On the leakage-power modeling for optimal server operation

Leakage power consumption is a com- ponent of the total power consumption in data cen- ters that is not traditionally considered in the set- point temperature of the room. However, the effect of this power component, increased with temperature, can determine the savings associated with the careful management of the cooling system, as well as the re- liability of the system. The work presented in this paper detects the need of addressing leakage power in order to achieve substantial savings in the energy consumption of servers. In particular, our work shows that, by a careful detection and management of two working regions (low and high impact of thermal- dependent leakage), energy consumption of the data- center can be optimized by a reduction of the cooling budget.

Leakage temperature dependency aware real-time scheduling for power and thermal optimization

Catering to society's demand for high performance computing, billions of transistors are now integrated on IC chips to deliver unprecedented performances. With increasing transistor density, the power consumption/density is growing exponentially. The increasing power consumption directly translates to the high chip temperature, which not only raises the packaging/cooling costs, but also degrades the performance/reliability and life span of the computing systems. Moreover, high chip temperature also greatly increases the leakage power consumption, which is becoming more and more significant with the continuous scaling of the transistor size. As the semiconductor industry continues to evolve, power and thermal challenges have become the most critical challenges in the design of new generations of computing systems. ^ In this dissertation, we addressed the power/thermal issues from the system-level perspective. Specifically, we sought to employ real-time scheduling methods to optimize the power/thermal efficiency of the real-time computing systems, with leakage/ temperature dependency taken into consideration. In our research, we first explored the fundamental principles on how to employ dynamic voltage scaling (DVS) techniques to reduce the peak operating temperature when running a real-time application on a single core platform. We further proposed a novel real-time scheduling method, “M-Oscillations” to reduce the peak temperature when scheduling a hard real-time periodic task set. We also developed three checking methods to guarantee the feasibility of a periodic real-time schedule under peak temperature constraint. We further extended our research from single core platform to multi-core platform. We investigated the energy estimation problem on the multi-core platforms and developed a light weight and accurate method to calculate the energy consumption for a given voltage schedule on a multi-core platform. Finally, we concluded the dissertation with elaborated discussions of future extensions of our research. ^

Leakage Temperature Dependency Aware Real-Time Scheduling for Power and Thermal Optimization

Catering to society’s demand for high performance computing, billions of transistors are now integrated on IC chips to deliver unprecedented performances. With increasing transistor density, the power consumption/density is growing exponentially. The increasing power consumption directly translates to the high chip temperature, which not only raises the packaging/cooling costs, but also degrades the performance/reliability and life span of the computing systems. Moreover, high chip temperature also greatly increases the leakage power consumption, which is becoming more and more significant with the continuous scaling of the transistor size. As the semiconductor industry continues to evolve, power and thermal challenges have become the most critical challenges in the design of new generations of computing systems. In this dissertation, we addressed the power/thermal issues from the system-level perspective. Specifically, we sought to employ real-time scheduling methods to optimize the power/thermal efficiency of the real-time computing systems, with leakage/ temperature dependency taken into consideration. In our research, we first explored the fundamental principles on how to employ dynamic voltage scaling (DVS) techniques to reduce the peak operating temperature when running a real-time application on a single core platform. We further proposed a novel real-time scheduling method, “M-Oscillations” to reduce the peak temperature when scheduling a hard real-time periodic task set. We also developed three checking methods to guarantee the feasibility of a periodic real-time schedule under peak temperature constraint. We further extended our research from single core platform to multi-core platform. We investigated the energy estimation problem on the multi-core platforms and developed a light weight and accurate method to calculate the energy consumption for a given voltage schedule on a multi-core platform. Finally, we concluded the dissertation with elaborated discussions of future extensions of our research.

Enhanced Race-to-halt: a leakage-aware energy management approach for dynamic priority systems

With progressing CMOS technology miniaturization, the leakage power consumption starts to dominate the dynamic power consumption. The recent technology trends have equipped the modern embedded processors with the several sleep states and reduced their overhead (energy/time) of the sleep transition. The dynamic voltage frequency scaling (DVFS) potential to save energy is diminishing due to efficient (low overhead) sleep states and increased static (leakage) power consumption. The state-of-the-art research on static power reduction at system level is based on assumptions that cannot easily be integrated into practical systems. We propose a novel enhanced race-to-halt approach (ERTH) to reduce the overall system energy consumption. The exhaustive simulations demonstrate the effectiveness of our approach showing an improvement of up to 8 % over an existing work.

