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On Self-adaptive Resource Allocation through Reinforcement Learning

Panerati, Jacopo; Sironi, Filippo; Carminati, Matteo; Maggio, Martina LU ; Beltrame, Giovanni; Gmytrasiewicz, Piotr; Sciuto, Donatella and Santambrogio, Marco Domenico (2013) NASA/ESA Conference on Adaptive Hardware and Systems (AHS-2013) In 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)
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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
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)
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
2013-07-22 08:45:49
date last changed
2017-02-19 04:28:18
@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},
  year         = {2013},
}