"{highest_cost_service} costs ${highest_cost_amount:,.2f}/month",
            proposed_solution=f"Review {highest_cost_service} configuration and usage patterns",
            estimated_savings=highest_cost_amount * 0.2,  # Estimate 20% savings
            implementation_effort="Medium",
            risk_level="Medium",
            priority="High",
            status=OptimizationStatus.IDENTIFIED,
            identified_date=datetime.now()
        ))
    
    return opportunities

def identify_performance_opportunities(self, workload_id: str, metrics: WorkloadMetrics) -> List[OptimizationOpportunity]:
    """Identify performance optimization opportunities"""
    
    opportunities = []
    
    # High response time
    response_time = metrics.performance_metrics.get('responsetime_avg', 0)
    if response_time > 2.0:  # > 2 seconds average response time
        opportunities.append(OptimizationOpportunity(
            opportunity_id=f"PERF_{workload_id}_{datetime.now().strftime('%Y%m%d')}_001",
            workload_id=workload_id,
            category=AnalysisCategory.PERFORMANCE_IMPROVEMENT,
            description="High response time detected",
            current_state=f"Average response time: {response_time:.2f} seconds",
            proposed_solution="Implement caching, optimize database queries, or consider CDN",
            estimated_savings=0.0,  # Performance improvement, not direct cost savings
            implementation_effort="Medium",
            risk_level="Medium",
            priority="High",
            status=OptimizationStatus.IDENTIFIED,
            identified_date=datetime.now()
        ))
    
    # High Lambda duration
    lambda_duration = metrics.performance_metrics.get('duration_avg', 0)
    if lambda_duration > 10000:  # > 10 seconds average duration
        opportunities.append(OptimizationOpportunity(
            opportunity_id=f"PERF_{workload_id}_{datetime.now().strftime('%Y%m%d')}_002",
            workload_id=workload_id,
            category=AnalysisCategory.PERFORMANCE_IMPROVEMENT,
            description="High Lambda function duration",
            current_state=f"Average Lambda duration: {lambda_duration:.0f}ms",
            proposed_solution="Optimize Lambda function code and consider provisioned concurrency",
            estimated_savings=100.0,  # Estimated cost savings from optimization
            implementation_effort="Medium",
            risk_level="Low",
            priority="Medium",
            status=OptimizationStatus.IDENTIFIED,
            identified_date=datetime.now()
        ))
    
    return opportunities

def identify_utilization_opportunities(self, workload_id: str, metrics: WorkloadMetrics) -> List[OptimizationOpportunity]:
    """Identify resource utilization optimization opportunities"""
    
    opportunities = []
    
    # Low CPU utilization
    avg_cpu = metrics.utilization_metrics.get('avg_cpu_utilization', 100)
    if avg_cpu < 20:  # < 20% average CPU utilization
        opportunities.append(OptimizationOpportunity(
            opportunity_id=f"UTIL_{workload_id}_{datetime.now().strftime('%Y%m%d')}_001",
            workload_id=workload_id,
            category=AnalysisCategory.COST_OPTIMIZATION,
            description="Low CPU utilization - right-sizing opportunity",
            current_state=f"Average CPU utilization: {avg_cpu:.1f}%",
            proposed_solution="Right-size EC2 instances to smaller instance types",
            estimated_savings=metrics.cost_metrics.get('total_monthly_cost', 0) * 0.3,
            implementation_effort="Low",
            risk_level="Medium",
            priority="High",
            status=OptimizationStatus.IDENTIFIED,
            identified_date=datetime.now()
        ))
    
    # High CPU utilization
    max_cpu = metrics.utilization_metrics.get('max_cpu_utilization', 0)
    if max_cpu > 90:  # > 90% max CPU utilization
        opportunities.append(OptimizationOpportunity(
            opportunity_id=f"UTIL_{workload_id}_{datetime.now().strftime('%Y%m%d')}_002",
            workload_id=workload_id,
            category=AnalysisCategory.PERFORMANCE_IMPROVEMENT,
            description="High CPU utilization - scaling opportunity",
            current_state=f"Maximum CPU utilization: {max_cpu:.1f}%",
            proposed_solution="Implement auto-scaling or upgrade to larger instance types",
            estimated_savings=0.0,  # Performance improvement
            implementation_effort="Medium",
            risk_level="Low",
            priority="High",
            status=OptimizationStatus.IDENTIFIED,
            identified_date=datetime.now()
        ))
    
    return opportunities

def identify_security_opportunities(self, workload_id: str, metrics: WorkloadMetrics) -> List[OptimizationOpportunity]:
    """Identify security optimization opportunities"""
    
    opportunities = []
    
