Distributed Management of CPU Resources for Time-Sensitive Applications
Chasparis, Georgios LU ; Maggio, Martina LU ; Årzén, Karl-Erik LU
and Bini, Enrico
LU
(2013)
American Control Conference, 2013
p.5305-5312
- Abstract
- The number of applications sharing the same embedded device is increasing dramatically. Very efficient mechanisms (resource managers) for assigning the CPU time to all demanding applications are needed. Unfortunately, existing optimization-based resource managers consume too much resource themselves. In this paper, we address the problem of distributed convergence to fair allocation of CPU resources for time-sensitive applications. We propose a novel resource management framework where both applications and the resource manager act independently trying to maximize their own performance measure according to a utility-based adjustment process. Contrary to prior work on centralized optimization schemes, the proposed framework exhibits... (More)
- The number of applications sharing the same embedded device is increasing dramatically. Very efficient mechanisms (resource managers) for assigning the CPU time to all demanding applications are needed. Unfortunately, existing optimization-based resource managers consume too much resource themselves. In this paper, we address the problem of distributed convergence to fair allocation of CPU resources for time-sensitive applications. We propose a novel resource management framework where both applications and the resource manager act independently trying to maximize their own performance measure according to a utility-based adjustment process. Contrary to prior work on centralized optimization schemes, the proposed framework exhibits adaptivity and robustness to changes both in the number and nature of applications, while it assumes minimum information available to both applications and the resource manager. It is shown analytically that fair resource allocation can be achieved through the proposed adjustment process when the CPU is overloaded. Experiments using the TrueTime Matlab toolbox show the validity of the proposed approach. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3567845
- author
- Chasparis, Georgios
LU
; Maggio, Martina
LU
; Årzén, Karl-Erik
LU
and Bini, Enrico
LU
- organization
- publishing date
- 2013
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- [Host publication title missing]
- pages
- 8 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- American Control Conference, 2013
- conference location
- Washington, DC, United States
- conference dates
- 2013-06-17 - 2016-06-19
- external identifiers
-
- wos:000327210205085
- scopus:84883533382
- ISSN
- 0743-1619
- language
- English
- LU publication?
- yes
- id
- 1c8e1a6f-5588-4f7d-aba7-475ccff905cf (old id 3567845)
- date added to LUP
- 2016-04-01 13:55:06
- date last changed
- 2022-03-29 18:11:49
@inproceedings{1c8e1a6f-5588-4f7d-aba7-475ccff905cf,
abstract = {{The number of applications sharing the same embedded device is increasing dramatically. Very efficient mechanisms (resource managers) for assigning the CPU time to all demanding applications are needed. Unfortunately, existing optimization-based resource managers consume too much resource themselves. In this paper, we address the problem of distributed convergence to fair allocation of CPU resources for time-sensitive applications. We propose a novel resource management framework where both applications and the resource manager act independently trying to maximize their own performance measure according to a utility-based adjustment process. Contrary to prior work on centralized optimization schemes, the proposed framework exhibits adaptivity and robustness to changes both in the number and nature of applications, while it assumes minimum information available to both applications and the resource manager. It is shown analytically that fair resource allocation can be achieved through the proposed adjustment process when the CPU is overloaded. Experiments using the TrueTime Matlab toolbox show the validity of the proposed approach.}},
author = {{Chasparis, Georgios and Maggio, Martina and Årzén, Karl-Erik and Bini, Enrico}},
booktitle = {{[Host publication title missing]}},
issn = {{0743-1619}},
language = {{eng}},
pages = {{5305--5312}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Distributed Management of CPU Resources for Time-Sensitive Applications}},
year = {{2013}},
}