Close to 1% of the world's electricity is consumed by computer servers. Given that the increased use of electricity raises costs and damages the environment, optimizing the world's computing infrastructure for power consumption is worthwhile. This thesis is one attempt at such an optimization. In particular, I began by building a cluster of 6 Intel Atom based low-power nodes to perform work analogous to data center clusters. Then, I installed a version of Hadoop modified with a novel power management system on the cluster. The power management system uses different algorithms to determine when to turn off idle nodes in the cluster. Using the experimental cluster running a modified Hadoop installation, I performed a series of experiments. These tests assessed various strategies for choosing nodes to suspend across a variety of workloads. The experiments validated that turning off idle nodes can yield power savings. While my experimental procedure caused the apparent throughput to significantly decrease, I argue that using more realistic workloads would have yielded much better throughput with slightly reduced power consumption. Additionally, my analysis of the results, show that the percentage power savings in a larger, more realistically sized cluster would be higher than shown in my experiments.

Ever faster data transmission in wireless communication is desired to satisfy emerging markets for various media services, such as voice, picture and video calls, multimedia messaging, music and video downloads, and even television. With the explosive increase in the use of mobile devices such as cellular phones, PDAs, GPS, and laptop computers, power consumption has become a prime consideration in the design of mobile communication systems. In order to reliably maintain a high rate of transmission and low power consumption, it is imperative that the receiver obtains as much knowledge as possible about the current state of the channel. A more accurate model of wireless communication channels will indisputably help in obtaining more knowledge about the transient channel state, providing a more accurate and efficient reproduction of the transmitted signal, and decreased power consumption by the receiver. With careful choice and consideration of the channel model, systemic optimization based on the selected channel model will improve the system performance of the transmitter and receiver through better encoding and decoding, as well as through better control of transmitted signal's power level. This thesis focuses on understanding the physical and statistical characteristics of wireless channels, and investigates how to represent wireless channels using simple mathematical models. This thesis initially studied a simple time-varying stationary channel, i.e.a multipath fiat fading channel without terminal motion, which is typically used for indoor wireless communication. With an introduction of stochastic differential equations, we derived a first-order AR stochastic process to represent this stationary channel. For a general multipath fiat fading channel with terminal motion, the traditional Clarke's model was then extended by incorporating the effects of fluctuations in the component phases and analyzed statistically. The resulting theoretical power spectrum was shown to fit practical measured spectra, in contrast to the traditional theoretical fiat fading channel spectra (Jakes' spectrum in [19]) . Finally, we developed a state-space model that represents a wireless channel using these modified spectral characteristics. This was achieved by developing a relationship between the state-space model and the theory of a rational transfer function. A novel method for designing a rational transfer function for linear systems was then proposed. In this method, the rational transfer function is represented via the Observable Canonical Form (OCF) to obtain the state-space model, which can be used to represent and simulate a fiat fading wireless channel. The presented state-space approach is simple and provides rapid computation. The present AR and state-space models provide valuable contributions that can be integrated with other algorithms for better system optimization of wireless communication networks. / Thesis / Doctor of Philosophy (PhD)

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The technology in cellular phones, portable computing systems, intelligent- andconnected- devices are evolving in a high pace and in many cases these devices arerequired to operate in a low-power environment. The problem that continues toemerge, is the power consumption in microcontrollers and DSP devices. This issue hasover time become important to solve in order to maximize battery life. To ease thechoice of power efficient microcontrollers, controlled experiments were thereforeperformed with the Cortex-M4, this microcontroller was chosen because of theupgraded hardware, which has led to an appreciable change in both power- and speedefficiency compared to its predecessors.The conclusion presents important points, along with advantages and difficulties toconsider when implementing a DSP application. By comparing different optimizationswith the Floating Point Unit(FPU), Fixed-point and software Floating-point, the resultsshow that there are major differences in power consumption between these threeoptions. Depending on which option and optimization used then the powerconsumption can exceed over 70% more compared to the other options available.

