Modelling and analysis techniques for capacity planning
Abstract: "This study examines techniques and tools of performance modelling and analysis for use in capacity planning. The study is based on three fundamental assumptions. First, the general plans of an organization must dominate the workload forecasting. Therefore hierarchical models should b...
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| Format: | Buch |
| Sprache: | English |
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1989
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| Schriftenreihe: | Helsingin Yliopisto / Department of Computer Science: Series of publications / A
1989,6 |
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| 100 | 1 | |a Raatikainen, Kimmo E. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Modelling and analysis techniques for capacity planning |c Kimmo E. E. Raatikainen |
| 264 | 1 | |c 1989 | |
| 300 | |a Getr. Zählung | ||
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| 490 | 1 | |a Helsingin Yliopisto / Department of Computer Science: Series of publications / A |v 1989,6 | |
| 500 | |a Helsinki, Univ., Diss., 1990 | ||
| 520 | 3 | |a Abstract: "This study examines techniques and tools of performance modelling and analysis for use in capacity planning. The study is based on three fundamental assumptions. First, the general plans of an organization must dominate the workload forecasting. Therefore hierarchical models should be used to obtain a well-structured description of the workload. Secondly, the performance objectives must be stated from the users' point of view. Since users experience individual response times instead of the average system response-time, the performance objectives are assumed to be related to quantiles of the response-time distribution. Thirdly, model specification and workload identification are based on data that can be efficiently collected | |
| 520 | 3 | |a When quantiles of response times are considered as primary performance indices, queueing network modelling and analysis are challenging. In this study, new techniques are developed both to approximate analytically and to simulate quantiles of response times. The accuracy of the approximation method developed is shown to be reasonable in the practical cases examined. The simulation techniques developed are based on sequential estimation. The length of a simulation run is not predefined, but controlled by the developed mechanisms. The decision to terminate the simulation is based on given accuracy requirements | |
| 520 | 3 | |a Response-time distributions are usually sensitive to shapes of service distributions. Traditional parameteric method have a limited ability to capture the desired shape. Instead, the developed distribution models, based on a nonparametric method, are shown to approximate the shape of service distributions quite accurately. The developed tools and techniques are applied in a real environment. Throughout the study a VAXCluster system, used in an academic research environment, is analyzed. The results reported indicate that the proposed methods are good for practical modelling situations. | |
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| 650 | 4 | |a Queuing theory | |
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Datensatz im Suchindex
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| author | Raatikainen, Kimmo E. |
| author_facet | Raatikainen, Kimmo E. |
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| bvnumber | BV008974515 |
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| genre_facet | Hochschulschrift |
| id | DE-604.BV008974515 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-23T12:55:18Z |
| institution | BVB |
| isbn | 9514551753 |
| language | English |
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| series2 | Helsingin Yliopisto / Department of Computer Science: Series of publications / A |
| spelling | Raatikainen, Kimmo E. Verfasser aut Modelling and analysis techniques for capacity planning Kimmo E. E. Raatikainen 1989 Getr. Zählung txt rdacontent n rdamedia nc rdacarrier Helsingin Yliopisto / Department of Computer Science: Series of publications / A 1989,6 Helsinki, Univ., Diss., 1990 Abstract: "This study examines techniques and tools of performance modelling and analysis for use in capacity planning. The study is based on three fundamental assumptions. First, the general plans of an organization must dominate the workload forecasting. Therefore hierarchical models should be used to obtain a well-structured description of the workload. Secondly, the performance objectives must be stated from the users' point of view. Since users experience individual response times instead of the average system response-time, the performance objectives are assumed to be related to quantiles of the response-time distribution. Thirdly, model specification and workload identification are based on data that can be efficiently collected When quantiles of response times are considered as primary performance indices, queueing network modelling and analysis are challenging. In this study, new techniques are developed both to approximate analytically and to simulate quantiles of response times. The accuracy of the approximation method developed is shown to be reasonable in the practical cases examined. The simulation techniques developed are based on sequential estimation. The length of a simulation run is not predefined, but controlled by the developed mechanisms. The decision to terminate the simulation is based on given accuracy requirements Response-time distributions are usually sensitive to shapes of service distributions. Traditional parameteric method have a limited ability to capture the desired shape. Instead, the developed distribution models, based on a nonparametric method, are shown to approximate the shape of service distributions quite accurately. The developed tools and techniques are applied in a real environment. Throughout the study a VAXCluster system, used in an academic research environment, is analyzed. The results reported indicate that the proposed methods are good for practical modelling situations. Network analysis (Planning) Queuing theory Benchmark (DE-588)4144457-7 gnd rswk-swf Warteschlangentheorie (DE-588)4255044-0 gnd rswk-swf Kapazitätsplanung (DE-588)4120544-3 gnd rswk-swf Modellierung (DE-588)4170297-9 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Kapazitätsplanung (DE-588)4120544-3 s Modellierung (DE-588)4170297-9 s DE-604 Warteschlangentheorie (DE-588)4255044-0 s 1\p DE-604 Benchmark (DE-588)4144457-7 s 2\p DE-604 Department of Computer Science: Series of publications / A Helsingin Yliopisto 1989,6 (DE-604)BV000904448 1989,6 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Raatikainen, Kimmo E. Modelling and analysis techniques for capacity planning Network analysis (Planning) Queuing theory Benchmark (DE-588)4144457-7 gnd Warteschlangentheorie (DE-588)4255044-0 gnd Kapazitätsplanung (DE-588)4120544-3 gnd Modellierung (DE-588)4170297-9 gnd |
| subject_GND | (DE-588)4144457-7 (DE-588)4255044-0 (DE-588)4120544-3 (DE-588)4170297-9 (DE-588)4113937-9 |
| title | Modelling and analysis techniques for capacity planning |
| title_auth | Modelling and analysis techniques for capacity planning |
| title_exact_search | Modelling and analysis techniques for capacity planning |
| title_full | Modelling and analysis techniques for capacity planning Kimmo E. E. Raatikainen |
| title_fullStr | Modelling and analysis techniques for capacity planning Kimmo E. E. Raatikainen |
| title_full_unstemmed | Modelling and analysis techniques for capacity planning Kimmo E. E. Raatikainen |
| title_short | Modelling and analysis techniques for capacity planning |
| title_sort | modelling and analysis techniques for capacity planning |
| topic | Network analysis (Planning) Queuing theory Benchmark (DE-588)4144457-7 gnd Warteschlangentheorie (DE-588)4255044-0 gnd Kapazitätsplanung (DE-588)4120544-3 gnd Modellierung (DE-588)4170297-9 gnd |
| topic_facet | Network analysis (Planning) Queuing theory Benchmark Warteschlangentheorie Kapazitätsplanung Modellierung Hochschulschrift |
| volume_link | (DE-604)BV000904448 |
| work_keys_str_mv | AT raatikainenkimmoe modellingandanalysistechniquesforcapacityplanning |