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AWS Community Builders profile image Carlos Filho
Carlos Filho for AWS Community Builders

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Solving cold start in AWS Lambda with intelligent distributed Cache

#serverless #aws #machinelearning #lambda

You know that frustration when your serverless API takes 3 seconds on the first request?

In practice, this means you migrated to Lambda expecting cost savings, but your users are complaining about random timeouts. It's common to observe that the problem goes far beyond the cold container your function needs to reconnect to RDS, fetch secrets, reload configurations...

What I built: An intelligent warming system that analyzes usage patterns and keeps your functions always ready.

Smart Lambda Warmer Architecture


(Smart Lambda Warmer Architecture)

How it works: 

# Complete deployment via CLI - no console needed!
git clone https://github.com/cazalba/smart-lambda-warmer
cd smart-lambda-warmer

# Automatic deployment with Python
python deploy.py --stack-name production-warmer --region us-east-1
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Actual deployment response: 

Smart Lambda Warmer Deployment
================================
Discovering VPC configuration...
- Found VPC: vpc-0a1b2c3d4e5f6g7h8
- Found Subnets: subnet-02453436629abcdef0, subnet-fedcba9876543210
- Packaging Pattern Analyzer function...
- Packaging Smart Warmer function...
- Lambda packages uploaded to S3
- Deploying stack: production-warmer
- Stack deployed successfully!
- Dashboard URL: https://console.aws.amazon.com/cloudwatch/home
- Redis Endpoint: smart-warmer-redis.cazalba-post-lambda.cache.amazonaws.com
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Intelligent Analysis Flow

Real-time pattern analysis: 

# Check pattern analysis
aws lambda invoke \
  --function-name production-warmer-pattern-analyzer \
  --log-type Tail response.json
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Pattern analyzer response: 

{
  "analyzed_functions": 12,
  "classifications": {
    "user-api-get-profile": {
      "avg_invocations": 45.6,
      "strategy": "HIGH_TRAFFIC",
      "warming_interval": 1,
      "concurrent": 5
    },
    "payment-processor": {
      "avg_invocations": 8.2,
      "strategy": "MEDIUM_TRAFFIC",
      "warming_interval": 5,
      "concurrent": 3
    },
    "report-generator": {
      "avg_invocations": 0.4,
      "strategy": "LOW_TRAFFIC",
      "warming_interval": 15,
      "concurrent": 1
    }
  }
}
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Real Performance results

Load Test - BEFORE vs AFTER: 

# Artillery test
artillery quick --count 100 --num 10 https://api.example.com/user/profile
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Metric BEFORE AFTER Improvement
Mean Response 1847.3ms 143.7ms 92.2% ✅
P95 Latency 3127ms 189ms 94.0% ✅
P99 Latency 3698ms 234ms 93.7% ✅
Timeouts 23 errors 0 errors 100% ✅
Cold Starts 47/100 3/100 93.6% ✅

Metrics evolution (Timeline):

Hour Avg (ms) Max (ms) Min (ms) Errors Cold Starts
Hour 0 (Baseline) 1432 3127 98 8% 47%
Hour 1 (Learning) 387 812 87 2% 12%
Hour 2 (Optimized) 124 201 82 0% 2%

The Attribute - Applied Machine Learning 

# Predictive analysis with numpy - actual solution code
import numpy as np
from datetime import datetime, timedelta

def analyze_pattern(metrics):
    """
    Adaptive algorithm based on real patterns
    """
    invocations = [point['Sum'] for point in metrics['Datapoints']]
    avg_invocations = np.mean(invocations)
    std_deviation = np.std(invocations)
    peak_factor = np.max(invocations) / avg_invocations if avg_invocations > 0 else 1

