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Avoid tracking unnecessary data

User tracking, user data collection and targeting in things like advertisements are responsible for significant energy use in many digital products, and services.

Cache static data

From an energy-efficiency perspective, it's better to reduce network traffic by reading the data locally through a cache rather than accessing it remotely over the network. Shortening the distance a network packet travels means that less energy is required to transmit it. Similarly, from an embodied carbon perspective, we are more efficient with hardware when a network packet traverses through less computing equipment.

Choose the region that is closest to users

From an energy-efficiency perspective, it's better to shorten the distance a network packet travels so that less energy is required to transmit it. Similarly, from an embodied-carbon perspective, when a network packet traverses through less computing equipment, we are more efficient with hardware.

Compress transmitted data

From an energy-efficiency perspective, it's better to minimise the size of the data transmitted so that less energy is required because the network traffic is reduced.

Defer offscreen images

Web pages offer a lot of images that aren't displayed on the first loaded screen and can thus be loaded dynamically.

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.

Enable text compression

Web browsers often communicate with web servers in a human readable format. These can be HTML, JavaScript and/or CSS files and REST requests which can return a response in JSON. This human readable communication is redundant and, as such, can be compressed to save bandwidth.

Evaluate other CPU architectures

Applications are built with a software architecture that best fits the business need they are serving. Cloud providers make it easy to evaluate other CPU types

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.

Minify web assets

Minification removes unnecessary or redundant data without affecting how the resource is processed by the web browser.

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.

Queue non-urgent processing requests

All systems have periods of peak and low load. From a hardware-efficiency perspective, we are more efficient with hardware if we minimise the impact of request spikes with an implementation that allows an even utilization of components. From an energy-efficiency perspective, we are more efficient with energy if we ensure that idle resources are kept to a minimum.

Reduce transmitted data

From an energy-efficiency perspective, it's better to minimize the size of the data transmitted so that less energy is required because the network traffic is reduced.

Remove unused CSS definitions

CSS files are very complex and need energy intensive parsing and processing. Each added CSS definition increases the amount of time and processing power needed in this process.

Run AI models at the edge

Data computation for ML workloads and ML inference is a significant contributor to the carbon footprint of the ML application. Also, if the ML model is running on the cloud, the data needs to be transferred and processed on the cloud to the required format that can be used by the ML model for inference.

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.

Select a more energy efficient AI/ML framework

Training an AI model implies a significant carbon footprint. The underlying framework used for the development, training, and deployment of AI/ML needs to be evaluated and considered to ensure the process is as energy efficient as possible.

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.

Use DDoS protection

Distributed denial of service (DDoS) attacks are used to increase the server load so that it is unable to respond to any legitimate requests. This is usually done to harm the owner of the service or hardware.

Use sustainable regions for AI/ML training

Depending on the model parameters and training iterations, training an AI/ML model consumes a lot of power and requires many servers which contribute to embodied emissions.