Mapas estratégicos de ruido 2ª fase. Estudio comparativo

El presente proyecto tiene el objeto de realizar, analizar y evaluar un estudio comparativo de los Mapas Estratégicos de Ruido (MER), 2a Fase, de España y el resto de países europeos. Los objetivos principales del proyecto son: 1. Revisión de los mapas estratégicos de ruido realizados en España correspondientes a la segunda fase de la Directiva 2002/49/EC. Análisis comparativo de las metodologías y resultados obtenidos. 2. Revisión de los mapas estratégicos de ruido realizados en países europeos. 3. Evaluación de la población afectada por el ruido ambiental, tanto en España como en países europeos.Ingeniería de TelecomunicaciónTelekomunikazio Ingeniaritz

Caracterización rápida y en tiempo de ejecución de grandes despliegues de aplicaciones

Tesis de la Universidad Complutense de Madrid, Facultad de Informática, leída el 19/01/2021Data centers are one of the most power hungry sections of the Information and Communications Technologies (ICT) sector. In the U.S. in 2014, data centers consumed around the 1.8% of the total U.S. electricity consumption. Worldwide data centers consumed in 2015 around 200 TWh of the global electricity usage. This electricity consumption is expected to increase to around 1200 TWh in 2025, which would represent 4.% of the global electricity usage. One of the mejor contributors to the overall data center power is the IT or computing power, therefore there is a special interest to imporve its energy efficiency. Scientific community has developed energy efficient techniques to reduce the energy consumption of IT equipment, such as resource management, power budgeting or power capping...Los centros de datos son una de las secciones del sector de Tecnologías de la Información y Comunicaciones (TIC) que tienen mayor consumo energético. Durante el año 2014 en EE.UU., los centros de datos consumieron alrededor del 1.8% del consumo eléctrico total en dicho país. A nivel mundial, los centros de datos representaron en el añó 2015 alrededor de 200TWh respecto al consumo eléctrico mundial. Según estimaciones, este consumo eléctrico puede aumentar hasta unos 1200 TWh en año 2025, lo que representaría el 4.5% del consumo eléctrico global. Uno de los mayores contribuidores al consumo global en los centros de datos es el representado por los equipos de computación o consumo de IT. A nivel computacional, se han desarrollado diversas técnicas para reducir el consumo de IT como pueden ser, la gestión de recursos, presupuestos de potencia y la limitación de consumo de los servidores ubicados en los centros de datos...Fac. de InformáticaTRUEunpu

Energy-aware task scheduling in data centers using an application signature

Data centers are power hungry facilities. Energy-aware task scheduling approaches are of utmost importance to improve energy savings in data centers, although they need to know beforehand the energy consumption of the applications that will run in the servers. This is usually done through a full profiling of the applications, which is not feasible in long-running application scenarios due to the long execution times. In the present work we use an application signature that allows to estimate the energy without the need to execute the application completely. We use different scheduling approaches together with the information of the application signature to improve the makespan of the scheduling process and therefore improve the energy savings in data centers. We evaluate the accuracy of using the application signature by means of comparing against an oracle method obtaining an error below 1.5%, and Compression Ratios around 39.7 to 45.8

Estudio del programa SoundPlan para la evaluación del impacto acústico de infraestructuras aeroportuarias

Los objetivos principales de este proyecto son: - Estudio en detalle del módulo de ruido de aeronaves del programa SoundPlan para evaluar el impacto acústico de las infraestructuras aeroportuarias. - Análisis comparativo entre resultados experimentales de afección y simulados para el Aeropuerto de Pamplona. Elaboración del Mapa de Ruido del Aeropuerto de Pamplona. Los objetivos secundarios son: - Análisis comparativo de los niveles máximos sonoros entre aeronaves con motor de hélice y aeronaves con motor a reacción utilizando el programa SoundPlan. - Análisis comparativo de los niveles Lden entre resultados con viento moderado y viento en calma utilizando el programa SoundPlan.Ingeniería Técnica de Telecomunicación, especialidad Sonido e ImagenTelekomunikazio Ingeniaritza Teknikoa. Soinua eta Irudia Berezitasun

