Evaluating decision-making performance in a grid-computing environment using DEA

Energy saving involves two direct benefits: sustainability and cost reduction, both of which Information Technologies must be aware. In this context, clusters, grids and data centres represent the hungriest con sumers of energy. Energy-saving policies for these infrastructures must be applied in order to maximize their resources. The aim of this paper is to compare how efficient these policies are in each location of a grid infrastructure. By identifying efficient policies in each location and the slack in inputs and outputs of the inefficient locations, Data Envelopment Analysis presents a very useful technique for comparing and improving efficiency level. This work enables managers to uncover any misuse of resources so that cor rective action can be taken.Ministerio de Economía y Competitividad TIN2009-14378-C02-01 (ARTEMISA)Junta de Andalucía TIC-8052 (Simon

Prediction of Safety Performance by Using Machine Learning Algorithms: Evidence from Indian Construction Project Sites

The construction industry in India happens to be the second most contributor to its gross domestic product (GDP) but high rates of accidents and fatalities have tarnished the image of the industry in India. To enhance the importance and alertness among the stakeholders in construction project sites, the present study proposes a framework for predicting safety performance. In this retrospective study, the data pertaining to the 69 construction project sites across India from January, 2021, to July, 2022 was analysed. The data analysis was conducted in two phases, in the first phase of the study the efficiency of project sites was computed by implementing data envelopment analysis (DEA). In the second phase, the results of the first phase are utilized to predict the safety performance of construction sites by applying four machine learning (ML) algorithms. In the first phase of the study, three input and three output variables were considered to compute the efficiency of the project sites. Results of four ML classifiers revealed that the random forest classifier with high recall percentage of 95.0 is considered the best in predicting the safety performance. Finally, the results indicate that the ML classifiers enable a good accuracy level in predicting the safety performance of project sites. Among the four ML classifiers, notably the Random Forest Classifier enables identifying the inefficient project sites and advising the site management to implement control measures. Finally, a safety performance prediction tool was developed to understand the results

Productive Efficiency of Energy-Aware Data Centers

Information technologies must be made aware of the sustainability of cost reduction. Data centers may reach energy consumption levels comparable to many industrial facilities and small-sized towns. Therefore, innovative and transparent energy policies should be applied to improve energy consumption and deliver the best performance. This paper compares, analyzes and evaluates various energy efficiency policies, which shut down underutilized machines, on an extensive set of data-center environments. Data envelopment analysis (DEA) is then conducted for the detection of the best energy efficiency policy and data-center characterization for each case. This analysis evaluates energy consumption and performance indicators for natural DEA and constant returns to scale (CRS). We identify the best energy policies and scheduling strategies for high and low data-center demands and for medium-sized and large data-centers; moreover, this work enables data-center managers to detect inefficiencies and to implement further corrective actions.Universidad de Sevilla 2018/0000052

National culture and multinational performance

The question of why some multinational corporations perform better than others is in the centre of the analysis of many international business disciplines and the subject of a never-ending debate. In that respect this paper provides empirical evidence by combining strategic management theories and performance measurement techniques. Specifically, it illustrates a way of strategic performance measurement by emphasising the impact of home country’s national culture on MNCs’ performance. Our empirical evidences suggest that home country’s national culture has a direct impact on MNCs’ performance. Additionally, the results clearly indicate that MNCs with higher performance have clear and distinct characteristics.Multinational Performance, National Culture, Cultural Distance Index, Data Envelopment Analysis.

Energy and performance-aware scheduling and shut-down models for efficient cloud-computing data centers.

This Doctoral Dissertation, presented as a set of research contributions, focuses on resource efficiency in data centers. This topic has been faced mainly by the development of several energy-efficiency, resource managing and scheduling policies, as well as the simulation tools required to test them in realistic cloud computing environments. Several models have been implemented in order to minimize energy consumption in Cloud Computing environments. Among them: a) Fifteen probabilistic and deterministic energy-policies which shut-down idle machines; b) Five energy-aware scheduling algorithms, including several genetic algorithm models; c) A Stackelberg game-based strategy which models the concurrency between opposite requirements of Cloud-Computing systems in order to dynamically apply the most optimal scheduling algorithms and energy-efficiency policies depending on the environment; and d) A productive analysis on the resource efficiency of several realistic cloud–computing environments. A novel simulation tool called SCORE, able to simulate several data-center sizes, machine heterogeneity, security levels, workload composition and patterns, scheduling strategies and energy-efficiency strategies, was developed in order to test these strategies in large-scale cloud-computing clusters. As results, more than fifty Key Performance Indicators (KPI) show that more than 20% of energy consumption can be reduced in realistic high-utilization environments when proper policies are employed.Esta Tesis Doctoral, que se presenta como compendio de artículos de investigación, se centra en la eficiencia en la utilización de los recursos en centros de datos de internet. Este problema ha sido abordado esencialmente desarrollando diferentes estrategias de eficiencia energética, gestión y distribución de recursos, así como todas las herramientas de simulación y análisis necesarias para su validación en entornos realistas de Cloud Computing. Numerosas estrategias han sido desarrolladas para minimizar el consumo energético en entornos de Cloud Computing. Entre ellos: 1. Quince políticas de eficiencia energética, tanto probabilísticas como deterministas, que apagan máquinas en estado de espera siempre que sea posible; 2. Cinco algoritmos de distribución de tareas que tienen en cuenta el consumo energético, incluyendo varios modelos de algoritmos genéticos; 3. Una estrategia basada en la teoría de juegos de Stackelberg que modela la competición entre diferentes partes de los centros de datos que tienen objetivos encontrados. Este modelo aplica dinámicamente las estrategias de distribución de tareas y las políticas de eficiencia energética dependiendo de las características del entorno; y 4. Un análisis productivo sobre la eficiencia en la utilización de recursos en numerosos escenarios de Cloud Computing. Una nueva herramienta de simulación llamada SCORE se ha desarrollado para analizar las estrategias antes mencionadas en clústers de Cloud Computing de grandes dimensiones. Los resultados obtenidos muestran que se puede conseguir un ahorro de energía superior al 20% en entornos realistas de alta utilización si se emplean las estrategias de eficiencia energética adecuadas. SCORE es open source y puede simular diferentes centros de datos con, entre otros muchos, los siguientes parámetros: Tamaño del centro de datos; heterogeneidad de los servidores; tipo, composición y patrones de carga de trabajo, estrategias de distribución de tareas y políticas de eficiencia energética, así como tres gestores de recursos centralizados: Monolítico, Two-level y Shared-state. Como resultados, esta herramienta de simulación arroja más de 50 Key Performance Indicators (KPI) de rendimiento general, de distribucin de tareas y de energía.Premio Extraordinario de Doctorado U

