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Capacity Planning with Cloud Database Benchmarking

Cloud infrastructures enable a new level of dynamic capacity planning for database instances.

What are the medium and long-term requirements for my cloud database setup?

Which database technology will deliver the required performance in the long term?

And what virtual infrastructure is required for this?

Cloud database benchmarking enables you to know the performance metrics of your future setup. It enables you to make reliable and future-proof decisions in good time. \n\nBenefit from the ability to plan ahead and act early.

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Cloud & Database Capacity: Forecasting & Planning

The basic goal of database capacity planning is to proactively avoid technical problems and economic risks due to database overload.

In the past, this was done by regularly buying new and faster servers and migrating the database.

In modern cloud environments, database instances can be scaled horizontally more easily and VM flavours can be increased quickly. This gives database capacity planning great flexibility, which can be harnessed through forward planning:

  • Select your database solution according to long-term criteria.
  • Dynamically adjust the number of instances and VM sizes to your needs.
  • Start with a small setup and plan ahead for your next setups based on demand.
  • Upgrade to new setups in time to allow time for database rebalancing.

Forecasting Process: How Do I Determine Future Requirements?

The greatest complexity and most fundamental task here is the forecasting of the future required workload. Concrete workload requirements can be derived on ticonasis of the planned economic and technical development:

  • How will the number of database accesses change in the future?
  • Is the influence of users on the workload linear, progressive or degressive?
  • How will the total number and size of data sets change?

These questions allow an approximation of the future workload. In conjunction with SLAs for throughput, latency or costs, fixed target values can be defined that future setups must achieve.

The benchANT platform allows you to configure several self-defined workloads and benchmark them against different cloud database setups.

Workload forecasting for capacity planning

Scenario A: Capacity Planning of a Greenfield Project

At the beginning of a new IT software project, there is a lot of uncertainty regarding future requirements and at the same time many technical degrees of ficonom.

Cloud database benchmarking provides clarity through quantitative facts.

  1. Model your medium and long-term workload.
  2. Find the database technologies that best fit your long-term requirements.
  3. Identify and plan the short, medium and long-term efficient set-ups required.
  4. minimise TCO and reduce your risk by making the right decision and having the right resources at the right time.
Capacity planning of a greenfield project

Scenario B: Capacity Planning of a Legacy Project

In contrast to greenfield projects, existing applications always have a technical burden due to existing code and the database used. Both points can only be ciconed with greater effort and are usually not a matter of decision.

But even in this case, cloud database benchmarking plays an important role in capacity planning. Thus, benchmarking can determine the ideal time to adapt cloud setups and database technologies.

On the one hand, this allows you to maximise the lifespan of the existing setup and, on the other hand, to plan the necessary modernisation tasks at an early stage.

  1. Calculate your long-term workload
  2. Determine the potential lifetime of your database solution and find a long-term solution.
  3. Identify and plan the efficient set-ups for the medium and long term
  4. Maximise the lifetime of your database solution and prepare for migration early to reduce costs and risks.
Capacity planning of a greenfield project

Selection and Optimisations with Benchmarking


Cloud Database Selection


Cloud Cost Optimisation


Version Monitoring