Cloud Computing Architectures: What needs to be scaled?

Whenever we think about scaling our Applications, we basically think about building a software architecture that supports scaling and selecting a technology such as IaaS or PaaS Platforms to achieve that goal. But scaling is more compliated than it seems. It is not only a thing that needs to be achieved in technology or software architectures.

An important question is, what needs to be scaled in an enterprise. It is not only the software architecture but also other factors:

  • Websites
  • Applications
  • Teams
  • Organisations

When talking about scaling organizations, some questions may arise:

  • How easy is it to add a Person to a Company/Team or remove a Person?
  • How can the work force be measured within the organisational structure?
  • What effort has to be made if a new Person is added to a company?
  • Does the company structure allows rapid organisational growth?

The Output of a team is not proportional to the number of people in a team. This is similar with Applications!


Scaling teams in a software project
Scaling teams in a software project

To achieve scalability, it is not only necessary to built an architecture that is made for scale but also to think about how to scale a team. Imagine you start with 5 employees and your start-up becomes super-famous. Your team might grow to 1,000 employees in some years. You need to think about how to solve this problem.

The following picture demonstrates how scaling problems might start in a company:

Productivity inhibitors in an IT project
Productivity inhibitors in an IT project



Picture Copyright by Moyan Brenn

I lead a team of Senior Experts in Data & Data Science as Head of Data & Analytics and AI at A1 Telekom Austria Group. I also teach this topic at various universities and frequently speak at various Conferences. In 2010 I wrote a book about Cloud Computing, which is often used at German & Austrian Universities. In my home country (Austria) I am part of several organisations on Big Data & Data Science.

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