Computer system sizing involves estimating the hardware resources needed to support a new workload that has not been run in a production environment. Sizing assumes that little system environment information or performance measurements are available, thus a sizing expert must rely on extrapolations from similar workloads, industry benchmarks, rules-of-thumb, and hardware performance guidelines to determine the type and quantity of required resources.;The main contribution of this thesis is a structured approach to size the database tier of a business intelligence application. Other contributions include a formal model of the database system sizing process, a characterization of business intelligence workloads, and an easy-to-use software tool to support the approach.;The proposed approach uses a combination of industry rules-of-thumb and extrapolations from collected performance data to determine the type and quantity of hardware resources needed. A customer's online and batch workloads are characterized in terms of high-impact parameters, which are used to determine processor and disk requirements. Memory and storage requirements are determined using common rules-of-thumb. An evaluation of a tool based on the sizing approach shows that the results obtained range within a factor of one to eleven of those produced by a sizing expert.
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