    # Low Config compliance
    compliance_percentage = metrics.security_metrics.get('config_compliance_percentage', 100)
    if compliance_percentage < 90:
        opportunities.append(OptimizationOpportunity(
            opportunity_id=f"SEC_{workload_id}_{datetime.now().strftime('%Y%m%d')}_001",
            workload_id=workload_id,
            category=AnalysisCategory.SECURITY_ENHANCEMENT,
            description="Low Config compliance score",
            current_state=f"Config compliance: {compliance_percentage:.1f}%",
            proposed_solution="Review and remediate Config rule violations",
            estimated_savings=0.0,  # Security improvement
            implementation_effort="Medium",
            risk_level="High",
            priority="High",
            status=OptimizationStatus.IDENTIFIED,
            identified_date=datetime.now()
        ))
    
    return opportunities

def identify_new_service_opportunities(self, workload_id: str, metrics: WorkloadMetrics) -> List[OptimizationOpportunity]:
    """Identify opportunities to adopt new AWS services"""
    
    opportunities = []
    
    # Serverless migration opportunity
    avg_cpu = metrics.utilization_metrics.get('avg_cpu_utilization', 100)
    if avg_cpu < 30 and metrics.cost_metrics.get('total_monthly_cost', 0) > 200:
        opportunities.append(OptimizationOpportunity(
            opportunity_id=f"NEW_{workload_id}_{datetime.now().strftime('%Y%m%d')}_001",
            workload_id=workload_id,
            category=AnalysisCategory.COST_OPTIMIZATION,
            description="Serverless migration opportunity",
            current_state=f"Low utilization ({avg_cpu:.1f}%) with significant costs",
            proposed_solution="Evaluate migration to AWS Lambda or Fargate",
            estimated_savings=metrics.cost_metrics.get('total_monthly_cost', 0) * 0.4,
            implementation_effort="High",
            risk_level="Medium",
            priority="Medium",
            status=OptimizationStatus.IDENTIFIED,
            identified_date=datetime.now()
        ))
    
    # Graviton processor opportunity
    if metrics.cost_metrics.get('highest_cost_service', '') == 'Amazon Elastic Compute Cloud - Compute':
        opportunities.append(OptimizationOpportunity(
            opportunity_id=f"NEW_{workload_id}_{datetime.now().strftime('%Y%m%d')}_002",
            workload_id=workload_id,
            category=AnalysisCategory.COST_OPTIMIZATION,
            description="AWS Graviton processor migration opportunity",
            current_state="Using x86-based EC2 instances",
            proposed_solution="Evaluate migration to Graviton-based instances for cost savings",
            estimated_savings=metrics.cost_metrics.get('highest_cost_service_amount', 0) * 0.2,
            implementation_effort="Medium",
            risk_level="Low",
            priority="Medium",
            status=OptimizationStatus.IDENTIFIED,
            identified_date=datetime.now()
        ))
    
    return opportunities

def generate_optimization_recommendations(self, opportunities: List[OptimizationOpportunity], 
                                        metrics: WorkloadMetrics) -> List[Dict]:
    """Generate actionable optimization recommendations"""
    
    recommendations = []
    
    # Prioritize opportunities by potential savings and effort
    high_value_opportunities = [
        opp for opp in opportunities 
        if opp.estimated_savings > 500 and opp.implementation_effort in ['Low', 'Medium']
    ]
    
    if high_value_opportunities:
        recommendations.append({
            'type': 'high_value_optimization',
            'priority': 'High',
            'title': 'High-Value Optimization Opportunities',
            'description': f"Identified {len(high_value_opportunities)} high-value optimization opportunities",
            'action_items': [
                f"{opp.description} - Estimated savings: ${opp.estimated_savings:,.2f}"
                for opp in high_value_opportunities[:5]  # Top 5
            ],
            'estimated_total_savings': sum(opp.estimated_savings for opp in high_value_opportunities)
        })
    
