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.
Speaker: Igor Trubin
Innovative and experienced System Management specialist with the ability to organize and manage world-class multiplatform Availability and Capacity Management services and teams. Expert in cloud computing optimization, the statistical analysis of performance data, queuing theory, modeling and business driver-based forecasting for distributed mainframe and cloud platforms
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