REL04-BP03: Do constant work

Overview

Implement constant work patterns to maintain consistent resource utilization and avoid the thundering herd problem that occurs when systems experience sudden spikes in demand. By performing work at a steady rate rather than in bursts, you can improve system predictability, reduce resource contention, and prevent cascading failures caused by sudden load changes.

Implementation Steps

1. Implement Steady-State Processing Patterns

Design systems to process work at consistent rates
Use background processing for non-urgent tasks
Implement work smoothing algorithms to distribute load over time
Avoid batch processing that creates resource spikes

2. Design Proactive Resource Management

Pre-warm resources before they are needed
Maintain connection pools at steady levels
Implement predictive scaling based on patterns
Use health checks and monitoring to maintain readiness

3. Implement Rate Limiting and Throttling

Apply consistent rate limits to prevent sudden spikes
Use token bucket algorithms for smooth traffic shaping
Implement backpressure mechanisms to control flow
Design adaptive throttling based on system capacity

4. Establish Predictable Caching Patterns

Implement cache warming strategies
Use consistent cache refresh patterns
Avoid cache stampede scenarios
Design cache hierarchies for predictable performance

5. Design Consistent Database Access Patterns

Implement read-through and write-through caching
Use connection pooling with steady connection counts
Avoid batch operations that create resource spikes
Implement consistent query patterns and indexing

6. Monitor and Optimize Work Distribution

Track resource utilization patterns and identify spikes
Implement metrics for work distribution consistency
Use automated scaling based on steady-state metrics
Optimize algorithms to maintain consistent performance

Implementation Examples

Example 1: Constant Work Processing System

AWS Services Used

Amazon SQS: Message queuing with consistent polling patterns for steady work distribution
AWS Lambda: Serverless functions with reserved concurrency for predictable execution
Amazon CloudWatch: Monitoring and metrics for tracking work distribution patterns
Amazon Kinesis: Stream processing with consistent shard allocation and processing
AWS Step Functions: Workflow orchestration with consistent execution patterns
Amazon DynamoDB: Database with consistent read/write patterns and auto-scaling
Amazon ElastiCache: Caching with consistent connection pools and refresh patterns
AWS Auto Scaling: Predictive scaling based on historical patterns
Amazon EventBridge: Event processing with consistent rate limiting
AWS Batch: Batch processing with steady job submission patterns
Amazon ECS/EKS: Container orchestration with consistent resource allocation
AWS Systems Manager: Parameter store for configuration management
Amazon CloudFront: CDN with consistent cache warming patterns
Elastic Load Balancing: Load balancing with consistent health checking
AWS X-Ray: Distributed tracing for monitoring consistent performance patterns

Benefits

Predictable Performance: Consistent resource utilization leads to predictable system behavior
Reduced Resource Contention: Steady work patterns prevent resource spikes and contention
Improved Scalability: Consistent load patterns enable better auto-scaling decisions
Better Cost Management: Predictable resource usage enables better cost optimization
Enhanced Reliability: Avoiding sudden spikes reduces the risk of cascading failures
Simplified Monitoring: Consistent patterns make it easier to detect anomalies
Better User Experience: Steady performance provides consistent response times
Reduced Thundering Herd: Constant work patterns prevent sudden demand spikes
Improved Resource Planning: Predictable patterns enable better capacity planning
Enhanced System Stability: Consistent work distribution improves overall system stability