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A Generic Platform for Transforming Monitoring Data into Performance Models

Published: 18 April 2017 Publication History

Abstract

The performance of software systems is an ongoing issue in the industry, including the development of corresponding performance models. Recently several approaches for deriving such performance models from monitoring data have been proposed. A current limitation of these approaches is that most of them are bound to certain monitoring tools for providing the data, limiting their applicability.
We therefore propose a generic platform for transforming monitoring data into performance models, encapsulating these approaches for deriving performance models. This platform gives the flexibility of exchanging the monitoring tool or the used performance modeling approach, allowing more comprehensive performance analysis without additional manual transformation work. A seamless exchangeability of the performance modeling approach enables the generation of different types of performance models based on the same monitoring data, while the exchangeability of the monitoring tool enables the same approaches to be employed on a wider range of systems, as often the applicability of certain monitoring tools is limited by environmental properties. In addition, the generic nature of the platform aims to support the rapid development of prototypes of new, upcoming ideas within the context of performance modeling based on monitoring data.
During our evaluation we examine the quality of our approach in terms of accuracy and scalability. We show that our platform for transforming monitoring data into performance models scales with a very low overhead and that the results of the integrated performance modeling approaches are very accurate in comparison to the results of the non-integrated versions.

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cover image ACM Conferences
ICPE '17 Companion: Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering Companion
April 2017
248 pages
ISBN:9781450348997
DOI:10.1145/3053600
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 April 2017

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Author Tags

  1. application monitoring
  2. model transformation
  3. performance model generation
  4. usage profile extraction

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  • Research-article

Funding Sources

  • German Federal Min- istry of Education and Research
  • German Research Foundation

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ICPE '17
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ICPE '17 Companion Paper Acceptance Rate 24 of 65 submissions, 37%;
Overall Acceptance Rate 252 of 851 submissions, 30%

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