Power and thermal aware scheduling for real-time computing systems

Over the past few decades, we have been enjoying tremendous benefits thanks to the revolutionary advancement of computing systems, driven mainly by the remarkable semiconductor technology scaling and the increasingly complicated processor architecture. However, the exponentially increased transistor density has directly led to exponentially increased power consumption and dramatically elevated system temperature, which not only adversely impacts the system's cost, performance and reliability, but also increases the leakage and thus the overall power consumption. Today, the power and thermal issues have posed enormous challenges and threaten to slow down the continuous evolvement of computer technology. Effective power/thermal-aware design techniques are urgently demanded, at all design abstraction levels, from the circuit-level, the logic-level, to the architectural-level and the system-level. ^ In this dissertation, we present our research efforts to employ real-time scheduling techniques to solve the resource-constrained power/thermal-aware, design-optimization problems. In our research, we developed a set of simple yet accurate system-level models to capture the processor's thermal dynamic as well as the interdependency of leakage power consumption, temperature, and supply voltage. Based on these models, we investigated the fundamental principles in power/thermal-aware scheduling, and developed real-time scheduling techniques targeting at a variety of design objectives, including peak temperature minimization, overall energy reduction, and performance maximization. ^ The novelty of this work is that we integrate the cutting-edge research on power and thermal at the circuit and architectural-level into a set of accurate yet simplified system-level models, and are able to conduct system-level analysis and design based on these models. The theoretical study in this work serves as a solid foundation for the guidance of the power/thermal-aware scheduling algorithms development in practical computing systems.^

Proactive and reactive thermal aware optimization techniques to minimize the environmental impact of data centers

