Overview of Available Flavours in Islet OpenStack

Islet OpenStack provides a variety of virtual machine (VM) flavours, each optimized for different use cases. The choice of flavour significantly affects the performance, cost, and functionality of your virtual machines. This guide will help you understand the different flavours, their use cases, and provide advice on choosing the right one based on your specific needs.

Choosing the Right Flavour

The flavour you choose in Islet OpenStack determines the performance characteristics of your VM. This selection is influenced by factors such as CPU, memory, storage, and specialized hardware capabilities like GPUs. However, your choice may also be constrained by the type of image you select.

Image Selection and Flavour Limitations

The operating system image you choose may limit the flavours available to you. For instance, a lightweight Linux image might not support flavours that require specialized hardware, such as GPUs. Similarly, certain images might only be compatible with particular flavours optimized for high performance, such as those offering more CPU cores or greater memory allocations.

When selecting a flavour, ensure that the image you intend to use supports the resources needed for your workload. If you’re unsure, it’s recommended to consult with your system administrator or check the specific image documentation in Islet OpenStack for compatibility.

The Real Meaning Behind Flavour Names

In Islet OpenStack, the names of flavours can seem cryptic, especially when it comes to specialized variants. Understanding what these names represent will help you make an informed choice.

Common Flavour Names Explained

eo2a

This typically refers to a flavour that is optimized for compute-heavy tasks. It is commonly used for running simulations, AI workloads, or other processes requiring high CPU performance but not necessarily large amounts of memory or specialized hardware.

hma

Flavours with this designation are geared toward high-memory applications. They are ideal for tasks that require a large amount of memory but don’t necessarily need as many CPU cores, such as in-memory databases or large-scale data analysis.

hmad

These flavours offer high memory combined with additional disk space for tasks that require both significant memory and storage. They are suited for workloads like large-scale data processing or for services that require a large working dataset in memory and disk.

Flavour Variants: vm.a6000 vs gpu.A6000

The distinction between vm.a6000 and gpu.A6000 flavours pertains to whether or not the flavour includes GPU acceleration.

vm.a6000

These flavours are designed for high-performance workloads that require significant CPU and memory resources but no GPU acceleration. Ideal for most general-purpose computing tasks.

gpu.A6000

This flavour is specifically configured for GPU-accelerated workloads, such as machine learning, AI, and rendering tasks. It includes access to specialized NVIDIA A6000 GPUs, making it suitable for workloads that require high-performance graphics processing.

Note

Access to gpu.A6000 flavours is restricted and is only available to specific users who have been granted permission by the system administrator. Ensure you have the necessary rights before attempting to provision these resources.

When to Use Each Flavour

Selecting the appropriate flavour depends on the nature of the tasks you need to perform:

For CPU-intensive tasks

Simulations, web servers, batch processing:

Choose a high-performance flavour such as eo2a.

For memory-heavy applications

Large-scale databases, data analytics:

Opt for hma or hmad flavours depending on the amount of memory and storage you require.

For GPU-accelerated tasks

Deep learning, 3D rendering

Use the gpu.A6000 flavour (only available to users with specific rights).

For general-purpose computing tasks

Small to medium web applications

A vm.a6000 flavour would be sufficient, providing a good balance of CPU, memory, and storage.

Conclusion

Choosing the right flavour is a critical decision in Islet OpenStack. Understanding the hardware requirements of your workloads and the limits imposed by the selected image will help ensure optimal performance. Always check whether your user account has the necessary rights to provision specialized resources like GPUs, and consult with system administrators if you have any questions about compatibility.