PCP: A Generalized Approach to Optimizing Performance Under Power Constraints through Resource Management
(2014) 11th International Conference on Autonomic Computing- Abstract
- Many computing systems are constrained by power budgets. While they could temporarily draw more power, doing so creates unsustainable temperatures and unwanted electricity consumption. Developing systems that operate within power budgets is a constrained optimization problem: configuring the components within the system to maximize performance while maintaining sustainable power consumption. This is a challenging problem because many different components within a system affect power/performance tradeoffs and they interact in complex ways. Prior approaches address these challenges by fixing a set of components and designing a power budgeting framework that manages only that one set of components.
If new components become... (More) - Many computing systems are constrained by power budgets. While they could temporarily draw more power, doing so creates unsustainable temperatures and unwanted electricity consumption. Developing systems that operate within power budgets is a constrained optimization problem: configuring the components within the system to maximize performance while maintaining sustainable power consumption. This is a challenging problem because many different components within a system affect power/performance tradeoffs and they interact in complex ways. Prior approaches address these challenges by fixing a set of components and designing a power budgeting framework that manages only that one set of components.
If new components become available, then this framework must be redesigned and reimplemented. This paper presents PCP, a general solution to the power budgeting problem that works with arbitrary sets of components, even if they are not known at design time or change during runtime. To demonstrate PCP we implement it in software and deploy it on a Linux/x86 platform. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4465868
- author
- Hoffmann, Henry and Maggio, Martina LU
- organization
- publishing date
- 2014
- type
- Contribution to conference
- publication status
- in press
- subject
- conference name
- 11th International Conference on Autonomic Computing
- conference location
- Philadelphia, Pennsylvania, United States
- conference dates
- 2014-06-20
- language
- English
- LU publication?
- yes
- additional info
- Published at the International Conference on Autonomic ComputingICAC, International Conference on Autonomic Computing
- id
- 44544051-23aa-4be2-904f-b780181c3f90 (old id 4465868)
- date added to LUP
- 2016-04-04 13:41:50
- date last changed
- 2019-04-30 20:37:16
@misc{44544051-23aa-4be2-904f-b780181c3f90,
abstract = {{Many computing systems are constrained by power budgets. While they could temporarily draw more power, doing so creates unsustainable temperatures and unwanted electricity consumption. Developing systems that operate within power budgets is a constrained optimization problem: configuring the components within the system to maximize performance while maintaining sustainable power consumption. This is a challenging problem because many different components within a system affect power/performance tradeoffs and they interact in complex ways. Prior approaches address these challenges by fixing a set of components and designing a power budgeting framework that manages only that one set of components.<br/><br>
<br/><br>
If new components become available, then this framework must be redesigned and reimplemented. This paper presents PCP, a general solution to the power budgeting problem that works with arbitrary sets of components, even if they are not known at design time or change during runtime. To demonstrate PCP we implement it in software and deploy it on a Linux/x86 platform.}},
author = {{Hoffmann, Henry and Maggio, Martina}},
language = {{eng}},
title = {{PCP: A Generalized Approach to Optimizing Performance Under Power Constraints through Resource Management}},
year = {{2014}},
}