Calculating indices for urban sprawl

Urban sprawl is a complex concept, that is generally associated with auto-oriented, low-density development. It is the subject of a wide range of research efforts, aiming at understanding and characterizing the underlying driving factors. This report follows an effort by Burchfield et al. who proposed in [burchfield06] a simple measure for urban sprawl, a so-called sprawl index. It proposes variants of this index and describes their implementation using the R statistical computation environment, the Geospatial Data Abstraction Library and the Quantum GIS (Geographic Information System).L'étalement urbain est un concept complexe qui est généralement associé à un développement de faible densité et basé sur l'automobile. Il fait l'objet de beaucoup de recherches ayant pour but la compréhension et la caractérisation des facteurs sous-jacents. Ce rapport suit un travail par Burchfield et al. qui ont proposé, dans [burchfield06], une mesure simple pour l'étalement urbain, appelé indexe d'étalement. Nous proposons des variantes pour cet indexe et décrivons leur implémentation utilisant l'environnement de calcul statistique R, la Geospatial Data Abstraction Library et le SIG (Système d'Information Géographique) Quantum

A Sensor-Actuator Model for Data Center Optimization

Cloud data centers commonly use virtualization technologies to provision compute capacity with a level of indirection between virtual machines and physical resources. In this paper we explore the use of that level of indirection as a means for autonomic data center configuration optimization and propose a sensor-actuator model to capture optimization-relevant relationships between data center events, monitored metrics (sensors data), and management actions (actuators). The model characterizes a wide spectrum of actions to help identify the suitability of different actions in specific situations, and outlines what (and how often) data needs to be monitored to capture, classify, and respond to events that affect the performance of data center operations

Må kraften vara med dig : dynamisk avvägning mellan prestanda och strömförbrukning i datacenter

The overall goal of the work presented in this thesis was to find ways of managing power-performance tradeoffs in cloud data centers. To this end, the relationships between the power consumption of data center servers and the performance of applications hosted in data centers are analyzed, models that capture these relationships are developed, and controllers to optimize the use of data center infrastructures are proposed. The studies were motivated by the massive power consumption of modern data centers, which is a matter of significant financial and environmental concern. Various strategies for improving the power efficiency of data centers have been proposed, including server consolidation, server throttling, and power budgeting. However, no matter what strategy is used to enhance data center power efficiency, substantial reductions in the power consumption of data center servers can easily degrade the performance of hosted applications, causing customer dissatisfaction. It is therefore crucial for data center operators to understand and control power-performance tradeoffs. The research methods used in this work include experiments on real testbeds, the application of statistical methods to create power-performance models, development of various optimization techniques to improve the power efficiency of servers, and simulations to evaluate the proposed solutions at scale. This thesis makes multiple contributions. First, it introduces taxonomies for various aspects of data center configuration, events, management actions, and monitored metrics. We discuss the relationships between these elements and support our analysis with results from a set of testbed experiments. We demonstrate limitations on the usefulness of various data center management actions for controlling power consumption, including Dynamic Voltage Frequency Scaling (DVFS) and Running Average Power Limit (RAPL). We also demonstrate similar limitations on common measures for controlling application performance, including variation of operating system scheduling parameters, CPU pinning, and horizontal and vertical scaling. Finally, we propose a set of power budgeting controllers that act at the application, server, and cluster levels to minimize performance degradation while enforcing power limits. The results and analysis presented in this thesis can be used by data center operators to improve the power-efficiency of servers and reduce overall operational costs while minimizing performance degradation. All of the software generated during this work, including controller source code, virtual machine images, scripts, and simulators, has been open-sourced

Må kraften vara med dig : dynamisk avvägning mellan prestanda och strömförbrukning i datacenter

