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Feedback and time are essential for the optimal control of computing systems

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Title: Feedback and time are essential for the optimal control of computing systems
Authors: Kerrigan, EC
Item Type: Conference Paper
Abstract: The performance, reliability, cost, size and energy usage of computing systems can be improved by one or more orders of magnitude by the systematic use of modern control and optimization methods. Computing systems rely on the use of feedback algorithms to schedule tasks, data and resources, but the models that are used to design these algorithms are validated using open-loop metrics. By using closed-loop metrics instead, such as the gap metric developed in the control community, it should be possible to develop improved scheduling algorithms and computing systems that have not been over-engineered. Furthermore, scheduling problems are most naturally formulated as constraint satisfaction or mathematical optimization problems, but these are seldom implemented using state of the art numerical methods, nor do they explicitly take into account the fact that the scheduling problem itself takes time to solve. This paper makes the case that recent results in real-time model predictive control, where optimization problems are solved in order to control a process that evolves in time, are likely to form the basis of scheduling algorithms of the future. We therefore outline some of the research problems and opportunities that could arise by explicitly considering feedback and time when designing optimal scheduling algorithms for computing systems.
Issue Date: 17-Dec-2015
Date of Acceptance: 25-Jun-2015
URI: http://hdl.handle.net/10044/1/26698
DOI: https://dx.doi.org/10.1016/j.ifacol.2015.11.309
ISSN: 1474-6670
Publisher: Elsevier
Start Page: 380
End Page: 387
Journal / Book Title: IFAC Proceedings Volumes (IFAC-PapersOnline)
Volume: 48
Issue: 23
Copyright Statement: © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
Conference Name: 5th IFAC Conference on Nonlinear Model Predictive Control
Publication Status: Published
Start Date: 2015-09-17
Finish Date: 2015-09-20
Conference Place: Seville, Spain
Open Access location: http://arxiv.org/abs/1510.01135
Appears in Collections:Electrical and Electronic Engineering
Faculty of Engineering