Estimating the hardware capabilities

Since the architecture is being designed to scale horizontally, we can add more servers to the setup. We will start by using commodity class, cost-effective hardware.

In order to expect our infrastructure economy, it would be great to make some basic hardware calculations for the first estimation of our exact requirements.

Considering the possibility of experiencing contentions for resources such as CPU, RAM, network, and disk, you cannot wait for a particular physical component to fail before you take corrective action, which might be more complicated.

Let's inspect a real-life example of the impact of underestimating capacity planning. A cloud-hosting company set up two medium servers, one for an e-mail server and the other to host the official website. The company, which is one of our several clients, grew in a few months and eventually ran out of disk space. The expected time to resolve such an issue is a few hours, but it took days. The problem was that all the parties did not make proper use of the cloud, due to the on demand nature of the service. This led to Mean Time To Repair (MTTR) increasing exponentially. The cloud provider did not expect this!

Incidents like this highlight the importance of proper capacity planning for your cloud infrastructure. Capacity management is considered a day-to-day responsibility where you have to stay updated with regard to software or hardware upgrades.

Through a continuous monitoring process of service consumption, you will be able to reduce the IT risk and provide a quick response to the customer's needs.

From your first hardware deployment, keep running your capacity management processes by looping through tuning, monitoring, and analysis.

The next stop will take into account your tuned parameters and introduce, within your hardware/software, the right change, which involves a synergy of the change management process.

Let's make our first calculation based on certain requirements. For example, let's say we aim to run 200 VMs in our OpenStack environment.