Fast energy estimation framework for long-running applications

The computation power in data center facilities is increasing significantly. This brings with it an increase of power consumption in data centers. Techniques such as power budgeting or resource management are used in data centers to increase energy efficiency. These techniques require to know beforehand the energy consumption throughout a full profiling of the applications. This is not feasible in scenarios with long-running applications that have long execution times. To tackle this problem we present a fast energy estimation framework for long-running applications. The framework is able to estimate the dynamic CPU and memory energy of the application without the need to perform a complete execution. For that purpose, we leverage the concept of application signature. The application signature is a reduced version, in terms of execution time, of the original application. Our fast energy estimation framework is validated with a set of long-running applications and obtains RMS values of 11.4% and 12.8% for the CPU and memory energy estimation errors, respectively. We define the concept of Compression Ratio as an indicator of the acceleration of the energy estimation process. Our framework is able to obtain Compression Ratio values in the range of 10.1 to 191.2

Fast Energy Estimation Through Partial Execution of HPC Applications

In order to optimize the energy use of servers in Data Centers, techniques such as power capping or power budgeting are usually deployed. These techniques rely on the prediction of the power and execution time of applications. These data are obtained via dynamic profiling which requires a full execution of the application. This is not feasible in High Performance Computing (HPC) applications with long execution times. In this paper, we present a methodology to estimate the dynamic CPU and memory energy consumption of an application without executing it completely. Our methodology merges static code analysis information and dynamic profiling via the partial execution of the application. We do so by leveraging the concept of application signature, defined as a reduced version of the application in terms of execution time and power profile. We validate our methodology with a set of CPU-intensive, memory-intensive benchmarks and multi-threaded applications in a presently shipping enterprise server. Our energy estimation methodology shows an overall error below 8.0% when compared to the dynamic energy of the whole execution of the application. Also, our energy estimation methodology allows to estimate the energy of multi-threaded applications with an RMSE equal to 12.7% when compared to the dynamic energy from the complete parallel execution

Energy-aware task scheduling in data centers using an application signature

Data centers are power hungry facilities. Energy-aware task scheduling approaches are of utmost importance to improve energy savings in data centers, although they need to know beforehand the energy consumption of the applications that will run in the servers. This is usually done through a full profiling of the applications, which is not feasible in long-running application scenarios due to the long execution times. In the present work we use an application signature that allows to estimate the energy without the need to execute the application completely. We use different scheduling approaches together with the information of the application signature to improve the makespan of the scheduling process and therefore improve the energy savings in data centers. We evaluate the accuracy of using the application signature by means of comparing against an oracle method obtaining an error below 1.5%, and Compression Ratios around 39.7 to 45.8

Fast energy estimation through partial execution of HPC applications

In order to optimize the energy use of servers in Data Centers, techniques such as power capping or power budgeting are usually deployed. These techniques rely on the prediction of the power and execution time of applications. These data are obtained via dynamic profiling which requires a full execution of the application. This is not feasible in High Performance Computing (HPC) applications with long execution times. In this paper, we present a methodology to estimate the dynamic CPU and memory energy consumption of an application without executing it completely. Our methodology merges static code analysis information and dynamic profiling via the partial execution of the application. We do so by leveraging the concept of application signature, defined as a reduced version of the application in terms of execution time and power profile. We validate our methodology with a set of CPU -intensive, memory-intensive benchmarks and multi-threaded applications in a presently shipping enterprise server. Our energy estimation methodology shows an overall error below 8.0% when compared to the dynamic energy of the whole execution of the application. Also, our energy estimation methodology allows to estimate the energy of multi-threaded applications with an RMSE equal to 12.7% when compared to the dynamic energy from the complete parallel execution