The design, simulation and evaluation of an intelligent and integrated load management system is presented in this dissertation. The objective of this research was to apply modern computer and communication technology to influence customer use of electricity in ways that would produce desired changes in the utility's load shape. Peak clipping (reduction of peak load) using direct load control is the primary application of this research. The prototype computerized communication and control package developed during this work has demonstrated the feasibility of this concept. The load management system consists of a network of computers, data and graphics terminals, controllers, modems and other communication hardware, and the necessary software. The network of interactive computers divides the responsibility of monitoring of meteorological data, electric load, and performing other functions. These functions include: data collection, processing and archiving, load forecasting, load modeling, information display and alarm processing. Each of these functions requires a certain amount of intelligence depending on the sophistication and complication of that function. Also, a high level of reliability has been provided to each function to guarantee an uninterrupted operation of the system. A full scale simulation of this concept was carried out in the laboratory using five microcomputers and the necessary communication hardware. An important and integral part of the research effort is the development of the short-term load forecast, load models and the decision support system using rule-based algorithms and expert systems. Each of these functions has shown the ability to produce more accurate results compared to classical techniques while at the same time requiring much less computing time and historical data. Development of these functions has made the use of microcomputers for constructing an integrated load management system possible and practical. Also, these functions can be applied for other applications in the electric utility industry and maintain their importance and contribution. In addition to that, the use of rule-based algorithms and expert systems promises to yield significant benefits in using microcomputers in the load management area. / Ph. D.

The continuous shrink of transistor sizes has allowed more complex and powerful devices to be implemented in the same area, which provides new capabilities and functionalities. However, this complexity increase comes with a considerable rise in power consumption. This situation is critical in portable devices where the energy budget is limited and, hence, battery lifetime defines the usefulness of the system. Therefore, power consumption has become a major concern in the design of real-time multicore embedded systems. This dissertation proposes several techniques aimed to save energy without sacrifying real-time schedulability in this type of systems. The proposed techniques deal with different main components of the system. In particular, the techniques affect the task partitioner and the scheduler, as well as the memory controller. Some of the techniques are especially tailored for multicores with shared Dynamic Voltage and Frequency Scaling (DVFS) domains. Workload balancing among cores in a given domain has a strong impact on power consumption, since all the cores sharing a DVFS domain must run at the speed required by the most loaded core. In this thesis, a novel workload partitioning algorithm is proposed, namely Loadbounded Resource Balancing (LRB). The proposal allocates tasks to cores to balance a given resource (processor or memory) consumption among cores, improving real-time schedulability by increasing overlapping between processor and memory. However, distributing tasks in this way regardless the individual core utilizations could lead to unfair load distributions. That is, one of the cores could become much loaded than the others. To avoid this scenario, when a given utilization threshold is exceeded, tasks are assigned to the least loaded core. Unfortunately, workload partitioning alone is sometimes not able to achieve a good workload balance among cores. Therefore, this work also explores novel task migration approaches. Two task migration heuristics are proposed. The first heuristic, referred to as Single Option Migration (SOM ), attempts to perform only one migration when the workload changes to improve utilization balance. Three variants of the SOM algorithm have been devised, depending on the point of time the migration attempt is performed: when a task arrives to the system (SOMin), when a task leaves the system (SOMout), and in both cases (SOMin−out). The second heuristic, referred to as Multiple Option Migration (MOM ) explores an additional alternative workload partitioning before performing the migration attempt. Regarding the memory controller, memory controller scheduling policies are devised. Conventional policies used in Non Real-Time (NRT) systems are not appropriate for systems providing support for both Hard Real-Time (HRT) and Soft Real-Time (SRT) tasks. Those policies can introduce variability in the latencies of the memory requests and, hence, cause an HRT deadline miss that could lead to a critical failure of the real-time system. To deal with this drawback, a simple policy, referred to as HR- first, which prioritizes requests of HRT tasks, is proposed. In addition, a more advanced approach, namely ATR-first, is presented. ATR-first prioritizes only those requests of HRT tasks that are necessary to ensure real-time schedulability, improving the Quality of Service (QoS) of SRT tasks. Finally, this thesis also tackles dynamic execution time estimation. The accuracy of this estimation is important to avoid deadline misses of HRT tasks but also to increase QoS in SRT systems. Besides, it can also help to improve the schedulability of the systems and reduce power consumption. The Processor-Memory (Proc-Mem) model, that dynamically predicts the execution time of real-time application for each frequency level, is proposed. This model measures at the first hyperperiod, making use of Performance Monitoring Counters (PMCs) at run-time, the portion of time that each core is performing computation (CPU ), waiting for memory (MEM ), or both (OVERLAP). This information will be used to estimate the execution time at any other working frequency / March Cabrelles, JL. (2014). Dynamic Power-Aware Techniques for Real-Time Multicore Embedded Systems [Tesis doctoral]. Editorial Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/48464

