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Load Balancing Overview

access911 is designed to handle high-volume emergency calls during disaster scenarios. The platform uses AWS cloud services with automatic scaling and load balancing to ensure reliable performance under extreme load conditions.

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Architecture Components

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API Gateway

API Gateway acts as the frontend HTTP layer providing:
  • Request Throttling: Prevents system overload
  • Request Validation: Ensures data integrity
  • Rate Limiting: Controls request frequency
  • CORS Support: Enables cross-origin requests
# API Gateway Configuration
ThrottleSettings:
  BurstLimit: 5000
  RateLimit: 2000
RequestValidator:
  ValidateRequestBody: true
  ValidateRequestParameters: true

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AWS Lambda

Lambda functions provide on-demand compute with automatic scaling:
  • Concurrent Executions: Automatic scaling based on demand
  • Reserved Concurrency: Optional limits to protect downstream resources
  • Memory Configuration: Optimized for performance
  • Timeout Settings: Appropriate timeouts for emergency processing
# Lambda Configuration
RESERVED_CONCURRENCY = 100  # Limit concurrent executions
MEMORY_SIZE = 512  # MB
TIMEOUT = 30  # seconds

def lambda_handler(event, context):
    # Process emergency call
    # Handle concurrent requests efficiently
    pass

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DynamoDB

DynamoDB provides low-latency reads/writes with automatic scaling:
  • On-Demand Capacity: Automatic scaling for unpredictable spikes
  • Provisioned Capacity: Predictable performance with autoscaling
  • Adaptive Capacity: Handles hot partitions automatically
  • Global Secondary Indexes: Optimized query patterns
# DynamoDB Configuration
TableConfiguration = {
    'BillingMode': 'ON_DEMAND',  # or 'PROVISIONED'
    'PointInTimeRecoverySpecification': {
        'PointInTimeRecoveryEnabled': True
    },
    'GlobalSecondaryIndexes': [
        {
            'IndexName': 'emergency-type-index',
            'KeySchema': [
                {'AttributeName': 'emergency_type', 'KeyType': 'HASH'},
                {'AttributeName': 'timestamp', 'KeyType': 'RANGE'}
            ]
        }
    ]
}

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S3 Storage

S3 provides essentially unlimited storage for call payloads:
  • Lifecycle Policies: Automatic tiering to cheaper storage
  • Cross-Region Replication: Disaster recovery
  • Versioning: Data protection and audit trails
  • Encryption: Data security at rest
{
  "LifecycleConfiguration": {
    "Rules": [
      {
        "Id": "ArchiveOldCalls",
        "Status": "Enabled",
        "Transitions": [
          {
            "Days": 30,
            "StorageClass": "STANDARD_IA"
          },
          {
            "Days": 90,
            "StorageClass": "GLACIER"
          }
        ]
      }
    ]
  }
}

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Load Balancing Strategies

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Horizontal Scaling

The platform scales horizontally by adding more Lambda instances: 
# Automatic scaling based on queue depth
def calculate_scaling_factor(queue_depth, current_instances):
    if queue_depth > current_instances * 10:
        return min(current_instances * 2, MAX_INSTANCES)
    elif queue_depth < current_instances * 5:
        return max(current_instances // 2, MIN_INSTANCES)
    return current_instances

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Vertical Scaling

Lambda functions can be scaled vertically by adjusting memory: 
# Memory scaling based on workload
def optimize_memory_configuration(avg_processing_time, memory_usage):
    if avg_processing_time > 10:  # seconds
        return min(memory_usage * 2, 3008)  # Max Lambda memory
    elif avg_processing_time < 2:
        return max(memory_usage // 2, 128)  # Min Lambda memory
    return memory_usage

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Database Scaling

DynamoDB scales automatically with demand: 
# DynamoDB scaling configuration
def configure_dynamodb_scaling(table_name, expected_load):
    if expected_load > 1000:  # requests per second
        # Use on-demand billing for unpredictable spikes
        return {
            'BillingMode': 'ON_DEMAND',
            'PointInTimeRecoveryEnabled': True
        }
    else:
        # Use provisioned capacity with autoscaling
        return {
            'BillingMode': 'PROVISIONED',
            'ProvisionedThroughput': {
                'ReadCapacityUnits': expected_load // 2,
                'WriteCapacityUnits': expected_load // 2
            },
            'AutoScalingEnabled': True
        }

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Performance Optimization

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Caching Strategies

Implement caching to reduce database load: 
# Redis cache for frequently accessed data
import redis

redis_client = redis.Redis(host='your-redis-endpoint', port=6379)

def get_cached_call(call_id):
    cached = redis_client.get(f"call:{call_id}")
    if cached:
        return json.loads(cached)
    return None

def cache_call(call_id, call_data, ttl=300):
    redis_client.setex(f"call:{call_id}", ttl, json.dumps(call_data))