    # Quick wins (low effort, medium savings)
    quick_wins = [
        opp for opp in opportunities 
        if opp.implementation_effort == 'Low' and opp.estimated_savings > 100
    ]
    
    if quick_wins:
        recommendations.append({
            'type': 'quick_wins',
            'priority': 'Medium',
            'title': 'Quick Win Opportunities',
            'description': f"Identified {len(quick_wins)} quick win opportunities",
            'action_items': [
                f"{opp.description} - Effort: {opp.implementation_effort}"
                for opp in quick_wins[:3]  # Top 3
            ],
            'estimated_total_savings': sum(opp.estimated_savings for opp in quick_wins)
        })
    
    # Performance improvements
    performance_opportunities = [
        opp for opp in opportunities 
        if opp.category == AnalysisCategory.PERFORMANCE_IMPROVEMENT
    ]
    
    if performance_opportunities:
        recommendations.append({
            'type': 'performance_improvement',
            'priority': 'Medium',
            'title': 'Performance Improvement Opportunities',
            'description': f"Identified {len(performance_opportunities)} performance improvement opportunities",
            'action_items': [
                opp.description for opp in performance_opportunities[:3]
            ],
            'estimated_total_savings': sum(opp.estimated_savings for opp in performance_opportunities)
        })
    
    # Security enhancements
    security_opportunities = [
        opp for opp in opportunities 
        if opp.category == AnalysisCategory.SECURITY_ENHANCEMENT
    ]
    
    if security_opportunities:
        recommendations.append({
            'type': 'security_enhancement',
            'priority': 'High',
            'title': 'Security Enhancement Opportunities',
            'description': f"Identified {len(security_opportunities)} security improvement opportunities",
            'action_items': [
                opp.description for opp in security_opportunities
            ],
            'estimated_total_savings': 0.0  # Security improvements don't have direct cost savings
        })
    
    return recommendations

def calculate_workload_score(self, metrics: WorkloadMetrics, 
                           opportunities: List[OptimizationOpportunity]) -> float:
    """Calculate overall workload optimization score (0-100)"""
    
    score = 100.0  # Start with perfect score
    
    # Deduct points for cost issues
    cost_trend = metrics.cost_metrics.get('cost_trend', 0)
    if cost_trend > 0.1:  # > 10% cost increase
        score -= 20
    elif cost_trend > 0.05:  # > 5% cost increase
        score -= 10
    
    # Deduct points for utilization issues
    avg_cpu = metrics.utilization_metrics.get('avg_cpu_utilization', 50)
    if avg_cpu < 20:  # Very low utilization
        score -= 15
    elif avg_cpu > 90:  # Very high utilization
        score -= 10
    
    # Deduct points for performance issues
    response_time = metrics.performance_metrics.get('responsetime_avg', 1.0)
    if response_time > 3.0:  # > 3 seconds
        score -= 20
    elif response_time > 2.0:  # > 2 seconds
        score -= 10
    
    # Deduct points for security issues
    compliance = metrics.security_metrics.get('config_compliance_percentage', 100)
    if compliance < 80:
        score -= 25
    elif compliance < 90:
        score -= 15
    
    # Deduct points for number of high-priority opportunities
    high_priority_opportunities = len([
        opp for opp in opportunities 
        if opp.priority == 'High'
    ])
    score -= min(high_priority_opportunities * 5, 30)  # Max 30 points deduction
    
    return max(0.0, score)  # Ensure score doesn't go below 0

def identify_improvement_areas(self, metrics: WorkloadMetrics, 
                             opportunities: List[OptimizationOpportunity]) -> List[str]:
    """Identify key areas for improvement"""
    
    improvement_areas = []
    
    # Cost optimization
    cost_opportunities = [opp for opp in opportunities if opp.category == AnalysisCategory.COST_OPTIMIZATION]
    if cost_opportunities:
        improvement_areas.append("Cost Optimization")
    
    # Performance improvement
    performance_opportunities = [opp for opp in opportunities if opp.category == AnalysisCategory.PERFORMANCE_IMPROVEMENT]
    if performance_opportunities:
        improvement_areas.append("Performance Optimization")
    
    # Security enhancement
    security_opportunities = [opp for opp in opportunities if opp.category == AnalysisCategory.SECURITY_ENHANCEMENT]
    if security_opportunities:
        improvement_areas.append("Security Enhancement")
    