The electric power policies in Nigeria and South Africa are considered the governments’ intention to provide quality and affordable electricity to the people. A comparative study on the electric power policies focuses on the similarities and differences in the policy approaches, the policy issues that affect electrification, and the impact of the policy issues in achieving the goal of universal access to quality and affordable electricity power in both countries. The methodological approach allows for an in depth textual study on the electric power policy documents in both countries. In Nigeria, the government intends to address the massive demand-supply imbalance and achieve the goal of electrification through reforms that focus on private sector-led growth in the sub-sector. In South Africa, the identification of electrification as a public problem by the post apartheid government leads to an integrated policy framework that focuses on balancing economic concerns with social and environmental considerations. The study identifies electricity provision as a social welfare responsibility of the governments in both countries and examines the policy issues in the context of public welfare. In Nigeria, the policy issues are found to be self serving and not in line with sustainable public interest, given the socio-economic challenges. As a result they, exert less impact on achieving the goal of universal access to quality and affordable electricity in the country. In South Africa, good governance in the sub-sector has enabled the identification of policy issues in line with sustainable public interests of social equity, poverty alleviation and environmental sustainability; and government using public administration agencies to play a key role in service delivery. Recommendations of the study mainly derive from the South African experience on electrification, and are intended to offer some policy-lessons to Nigeria in the sub-sector. The study contributes to new knowledge in the discipline of public administration by opening up new vistas for a comparative analysis of electric power policy issues in both countries in the context of public welfare. Besides, a comparative study on electrification in Nigeria and South Africa from a policy angle contributes to the existing knowledge base in the discipline.

Power management is an important part when developing a smartwatch to meet user expectations of battery lifetime and functionality. To develop a good power management structure, measurements of the device is crucial to understand the behavior and power consumption of hardware and software. In this thesis, measurements have been made to hardware connected to the PineTime smartwatch to get knowledge of its behavior and power consumption. A structure for hardware and software was developed to enable the possibility to debug, flash, and perform measurements. The purpose of the measurements is to find possible areas of improvement that have the potential to lower the overall power consumption of the device. From the measurements, the st7789V display driver and the hardware design was detected to have a high power consumption. Bluetooth was also found to have an odd behavior in terms of power consumption. From these findings, suggestions on how to lower the power consumption are presented.

published_or_final_version / Environmental Management / Master / Master of Science in Environmental Management

M.A. (Environmental Management) / The Small Micro Medium Enterprise (SMME) sector plays a critical role in the economy of South Africa by reducing poverty and providing employment. South Africa has about 6 million SMMEs that employ more than 61% of citizens and contribute about 37% to the Gross Domestic Product (GDP). The growth and development of the sector over the years has however been compromised and threatened by the shortage of electricity and increasing electricity tariffs. Whereas large companies can manage to afford the adoption of energy efficiency measures in order to reduce energy costs, SMMEs have limited resources and may therefore struggle to afford energy efficiency adoption. However, there is a lack of scientific documentation about energy efficiency adoption amongst manufacturing SMMEs (M-SMMEs) in South Africa. The purpose of the research is to therefore assess the state of energy efficiency adoption amongst M-SMMEs, despite the challenges they experience. To achieve this research goal, the study surveyed and characterised the extent of energy efficiency adoption and also unveiled the drivers and barriers to energy efficiency adoption. Data were collected by means of surveys, making use of questionnaires. A sample of 30 firms was selected for the research. Almost all (96%) firms perceived EE as an opportunity. However, only 50% had adopted EE measures. Key drivers to adoption included the motive to reduce production costs, mitigate the impact of increasing electricity prices, gain competitive advantage and payback period. The gap between EE perception and adoption was caused by several barriers like the lack of finance, skills and time. Among non-adopters, 60% indicated their willingness to be trained in EE. The study therefore recommends improved capacity building through workshops for M-SMMEs.