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Connection Pooling

Optimize database connections: 
# DynamoDB connection pooling
import boto3
from botocore.config import Config

# Configure connection pooling
config = Config(
    max_pool_connections=50,
    retries={'max_attempts': 3}
)

dynamodb = boto3.resource('dynamodb', config=config)

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Batch Processing

Process multiple calls efficiently: 
# Batch DynamoDB operations
def batch_write_calls(calls):
    with dynamodb.Table('emergency-calls').batch_writer() as batch:
        for call in calls:
            batch.put_item(Item=call)

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Monitoring and Metrics

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CloudWatch Metrics

Monitor key performance indicators: 
# CloudWatch metrics
import boto3

cloudwatch = boto3.client('cloudwatch')

def publish_metrics(metric_name, value, unit='Count'):
    cloudwatch.put_metric_data(
        Namespace='DispatchAI',
        MetricData=[
            {
                'MetricName': metric_name,
                'Value': value,
                'Unit': unit,
                'Timestamp': datetime.utcnow()
            }
        ]
    )

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Key Metrics to Monitor

  • Request Rate: Number of requests per second
  • Response Time: Average response time
  • Error Rate: Percentage of failed requests
  • Concurrent Executions: Number of active Lambda instances
  • DynamoDB Throttles: Number of throttled requests
  • Queue Depth: Number of pending requests

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Alerting

Set up alerts for critical metrics: 
# CloudWatch Alarms
Alarms:
  - AlarmName: HighErrorRate
    MetricName: ErrorRate
    Threshold: 5.0
    ComparisonOperator: GreaterThanThreshold
    EvaluationPeriods: 2
    
  - AlarmName: HighResponseTime
    MetricName: ResponseTime
    Threshold: 10.0
    ComparisonOperator: GreaterThanThreshold
    EvaluationPeriods: 3

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Disaster Recovery

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Multi-Region Deployment

Deploy across multiple AWS regions: 
# Multi-region configuration
REGIONS = ['us-east-1', 'us-west-2', 'eu-west-1']

def deploy_to_region(region):
    # Deploy Lambda functions
    # Deploy DynamoDB tables
    # Deploy S3 buckets
    # Configure cross-region replication
    pass

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Backup Strategies

Implement comprehensive backup strategies: 
# Automated backups
def create_backup():
    # DynamoDB point-in-time recovery
    # S3 cross-region replication
    # Lambda function code backup
    pass

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Best Practices

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Performance Optimization

Use appropriate memory settings and optimize code for performance.
Design DynamoDB tables for expected query patterns and access patterns.
Implement comprehensive monitoring and alerting for all components.
Use least-privilege IAM policies and enable encryption at rest.

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Scaling Guidelines

  • Start Small: Begin with conservative scaling settings
  • Monitor Closely: Watch metrics during initial deployments
  • Test Limits: Conduct load testing to understand system limits
  • Plan for Spikes: Design for 10x normal load during emergencies

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Load Testing

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Simulation Load Testing

Use the simulation engine to test system limits: 
# Generate high-volume test load
curl -X POST https://v2y08vmfga.execute-api.us-east-1.amazonaws.com/simulate \
  -H "Content-Type: application/json" \
  -d '{
    "scenario": "nashville_tornado",
    "num_calls": 1000
  }'

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Performance Testing Tools

Use tools like Apache JMeter or Artillery for load testing: 
# Artillery configuration
config:
  target: 'https://your-api-gateway-url'
  phases:
    - duration: 300
      arrivalRate: 100
scenarios:
  - name: "Emergency Call Simulation"
    requests:
      - post:
          url: "/simulate"
          json:
            scenario: "nashville_tornado"
            num_calls: 10

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Troubleshooting

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Common Issues

Monitor consumed capacity and adjust provisioned capacity or use on-demand billing.
Increase timeout settings and optimize function performance.
Increase throttling limits and implement request queuing.
Monitor memory usage and adjust Lambda memory configuration.

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Performance Tuning

# Performance tuning checklist
def performance_tuning_checklist():
    return {
        'lambda_memory': 'Optimize memory settings',
        'dynamodb_capacity': 'Monitor and adjust capacity',
        'api_gateway_limits': 'Increase throttling limits',
        'connection_pooling': 'Implement connection pooling',
        'caching': 'Add caching layers',
        'batch_processing': 'Use batch operations'
    }
Production Deployment: Ensure proper load testing and monitoring before deploying to production emergency response systems.