Capacity Management as a Service for Enterprise Standard Software.
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| Title: | Capacity Management as a Service for Enterprise Standard Software. |
|---|---|
| Authors: | Müller, Hendrik1 hendrik.mueller@ovgu.de, Bosse, Sascha1 sascha.bosse@ovgu.de, Turowski, Klaus1 klaus.turowski@ovgu.de |
| Source: | Complex Systems Informatics & Modeling Quarterly. Dec2017/Jan2018, Issue 13, p1-21. 21p. 1 Diagram, 1 Chart, 3 Graphs. |
| Subjects: | Capacity management (Computers), Workload of computers, Business enterprises, Computer software, Task performance, Prediction models |
| Abstract: | Capacity management approaches optimize component utilization from a strong technical perspective. In fact, the quality of involved services is considered implicitly by linking it to resource capacity values. This practice hinders to evaluate design alternatives with respect to given service levels that are expressed in user-centric metrics such as the mean response time for a business transaction. We argue that utilized historical workload traces often contain a variety of performance-related information that allows for the integration of performance prediction techniques through machine learning. Since enterprise applications excessively make use of standard software that is shipped by large software vendors to a wide range of customers, standardized prediction models can be trained and provisioned as part of a capacity management service which we propose in this article. Therefore, we integrate knowledge discovery activities into well-known capacity planning steps, which we adapt to the special characteristics of enterprise applications. Using a realworld example, we demonstrate how prediction models that were trained on a large scale of monitoring data enable cost-efficient measurement-based prediction techniques to be used in early design and redesign phases of planned or running applications. Finally, based on the trained model, we demonstrate how to simulate and analyze future workload scenarios. Using a Pareto approach, we were able to identify cost-effective design alternatives for an enterprise application whose capacity is being managed. [ABSTRACT FROM AUTHOR] |
| Copyright of Complex Systems Informatics & Modeling Quarterly is the property of RTU Publishing House and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 127158823 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Capacity Management as a Service for Enterprise Standard Software. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Müller%2C+Hendrik%22">Müller, Hendrik</searchLink><relatesTo>1</relatesTo><i> hendrik.mueller@ovgu.de</i><br /><searchLink fieldCode="AR" term="%22Bosse%2C+Sascha%22">Bosse, Sascha</searchLink><relatesTo>1</relatesTo><i> sascha.bosse@ovgu.de</i><br /><searchLink fieldCode="AR" term="%22Turowski%2C+Klaus%22">Turowski, Klaus</searchLink><relatesTo>1</relatesTo><i> klaus.turowski@ovgu.de</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Complex+Systems+Informatics+%26+Modeling+Quarterly%22">Complex Systems Informatics & Modeling Quarterly</searchLink>. Dec2017/Jan2018, Issue 13, p1-21. 21p. 1 Diagram, 1 Chart, 3 Graphs. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Capacity+management+%28Computers%29%22">Capacity management (Computers)</searchLink><br /><searchLink fieldCode="DE" term="%22Workload+of+computers%22">Workload of computers</searchLink><br /><searchLink fieldCode="DE" term="%22Business+enterprises%22">Business enterprises</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software%22">Computer software</searchLink><br /><searchLink fieldCode="DE" term="%22Task+performance%22">Task performance</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Capacity management approaches optimize component utilization from a strong technical perspective. In fact, the quality of involved services is considered implicitly by linking it to resource capacity values. This practice hinders to evaluate design alternatives with respect to given service levels that are expressed in user-centric metrics such as the mean response time for a business transaction. We argue that utilized historical workload traces often contain a variety of performance-related information that allows for the integration of performance prediction techniques through machine learning. Since enterprise applications excessively make use of standard software that is shipped by large software vendors to a wide range of customers, standardized prediction models can be trained and provisioned as part of a capacity management service which we propose in this article. Therefore, we integrate knowledge discovery activities into well-known capacity planning steps, which we adapt to the special characteristics of enterprise applications. Using a realworld example, we demonstrate how prediction models that were trained on a large scale of monitoring data enable cost-efficient measurement-based prediction techniques to be used in early design and redesign phases of planned or running applications. Finally, based on the trained model, we demonstrate how to simulate and analyze future workload scenarios. Using a Pareto approach, we were able to identify cost-effective design alternatives for an enterprise application whose capacity is being managed. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Complex Systems Informatics & Modeling Quarterly is the property of RTU Publishing House and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.7250/csimq.2017-13.01 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 1 Subjects: – SubjectFull: Capacity management (Computers) Type: general – SubjectFull: Workload of computers Type: general – SubjectFull: Business enterprises Type: general – SubjectFull: Computer software Type: general – SubjectFull: Task performance Type: general – SubjectFull: Prediction models Type: general Titles: – TitleFull: Capacity Management as a Service for Enterprise Standard Software. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Müller, Hendrik – PersonEntity: Name: NameFull: Bosse, Sascha – PersonEntity: Name: NameFull: Turowski, Klaus IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2017/Jan2018 Type: published Y: 2017 Identifiers: – Type: issn-print Value: 22559922 Numbering: – Type: issue Value: 13 Titles: – TitleFull: Complex Systems Informatics & Modeling Quarterly Type: main |
| ResultId | 1 |
