Green Software Patterns

Delete unused storage resources

From an embodied carbon perspective, it's better to delete unused storage resources so we are efficient with hardware and so that the storage layer is optimised for the task.

Match utilization requirements of virtual machines (VMs)

It's better to have one VM running at a higher utilization than two running at low utilization rates, not only in terms of energy proportionality but also in terms of embodied carbon. Two servers running at low utilization rates will consume more energy than one running at a high utilization rate. In addition, the unused capacity on the underutilized server could be more efficiently used for another task or process.

Match utilization requirements with pre-configured servers

It's better to have one VM running at a higher utilization than two running at low utilization rates, not only in terms of energy proportionality but also in terms of embodied carbon. Two servers running at low utilization rates will consume more energy than one running at a high utilization rate. In addition, the unused capacity on the underutilized server could be more efficiently used for another task or process.

Optimise storage utilization

It's better to maximise storage utilisation so the storage layer is optimised for the task, not only in terms of energy proportionality but also in terms of embodied carbon. Two storage units running at low utilization rates will consume more energy than one running at a high utilization rate. In addition, the unused capacity on the underutilised storage unit could be more efficiently used for another task or process.

Optimize average CPU utilization

CPU usage and utilization varies throughout the day, sometimes wildly for different computational requirements. The larger the variance between the average and peak CPU utilization values, the more resources need to be provisioned in stand-by mode to absorb those spikes in traffic.

Optimize peak CPU utilization

CPU usage and utilization varies throughout the day, sometimes wildly for different computational requirements. The larger the variance between the average and peak CPU utilization values, the more resources need to be provisioned in stand-by mode to absorb those spikes in traffic.

Remove unused assets

Unused cloud resources such as databases, storage buckets, and compute instances continue to consume energy and generate embodied carbon; identifying and decommissioning them eliminates unnecessary waste.

Scale down applications when not in use

Applications consume CPU even when they are not actively in use. For example, background timers, garbage collection, health checks, etc. Even when the application is shut down, the underlying hardware is consuming idle power.

Scale down kubernetes applications when not in use

In order to reduce carbon emissions and costs, Dev&Test Kubernetes clusters can turn off nodes out of office hours. Thereby, optimization is implemented at the cluster level. For production clusters, where nodes need to stay up and running, optimization needs to be implemented at the application level.

Scale infrastructure with user load

Demand for resources depends on user load at any given time. However, most applications run without taking this into consideration. As a result,resources are underused and inefficient.

Scale Kubernetes workloads based on relevant demand metrics

By default, Kubernetes scales workloads based on CPU and RAM utilization. In practice, however, it's difficult to correlate your application's demand drivers with CPU and RAM utilization. Scaling your workload based on relevant demand metrics that drive scaling of your applications, such as HTTP requests, queue length, and cloud alerting events can help reduce resource utilization, and therefore also your carbon emissions.

Set storage retention policies

From an embodied carbon perspective, it's better to have an automated mechanism to delete unused storage resources so we are efficient with hardware and so that the storage layer is optimised for the task.

Time-shift Kubernetes cron jobs

The carbon emissions of a software system depends on the power consumed by that software, but also on the Carbon intensity of the electricity it is powered on. For this reason, running energy-efficient software on carbon intensive electricity grid, might be inefficient to reduce its global carbon emissions. Carbon aware time scheduling, is about scheduling workloads to execute, when electricity carbon intensity is low.

Use circuit breaker patterns

Modern applications need to communicate with other applications on a regular basis. Since these other applications have their own deployment schedule, downtimes and availability, the network connection to these application might have problems. If the other application is not reachable, all network requests against this other application will fail and future network requests are futile.