Theses and Dissertations from UMD
Permanent URI for this communityhttp://hdl.handle.net/1903/2
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item Runtime Adaptation in Embedded Computing Systems using Markov Decision Processes(2019) Sapio, Adrian; Bhattacharyya, Shuvra S; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)During the design and implementation of embedded computing systems (ECSs), engineers must make assumptions on how the system will be used after being built and deployed. Traditionally, these important decisions were made at design time for a fleet of ECSs prior to deployment. In contrast to this approach, this research explores and develops techniques to enable adaptation of ECSs at runtime to the environments and applications in which they operate. Adaptation is enabled such that the usage assumptions and performance optimization decisions can be made autonomously at runtime in the deployed system. This thesis utilizes Markov Decision Processes (MDPs), a powerful and well established mathematical framework used for decision making under uncertainty, to control computing systems at runtime. The resulting control is performed in ways that are more dynamic, robust and adaptable than alternatives in many scenarios. The techniques developed in this thesis are first applied to a reconfigurable embedded digital signal processing system. In this effort, several challenges are encountered and resolved using novel approaches. Through extensive simulations and a prototype implementation, the robustness of the adaptation is demonstrated in comparison with the prior state-of-the-art. The thesis continues by developing an efficient algorithm for conversion of MDP models to actionable control policies - a required step known as solving the MDP. The solver algorithm is developed in the context of ECSs that contain general purpose embedded GPUs (graphics processing units). The novel solver algorithm, Sparse Parallel Value Iteration (SPVI), makes use of the parallel processing capabilities provided by such GPUs, and also exploits the sparsity that typically exists in MDPs when used to model and control ECSs. To extend the applicability of the runtime adaptation techniques to smaller and more strictly resource constrained ECSs, another solver - Sparse Value Iteration (SVI) is developed for use on microcontrollers. The method is explored in a detailed case study involving a cellular (LTE-M) connected sensor that adapts to varying communications profiles. The case study reveals that the proposed adaptation framework outperforms a competing approach based on Reinforcement Learning (RL) in terms of robustness and adaptation, while consuming comparable resource requirements. Finally, the thesis concludes by analyzing the various logistical challenges that exist when deploying MDPs on ECSs. In response to these challenges, the thesis contributes an open source software package to the engineering community. The package contains libraries of MDP solvers, parsers, datasets and reference solutions, which provide a comprehensive infrastructure for exploring the trade-offs among existing embedded MDP techniques, and experimenting with novel approaches.Item Comparing Regime-Switching Models In Time Series: Logistic Mixtures vs. Markov Switching(2007-05-16) Paliouras, Dimitrios V.; Kedem, Benjamin; Applied Mathematics and Scientific Computation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The purpose of this thesis is to review several related regime-switching time series models. Specifically, we use simulated data to compare models where the unobserved state vector follows a Markov process against an independent logistic mixture process. We apply these techniques to crude oil and heating oil futures prices using several explanatory variables to estimate the unobserved regimes. We find that crude oil is characterized by regime switching, where prices alternate between a high volatility state with low returns and significant mean reversion and a low volatility state with positive returns and some trending. The spread between one-month and three-month futures prices is an important determinant in the dynamics of crude oil prices.