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Throughout the system life cycle, the ability to predict a software system's performance under different configurations and workloads is highly valuable to ensure that the system meets its performance requirements. During the design phase, performance prediction helps to evaluate different design alternatives. At deployment time, it facilitates system sizing and capacity planning. During operation, predicting the effect of changes in the workload or in the system configuration is beneficial for run-time performance management. The alternative to performance prediction is to deploy the system in an environment reflecting the configuration of interest and conduct experiments measuring the system performance under the respective workloads. Such experiments, however, are normally very expensive and time-consuming and therefore often considered not to be economically viable.
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