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

Impact of the Information and Communication Technologies on the Education of Students with Down Syndrome: a Bibliometric Study (2008- 2018)

This article analyzes the impact of the Information and Communication Technologies (ICT) on students with Down syndrome through the consult of scientific articles published during the 2008 to 2018 period, in five scientific journal databases utilized in the academic world. Through a descriptive and quantitative methodology, the most significant bibliometric data according to citation index is shown. Likewise, a methodology based on the analysis of co-words and clustering techniques is applied through a bibliometric maps, in order to determine the fields of scientific study. The results show that articles published have a medium-low index of impact. There are linked with the importance of using ICT with these students, from educational inclusion and accessibility perspective

Limiting Global Warming by Improving Data-Centre Software

Carbon emissions, greenhouse gases and pollution in general are usually related to traditional factories, so the most modern computing factories have gone unnoticed for the general-public opinion. We empirically show through extensive and realistic simulation that: 1) energy consumption, and consequently CO2 emissions, could be reduced from ~15% to ~60% if the correct energy-efficiency policies are applied; and 2) such energy-consumption reduction can be achieved without negatively impacting the correct operation of these infrastructures. To this end, this work is focused on the proposal and analysis of a set of energy-efficiency policies which are applied to traditional and hyper-scale data centres, as well as numerous operation environments, including: 1) the top resource managers used in industry; 2) eight energy-efficiency policies, including aggressive, fine-tuned and adaptive models; and 3) three types of workload-arrival patterns. Finally, we present a realistic analysis of the environmental impact of the application of such energy-efficiency policies on USA data centres. The presented results estimate that 11.5 million of tons of CO2 could be saved, which is equivalent to the removal of 4.79 million of combustion cars, that is, the total car fleet of countries such as Portugal, Austria and Sweden.Ministerio de Ciencia e Innovación RTI2018-098062-A-I0

Single-Board-Computer Clusters for Cloudlet Computing in Internet of Things

The number of connected sensors and devices is expected to increase to billions in the near future. However, centralised cloud-computing data centres present various challenges to meet the requirements inherent to Internet of Things (IoT) workloads, such as low latency, high throughput and bandwidth constraints. Edge computing is becoming the standard computing paradigm for latency-sensitive real-time IoT workloads, since it addresses the aforementioned limitations related to centralised cloud-computing models. Such a paradigm relies on bringing computation close to the source of data, which presents serious operational challenges for large-scale cloud-computing providers. In this work, we present an architecture composed of low-cost Single-Board-Computer clusters near to data sources, and centralised cloud-computing data centres. The proposed cost-efficient model may be employed as an alternative to fog computing to meet real-time IoT workload requirements while keeping scalability. We include an extensive empirical analysis to assess the suitability of single-board-computer clusters as cost-effective edge-computing micro data centres. Additionally, we compare the proposed architecture with traditional cloudlet and cloud architectures, and evaluate them through extensive simulation. We finally show that acquisition costs can be drastically reduced while keeping performance levels in data-intensive IoT use cases.Ministerio de Economía y Competitividad TIN2017-82113-C2-1-RMinisterio de Economía y Competitividad RTI2018-098062-A-I00European Union’s Horizon 2020 No. 754489Science Foundation Ireland grant 13/RC/209

Use of Augmented Reality for Students with Educational Needs: A Systematic Review (2016–2021)

In recent years, interest in applying Augmented Reality technology as a teaching/learning resource in education has increased. However, few studies focus on the possibilities and challenges of these tools to support learners with educational needs. In this review, we aggregate the current knowledge of how Augmented Reality technologies are applicable and their impact on the learning of students with educational needs considering the above-mentioned factors. In total, 18 studies indexed in the Scopus and Web of Science databases were analysed. The main findings of this review provide the current state of Augmented Reality research in special education and show positive results in the learning of students with educational needs.Ministry of Economy, Industry and Competitiveness PID2019-108230RB-I0

Barreras en la capacitación tecnológica del profesorado de ciencias de la salud. Un estudio de caso

Las competencias digitales de los docentes constituyen una variable clave para integrar las TIC en el proceso de enseñanza-aprendizaje. Su desarrollo se ha convertido en uno de los principales problemas formativos que afecta de forma general al ámbito educativo, y más aún a la formación de profesionales de Ciencias de la Salud. El propósito del estudio ha sido conocer las principales barreras que encuentran los profesores de educación secundaria que imparten asignaturas de educación para la salud, en Sevilla (España) y su provincia, para su capacitación tecnológica. El diseño de la investigación se sitúa desde una perspectiva de investigación etnográfica de carácter descriptivo, donde se analizaron 60 entrevistas. Entre las conclusiones podemos destacar que las principales barreras que obstaculizan la realización de actividades de formación tecnológica en el profesorado de ciencias de la salud vienen determinadas en primer lugar por factores económicos, de tiempo y de actitud del profesorado

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

Stackelberg Game-based Models in Energy-aware Cloud Scheduling

Energy-awareness remians the important problem in today’s cloud computing (CC). Optimization of the energy consumed in cloud data centers and computing servers is usually related to the scheduling prob lems. It is very difficult to define an optimal schedul ing policy without negoative influence into the system performance and task completion time. In this work, we define a general cloud scheduling model based on a Stackelberg game with the workload scheduler and energy-efficiency agent as the main players. In this game, the aim of the scheduler is the minimization of the makespan of the workload, which is achieved by the employ of a genetic scheduling algorithm that maps the workload tasks into the computational nodes. The energy-efficiency agent selects the energy-optimization techniques based on the idea of switchin-off of the idle machines, in response to the scheduler decisions. The efficiency of the proposed model has been tested using a SCORE cloud simmulator. Obtained results show that the proposed model performs better than static energy-optimization strategies, achieving a fair balance between low energy consumption and short queue times and makespan

Quality of cloud services determined by the dynamic management of scheduling models for complex heterogeneous workloads

The quality of services in Cloud Computing (CC) depends on the scheduling strategies selected for processing of the complex workloads in the physical cloud clusters. Using the scheduler of the single type does not guarantee of the optimal mapping of jobs onto cloud resources, especially in the case of the processing of the big data workloads. In this paper, we compare the performances of the cloud schedulers for various combinations of the cloud workloads with different characteristics. We define several scenarios where the proper types of schedulers can be selected from a list of scheduling models implemented in the system, and used to schedule the concrete workloads based on the workloads’ parameters and the feedback on the efficiency of the schedulers. The presented work is the first step in the development and implementation of an automatic intelligent scheduler selection system. In our simple experimental analysis, we confirm the usefulness of such a system in today’s data-intensive cloud computin