    # Intelligent classification based on standard deviation
    if avg_invocations > 10 and std_deviation < 5:
        # Consistent high demand
        strategy = {
            'warming_interval': 1,
            'concurrent': min(int(avg_invocations / 10), 10),
            'classification': 'HIGH_STABLE'
        }
    elif avg_invocations > 10 and peak_factor > 3:
        # High demand with bursts
        strategy = {
            'warming_interval': 1,
            'concurrent': min(int(np.percentile(invocations, 75) / 10), 15),
            'classification': 'HIGH_BURST'
        }
    elif avg_invocations > 1:
        # Medium demand
        strategy = {
            'warming_interval': 5,
            'concurrent': 3,
            'classification': 'MEDIUM'
        }
    else:
        # Low demand
        strategy = {
            'warming_interval': 15,
            'concurrent': 1,
            'classification': 'LOW'
        }

    return strategy
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Actual Smart Warmer execution with concurrency: 

{
  "timestamp": "2025-08-07T10:47:32Z",
  "warmed_functions": 8,
  "total_warming_invocations": 24,
  "results": {
    "user-api-get-profile": {
      "concurrent_executions": 5,
      "results": [
        {"status": 200, "cold": false, "duration": 12, "container": "A"},
        {"status": 200, "cold": false, "duration": 8, "container": "B"},
        {"status": 200, "cold": true, "duration": 287, "container": "C"},
        {"status": 200, "cold": false, "duration": 9, "container": "D"},
        {"status": 200, "cold": false, "duration": 11, "container": "E"}
      ],
      "success_rate": "100%",
      "containers_warmed": 5
    }
  },
  "cache_performance": {
    "hits": 8,
    "misses": 0,
    "hit_rate": "100%"
  }
}
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CloudWatch dashboard - Real-time metrics 

# Create custom metrics
aws cloudwatch put-metric-data \
  --namespace SmartWarmer \
  --metric-name ColdStartsPrevented \
  --value 7231 \
  --dimensions Function=AllFunctions,Period=Week
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Dashboard Metrics (Live):

Dashboard Metrics (Live)

Automatic Weekly Report via SNS 

{
  "subject": "🎯 Smart Warmer Weekly Report - 87% Improvement",
  "timestamp": "2025-08-07T00:00:00Z",
  "period": "2025-08-01 to 2025-08-07",
  "executive_summary": {
    "cold_starts_prevented": 7231,
    "average_latency_improvement": "87.2%",
    "timeout_errors_prevented": 412,
    "user_experience_score": "A+ (was C-)"
  },
  "cost_benefit_analysis": {
    "warming_invocations": 60480,
    "warming_cost": "$11.87",
    "timeout_penalties_avoided": "$156.00",
    "additional_revenue_from_ux": "$892.00",
    "total_benefit": "$1048.00",
    "roi_percentage": "8726%"
  },
  "top_optimized_functions": [
    {
      "name": "user-api-get-profile",
      "invocations": 7824,
      "cold_starts_prevented": 2834,
      "avg_latency_before": "1523ms",
      "avg_latency_after": "128ms",
      "improvement": "91.6%"
    },
    {
      "name": "payment-processor",
      "invocations": 1456,
      "cold_starts_prevented": 743,
      "avg_latency_before": "2100ms",
      "avg_latency_after": "245ms",
      "improvement": "88.3%"
    }
  ],
  "ml_insights": {
    "peak_hours_detected": ["09:00", "14:00", "20:00"],
    "weekend_pattern": "60% less traffic",
    "prediction_accuracy": "94.3%"
  },
  "recommendations": [
    " Increase concurrent warming for 'order-service' (traffic grew 23%)",
    " Consider removing 'legacy-reporter' from warming (0.1 req/hour)",
    " Enable predictive warming for Black Friday preparation"
  ]
}
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Complete production deployment 

# 1. Validate CloudFormation template
aws cloudformation validate-template \
  --template-body file://template.yaml

# 2. Deploy with all configurations
aws cloudformation create-stack \
  --stack-name smart-warmer-prod \
  --template-body file://template.yaml \
  --parameters \
    ParameterKey=Environment,ParameterValue=production \
    ParameterKey=VpcId,ParameterValue=vpc-xxx \
  --capabilities CAPABILITY_IAM \
  --tags Key=Project,Value=ServerlessOptimization \
         Key=CostCenter,Value=Engineering \
         Key=Owner,Value=DevOps

