Modern data centers must provide performance assurance for complex system software such as web applications. In addition, the power consumption of data centers needs to be minimized to reduce operating costs and avoid system overheating. In recent years, more and more data centers start to adopt server virtualization strategies for resource sharing to reduce hardware and operating costs by consolidating applications previously running on multiple physical servers onto a single physical server. In this dissertation, several power efficient algorithms are proposed to effectively reduce server power consumption while achieving the required application-level performance for virtualized servers. First, at the server level this dissertation proposes two control solutions based on dynamic voltage and frequency scaling (DVFS) technology and request batching technology. The two solutions share a performance balancing technique that maintains performance balancing among all virtual machines so that they can have approximately the same performance level relative to their allowed peak values. Then, when the workload intensity is light, we adopt the request batching technology by using a controller to determine the time length for periodically batching incoming requests and putting the processor into sleep mode. When the workload intensity changes from light to moderate, request batching is automatically switched to DVFS to increase the processor frequency for performance guarantees. Second, at the datacenter level, this dissertation proposes a performance-controlled power optimization solution for virtualized server clusters with multi-tier applications. The solution utilizes both DVFS and server consolidation strategies for maximized power savings by integrating feedback control with optimization strategies. At the application level, a multi-input-multi-output controller is designed to achieve the desired performance for applications spanning multiple VMs, on a short time scale, by reallocating the CPU resources and DVFS. At the cluster level, a power optimizer is proposed to incrementally consolidate VMs onto the most power-efficient servers on a longer time scale. Finally, this dissertation proposes a VM scheduling algorithm that exploits core performance heterogeneity to optimize the overall system energy efficiency. The four algorithms at the three different levels are demonstrated with empirical results on hardware testbeds and trace-driven simulations and compared against state-of-the-art baselines.

The majority of current internet traffic is based on TCP. With the emergence of new applications, especially new multimedia applications, however, UDP-based traffic is expected to increase. Furthermore, multimedia applications have sparkled the development of protocols responding to congestion while behaving differently from TCP. As a result, network traffc is expected to become more and more diverse. The increasing link capacity further stimulates new applications utilizing higher bandwidths of future. Besides the traffic diversity, the network is also evolving around new technologies. These trends in the Internet motivate our research work. In this dissertation, modeling and estimation techniques of heterogeneous traffic at a router are presented. The idea of the presented techniques is that if the observed queue length and packet drop probability do not match the predictions from a model of responsive (TCP) traffic, then the error must come from non-responsive traffic; it can then be used for estimating the proportion of non-responsive traffic. The proposed scheme is based on the queue length history, packet drop history, expected TCP and queue dynamics. The effectiveness of the proposed techniques over a wide range of traffic scenarios is corroborated using NS-2 based simulations. Possible applications based on the estimation technique are discussed. The implementation of the estimation technique in the Linux kernel is presented in order to validate our estimation technique in a realistic network environment.

The solution of optimal control problems through the Hamilton-Jacobi-Bellman (HJB) equation offers guaranteed satisfaction of both the necessary and sufficient conditions for optimality. However, finding an exact solution to the HJB equation is a near impossible task for many optimal control problems. This thesis presents an approximation method for solving finite-horizon optimal control problems involving nonlinear dynamical systems. The method uses finite-order approximations of the partial derivatives of the cost-to-go function, and successive higher-order differentiations of the HJB equation. Natural byproducts of the proposed method provide sensitivities of the controls to changes in the initial states, which can be used to approximate the solution to neighboring optimal control problems. For highly nonlinear problems, the method is modified to calculate control sensitivities about a nominal trajectory. In this framework, the method is shown to provide accurate control sensitivities at much lower orders of approximation. Several numerical examples are presented to illustrate both applications of the approximation method.

Quantum information and quantum computation are linked by a common mathematical and physical framework of quantum mechanics. The manipulation of the predicted dynamics and its optimization is known as quantum control. Many techniques, originating in the study of nuclear magnetic resonance, have found common usage in methods for processing quantum information and steering physical systems into desired states. This thesis expands on these techniques, with careful attention to the regime where competing effects in the dynamics are present, and no semi-classical picture exists where one effect dominates over the others. That is, the transition between the diabatic and adiabatic error regimes is examined, with the use of such techniques as time-dependent diagonalization, interaction frames, average-Hamiltonian expansion, and numerical optimization with multiple time-dependences. The results are applied specifically to superconducting systems, but are general and improve on existing methods with regard to selectivity and crosstalk problems, filtering of modulation of resonance between qubits, leakage to non-compuational states, multi-photon virtual transitions, and the strong driving limit.