The overall goal of the work presented in this thesis was to find ways of managing power-performance tradeoffs in cloud data centers. To this end, the relationships between the power consumption of data center servers and the performance of applications hosted in data centers are analyzed, models that capture these relationships are developed, and controllers to optimize the use of data center infrastructures are proposed. The studies were motivated by the massive power consumption of modern data centers, which is a matter of significant financial and environmental concern. Various strategies for improving the power efficiency of data centers have been proposed, including server consolidation, server throttling, and power budgeting. However, no matter what strategy is used to enhance data center power efficiency, substantial reductions in the power consumption of data center servers can easily degrade the performance of hosted applications, causing customer dissatisfaction. It is therefore crucial for data center operators to understand and control power-performance tradeoffs. The research methods used in this work include experiments on real testbeds, the application of statistical methods to create power-performance models, development of various optimization techniques to improve the power efficiency of servers, and simulations to evaluate the proposed solutions at scale. This thesis makes multiple contributions. First, it introduces taxonomies for various aspects of data center configuration, events, management actions, and monitored metrics. We discuss the relationships between these elements and support our analysis with results from a set of testbed experiments. We demonstrate limitations on the usefulness of various data center management actions for controlling power consumption, including Dynamic Voltage Frequency Scaling (DVFS) and Running Average Power Limit (RAPL). We also demonstrate similar limitations on common measures for controlling application performance, including variation of operating system scheduling parameters, CPU pinning, and horizontal and vertical scaling. Finally, we propose a set of power budgeting controllers that act at the application, server, and cluster levels to minimize performance degradation while enforcing power limits. The results and analysis presented in this thesis can be used by data center operators to improve the power-efficiency of servers and reduce overall operational costs while minimizing performance degradation. All of the software generated during this work, including controller source code, virtual machine images, scripts, and simulators, has been open-sourced

How might residential PV change the energy demand curve in Poland

Photovoltaics (PV) in terms of installed capacity play a minor role in the portfolio of renewable energy sources (RES) in Poland. However current market tendencies indicate that residential PV installations are gaining on popularity and may in future significantly contribute to covering national energy demand. This study investigates the potential impact of numerous residential PV installations on the shape and statistical properties of the polish energy demand curve. Analysis employed statistical data on mean household energy consumption in different districts, typical energy demand patterns and hourly values of irradiation for the year 2012. Obtained results indicate that there is a possibility to integrate in total as much as 300 000 residential PV installations (0.9 GW) from which generated energy will be utilized by households within given district. Further analysis has shown that to some extent increasing number of residential PV decreases the value of energy demand coefficient of variation

The use of photovoltaics and electric vehicles for electricity peak shaving in office buildings

The use of electric vehicles and photovoltaics is perceived as a viable option to reduce the human impact on the natural environment. This paper investigates the opportunity of managing a fleet of EVs along with PV installation in such a manner that shaves the peak load in an office building. The simulation used hourly load data representative for a small office building located in Cracow (Poland). For the same location hourly irradiation data was obtained. A deterministic model was created and implemented in MS Excel software. The study showed that 30 kW installed capacity in photovoltaics can reduce the observed peak load by 36% (from 19.8 kW to 14.52 kW) in a building consuming on an annual basis 54.7 MWh of electricity. Additionally, an appropriate management of the charging process of electric vehicles can increase the energy from photovoltaics self-consumption and level the observed energy demand in normal office building operating hours

Concept of large scale PV-WT-PSH energy sources coupled with the national power system

Intermittent/non-dispatchable energy sources are characterized by a significant variation of their energy yield over time. In majority of cases their role in energy systems is marginalized. However, even in Poland which is strongly dedicated to its hard and brown coal fired power plants, the wind generation in terms of installed capacity starts to play a significant role. This paper briefly introduces a concept of wind (WT) and solar (PV) powered pumped storage hydroelectricity (PSH) which seems to be a viable option for solving the problem of the variable nature of PV and WT generation. Additionally we summarize the results of our so far conducted research on the integration of variable renewable energy sources (VRES) to the energy systems and present conclusions which strictly refer to the prospects of large scale PV-WT-PSH operating as a part of the polish energy system

Power-aware cloud brownout : Response time and power consumption control

Cloud computing infrastructures are powering most of the web hosting services that we use at all times. A recent failure in the Amazon cloud infrastructure made many of the website that we use on a hourly basis unavailable1. This illustrates the importance of cloud applications being able to absorb peaks in workload, and at the same time to tune their power requirements to the power and energy capacity offered by the data center infrastructure. In this paper we combine an established technique for response time control-brownout-with power capping. We use cascaded control to take into account both the need for predictability in the response times (the inner loop), and the power cap (the outer loop). We execute tests on real machines to determine power usage and response times models and extend an existing simulator. We then evaluate the cascaded controller approach with a variety of workloads and both open-and closed-loop client models