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Asynchronous Processing Runbook Example
PostedMarch 29, 2025
UpdatedMarch 29, 2025
ByKevin McCaffrey
ID: SUS_SUS3_1_asynchronous-processing-runbook
Code: SUS3_1
Asynchronous processing is an effective way to optimize resource usage and minimize energy consumption. Below is an example of how to implement a queue-driven system and scheduled jobs for consistent resource utilization:
1. Use Queue-Driven Architectures
- Queue Setup: Create a message queue to handle incoming tasks. Using managed services like Amazon SQS ensures tasks are processed only when capacity is available, reducing idle resource usage.
- Worker Processes: Configure auto-scaling for the worker instances that poll the queue. Scale down during low traffic, and scale up when demand spikes, ensuring resources are appropriately utilized.
- Failover and Retries: Set up dead-letter queues to handle unprocessed or failed tasks, minimizing wasted compute time and reducing the need to maintain large instance pools 24/7.
2. Schedule Non-Real-Time Jobs
- Batch Windows: Identify off-peak periods to schedule resource-intensive jobs. This approach takes advantage of existing capacity, fostering better resource efficiency.
- Event Triggers: Use serverless event-driven triggers (e.g., AWS Lambda) to invoke jobs only when needed. This reduces idle compute usage and limits emissions.
- Monitoring and Metrics: Track key metrics like CPU utilization, queue lengths, and job completion times to continually optimize scheduling and scaling strategies.
3. Best Practices
- Optimize at the Code Level: Write efficient, load-based code to ensure minimal resource requirements per job, and adopt modern X-Ray or distributed tracing to locate inefficiencies.
- Right-Sizing: Choose resource configurations that match actual demand. Over-provisioning leads to higher carbon footprints and increased costs.
- Continuous Improvement: Regularly review utilization data to fine-tune the asynchronous architecture, ensuring continuous alignment with sustainability goals.
By leveraging queue-driven systems and timed batch jobs, you ensure resources are used efficiently and only when necessary, thereby reducing waste, lowering your carbon footprint, and advancing sustainability objectives.
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