8 docs tagged with "role:cloud-engineer"

Containerizing workloads enables better resource utilisation and bin packing, reducing unnecessary compute allocation and embodied carbon compared to running full virtual machines.

Data protection through encryption is a crucial aspect of our security measures. However, the encryption process can be resource-intensive at multiple levels.

Placing VMs in the same region or availability zone minimises the physical distance data must travel between instances, reducing the energy consumed by network traversal.

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.

Decomposing applications into independently scalable microservices allows each component to be right-sized for its own demand, reducing overall compute resource consumption and embodied carbon.

Many attacks on cloud infrastructure seek to misuse deployed resources, which leads to an unnecessary spike in usage and cost.

Service meshes add overhead through additional containers and increased network traffic, so they should only be deployed for applications that genuinely require the capabilities they provide.

Cloud VMs built on energy-efficient processors, such as ARM-based cloud-native chips, can run scale-out workloads with significantly lower energy consumption and embodied carbon than traditional alternatives.