On Self-adaptive Resource Allocation through Reinforcement Learning
(2013) NASA/ESA Conference on Adaptive Hardware and Systems (AHS-2013) p.23-30- Abstract
- Autonomic computing was proposed as a promising solution to overcome the complexity of modern systems, which is causing management operations to become increasingly difficult for human beings. This work proposes the Adaptation Manager, a comprehensive framework to implement autonomic managers capable of pursuing some of the objectives of autonomic computing (i.e., self-optimization and self-healing). The Adaptation Manager features an active performance monitoring infrastructure and two dynamic knobs to tune the scheduling decisions of an operating system and the working frequency of cores. The Adaptation Manager exploits artificial intelligence and reinforcement learning to close the Monitor-Plan-Analyze- Execute with Knowledge adaptation... (More)
- Autonomic computing was proposed as a promising solution to overcome the complexity of modern systems, which is causing management operations to become increasingly difficult for human beings. This work proposes the Adaptation Manager, a comprehensive framework to implement autonomic managers capable of pursuing some of the objectives of autonomic computing (i.e., self-optimization and self-healing). The Adaptation Manager features an active performance monitoring infrastructure and two dynamic knobs to tune the scheduling decisions of an operating system and the working frequency of cores. The Adaptation Manager exploits artificial intelligence and reinforcement learning to close the Monitor-Plan-Analyze- Execute with Knowledge adaptation loop at the very base of every autonomic manager. We evaluate the Adaptation Manager, and especially the adaptation policies it learns by means of reinforcement learning, using a set of representative applications for multicore processors and show the effectiveness of our prototype on commodity computing systems. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3920589
- author
- Panerati, Jacopo ; Sironi, Filippo ; Carminati, Matteo ; Maggio, Martina LU ; Beltrame, Giovanni ; Gmytrasiewicz, Piotr ; Sciuto, Donatella and Santambrogio, Marco Domenico
- organization
- publishing date
- 2013
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- NASA/ESA Conference on Adaptive Hardware and Systems (AHS), 2013
- pages
- 23 - 30
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- NASA/ESA Conference on Adaptive Hardware and Systems (AHS-2013)
- conference location
- Torino, Italy
- conference dates
- 2013-06-25
- external identifiers
-
- scopus:84885417645
- ISBN
- 9781467363822
- DOI
- 10.1109/AHS.2013.6604222
- language
- English
- LU publication?
- yes
- id
- 329ffa45-1849-4b73-acd0-a21f9da4e864 (old id 3920589)
- date added to LUP
- 2016-04-04 10:01:41
- date last changed
- 2024-01-27 16:23:55
@inproceedings{329ffa45-1849-4b73-acd0-a21f9da4e864, abstract = {{Autonomic computing was proposed as a promising solution to overcome the complexity of modern systems, which is causing management operations to become increasingly difficult for human beings. This work proposes the Adaptation Manager, a comprehensive framework to implement autonomic managers capable of pursuing some of the objectives of autonomic computing (i.e., self-optimization and self-healing). The Adaptation Manager features an active performance monitoring infrastructure and two dynamic knobs to tune the scheduling decisions of an operating system and the working frequency of cores. The Adaptation Manager exploits artificial intelligence and reinforcement learning to close the Monitor-Plan-Analyze- Execute with Knowledge adaptation loop at the very base of every autonomic manager. We evaluate the Adaptation Manager, and especially the adaptation policies it learns by means of reinforcement learning, using a set of representative applications for multicore processors and show the effectiveness of our prototype on commodity computing systems.}}, author = {{Panerati, Jacopo and Sironi, Filippo and Carminati, Matteo and Maggio, Martina and Beltrame, Giovanni and Gmytrasiewicz, Piotr and Sciuto, Donatella and Santambrogio, Marco Domenico}}, booktitle = {{NASA/ESA Conference on Adaptive Hardware and Systems (AHS), 2013}}, isbn = {{9781467363822}}, language = {{eng}}, pages = {{23--30}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, title = {{On Self-adaptive Resource Allocation through Reinforcement Learning}}, url = {{http://dx.doi.org/10.1109/AHS.2013.6604222}}, doi = {{10.1109/AHS.2013.6604222}}, year = {{2013}}, }