    # Utilization optimization
    avg_cpu = metrics.utilization_metrics.get('avg_cpu_utilization', 50)
    if avg_cpu < 30 or avg_cpu > 85:
        improvement_areas.append("Resource Utilization")
    
    # Operational efficiency
    if metrics.cost_metrics.get('service_count', 0) > 10:
        improvement_areas.append("Architecture Simplification")
    
    return improvement_areas

def calculate_next_review_date(self, workload_id: str, review_score: float) -> datetime:
    """Calculate next review date based on workload score and criticality"""
    
    # Get workload criticality (this would come from workload metadata)
    workload_criticality = self.get_workload_criticality(workload_id)
    
    # Determine review frequency based on score and criticality
    if review_score < 60:  # Poor score
        days_until_next_review = 30  # Monthly review
    elif review_score < 80:  # Fair score
        days_until_next_review = 60  # Bi-monthly review
    else:  # Good score
        if workload_criticality == 'critical':
            days_until_next_review = 90  # Quarterly review
        elif workload_criticality == 'important':
            days_until_next_review = 180  # Semi-annual review
        else:
            days_until_next_review = 365  # Annual review
    
    return datetime.now() + timedelta(days=days_until_next_review)

def get_workload_criticality(self, workload_id: str) -> str:
    """Get workload criticality level"""
    # This would typically come from a workload registry or tagging
    # For now, return a default value
    return 'important'

def store_review_results(self, review_result: WorkloadReviewResult):
    """Store review results for tracking and historical analysis"""
    
    try:
        # Store in Systems Manager Parameter Store
        parameter_name = f"/workload-reviews/{review_result.workload_id}/{review_result.review_id}"
        
        review_data = {
            'review_id': review_result.review_id,
            'workload_id': review_result.workload_id,
            'review_date': review_result.review_date.isoformat(),
            'review_score': review_result.review_score,
            'opportunities_count': len(review_result.opportunities_identified),
            'total_estimated_savings': sum(opp.estimated_savings for opp in review_result.opportunities_identified),
            'improvement_areas': review_result.improvement_areas,
            'next_review_date': review_result.next_review_date.isoformat()
        }
        
        self.systems_manager.put_parameter(
            Name=parameter_name,
            Value=json.dumps(review_data),
            Type='String',
            Overwrite=True,
            Description=f'Workload review results for {review_result.workload_id}'
        )
        
        # Also send metrics to CloudWatch
        self.cloudwatch.put_metric_data(
            Namespace='WorkloadAnalysis',
            MetricData=[
                {
                    'MetricName': 'ReviewScore',
                    'Dimensions': [
                        {'Name': 'WorkloadId', 'Value': review_result.workload_id}
                    ],
                    'Value': review_result.review_score,
                    'Unit': 'None'
                },
                {
                    'MetricName': 'OptimizationOpportunities',
                    'Dimensions': [
                        {'Name': 'WorkloadId', 'Value': review_result.workload_id}
                    ],
                    'Value': len(review_result.opportunities_identified),
                    'Unit': 'Count'
                },
                {
                    'MetricName': 'EstimatedSavings',
                    'Dimensions': [
                        {'Name': 'WorkloadId', 'Value': review_result.workload_id}
                    ],
                    'Value': sum(opp.estimated_savings for opp in review_result.opportunities_identified),
                    'Unit': 'None'
                }
            ]
        )
        
        self.logger.info(f"Stored review results for workload {review_result.workload_id}")
        
    except Exception as e:
        self.logger.error(f"Error storing review results: {str(e)}")

def generate_workload_analysis_report(self, review_result: WorkloadReviewResult) -> str:
    """Generate comprehensive workload analysis report"""
    
    report = f""" # Workload Analysis Report

Executive Summary

• Workload ID: {review_result.workload_id}
• Review Date: {review_result.review_date.strftime(‘%Y-%m-%d %H:%M:%S’)}
• Overall Score: {review_result.review_score:.1f}/100
• Optimization Opportunities: {len(review_result.opportunities_identified)}
• Total Estimated Savings: ${sum(opp.estimated_savings for opp in review_result.opportunities_identified):,.2f}

Current Metrics

Cost Metrics

• Monthly Cost: ${review_result.metrics_analyzed.cost_metrics.get(‘total_monthly_cost’, 0):,.2f}
• Daily Average: ${review_result.metrics_analyzed.cost_metrics.get(‘average_daily_cost’, 0):,.2f}
• Cost Trend: {review_result.metrics_analyzed.cost_metrics.get(‘cost_trend’, 0):.1%}
• Highest Cost Service: {review_result.metrics_analyzed.cost_metrics.get(‘highest_cost_service’, ‘N/A’)}