Comparing Energy Efficiency Of Drivers And Vehicles Using Data Envelopment Analysis

In this paper, we propose a new methodology to compare and promote efficient driving by providing feedback to the user. The proposed methodology uses Data Envelopment Analysis (DEA) to enable comparisons between drivers and vehicles, by including parameters retrieved from vehicle as inputs or output for DEA method. Providing feedback to the user is essential in driving eco-systems for changing bad driving habits and not returning back to driving bad-habits. In our case, feedback is provided once the driver has finished some routes, by proposing which corrections or improvement has to deal with for future trips. The required vehicle’s telemetry data is obtained through the OBD2 port using an OBD2 adapterMinisterio de Economía y Competitividad TIN2009-14378-C02-01Junta de Andalucía TIC-805

Relative Performance of UK and Japanese Electricity Distribution Systems 1985-1998: Lessons for Incentive Regulation

International comparisons can be used to study relative efficiency of decision-making units in an industry in a wider context. In particular, cross-country comparisons can, help regulators of natural monopoly firms to assess the relative performance of their regulation regime and national firms with those of other countries. The relative performance of frontier firms is important as these may be subject to lax regulation and could constitute benchmarks for regulation of other firms. The results of empirical studies can be sensitive to the choice of techniques and models. The UK and Japanese electricity distribution utilities have been subject to yardstick regulation since 1990 and 1996 respectively. In this paper we present an analysis of the development and relative performance of electricity distribution utilities in the UK and Japan between 1985 and 1998. The results allow the examination of the impact of privatisation and regulation on the UK firms and their scope for further efficiency gains. The paper presents the findings from applying input distance functions with data envelopment analysis (DEA), stochastic frontier analysis (SFA), and corrected ordinary least square (COLS) techniques using cost based model specifications in a dynamic setting.Technical Efficiency, Efficiency Analysis, Electricity Distribution Systems, Incentive Regulation, International Comparison

Green Technologies for Production Processes

This book focuses on original research works about Green Technologies for Production Processes, including discrete production processes and process production processes, from various aspects that tackle product, process, and system issues in production. The aim is to report the state-of-the-art on relevant research topics and highlight the barriers, challenges, and opportunities we are facing. This book includes 22 research papers and involves energy-saving and waste reduction in production processes, design and manufacturing of green products, low carbon manufacturing and remanufacturing, management and policy for sustainable production, technologies of mitigating CO2 emissions, and other green technologies

New Fundamental Technologies in Data Mining

The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining

Toward a More Accurate Web Service Selection Using Modified Interval DEA Models with Undesirable Outputs

With the growing number of Web services on the internet, there is a challenge to select the best Web service which can offer more quality-of-service (QoS) values at the lowest price. Another challenge is the uncertainty of QoS values over time due to the unpredictable nature of the internet. In this paper, we modify the interval data envelopment analysis (DEA) models [Wang, Greatbanks and Yang (2005)] for QoS-aware Web service selection considering the uncertainty of QoS attributes in the presence of desirable and undesirable factors. We conduct a set of experiments using a synthesized dataset to show the capabilities of the proposed models. The experimental results show that the correlation between the proposed models and the interval DEA models is significant. Also, the proposed models provide almost robust results and represent more stable behavior than the interval DEA models against QoS variations. Finally, we demonstrate the usefulness of the proposed models for QoS-aware Web service composition. Experimental results indicate that the proposed models significantly improve the fitness of the resultant compositions when they filter out unsatisfactory candidate services for each abstract service in the preprocessing phase. These models help users to select the best possible cloud service considering the dynamic internet environment and they help service providers to improve their Web services in the marke