ARPE : A tool to build equation models of computing systems
(2013) 8th International Workshop on Feedback Computing- Abstract
An important challenge in the design and implementation of self-optimizing systems is that of finding a model that maps changes in a tunable parameter (or “knob”) into an effect on the performance, power, or energy, of the overall system. This paper describes ARPE (Analyzing the Relationship between Parameters and Effectors), an open source tool to analyze the effect of parameter changes on the behavior of applications in a complex system with interrelated knobs. We evaluate ARPE in several case studies on real systems with different sensors and parameters. Our results show that ARPE can help determine the best sensors for a system designed to predict application execution time. For space limitations, only one case study is here shown,... (More)
An important challenge in the design and implementation of self-optimizing systems is that of finding a model that maps changes in a tunable parameter (or “knob”) into an effect on the performance, power, or energy, of the overall system. This paper describes ARPE (Analyzing the Relationship between Parameters and Effectors), an open source tool to analyze the effect of parameter changes on the behavior of applications in a complex system with interrelated knobs. We evaluate ARPE in several case studies on real systems with different sensors and parameters. Our results show that ARPE can help determine the best sensors for a system designed to predict application execution time. For space limitations, only one case study is here shown, demonstrating that the error of modeling energy consumption is limited to the range 0.1−10% for previously unseen benchmarks.
(Less)
- author
- Maggio, Martina LU and Hoffmann, Henry
- organization
- publishing date
- 2013
- type
- Contribution to conference
- publication status
- published
- subject
- conference name
- 8th International Workshop on Feedback Computing
- conference location
- San Jose, CA, United States
- conference dates
- 2013-06-25
- external identifiers
-
- scopus:85091942272
- project
- Cloud Control
- Power Control for Computing Infrastructures
- language
- English
- LU publication?
- yes
- additional info
- Funding Information: This work was supported by the Swedish Research Council through the LCCC Linnaeus Center. Publisher Copyright: © 8th International Workshop on Feedback Computing 2013. All rights reserved. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.
- id
- ddcfc1e4-814f-4d97-a667-72962368c475
- date added to LUP
- 2021-03-22 11:09:34
- date last changed
- 2022-02-01 20:52:51
@misc{ddcfc1e4-814f-4d97-a667-72962368c475,
abstract = {{<p>An important challenge in the design and implementation of self-optimizing systems is that of finding a model that maps changes in a tunable parameter (or “knob”) into an effect on the performance, power, or energy, of the overall system. This paper describes ARPE (Analyzing the Relationship between Parameters and Effectors), an open source tool to analyze the effect of parameter changes on the behavior of applications in a complex system with interrelated knobs. We evaluate ARPE in several case studies on real systems with different sensors and parameters. Our results show that ARPE can help determine the best sensors for a system designed to predict application execution time. For space limitations, only one case study is here shown, demonstrating that the error of modeling energy consumption is limited to the range 0.1−10% for previously unseen benchmarks.</p>}},
author = {{Maggio, Martina and Hoffmann, Henry}},
language = {{eng}},
title = {{ARPE : A tool to build equation models of computing systems}},
year = {{2013}},
}