Capacity Planning for Computer Systems by Tim Browning

By Tim Browning

Intends to use the thoughts of ability making plans to desktops with a mix of dimension, modelling and analytical tools for exact projection of computing device workload and source characterization

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CPU hours); Χι,Χ^, · · ·, Xn are values of user reported business elements;fls»,«i,. , «„are coefficients that relate business elements to the resource element; and Oo is Chapter 3 · Business Elements and Service Levels 47 an error term containing the residual difference between the linear combination of the coefficients and the observed values of the business elements. 3 Validating the Business Element Statistical Model Model validation has basically two dimensions: (1) the mathematical appropriateness of the statistical model and (2) the actual ability of the model to provide realistic forecasts.

In addition, PDD will be high for installations with higher data manipulation requirements. 4 Process Density of Memory Resident Data (PDM) PDM represents the processing cost (per second) that is associated with having on-line 1 megabyte of nominal Main Storage capacity. As an installation's processing requirements grow over time, its process density of mem- 40 Capacity Planning for Computer Systems ory decreases. 5 (9) Data Remoteness Factor (DRF) DRFrepresents the ratio of on-line DASD resident data gigabytes to the Main Storage resident gigabytes.

The interval between client request for I / O and server response Response time metrics are most often used in establishing service targets for the system, such as an on-line response time of no greater than two seconds for X^type CICS transactions during a certain time-of-day period Tx to y. Re­ sponse time is sensitive to system load and increases as system load increases. , number of batch processes or jobs running, system resource management adjustments (in MVS), or other 32 Capacity Planning for Computer Systems means).

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