Los Centros de Datos se encuentran actualmente en cualquier sector de la economía mundial. Están compuestos por miles de servidores, dando servicio a los usuarios de forma global, las 24 horas del día y los 365 días del año. Durante los últimos años, las aplicaciones del ámbito de la e-Ciencia, como la e-Salud o las Ciudades Inteligentes han experimentado un desarrollo muy significativo. La necesidad de manejar de forma eficiente las necesidades de cómputo de aplicaciones de nueva generación, junto con la creciente demanda de recursos en aplicaciones tradicionales, han facilitado el rápido crecimiento y la proliferación de los Centros de Datos. El principal inconveniente de este aumento de capacidad ha sido el rápido y dramático incremento del consumo energético de estas infraestructuras. En 2010, la factura eléctrica de los Centros de Datos representaba el 1.3% del consumo eléctrico mundial. Sólo en el año 2012, el consumo de potencia de los Centros de Datos creció un 63%, alcanzando los 38GW. En 2013 se estimó un crecimiento de otro 17%, hasta llegar a los 43GW. Además, los Centros de Datos son responsables de más del 2% del total de emisiones de dióxido de carbono a la atmósfera. Esta tesis doctoral se enfrenta al problema energético proponiendo técnicas proactivas y reactivas conscientes de la temperatura y de la energía, que contribuyen a tener Centros de Datos más eficientes. Este trabajo desarrolla modelos de energía y utiliza el conocimiento sobre la demanda energética de la carga de trabajo a ejecutar y de los recursos de computación y refrigeración del Centro de Datos para optimizar el consumo. Además, los Centros de Datos son considerados como un elemento crucial dentro del marco de la aplicación ejecutada, optimizando no sólo el consumo del Centro de Datos sino el consumo energético global de la aplicación. Los principales componentes del consumo en los Centros de Datos son la potencia de computación utilizada por los equipos de IT, y la refrigeración necesaria para mantener los servidores dentro de un rango de temperatura de trabajo que asegure su correcto funcionamiento. Debido a la relación cúbica entre la velocidad de los ventiladores y el consumo de los mismos, las soluciones basadas en el sobre-aprovisionamiento de aire frío al servidor generalmente tienen como resultado ineficiencias energéticas. Por otro lado, temperaturas más elevadas en el procesador llevan a un consumo de fugas mayor, debido a la relación exponencial del consumo de fugas con la temperatura. Además, las características de la carga de trabajo y las políticas de asignación de recursos tienen un impacto importante en los balances entre corriente de fugas y consumo de refrigeración. La primera gran contribución de este trabajo es el desarrollo de modelos de potencia y temperatura que permiten describes estos balances entre corriente de fugas y refrigeración; así como la propuesta de estrategias para minimizar el consumo del servidor por medio de la asignación conjunta de refrigeración y carga desde una perspectiva multivariable. Cuando escalamos a nivel del Centro de Datos, observamos un comportamiento similar en términos del balance entre corrientes de fugas y refrigeración. Conforme aumenta la temperatura de la sala, mejora la eficiencia de la refrigeración. Sin embargo, este incremente de la temperatura de sala provoca un aumento en la temperatura de la CPU y, por tanto, también del consumo de fugas. Además, la dinámica de la sala tiene un comportamiento muy desigual, no equilibrado, debido a la asignación de carga y a la heterogeneidad en el equipamiento de IT. La segunda contribución de esta tesis es la propuesta de técnicas de asigación conscientes de la temperatura y heterogeneidad que permiten optimizar conjuntamente la asignación de tareas y refrigeración a los servidores. Estas estrategias necesitan estar respaldadas por modelos flexibles, que puedan trabajar en tiempo real, para describir el sistema desde un nivel de abstracción alto. Dentro del ámbito de las aplicaciones de nueva generación, las decisiones tomadas en el nivel de aplicación pueden tener un impacto dramático en el consumo energético de niveles de abstracción menores, como por ejemplo, en el Centro de Datos. Es importante considerar las relaciones entre todos los agentes computacionales implicados en el problema, de forma que puedan cooperar para conseguir el objetivo común de reducir el coste energético global del sistema. La tercera contribución de esta tesis es el desarrollo de optimizaciones energéticas para la aplicación global por medio de la evaluación de los costes de ejecutar parte del procesado necesario en otros niveles de abstracción, que van desde los nodos hasta el Centro de Datos, por medio de técnicas de balanceo de carga. Como resumen, el trabajo presentado en esta tesis lleva a cabo contribuciones en el modelado y optimización consciente del consumo por fugas y la refrigeración de servidores; el modelado de los Centros de Datos y el desarrollo de políticas de asignación conscientes de la heterogeneidad; y desarrolla mecanismos para la optimización energética de aplicaciones de nueva generación desde varios niveles de abstracción. ABSTRACT Data centers are easily found in every sector of the worldwide economy. They consist of tens of thousands of servers, serving millions of users globally and 24-7. In the last years, e-Science applications such e-Health or Smart Cities have experienced a significant development. The need to deal efficiently with the computational needs of next-generation applications together with the increasing demand for higher resources in traditional applications has facilitated the rapid proliferation and growing of data centers. A drawback to this capacity growth has been the rapid increase of the energy consumption of these facilities. In 2010, data center electricity represented 1.3% of all the electricity use in the world. In year 2012 alone, global data center power demand grew 63% to 38GW. A further rise of 17% to 43GW was estimated in 2013. Moreover, data centers are responsible for more than 2% of total carbon dioxide emissions. This PhD Thesis addresses the energy challenge by proposing proactive and reactive thermal and energy-aware optimization techniques that contribute to place data centers on a more scalable curve. This work develops energy models and uses the knowledge about the energy demand of the workload to be executed and the computational and cooling resources available at data center to optimize energy consumption. Moreover, data centers are considered as a crucial element within their application framework, optimizing not only the energy consumption of the facility, but the global energy consumption of the application. The main contributors to the energy consumption in a data center are the computing power drawn by IT equipment and the cooling power needed to keep the servers within a certain temperature range that ensures safe operation. Because of the cubic relation of fan power with fan speed, solutions based on over-provisioning cold air into the server usually lead to inefficiencies. On the other hand, higher chip temperatures lead to higher leakage power because of the exponential dependence of leakage on temperature. Moreover, workload characteristics as well as allocation policies also have an important impact on the leakage-cooling tradeoffs. The first key contribution of this work is the development of power and temperature models that accurately describe the leakage-cooling tradeoffs at the server level, and the proposal of strategies to minimize server energy via joint cooling and workload management from a multivariate perspective. When scaling to the data center level, a similar behavior in terms of leakage-temperature tradeoffs can be observed. As room temperature raises, the efficiency of data room cooling units improves. However, as we increase room temperature, CPU temperature raises and so does leakage power. Moreover, the thermal dynamics of a data room exhibit unbalanced patterns due to both the workload allocation and the heterogeneity of computing equipment. The second main contribution is the proposal of thermal- and heterogeneity-aware workload management techniques that jointly optimize the allocation of computation and cooling to servers. These strategies need to be backed up by flexible room level models, able to work on runtime, that describe the system from a high level perspective. Within the framework of next-generation applications, decisions taken at this scope can have a dramatical impact on the energy consumption of lower abstraction levels, i.e. the data center facility. It is important to consider the relationships between all the computational agents involved in the problem, so that they can cooperate to achieve the common goal of reducing energy in the overall system. The third main contribution is the energy optimization of the overall application by evaluating the energy costs of performing part of the processing in any of the different abstraction layers, from the node to the data center, via workload management and off-loading techniques. In summary, the work presented in this PhD Thesis, makes contributions on leakage and cooling aware server modeling and optimization, data center thermal modeling and heterogeneityaware data center resource allocation, and develops mechanisms for the energy optimization for next-generation applications from a multi-layer perspective.

