COST10-BP03: Implement new service evaluation automation
*This page contains guidance for implementing this best practice from the AWS Well-Architected Framework.*
Create automated systems to monitor AWS service announcements, evaluate their relevance to your workloads, and generate recommendations for potential adoption opportunities. Automation ensures you stay current with AWS innovations and can quickly identify optimization opportunities without manual monitoring overhead.
Overview
New service evaluation automation involves creating intelligent systems that continuously monitor AWS service announcements, analyze their relevance to your existing workloads, and generate actionable recommendations for adoption. This automation ensures you don’t miss optimization opportunities and can respond quickly to new AWS capabilities.
Key components of service evaluation automation include:
- Announcement Monitoring: Automated tracking of AWS service launches and updates
- Relevance Analysis: Intelligent matching of new services to existing workload patterns
- Impact Assessment: Automated evaluation of potential cost and performance benefits
- Recommendation Generation: Creation of prioritized adoption recommendations
- Integration Workflows: Automated processes for evaluation and decision-making
Implementation
Service Evaluation Automation Framework
Let me continue with the rest of the implementation and examples:
def send_recommendations(self, evaluations: List[ServiceEvaluationResult]):
"""Send recommendations based on evaluation results"""
# Group evaluations by priority
high_priority = [e for e in evaluations if e.recommendation == AdoptionRecommendation.IMMEDIATE]
medium_priority = [e for e in evaluations if e.recommendation == AdoptionRecommendation.PILOT]
# Send high priority notifications
if high_priority:
self.send_high_priority_notification(high_priority)
# Send medium priority notifications
if medium_priority:
self.send_medium_priority_notification(medium_priority)
# Generate summary report
self.generate_evaluation_summary_report(evaluations)
def send_high_priority_notification(self, evaluations: List[ServiceEvaluationResult]):
"""Send high priority notifications for immediate action items"""
message = {
"notification_type": "high_priority_service_evaluation",
"timestamp": datetime.now().isoformat(),
"summary": f"{len(evaluations)} high-priority service adoption opportunities identified",
"evaluations": []
}
for evaluation in evaluations:
message["evaluations"].append({
"workload_id": evaluation.workload_id,
"service_name": evaluation.service_announcement.service_name,
"relevance_score": evaluation.relevance_score,
"estimated_cost_impact": evaluation.estimated_cost_impact,
"recommendation": evaluation.recommendation.value,
"rationale": evaluation.rationale
})
try:
self.sns_client.publish(
TopicArn=self.config['notification_topics']['high_priority'],
Message=json.dumps(message, indent=2),
Subject="🚨 High Priority: New AWS Service Adoption Opportunities"
)
self.logger.info(f"Sent high priority notification for {len(evaluations)} evaluations")
except Exception as e:
self.logger.error(f"Error sending high priority notification: {str(e)}")
def create_automation_dashboard(self) -> Dict:
"""Create CloudWatch dashboard for monitoring automation"""
dashboard_body = {
"widgets": [
{
"type": "metric",
"properties": {
"metrics": [
["ServiceEvaluation", "AnnouncementsProcessed"],
["ServiceEvaluation", "EvaluationsGenerated"],
["ServiceEvaluation", "HighPriorityRecommendations"],
["ServiceEvaluation", "MediumPriorityRecommendations"]
],
"period": 3600,
"stat": "Sum",
"region": "us-east-1",
"title": "Service Evaluation Metrics"
}
},
{
"type": "log",
"properties": {
"query": "SOURCE '/aws/lambda/ServiceAnnouncementMonitor'\n| fields @timestamp, @message\n| filter @message like /ERROR/\n| sort @timestamp desc\n| limit 20",
"region": "us-east-1",
"title": "Recent Errors",
"view": "table"
}
},
{
"type": "metric",
"properties": {
"metrics": [
["AWS/Lambda", "Duration", "FunctionName", "ServiceAnnouncementMonitor"],
["AWS/Lambda", "Duration", "FunctionName", "ServiceEvaluator"],
["AWS/Lambda", "Duration", "FunctionName", "RecommendationGenerator"]
],
"period": 300,
"stat": "Average",
"region": "us-east-1",
"title": "Lambda Function Performance"
}
}
]
}
return {
"dashboard_name": "ServiceEvaluationAutomation",
"dashboard_body": json.dumps(dashboard_body)
}
# Helper methods for data management
def is_announcement_processed(self, announcement_id: str) -> bool:
"""Check if announcement has already been processed"""
try:
