Device drivers are Operating Systems (OS) extensions that enable the use of I/O devices in computing systems. However, studies have identified drivers as an Achilles’ heel of system reliability, their high fault rate accounting for a significant portion of system failures. Consequently, significant effort has been directed towards improving system robustness by protecting system components (e.g., OS kernel, I/O devices, etc.) from the harmful effects of driver faults. In contrast to prior techniques which focused on preventing unsafe driver interactions (e.g., with the OS kernel), my thesis is that checking a driver’s execution for correctness violations results in the detection and mitigation of more faults. To validate this thesis, I present Guardrail, a flexible and powerful framework that enables instruction-grained dynamic analysis (e.g., data race detection) of unmodified kernel-mode driver binaries to safeguard I/O operations and devices from driver faults. Guardrail decouples the analysis tool from driver execution to improve performance, and runs it in user-space to simplify the deployment of new tools. Moreover, Guardrail leverages virtualization to be transparent to both the driver and device, and enable support for arbitrary driver/device combinations. To demonstrate Guardrail’s generality, I implemented three novel dynamic checking tools within the framework for detecting memory faults, data races and DMA faults in drivers. These tools found 25 serious bugs, including previously unknown bugs, in Linux storage and network drivers. Some of the bugs existed in several Linux (and driver) releases, suggesting their elusiveness to existing approaches. Guardrail easily detected these bugs using common driver workloads. Finally, I present an evaluation of using Guardrail to protect network and storage I/O operations from memory faults, data races and DMA faults in drivers. The results show that with hardware-assisted logging for decoupling the heavyweight analyses from driver execution, standard I/O workloads generally experienced negligible slowdown on their end-to-end performance. In conclusion, Guardrail’s high fidelity fault detection and efficient monitoring performance makes it a promising approach for improving the resilience of computing systems to the wide variety of driver faults.

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<p>Self-tuning has been an elusive goal for operating systems and is becoming a pressing issue for modern operating systems. Well-trained system administrators are able to tune an operating system to achieve better system performance for a specific system class. Unfortunately, the system class can change when the running applications change. The model for self-tuning operating system is based on a monitor-classify-adjust loop. The idea of this loop is to continuously monitor certain performance metrics, and whenever these change, the system determines the new system class and dynamically adjusts tuning parameters for this new class. This thesis described KernTune, a prototype tool that identifies the system class and improves system performance automatically. A key aspect of KernTune is the notion of Artificial Intelligence oriented performance tuning. Its uses a support vector machine to identify the system class, and tunes the operating system for that specific system class. This thesis presented design and implementation details for KernTune. It showed how KernTune identifies a system class and tunes the operating system for improved performance.</p>

While the focus of this work is on turbo equalization, there is also an examination of equalization techniques including MMSE linear and DFE equalizers and Precoding. The losses and capacity associated with the ISI channel are also examined. Iterative decoding of concatenated codes is briefly reviewed and the MAP algorithm is explained.

Systems code is almost universally written in the C programming language or a variant. C has a very low level of type and memory abstraction and formal reasoning about C systems code requires a memory model that is able to capture the semantics of C pointers and types. At the same time, proof-based verification demands abstraction, in particular from the aliasing and frame problems. In this thesis, we study the mechanisation of a series of models, from semantic to separation logic, for achieving this abstraction when performing interactive theorem-prover based verification of C systems code in higher- order logic. We do not commit common oversimplifications, but correctly deal with C's model of programming language values and the heap, while developing the ability to reason abstractly and efficiently. We validate our work by demonstrating that the models are applicable to real, security- and safety-critical code by formally verifying the memory allocator of the L4 microkernel. All formalisations and proofs have been developed and machine-checked in the Isabelle/HOL theorem prover.

Thesis (M.S.S.I.S.)--Regis University, Denver, Colo., 2007. / Title from PDF title page (viewed on Nov. 02, 2007). Includes bibliographical references.

Thesis (M.S.)--Rochester Institute of Technology, 1990. / Spine title: Authentication models. Bibliography. p. [167-175].

Thesis (M.Sc.) - Carleton University, 2006. / Includes bibliographical references (p. 143-147). Also available in electronic format on the Internet.

Thesis (M.S.)--University of Copenhagen, 1995. / "Department of Computer Science, University of Copenhagen"--Cover. "Summer 1995." Includes bibliographical references.

Project (M.S.)--Virginia Polytechnic Institute and State University. M.S. 1992. / Vita. Abstract. Includes bibliographical references (leaves 202-204). Also available via the Internet.