Scheduling policies for open soft real-time systems must be able to balance the competing concerns of meeting their objectives under exceptional conditions while achieving good performance in the average case. Balancing these concerns requires modeling strategies that represent the range of possible task behaviors, and solution techniques that are capable of effectively managing uncertainty in order to discover scheduling policies that are effective across the range of system modes. We develop methods for solving a particular class of task scheduling problems in an open soft real-time setting involving repeating, non-preemptable tasks that contend for a single shared resource. We enforce timeliness by optimizing performance with respect to the proportional progress of tasks in the system.
We model this scheduling problem as an infinite-state Markov decision process, and provide guarantees regarding the existence of optimal solutions to this problem. We derive several methods for approximating optimal scheduling policies and provide theoretical justification and empirical evidence that these solutions are good approximations to the optimal solution. We consider cases in which task models are known, and adapt reinforcement learning methods to learn task models when they are not available.
|Advisor:||Smart, William D.|
|Commitee:||Chen, Yixin, Gill, Christopher, Goldman, Sally, Szepesvari, Csaba, Thoroughman, Kurt|
|School:||Washington University in St. Louis|
|School Location:||United States -- Missouri|
|Source:||DAI-B 70/12, Dissertation Abstracts International|
|Keywords:||Markov decision processes, Real-time systems, Reinforcement learning, Scheduling|
Copyright in each Dissertation and Thesis is retained by the author. All Rights Reserved
The supplemental file or files you are about to download were provided to ProQuest by the author as part of a
dissertation or thesis. The supplemental files are provided "AS IS" without warranty. ProQuest is not responsible for the
content, format or impact on the supplemental file(s) on our system. in some cases, the file type may be unknown or
may be a .exe file. We recommend caution as you open such files.
Copyright of the original materials contained in the supplemental file is retained by the author and your access to the
supplemental files is subject to the ProQuest Terms and Conditions of use.
Depending on the size of the file(s) you are downloading, the system may take some time to download them. Please be