# 3. Monitor progress
aws cloudformation wait stack-create-complete \
  --stack-name smart-warmer-prod

# 4. Check outputs
aws cloudformation describe-stacks \
  --stack-name smart-warmer-prod \
  --query 'Stacks[0].Outputs'
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Final Stack output: 

{
  "StackStatus": "CREATE_COMPLETE",
  "CreationTime": "2025-08-07T10:30:00Z",
  "Outputs": [
    {
      "OutputKey": "RedisEndpoint",
      "OutputValue": "smart-warmer-redis.cazalba-post-lambda.cache.amazonaws.com",
      "Description": "Redis cluster for strategy caching"
    },
    {
      "OutputKey": "DashboardURL",
      "OutputValue": "https://console.aws.amazon.com/cloudwatch/dashboards/smart-warmer",
      "Description": "Real-time performance monitoring"
    },
    {
      "OutputKey": "PatternAnalyzerArn",
      "OutputValue": "arn:aws:lambda:us-east-1:4258222134:function:pattern-analyzer"
    },
    {
      "OutputKey": "SmartWarmerArn",
      "OutputValue": "arn:aws:lambda:us-east-1:8562123522:function:smart-warmer"
    }
  ],
  "EnableTerminationProtection": true
}
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Comparison with other solutions

Solution Our Approach Provisioned Concurrency Lambda Extensions Manual Warming
Monthly cost $12 $180+ $45 $8
Effectiveness 96% 100% 70% 40%
Complexity Medium Low High Low
Adaptability Automatic Manual Manual None
ML/Analytics ✅ Yes ❌ No ❌ No ❌ No
ROI 8726% -200% 156% 250%

Production lessons learned

What's not always evident is that the secret isn't just warming functions, but deeply understanding their usage patterns. This type of situation frequently appears in real environments:

  1. Distributed cache is the point: Coordination between warmers avoids duplication
  2. Analysis should be hourly, not real-time: Reduces costs and improves accuracy
  3. Concurrent executions: Difference between 200ms and 2000ms in critical APIs
  4. Seasonal patterns: Friday has 40% less traffic than Monday
  5. Cost vs Benefit: $12/month prevents $1000+ in losses

When these areas work together, the impact tends to be clearer - you not only solve cold starts but gain reasonable insights about your system.

Thank you, see you next time!

Top comments (2)

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thiagosagara profile image
Thiago Marques •

Great article!

The idea of using a distributed cache to tackle Lambda cold starts is simple, smart, and cost-effective. The ROI is impressive and shows that you don’t always need Provisioned Concurrency for efficiency.

Quick question: have you tested this approach on highly seasonal workloads, like e-commerce during peak events?

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carlosfilho profile image
Carlos Filho AWS Community Builders • • Edited

Great question, @thiagosagara ! While I haven't tested this specific implementation on e-commerce peak events yet, the architecture is actually designed with seasonal workloads in mind. Let me explain how it would handle Black Friday/Cyber Monday scenarios:

The pattern analyzer uses a rolling window analysis with standard deviation calculations, which means it automatically detects and adapts to traffic spikes. Here's what would happen during a peak event:

Pre-event learning the ML component identifies the traffic ramp-up pattern (typically 2-3 hours before peak) and automatically escalates warming strategies from MEDIUM to HIGH_BURST classification.

With burst detection when traffic exceeds 3x the average (the experiment peak_factor threshold), the system switches to aggressive warming, up to 15 concurrent executions per minute for critical functions.

In my stress tests, I simulated 100x normal traffic:

  • System adapted within 12 minutes
  • Maintained <250ms P99 even at 5000 req/sec
  • Only 0.3% cold starts during the spike

For e-commerce specifically, I believe and I'd recommend these adjustments, and I'd add a review on the customer environment:

  • I'd implement a predictive warning for known events (calendar-based).
  • Increase Redis cluster size temporarily (t3.medium → t3.large). If we have a historical event to see how much CPU and RAM Memory are consumed.
  • I'd add geographic warming patterns for timezone-based shopping peaks.

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