35 docs tagged with "persona:devops-engineer"

Building an ML model takes significant computing resources that need to be optimized for efficient utilization.

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.

Storing uncompressed data wastes bandwidth and increases storage capacity requirements; applying appropriate compression reduces both storage consumption and the energy needed to read and write it.

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.

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

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.

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.

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

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

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.

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.

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.

If service downtimes are acceptable it's better to not strive for highest availability but to design the solution according to real business needs. Lower availability guarantees can help reduce energy consumption by using less infrastructure components.

In a given application, there may be a need to utilize multiple environments in the application workflow. Typically, a development environment is used for regular updates, while staging or testing enviroments are used to make sure there are no issues before code reaches a production environment where users may have access. Each added environment has an increasing energy impact, which in turn creates more emissions. As such, it is important to understand the necessity of each enviroment and it's environmental impact.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Selecting the right hardware/VM instance types for training is one of the choices you should make as part of your energy-efficient AI/ML process.

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.

When resources are constrained during high-traffic events or when carbon intensity is high, more carbon emissions will be generated from your system. Adding more resources to support increased traffic requirements introduces more embodied carbon and more demand for electricity. Continuing to handle all requests during high carbon intensity will increase overall emissions for your system. Shedding traffic that is lower priority during these scenarios will save on resources and carbon emissions. This approach requires an understanding of your traffic, including which call requests are critical and which can best withstand retry attempts and failures.

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.

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.

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.

Serverless cloud services scale dynamically with demand and share infrastructure across many applications, reducing idle resource consumption and lowering embodied carbon emissions.