The goal of the dissertation study was to evaluate the existing DG scheduling algorithm. The evaluation was developed through previously explored simulated analyses of DGs performed by researchers in the field of DG scheduling optimization and to improve the current RT framework of the DG at TTU. The author analyzed the RT of an actual DG,
thereby enabling other investigators to compare theoretical results with the results of this dissertation case study.
Two statistical methods were used to formulate and validate predictive models: multiple linear regression and graphical exploratory data analysis techniques. Using both statistical methods, the author was able to determine that the theoretical model was able to predict the significance of four independent variables of resource fragmentation,
computational volatility, resource management, and grid job scheduling on the dependent variables quality of service and job performance affecting RT. After an experimental case study analysis of the DG variables, the author identified the best DG resources to perform
optimization of run-time performance of DG at TTU. The projected outcome of this investigation is the improved job scheduling techniques of the DG at TTU.
| Identifer | oai:union.ndltd.org:nova.edu/oai:nsuworks.nova.edu:gscis_etd-1272 |
| Date | 01 January 2013 |
| Creators | Perez, Jerry Felix |
| Publisher | NSUWorks |
| Source Sets | Nova Southeastern University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | CEC Theses and Dissertations |
Page generated in 0.002 seconds