12 docs tagged with "kubernetes"

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

Service state refers to the in-memory or on-disk data required by a service to function. State includes the data structures and member variables that the service reads and writes. Depending on how the service is architected, the state might also include files or other resources stored on the disk. Applications that consume large memory or on-disk data require larger VM sizes, especially for cloud computing where you would need larger VM SKUs to support high RAM capacity and multiple data disks.

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.

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.

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.

Transport Layer Security (TLS) ensures that all data passed between the web server and web browsers remain private and encrypted. However, terminating and re-establishing TLS increases CPU usage and might be unnecessary in certain architectures.

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.

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.

Network and web application firewalls provide protection against most common attacks and load shedding bad bots. These tools help to remove unnecessary data transmission and reduce the burden on the cloud infrastructure, while also using lower bandwidth and less infrastructure.

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.