An investigation focusing on methods of estimation of robustness of nominally linear dynamic systems with unstructured uncertainties was performed. The algorithm proposed involves the consideration of an associated system, selection, and subsequent development, of Liapunov function candidate and integration of their derivatives along the solution trajectory. A nominally linear multi-dimensional dynamic system is considered with unstructured, nonlinear, time-varying and bounded perturbations. The examples illustrate the success of the method: better estimates of the bounds, than those which results from traditional approaches were obtained. Robustness of linear, time-invariant systems subject to nonlinear, time-varying perturbations has been a matter of considerable research interest recently. Design of conventional state-feedback controllers requires knowledge of the bounds for disturbances. The knowledge of disturbance bounds is also important in adaptive control and control of nonlinear & uncertain systems. Numerous applications can be found in the fields of automation, aircraft control, manipulator trajectory control, etc. The technique for the determination of robust stability bounds proposed in this paper can be utilized effectively in computerized robust control system design. / Graduation date: 1992

An adaptive decentralized nonlinear controller for a robot manipulator is presented in this thesis. Based on the adaptive control schemes designed by Seraji [18], Dai [30], and Jimenez [31], we redesigned and further simplified the control algorithm and, as a consequence, we achieved better path tracking performance. The proposed adaptive controller is made of a PD feedback controller which has time varying gains, a feedforward compensator based on the idea of inverse dynamics, and an auxiliary signal. Due to its adaptive structure, the controller shows robustness against disturbances and unmodeled dynamics. In order to ensure asymptotic tracking we select a Lyapunov function such that the controller forces the negative definiteness of the time derivative of such a Lyapunov function. To do this, the tracking position and velocity error are penalized and used as a part of the adaptive control gain. The main advantages of this scheme are the comparably faster convergence of tracking error, relatively simpler structure, and smoother control activity. This controller only requires the position and angular speed measurement, it does not require any knowledge about the mathematical model of the robot manipulator. Simulation shows the capacity of this controller and its robustness against disturbances. / Graduation date: 1993

Vaccinations provide protection against deadly diseases and children are scheduled to receive many immunization injections before the age of six. However, painful procedures, such as immunizations cause negative short- and long-term consequences for children. The Gate Control Theory of Pain suggests that physical interventions may be helpful, but they have not yet been validated as an effective intervention to manage children’s acute pain. This randomized trial examined the effectiveness of the ShotBlocker®, a physical intervention designed to decrease children’s injection pain, in a sample of 89 4- to 12- year-old children receiving immunizations at a pediatric practice. An ANOVA revealed no significant effect of treatment group (Typical Care Control, Placebo, and ShotBlocker®) on any measure of child distress. Clinical and theoretical implications are discussed.

Vocational psychologists have theorized that the congruence between self and occupations is the key to find fulfilling careers for individuals (Vondracek & Porfeli, 2011). However, the typical use of vocational interests to capture information about the self has been limited because it does not disentangle identity and work preferences in people’s responses in vocational assessments. People cannot be fully informed of careers most fitting to them if the vocational assessment does not capture distinct information about their identity. In this study, we strive to disentangle identity from preferences by including affective identity, which is sentiments that people hold towards themselves, as a predictor for career intent. Focusing on the context of entrepreneurship as a career, we examine how the congruence of affective identity and affective ratings of entrepreneurs provide additional information in predicting entrepreneurial intent beyond work preferences congruence. We invited undergraduate students from a Canadian University to complete an online-survey for an extra credit in their psychology course. We examined the impact of different congruence form of intent by including linear and polynomial terms of self and entrepreneur ratings when conducting a hierarchical linear regression. In general, we found support for the validity of our developed measure and demonstrated that contemporary congruence forms based on factors of affective identity brings new information in career choice perception. Affective identity accounts for unique predictability of self perception beyond vocational preference, which suggests the potential use of affective identity for career search feedback.

The solution of optimal control problems through the Hamilton-Jacobi-Bellman (HJB) equation offers guaranteed satisfaction of both the necessary and sufficient conditions for optimality. However, finding an exact solution to the HJB equation is a near impossible task for many optimal control problems. This thesis presents an approximation method for solving finite-horizon optimal control problems involving nonlinear dynamical systems. The method uses finite-order approximations of the partial derivatives of the cost-to-go function, and successive higher-order differentiations of the HJB equation. Natural byproducts of the proposed method provide sensitivities of the controls to changes in the initial states, which can be used to approximate the solution to neighboring optimal control problems. For highly nonlinear problems, the method is modified to calculate control sensitivities about a nominal trajectory. In this framework, the method is shown to provide accurate control sensitivities at much lower orders of approximation. Several numerical examples are presented to illustrate both applications of the approximation method.

Even though software testing takes more than 40% of the total development cost, especially the massive amount of devoted efforts and resources, software testing is nevertheless the least respectable part in many software projects when comparing to design and development. The challenge becomes bigger as the software becomes more complicated. This has been further compounded by lacking of appropriate attention and suitable resource allocation. As a result, it becomes a global concern and issue on how software testing can be more effective to guard the software quality control. Software testing techniques have evolved for decades and almost reached the maturity level. Why software testing is not successful is mostly related to lacking of enough respect by management. Therefore, creating a software testing institution is necessary to put enough control on the process and to establish a regulation for implementation. This research employs software testing theory standards, institutional theory and control theory to come out with an ideal software testing institution. A case study is used to validate the ideal software testing institution. Software testing theories are to create a software testing process, which can be divided into planning, design and execution phases. Institutional theory is to create a regulation and as a basis for implementation. Control theory is to empower control mechanisms on testing to ensure the progress comply with the final goals. The ideal software testing institution provided by this research is appropriate for joint-development outsourcing project. When both customer and vendor are involved in testing, it¡¦s recommended to define separate test plans with consistent schedule to prevent from resource idle or inconsistency between software and documentation. Since both parties will produce software source code and documentation, it¡¦s also recommended to define the working model and version control rules as a basis for cooperation. Finally, Employing configuration management can avoid unnecessary conflicts and confusion.