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Tuesday, November 23, 2021

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When the cloud servers rightsizing algorithm calculates the baseline level for the current year application server’s usage, the seasonal adjustment needs to be calculated and applied by adding the additional anticipated change, which could be increasing or decreasing the capacity usage. We describe the method and illustrate it against the real data.

The cloud servers rightsizing recommendation generated based on seasonality adjustments, would reflect the seasonal patterns, and prevent any potential capacity issues or reduce an excess capacity.
The ability to keep multi-year historical data of 4 main subsystems of application servers’ capacity usage opens the opportunity to detect seasonality changes and estimate additional capacity needs for CPU, memory, disk I/Os, and network. A multi-subsystem approach is necessary, as very often the nature of the application could be not CPU but I/Os or Memory or Network-intensive.

Applying the method daily allows downsizing correctly if the peak season passes and the available capacity should be decreased, which is a good way to achieve cost savings.

In the session, the detailed seasonality adjustment method is described and illustrated against the real data. The method is based on and developed by the author’s SETDS methodology, which treats the seasonal variation as an exception (anomaly) and calculates adjustments as variations from a linear trend.

Key Takeaways

  • How to build seasonal adjustments into the cloud rightsizing
  • To get familiar with cloud objects rightsizing techniques

Monday, November 22, 2021

The Change Point Detection SETDS based method is implemented as a Perfomalist API. Everybody is welcome to test!

How to use it explained HERE:


Example of the step jump event detected by the API (Output got from the API call via Postman and spreadsheet was used to chart the result):