Energy is a critical resource in all types of computing systems from servers, where energy costs dominate data centre expenses and carbon footprints, to embedded systems, where the system's battery life limits the device's functionality. In their efforts to reduce the energy use of these system's hardware manufacturers have implemented features which allow a reduced energy consumption under software control. This thesis shows that managing these settings is a more complex problem than previously considered. Where much (but not all) of the previous academic research investigates unrealistic scenarios, this thesis presents a solution to managing the power on varying hardware. Instead of making unrealistic assumptions, we extract a model from empirical data and characterise that model. Our models estimate the effect of different power management settings on the behaviour of the hardware platform, taking into account the workload, platform and environmental characteristics, but without any kind of a-priori knowledge of the specific workloads being run. These models encapsulate a system's knowledge of the platform. We also developed a \emph{generalised energy-delay} policy which allows us to quickly express the instantaneous importance of both performance and energy to the system. It allows us to select a power management strategy from a number of options. This thesis shows, by evaluation on a number of platforms, that our implementation, Koala, can accurately meet energy and performance goals. In some cases, our system saves 26\% of the system-level energy required for a task, while losing only 1\% performance. This is nearly 46\% of the dynamic energy. Taking advantage of all energy-saving opportunities requires detailed platform, workload and environmental information. Given this knowledge, we reach the exciting conclusion that near optimal power management is possible on real operating systems, with real platforms and real workloads.

This dissertation is dedicated to the problem of device driver reliability. Software defects in device drivers constitute the biggest source of failure in operating systems, causing significant damage through downtime and data loss. Previous research on driver reliability has concentrated on detecting and mitigating defects in existing drivers using static analysis or runtime isolation. In contrast, this dissertation presents an approach to reducing the number of defects through an improved device driver architecture and development process. In analysing factors that contribute to driver complexity and induce errors, I show that a large proportion of errors are due to two key shortcomings in the device-driver architecture enforced by current operating systems: poorly-defined communication protocols between drivers and the operating system, which confuse developers and lead to protocol violations, and a multithreaded model of computation, which leads to numerous race conditions and deadlocks. To address the first shortcoming, I propose to describe driver protocols using a formal, state-machine based, language, which avoids confusion and ambiguity and helps driver writers implement correct behaviour. The second issue is addressed by abandoning multithreading in drivers in favour of a more disciplined event-driven model of computation, which eliminates most concurrency-related faults. These improvements reduce the number of defects without radically changing the way drivers are developed. In order to further reduce the impact of human error on driver reliability, I propose to automate the driver development process by synthesising the implementation of a driver from the combination of three formal specifications: a device-class specification that describes common properties of a class of similar devices, a device specification that describes a concrete representative of the class, and an operating system interface specification that describes the communication protocol between the driver and the operating system. This approach allows those with the most appropriate skills and knowledge to develop specifications: device specifications are developed by device manufacturers, operating system specifications by the operating system designers. The device-class specification is the only one that requires understanding of both hardware and software-related issues. However writing such a specification is a one-off task that only needs to be completed once for a class of devices. This approach also facilitates the reuse of specifications: a single operating-system specification can be combined with many device specifications to synthesise drivers for multiple devices. Likewise, since device specifications are independent of any operating system, drivers for different systems can be synthesised from a single device specification. As a result, the likelihood of errors due to incorrect specifications is reduced because these specifications are shared by many drivers. I demonstrate that the proposed techniques can be incorporated into existing operating systems without sacrificing performance or functionality by presenting their implementation in Linux. This implementation allows drivers developed using these techniques to coexist with conventional Linux drivers, providing a gradual migration path to more reliable drivers.

Energy is a critical resource in all types of computing systems from servers, where energy costs dominate data centre expenses and carbon footprints, to embedded systems, where the system's battery life limits the device's functionality. In their efforts to reduce the energy use of these system's hardware manufacturers have implemented features which allow a reduced energy consumption under software control. This thesis shows that managing these settings is a more complex problem than previously considered. Where much (but not all) of the previous academic research investigates unrealistic scenarios, this thesis presents a solution to managing the power on varying hardware. Instead of making unrealistic assumptions, we extract a model from empirical data and characterise that model. Our models estimate the effect of different power management settings on the behaviour of the hardware platform, taking into account the workload, platform and environmental characteristics, but without any kind of a-priori knowledge of the specific workloads being run. These models encapsulate a system's knowledge of the platform. We also developed a \emph{generalised energy-delay} policy which allows us to quickly express the instantaneous importance of both performance and energy to the system. It allows us to select a power management strategy from a number of options. This thesis shows, by evaluation on a number of platforms, that our implementation, Koala, can accurately meet energy and performance goals. In some cases, our system saves 26\% of the system-level energy required for a task, while losing only 1\% performance. This is nearly 46\% of the dynamic energy. Taking advantage of all energy-saving opportunities requires detailed platform, workload and environmental information. Given this knowledge, we reach the exciting conclusion that near optimal power management is possible on real operating systems, with real platforms and real workloads.

Thesis (Ph. D.)--Ohio University, August, 1997. / Title from PDF t.p.

Thesis (M.S.)--Rochester Institute of Technology, 1989. / Includes bibliographical references (leaf 111).

Thesis (M.S.)--Ohio University, November, 2002. / Title from PDF t.p. Includes bibliographical references (leaves 81-86).

Thesis (Ph.D.)--George Mason University, 2008. / Vita: p. 192. Thesis director: Alexander H. Levis. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Information Technology. Title from PDF t.p. (viewed Jan. 11, 2009). Includes bibliographical references (p. 184-191). Also issued in print.

Thesis (M. S.)--Mechanical Engineering, Georgia Institute of Technology, 2008. / Committee Chair: Jeter, Sheldon; Committee Member: Ghiaasiaan, S. Mostafa; Committee Member: Joshi, Yogendra.

Thesis (M.Sc.) - Carleton University, 2002. / Includes bibliographical references (p. 92-94). Also available in electronic format on the Internet.

Thesis (M. Eng.)--Carleton University, 2004. / Includes bibliographical references (p. 106-109). Also available in electronic format on the Internet.