Implement buffering or throttling to flatten the demand curve

Implementing buffering and throttling techniques can significantly optimize resource consumption within your cloud environment. By flattening demand spikes, these methods allow for a stable use of resources throughout periods of variations, which is critical for achieving sustainability goals.

Best Practices

Implement Queue-Based Buffering to Handle Spikes Smoothly

• Use managed queue services (such as Amazon SQS) to store incoming requests during peak times, which prevents overwhelming downstream systems and helps avoid overprovisioning of resources.
• Configure your queue depth monitoring and dynamically scale consumers based on the length of the queue, ensuring that you efficiently process workloads while minimizing idle capacity.
• Use queue-based architectures for asynchronous workloads, balancing demand across servers and reducing the need to provision for worst-case load scenarios.

Leverage Throttling Mechanisms to Control Request Rates

• Define rate limits in your application or API Gateway (for example, set burst and rate limits in Amazon API Gateway) to regulate incoming traffic and prevent saturation of backend services.
• Implement graceful degradation strategies: if traffic exceeds preconfigured thresholds, your application can respond with a throttled status or reduced functionality, preserving critical resources and lowering energy usage.
• Monitor throttling metrics (like request rate and error messages) to fine-tune thresholds, ensuring you’re adequately controlling traffic and meeting user expectations.

Automate Load Management and Scaling Policies

• Use AWS Auto Scaling to match resources precisely to real-time demand, reducing overprovisioning and the associated energy consumption.
• Set up dynamic scaling policies based on key performance indicators like CPU or latency, and integrate them with throttling policies to create a balanced approach for cost and sustainability.
• Regularly review your scaling thresholds to ensure they remain aligned with traffic patterns, minimizing unnecessary resource usage without negatively impacting performance.

Optimize Application Logic and Resource Usage

• Design your application to handle variable workloads by splitting non-time-critical tasks into asynchronous processes that can run during off-peak periods, helping flatten overall resource utilization.
• Implement efficient coding patterns and data processing methods to prevent excessive CPU or memory usage, directly contributing to lower power consumption.
• Combine caching strategies with throttling and buffering to reduce redundant requests, improving overall efficiency and reducing the load on downstream systems.

Questions to ask your team

• How have you implemented buffering or throttling to handle peak demand while reducing over-provisioning?
• Are you monitoring demand fluctuations to adjust your buffering or throttling techniques in real time?
• How do you validate that your buffering or throttling approach successfully flattens the demand curve?
• Do you perform regular reviews of usage patterns to fine-tune buffering or throttling thresholds?
• Are you tracking the impact of buffering or throttling on resource utilization and sustainability outcomes?

Who should be doing this?

Cloud Architect

• Design buffering and throttling strategies to flatten peak demand
• Evaluate architectural trade-offs for sustainable resource usage
• Define capacity thresholds aligned with sustainability objectives

DevOps Engineer

• Implement buffering and throttling tools or services
• Automate scaling policies to match real-time demand
• Monitor system performance and adjust configurations to optimize resource usage

Application Owner

• Provide workload usage patterns to inform demand forecasting
• Coordinate feature rollouts with capacity planning in mind
• Review usage metrics to ensure efficient resource consumption

Sustainability Lead

• Set targets for reducing environmental impact of workloads
• Advise on sustainable metrics and performance standards
• Collaborate with technical teams to align solutions with organizational sustainability goals

What evidence shows this is happening in your organization?

• Buffering Demand Implementation Plan: A structured plan detailing how to implement buffering solutions that gradually process incoming requests, reducing resource spikes. It outlines provisioning paths, monitoring strategies, and performance benchmarks to match demand changes effectively while minimizing overprovisioning.
• Throttling Policy Checklist: A step-by-step checklist for implementing throttling rules. It includes guidelines on setting thresholds, integrating with monitoring tools, and validating that throttling mechanisms are effectively flattening peak demand while maintaining an optimal user experience.
• Flattened Demand Monitoring Dashboard: A real-time dashboard to track request rates, throttling activations, and resource utilization. It provides clear visibility into how effectively demand curves are being flattened, confirms minimal resource over-allocation, and identifies areas for further optimization.

Cloud Services

AWS

• Amazon SQS: Use Amazon SQS to queue and buffer requests, thereby smoothing out spikes in traffic and flattening the demand curve.
• Amazon Kinesis: Ingest and process streaming data with buffering, thereby aligning system capacity with demand.
• AWS Application Auto Scaling: Automatically scale resources to match demand, aiding in throttling or buffering workloads.

Azure

• Azure Service Bus: Provide messaging and queuing to decouple workloads, supporting buffering and throttling.
• Azure Event Hubs: Capture and process streaming data with throttling strategies to manage peaks in demand.

Google Cloud Platform

• Pub/Sub: Enable asynchronous communication through pub/sub messaging, flattening traffic spikes.
• Cloud Dataflow: Process streaming data at scale and apply buffering or throttling logic to manage workload.