response = self.announcements_table.get_item(
Key={'announcement_id': announcement_id}
)
return 'Item' in response
except Exception:
return False
def store_announcement(self, announcement: ServiceAnnouncement):
"""Store announcement in DynamoDB"""
try:
self.announcements_table.put_item(
Item={
'announcement_id': announcement.announcement_id,
'title': announcement.title,
'description': announcement.description,
'service_name': announcement.service_name,
'category': announcement.category.value,
'announcement_date': announcement.announcement_date.isoformat(),
'source_url': announcement.source_url,
'key_features': announcement.key_features,
'pricing_model': announcement.pricing_model,
'availability_regions': announcement.availability_regions
}
)
except Exception as e:
self.logger.error(f"Error storing announcement: {str(e)}")
def get_workload_profiles(self) -> List[WorkloadProfile]:
"""Get all workload profiles from DynamoDB"""
try:
response = self.workload_profiles_table.scan()
profiles = []
for item in response['Items']:
profile = WorkloadProfile(
workload_id=item['workload_id'],
architecture_components=item.get('architecture_components', []),
current_services=item.get('current_services', []),
cost_profile=item.get('cost_profile', {}),
performance_requirements=item.get('performance_requirements', {}),
compliance_requirements=item.get('compliance_requirements', []),
business_criticality=item.get('business_criticality', 'standard')
)
profiles.append(profile)
return profiles
except Exception as e:
self.logger.error(f"Error getting workload profiles: {str(e)}")
return []
def generate_announcement_id(self, source_id: str) -> str:
"""Generate unique announcement ID"""
import hashlib
return hashlib.md5(source_id.encode()).hexdigest()[:12]
def estimate_cost_impact(self, announcement: ServiceAnnouncement,
profile: WorkloadProfile) -> float:
"""Estimate cost impact of adopting new service"""
# This is a simplified estimation - in practice, you'd use more sophisticated models
base_cost = profile.cost_profile.get('monthly_cost', 1000.0)
# Positive impact factors
if 'cost' in announcement.description.lower():
return base_cost * 0.15 # Estimate 15% savings
if announcement.pricing_model == 'pay-as-you-go':
return base_cost * 0.10 # Estimate 10% savings
if 'managed' in announcement.description.lower():
return base_cost * 0.05 # Estimate 5% operational savings
# Neutral or negative impact
return 0.0
def assess_implementation_complexity(self, announcement: ServiceAnnouncement,
profile: WorkloadProfile) -> str:
"""Assess implementation complexity"""
complexity_factors = 0
# Check for integration complexity
if len(profile.current_services) > 5:
complexity_factors += 1
# Check for compliance requirements
if profile.compliance_requirements:
complexity_factors += 1
# Check for new technology
if announcement.category not in [ServiceCategory.COMPUTE, ServiceCategory.STORAGE, ServiceCategory.DATABASE]:
complexity_factors += 1
if complexity_factors >= 2:
return "High"
elif complexity_factors == 1:
return "Medium"
else:
return "Low"
def perform_risk_assessment(self, announcement: ServiceAnnouncement,
profile: WorkloadProfile) -> Dict[str, str]:
"""Perform risk assessment for service adoption"""
risks = {
'technical_risk': 'Low',
'operational_risk': 'Low',
'business_risk': 'Low',
'overall_risk': 'Low'
}
# Assess technical risk
if announcement.category in [ServiceCategory.MACHINE_LEARNING, ServiceCategory.ANALYTICS]:
risks['technical_risk'] = 'Medium'
# Assess operational risk
if profile.business_criticality == 'critical':
risks['operational_risk'] = 'Medium'
# Assess business risk
if profile.cost_profile.get('monthly_cost', 0) > 10000:
risks['business_risk'] = 'Medium'
# Calculate overall risk
risk_levels = [risks['technical_risk'], risks['operational_risk'], risks['business_risk']]
if 'High' in risk_levels:
risks['overall_risk'] = 'High'
elif 'Medium' in risk_levels:
risks['overall_risk'] = 'Medium'
return risks
def create_evaluation_rationale(self, announcement: ServiceAnnouncement,
profile: WorkloadProfile, relevance_score: float,
benefits: List[str], recommendation: AdoptionRecommendation) -> str:
"""Create rationale for evaluation recommendation"""
rationale = f"Service '{announcement.service_name}' evaluated for workload '{profile.workload_id}' "
rationale += f"with relevance score {relevance_score:.2f}. "
if benefits:
rationale += f"Potential benefits include: {', '.join(benefits[:3])}. "
if recommendation == AdoptionRecommendation.IMMEDIATE:
rationale += "Immediate adoption recommended due to high relevance and clear benefits."