Short-Circuit Impedance of Power Transformers: the Fractional Order Calculus Approach

First IFAC Workshop on Fractional Differentiation and Its Application - 19-21 July 2004, Enseirb, Bordeaux, France - FDA'04

On the Estimation of the Short-Circuit Impedance of Power Transformers Using Fractional Order Calculus

This paper reports investigation on the estimation of the short circuit impedance of power transformers, using fractional order calculus to analytically study the influence of the diffusion phenomena in the windings. The aim is to better characterize the medium frequency range behavior of leakage inductances of power transformer models, which include terms to represent the magnetic field diffusion process in the windings. Comparisons between calculated and measured values are shown and discussed.

Analysis of leakage properties and guiding conditions of rib antiresonant reflecting optical waveguides

Power leakage properties and guiding conditions of rib antiresonant reflecting optical waveguides (rib-ARROW) have been theoretically and experimentally studied as a function of wavelength and polarization of the light for different geometrical and optical parameters that characterize the rib-ARROW structure. Obtained results show that rib-ARROWs can only be fabricated with low losses in a wavelength range when determined rib configurations are adopted. Furthermore, these waveguides exhibit a polarization sensitivity that largely depends on the core-substrate refractive index difference. Together with the experimental results, theoretical calculations from different modeling methods are also presented and discussed.

Effect of Auger recombination and leakage on the droop in InGaN/GaN quantum well LEDs

We investigate the effect of the epitaxial structure and the acceptor doping profile on the efficiency droop in InGaN/GaN LEDs by the physics based simulation of experimental internal quantum efficiency (IQE) characteristics. The device geometry is an integral part of our simulation approach. We demonstrate that even for single quantum well LEDs the droop depends critically on the acceptor doping profile. The Auger recombination was found to increase stronger than with the third power of the carrier density and has been found to dominate the droop in the roll over zone of the IQE. The fitted Auger coefficients are in the range of the values predicted by atomistic simulations.

Life cycle analysis of UK coal fired power plants

This paper examines the life cycle GHG emissions from existing UK pulverized coal power plants. The life cycle of the electricity Generation plant includes construction, operation and decommissioning. The operation phase is extended to upstream and downstream processes. Upstream processes include the mining and transport of coal including methane leakage and the production and transport of limestone and ammonia, which are necessary for flue gas clean up. Downstream processes, on the other hand, include waste disposal and the recovery of land used for surface mining. The methodology used is material based process analysis that allows calculation of the total emissions for each process involved. A simple model for predicting the energy and material requirements of the power plant is developed. Preliminary calculations reveal that for a typical UK coal fired plant, the life cycle emissions amount to 990 g CO2-e/kWh of electricity generated, which compares well with previous UK studies. The majority of these emissions result from direct fuel combustion (882 g/kWh 89%) with methane leakage from mining operations accounting for 60% of indirect emissions. In total, mining operations (including methane leakage) account for 67.4% of indirect emissions, while limestone and other material production and transport account for 31.5%. The methodology developed is also applied to a typical IGCC power plant. It is found that IGCC life cycle emissions are 15% less than those from PC power plants. Furthermore, upon investigating the influence of power plant parameters on life cycle emissions, it is determined that, while the effect of changing the load factor is negligible, increasing efficiency from 35% to 38% can reduce emissions by 7.6%. The current study is funded by the UK National Environment Research Council (NERC) and is undertaken as part of the UK Carbon Capture and Storage Consortium (UKCCSC). Future work will investigate the life cycle emissions from other power generation technologies with and without carbon capture and storage. The current paper reveals that it might be possible that, when CCS is employed. the emissions during generation decrease to a level where the emissions from upstream processes (i.e. coal production and transport) become dominant, and so, the life cycle efficiency of the CCS system can be significantly reduced. The location of coal, coal composition and mining method are important in determining the overall impacts. In addition to studying the net emissions from CCS systems, future work will also investigate the feasibility and technoeconomics of these systems as a means of carbon abatement.