Performance Metrics

• Average Response Time: {review_result.metrics_analyzed.performance_metrics.get(‘responsetime_avg’, 0):.2f}s
• Average CPU Utilization: {review_result.metrics_analyzed.utilization_metrics.get(‘avg_cpu_utilization’, 0):.1f}%
• Security Compliance: {review_result.metrics_analyzed.security_metrics.get(‘config_compliance_percentage’, 0):.1f}%

Optimization Opportunities

”””

    # Group opportunities by category
    opportunities_by_category = {}
    for opp in review_result.opportunities_identified:
        category = opp.category.value
        if category not in opportunities_by_category:
            opportunities_by_category[category] = []
        opportunities_by_category[category].append(opp)
    
    for category, opportunities in opportunities_by_category.items():
        report += f"\n### {category.replace('_', ' ').title()}\n"
        for opp in opportunities[:3]:  # Top 3 per category
            report += f"- **{opp.description}**\n"
            report += f"  - Current State: {opp.current_state}\n"
            report += f"  - Proposed Solution: {opp.proposed_solution}\n"
            report += f"  - Estimated Savings: ${opp.estimated_savings:,.2f}\n"
            report += f"  - Priority: {opp.priority}\n\n"
    
    # Recommendations
    report += "\n## Recommendations\n"
    for i, rec in enumerate(review_result.recommendations, 1):
        report += f"\n### {i}. {rec['title']}\n"
        report += f"- **Priority**: {rec['priority']}\n"
        report += f"- **Description**: {rec['description']}\n"
        if rec.get('estimated_total_savings', 0) > 0:
            report += f"- **Total Estimated Savings**: ${rec['estimated_total_savings']:,.2f}\n"
        
        if rec.get('action_items'):
            report += "- **Action Items**:\n"
            for item in rec['action_items']:
                report += f"  - {item}\n"
    
    # Next steps
    report += f"\n## Next Steps\n"
    report += f"- **Next Review Date**: {review_result.next_review_date.strftime('%Y-%m-%d')}\n"
    report += f"- **Key Improvement Areas**: {', '.join(review_result.improvement_areas)}\n"
    report += f"- **Recommended Actions**: Prioritize high-value optimization opportunities\n"
    
    return report <!-- CODE SNIPPET HIDDEN - Original content below: ```

Now let me add the usage examples and complete the file:

Usage Examples

Example 1: Comprehensive Workload Analysis

CODE SNIPPET WILL BE PROVIDED SOON -->

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</div>python
# Initialize the workload analysis manager
analysis_manager = WorkloadAnalysisManager()

# Conduct comprehensive analysis for a critical workload
workload_id = "ecommerce-platform-prod"
review_result = analysis_manager.conduct_workload_analysis(
    workload_id=workload_id,
    analysis_type="comprehensive"
)

# Print summary results
print(f"Workload Score: {review_result.review_score:.1f}/100")
print(f"Opportunities Identified: {len(review_result.opportunities_identified)}")
print(f"Total Estimated Savings: ${sum(opp.estimated_savings for opp in review_result.opportunities_identified):,.2f}")

# Generate and display report
report = analysis_manager.generate_workload_analysis_report(review_result)
print(report)

# Check high-priority opportunities
high_priority_opportunities = [
    opp for opp in review_result.opportunities_identified 
    if opp.priority == 'High'
]

if high_priority_opportunities:
    print("\n🚨 High Priority Opportunities:")
    for opp in high_priority_opportunities:
        print(f"- {opp.description}")
        print(f"  Estimated Savings: ${opp.estimated_savings:,.2f}")
        print(f"  Implementation Effort: {opp.implementation_effort}")
<!-- CODE SNIPPET HIDDEN - Original content below:

Example 2: Automated Regular Review Process

CODE SNIPPET WILL BE PROVIDED SOON -->

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</div>python
# Set up automated review process for multiple workloads
workloads_to_review = [
    {"id": "web-app-prod", "criticality": "critical"},
    {"id": "data-pipeline", "criticality": "important"},
    {"id": "reporting-system", "criticality": "standard"}
]

def automated_workload_review_process():
    """Automated process for regular workload reviews"""
    
    analysis_manager = WorkloadAnalysisManager()
    review_results = []
    
    for workload in workloads_to_review:
        workload_id = workload["id"]
        criticality = workload["criticality"]
        
        print(f"\n📊 Analyzing workload: {workload_id} ({criticality})")
        
