Date of Graduation


Document Type


Degree Name

Doctor of Philosophy in Engineering (PhD)

Degree Level



Computer Science & Computer Engineering


Amy W. Apon

Committee Member

Jia Di

Second Committee Member

David Douglas

Third Committee Member

Craig W. Thompson


Applied sciences, Empirical mode decomposition, Enterprise clusters, Workload characterization, Workload forecasting


Characterization and forecasting are two important processes in capacity planning. While they are closely related, their approaches have been different. In this research, a decomposition method called Empirical Mode Decomposition (EMD) has been applied as a preprocessing tool in order to bridge the input of both characterization and forecasting processes of the job arrivals of an enterprise cluster. Based on the facts that an enterprise cluster follows a standard preset working schedule and that EMD has the capability to extract hidden patterns within a data stream, we have developed a set of procedures that can preprocess the data for characterization as well as for forecasting. This comprehensive empirical study demonstrates that the addition of the preprocessing step is an improvement over the standard approaches in both characterization and forecasting. In addition, it is also shown that EMD is better than the popular wavelet-based decomposition in term of extracting different patterns from within a data stream.