Marginal leakage in Class V cavities pretreated with different laser energy densities

The purpose of the present study was to evaluate in vitro the degree of marginal leakage in Class V cavities involving the cementoenamel junction. Cavities were 4 rum wide and 2 mm deep. The specimens received dentin pretreatment (37% phosphoric acid) followed by the Single Bond (3M) adhesive system application. The 40 specimens were then divided into four groups: Group I (control); Group 2 (Nd:YAG laser at 120 mJ/pulse, frequency of 10 Hz, power of 1.2 W); Group 3 (Nd:YAG laser at 140 mJ/pulse, frequency of 10 Hz, power of 1.4 W); Group 4 (Nd:YAG laser at 160 mJ/pulse, frequency of 10 Hz, power of 1.6 W). The cavities were restored with Z100 composite resin (3M) and light cured at 300-600 mW/cm(2) light intensity. Specimens were thermocycled to 500 cycles from 2-50 degrees C. After that, they were dried and sealed with nail varnish, respecting 1 mm around the restorations, and immersed in 0.5% methylene blue solution for 4 h. After this period, the teeth were rinsed, dried, sectioned, and analyzed in a stereoscopic loupe. The highest leakage scores were considered for each specimen. The results were statistically analyzed by the analysis of variance (ANOVA) Kruskal-Wallis test to the 5% level. For both the enamel and cementum, there was a decrease in marginal leakage with the application of laser energy; no significant differences were observed for Groups 2, 3, and 4. The results also showed a smaller tendency to marginal leakage on the cementum than on the enamel.

A low-voltage low-power analog memory cell with built-in 4-quadrant multiplication

An accurate switched-current (SI) memory cell and suitable for low-voltage low-power (LVLP) applications is proposed. Information is memorized as the gate-voltage of the input transistor, in a tunable gain-boosting triode-transconductor. Additionally, four-quadrant multiplication between the input voltage to the transconductor regulation-amplifier (X-operand) and the stored voltage (Y-operand) is provided. A simplified 2 x 2-memory array was prototyped according to a standard 0.8 mum n-well CMOS process and 1.8-V supply. Measured current-reproduction error is less than 0.26% for 0.25 muA less than or equal to I-SAMPLE less than or equal to 0.75 muA. Standby consumption is 6.75 muW per cell @I-SAMPLE = 0.75 muA. At room temperature, leakage-rate is 1.56 nA/ms. Four-quadrant multiplier (4QM) full-scale operands are 2x(max) = 320 mV(pp) and 2y(max). = 448 mV(pp), yielding a maximum output swing of 0.9 muA(pp). 4QM worst-case nonlinearity is 7.9%.

An active leakage-injection scheme applied to low-voltage SRAMs

An active leakage-injection scheme (ALIS) for low-voltage (LV) high-density (HD) SRAMs is presented. By means of a feedback loop comprising a servo-amplifier and a common-drain MOSFET, a current matching the respective bit-line leakage is injected onto the line during precharge and sensing, preventing the respective capacitances from erroneous discharges. The technique is able to handle leakages up to hundreds of μA at high operating temperatures. Since no additional timing is required, read-out operations are performed at no speed penalty. A simplified 256×1bit array was designed in accordance with a 0.35 CMOS process and 1.2V-supply. A range of PSPICE simulation attests the efficacy of ALIS. With an extra power consumption of 242 μW, a 200 μA-leakage @125°C, corresponding to 13.6 times the cell current, is compensated.

Influence of double excitation AC/DC in the reactive power demand of 3-phase transformers

In this work it is discussed the performance of the reactive power demand in three-leg transformer core and three-phase transformer bank, under different conditions of AC/DC double excitation. In order to analyse the influence of double excitation in reactive power theoretically a mathematical model was developed considering the mutual coupling between phases and the magnetic nonlinearity. The validity of the proposed model is verified by means of the experimental and simulated results.