        # Conduct analysis
        review_result = analysis_manager.conduct_workload_analysis(
            workload_id=workload_id,
            analysis_type="comprehensive" if criticality == "critical" else "focused"
        )
        
        review_results.append(review_result)
        
        # Alert on poor scores
        if review_result.review_score < 70:
            print(f"⚠️  LOW SCORE ALERT: {workload_id} scored {review_result.review_score:.1f}/100")
            
            # Send notification (implementation would depend on your notification system)
            send_low_score_alert(workload_id, review_result.review_score)
        
        # Alert on high-value opportunities
        high_value_opportunities = [
            opp for opp in review_result.opportunities_identified
            if opp.estimated_savings > 1000
        ]
        
        if high_value_opportunities:
            total_savings = sum(opp.estimated_savings for opp in high_value_opportunities)
            print(f"💰 HIGH VALUE OPPORTUNITIES: ${total_savings:,.2f} potential savings")
    
    # Generate summary report
    generate_portfolio_summary_report(review_results)
    
    return review_results

def send_low_score_alert(workload_id: str, score: float):
    """Send alert for workloads with low optimization scores"""
    # Implementation would integrate with your alerting system
    print(f"🚨 ALERT: Workload {workload_id} requires immediate attention (Score: {score:.1f})")

def generate_portfolio_summary_report(review_results: List[WorkloadReviewResult]):
    """Generate summary report across all workloads"""
    
    total_workloads = len(review_results)
    average_score = sum(result.review_score for result in review_results) / total_workloads
    total_opportunities = sum(len(result.opportunities_identified) for result in review_results)
    total_potential_savings = sum(
        sum(opp.estimated_savings for opp in result.opportunities_identified)
        for result in review_results
    )
    
    print(f"""
📈 PORTFOLIO OPTIMIZATION SUMMARY
================================
Total Workloads Analyzed: {total_workloads}
Average Optimization Score: {average_score:.1f}/100
Total Opportunities Identified: {total_opportunities}
Total Potential Savings: ${total_potential_savings:,.2f}

Top Recommendations:
- Focus on workloads with scores below 70
- Prioritize high-value optimization opportunities
- Implement quick wins for immediate impact
""")

# Run the automated review process
review_results = automated_workload_review_process()
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Example 3: Trend Analysis and Historical Comparison

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def analyze_workload_trends(workload_id: str, months_back: int = 6):
    """Analyze workload optimization trends over time"""
    
    analysis_manager = WorkloadAnalysisManager()
    
    # Get historical review data
    historical_reviews = get_historical_reviews(workload_id, months_back)
    
    if len(historical_reviews) < 2:
        print("Insufficient historical data for trend analysis")
        return
    
    # Analyze trends
    scores = [review['review_score'] for review in historical_reviews]
    costs = [review['monthly_cost'] for review in historical_reviews]
    opportunities = [review['opportunities_count'] for review in historical_reviews]
    
    # Calculate trends
    score_trend = (scores[-1] - scores[0]) / scores[0] if scores[0] > 0 else 0
    cost_trend = (costs[-1] - costs[0]) / costs[0] if costs[0] > 0 else 0
    
    print(f"""
📊 WORKLOAD TREND ANALYSIS: {workload_id}
==========================================
Review Period: {months_back} months
Number of Reviews: {len(historical_reviews)}

Score Trend: {score_trend:+.1%} ({'Improving' if score_trend > 0 else 'Declining'})
Cost Trend: {cost_trend:+.1%} ({'Increasing' if cost_trend > 0 else 'Decreasing'})

Current Status:
- Latest Score: {scores[-1]:.1f}/100
- Latest Monthly Cost: ${costs[-1]:,.2f}
- Active Opportunities: {opportunities[-1]}

