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25 docs tagged with "cloud"

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Dynamic scaling based on demand

Demand for resources depends on user load at any given time. However, most applications run without taking this into consideration, resulting in underused and inefficient resources. Scaling infrastructure dynamically based on actual demand eliminates excess capacity and improves efficiency.

Encrypt what is necessary

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

Evaluate and adopt energy-efficient processor architectures

Applications are built with a software architecture that best fits the business need they are serving. Cloud providers make it easy to evaluate different CPU architectures, including energy-efficient cloud-native processors that offer good performance per watt.

Implement stateless design

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.

Match your service level objectives to business needs

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.

Minimize the total number of deployed environments

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.

Minimize transmitted data size

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.

Optimize 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 storage resource utilization

From an embodied carbon perspective, it's better to identify and remove unused storage resources and implement automated retention policies so we are efficient with hardware and ensure the storage layer is optimized for the task.

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.

Right-size compute resources

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.

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. This can also happen with development and test applications or hardware in out-of-office hours.

Scan for vulnerabilities

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

Select the right hardware/VM instance types for AI/ML training

Selecting the right hardware/VM instance types for AI/ML training and inference is critical for energy efficiency. The hardware landscape has evolved dramatically with specialized AI accelerators, GPUs, and custom silicon offering vastly different performance-per-watt characteristics.

Terminate TLS at border gateway

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.

Use cloud native network security tools and controls

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.

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.