elif recommendation == AdoptionRecommendation.PILOT:
rationale += "Pilot program recommended to validate benefits and assess implementation."
elif recommendation == AdoptionRecommendation.EVALUATE:
rationale += "Further evaluation recommended to assess fit and implementation approach."
elif recommendation == AdoptionRecommendation.MONITOR:
rationale += "Service should be monitored for future relevance as workload evolves."
else:
rationale += "Service not currently applicable to this workload."
return rationale
def calculate_next_review_date(self, recommendation: AdoptionRecommendation,
relevance_level: RelevanceLevel) -> datetime:
"""Calculate when to next review this service-workload combination"""
if recommendation == AdoptionRecommendation.IMMEDIATE:
return datetime.now() + timedelta(days=30) # Review in 1 month
elif recommendation == AdoptionRecommendation.PILOT:
return datetime.now() + timedelta(days=60) # Review in 2 months
elif recommendation == AdoptionRecommendation.EVALUATE:
return datetime.now() + timedelta(days=90) # Review in 3 months
elif recommendation == AdoptionRecommendation.MONITOR:
return datetime.now() + timedelta(days=180) # Review in 6 months
else:
return datetime.now() + timedelta(days=365) # Review in 1 year
def store_evaluation_result(self, evaluation: ServiceEvaluationResult):
"""Store evaluation result in DynamoDB"""
try:
self.evaluations_table.put_item(
Item={
'evaluation_id': evaluation.evaluation_id,
'workload_id': evaluation.workload_id,
'service_name': evaluation.service_announcement.service_name,
'relevance_level': evaluation.relevance_level.value,
'relevance_score': evaluation.relevance_score,
'potential_benefits': evaluation.potential_benefits,
'estimated_cost_impact': evaluation.estimated_cost_impact,
'implementation_complexity': evaluation.implementation_complexity,
'recommendation': evaluation.recommendation.value,
'rationale': evaluation.rationale,
'evaluation_date': evaluation.evaluation_date.isoformat(),
'next_review_date': evaluation.next_review_date.isoformat()
}
)
except Exception as e:
self.logger.error(f"Error storing evaluation result: {str(e)}")
Usage Examples
Example 1: Setting Up Service Evaluation Automation
Example 2: Manual Service Announcement Processing
Example 3: Automated Evaluation Pipeline
Automation Configuration Templates
EventBridge Rules Configuration
Common Challenges and Solutions
Challenge: Information Overload
Solution: Implement intelligent filtering and relevance scoring to focus on the most applicable services. Use machine learning models to improve filtering accuracy over time.
Challenge: False Positives in Relevance Detection
Solution: Continuously refine relevance algorithms based on feedback. Implement human-in-the-loop validation for high-impact recommendations.
Challenge: Integration with Existing Workflows
Solution: Design automation to integrate with existing ticketing systems, approval workflows, and change management processes. Provide APIs for custom integrations.
Challenge: Keeping Up with AWS Innovation Pace
Solution: Use multiple information sources beyond RSS feeds, including AWS Partner Network updates, re:Invent announcements, and direct AWS account team communications.
Challenge: Cost-Benefit Analysis Accuracy
Solution: Develop sophisticated cost modeling based on historical data and workload patterns. Validate estimates against actual implementation results to improve accuracy.