Recommendations:
""")
    
    if score_trend < -0.1:  # Score declining by more than 10%
        print("- 🚨 Optimization score is declining - immediate review recommended")
    
    if cost_trend > 0.2:  # Cost increasing by more than 20%
        print("- 💸 Cost is increasing significantly - cost optimization review needed")
    
    if opportunities[-1] > 5:
        print("- 🎯 Multiple optimization opportunities available - prioritize implementation")

def get_historical_reviews(workload_id: str, months_back: int) -> List[Dict]:
    """Get historical review data for trend analysis"""
    # This would query your review data storage
    # For demonstration, returning sample data
    
    sample_data = []
    for i in range(months_back):
        date = datetime.now() - timedelta(days=30 * i)
        sample_data.append({
            'review_date': date,
            'review_score': 75 + (i * 2),  # Improving trend
            'monthly_cost': 5000 - (i * 100),  # Decreasing cost
            'opportunities_count': max(1, 8 - i)  # Decreasing opportunities
        })
    
    return list(reversed(sample_data))  # Chronological order

# Analyze trends for a specific workload
analyze_workload_trends("ecommerce-platform-prod", months_back=6)
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Review Schedule Templates

Workload Review Schedule Configuration

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Workload_Review_Schedules:
  critical_workloads:
    frequency: "monthly"
    analysis_type: "comprehensive"
    stakeholders: ["technical_lead", "business_owner", "cost_optimization_team"]
    duration: "2 weeks"
    deliverables:
      - "Detailed analysis report"
      - "Optimization roadmap"
      - "Cost-benefit analysis"
      - "Implementation timeline"
    
  important_workloads:
    frequency: "quarterly"
    analysis_type: "focused"
    stakeholders: ["technical_lead", "cost_optimization_team"]
    duration: "1 week"
    deliverables:
      - "Analysis summary"
      - "Priority optimization opportunities"
      - "Quick wins identification"
    
  standard_workloads:
    frequency: "annually"
    analysis_type: "rapid"
    stakeholders: ["technical_lead"]
    duration: "3 days"
    deliverables:
      - "Basic analysis report"
      - "High-level recommendations"

Review_Triggers:
  cost_spike:
    threshold: "20% increase over 7 days"
    action: "immediate_review"
    analysis_type: "focused"
    
  performance_degradation:
    threshold: "response_time > 3 seconds"
    action: "performance_review"
    analysis_type: "focused"
    
  security_compliance_drop:
    threshold: "compliance < 90%"
    action: "security_review"
    analysis_type: "comprehensive"
    
  new_service_announcement:
    trigger: "relevant_aws_service_launch"
    action: "service_evaluation_review"
    analysis_type: "rapid"

Analysis_Metrics:
  cost_optimization:
    - "monthly_cost_trend"
    - "service_cost_distribution"
    - "utilization_efficiency"
    - "reserved_instance_coverage"
    
  performance_optimization:
    - "response_time_percentiles"
    - "throughput_metrics"
    - "error_rates"
    - "availability_metrics"
    
  security_optimization:
    - "config_compliance_score"
    - "security_group_analysis"
    - "encryption_coverage"
    - "access_pattern_analysis"
    
  operational_optimization:
    - "automation_coverage"
    - "monitoring_completeness"
    - "backup_compliance"
    - "disaster_recovery_readiness"

Common Challenges and Solutions

Challenge: Data Collection Complexity

Solution: Implement automated data collection pipelines using AWS APIs and CloudWatch metrics. Create standardized data collection templates and use AWS Config for configuration tracking.

Challenge: Analysis Consistency

Solution: Develop standardized analysis frameworks and scoring methodologies. Use automated analysis tools and maintain consistent evaluation criteria across all workloads.

Challenge: Review Fatigue

Solution: Implement risk-based review scheduling where high-performing workloads are reviewed less frequently. Use automation to reduce manual effort and focus human attention on high-value activities.

Challenge: Tracking Implementation Progress

Solution: Create optimization opportunity tracking systems with status updates and progress monitoring. Implement automated reporting and dashboard visualization for stakeholder communication.

Challenge: Measuring Review Effectiveness

Solution: Track key metrics such as optimization implementation rates, actual vs. estimated savings, and workload score improvements over time. Use this data to continuously improve the review process.

Related Resources

Related Resources

• AWS Well-Architected Framework - Review and analyze this workload regularly
• AWS Cost Explorer User Guide
• Amazon CloudWatch User Guide
• AWS Config Developer Guide
• AWS Well-Architected Tool User Guide
• AWS Trusted Advisor
• AWS Systems Manager User Guide
• Amazon QuickSight User Guide