Grid Global Behavior Prediction

Complexity has always been one of the most important issues in distributed computing. From the first clusters to grid and now cloud computing, dealing correctly and efficiently with system complexity is the key to taking technology a step further. In this sense, global behavior modeling is an innovative methodology aimed at understanding the grid behavior. The main objective of this methodology is to synthesize the grid's vast, heterogeneous nature into a simple but powerful behavior model, represented in the form of a single, abstract entity, with a global state. Global behavior modeling has proved to be very useful in effectively managing grid complexity but, in many cases, deeper knowledge is needed. It generates a descriptive model that could be greatly improved if extended not only to explain behavior, but also to predict it. In this paper we present a prediction methodology whose objective is to define the techniques needed to create global behavior prediction models for grid systems. This global behavior prediction can benefit grid management, specially in areas such as fault tolerance or job scheduling. The paper presents experimental results obtained in real scenarios in order to validate this approach

Using Global Behavior Modeling to Improve QoS in Cloud Data Storage Services

The cloud computing model aims to make large-scale data-intensive computing affordable even for users with limited financial resources, that cannot invest into expensive infrastructures necesssary to run them. In this context, MapReduce is emerging as a highly scalable programming paradigm that enables high-throughput data-intensive processing as a cloud service. Its performance is highly dependent on the underlying storage service, responsible to efficiently support massively parallel data accesses by guaranteeing a high throughput under heavy access concurrency. In this context, quality of service plays a crucial role: the storage service needs to sustain a stable throughput for each individual accesss, in addition to achieving a high aggregated throughput under concurrency. In this paper we propose a technique to address this problem using component monitoring, application-side feedback and behavior pattern analysis to automatically infer useful knowledge about the causes of poor quality of service and provide an easy way to reason in about potential improvements. We apply our proposal to Blob Seer, a representative data storage service specifically designed to achieve high aggregated throughputs and show through extensive experimentation substantial improvements in the stability of individual data read accesses under MapReduce workloads

GMonE: a complete approach to cloud monitoring

The inherent complexity of modern cloud infrastructures has created the need for innovative monitoring approaches, as state-of-the-art solutions used for other large-scale environments do not address specific cloud features. Although cloud monitoring is nowadays an active research field, a comprehensive study covering all its aspects has not been presented yet. This paper provides a deep insight into cloud monitoring. It proposes a unified cloud monitoring taxonomy, based on which it defines a layered cloud monitoring architecture. To illustrate it, we have implemented GMonE, a general-purpose cloud monitoring tool which covers all aspects of cloud monitoring by specifically addressing the needs of modern cloud infrastructures. Furthermore, we have evaluated the performance, scalability and overhead of GMonE with Yahoo Cloud Serving Benchmark (YCSB), by using the OpenNebula cloud middleware on the Grid’5000 experimental testbed. The results of this evaluation demonstrate the benefits of our approach, surpassing the monitoring performance and capabilities of cloud monitoring alternatives such as those present in state-of-the-art systems such as Amazon EC2 and OpenNebula

An autonomic framework for enhancing the quality of data grid services

Data grid services have been used to deal with the increasing needs of applications in terms of data volume and throughput. The large scale, heterogeneity and dynamism of grid environments often make management and tuning of these data services very complex. Furthermore, current high-performance I/O approaches are characterized by their high complexity and specific features that usually require specialized administrator skills. Autonomic computing can help manage this complexity. The present paper describes an autonomic subsystem intended to provide self-management features aimed at efficiently reducing the I/O problem in a grid environment, thereby enhancing the quality of service (QoS) of data access and storage services in the grid. Our proposal takes into account that data produced in an I/O system is not usually immediately required. Therefore, performance improvements are related not only to current but also to any future I/O access, as the actual data access usually occurs later on. Nevertheless, the exact time of the next I/O operations is unknown. Thus, our approach proposes a long-term prediction designed to forecast the future workload of grid components. This enables the autonomic subsystem to determine the optimal data placement to improve both current and future I/O operations

Motivando el aprendizaje de Física a través de experiencias sencillas en el aula

Memoria ID-0038. Ayudas de la Universidad de Salamanca para la innovación docente, curso 2015-2016

Improving Motivation And Continuous Assessment In Engineering Classrooms Through Student Response Systems

The use of Student Response Systems (SRS) is highly recommended to encourage the active and meaningful learning of students in each lecture. SRS promotes the motivation of students and improves the system of continuous assessment. One of the most popular applications designed for SRS is Socrative (Socrative n.d.). The use of Socrative gives real meaning to continuous assessment, since the teacher has an easily manageable record of the evolution of their students‘learning and will help the teacher to schedule both formative and summative assessment. The application allows the detection of topics that each student may not have understood and determines the percentage of the entire class with the same difficulty. Beyond the use of Socrative as an evaluation instrument, sufficiently referenced, in this article we present different methodologies supported by SRS implemented in engineering studies at the University the Salamanca. The methodologies aim to promote autonomous work outside the classroom, and in face-to-face classes, to maintain the attention and lead the reasoning of the students to facilitate learning. The influence of the methodologies proposed by the authors on a series of indicators related to the motivation and commitment of the students to the subjects will be presented. To the best of our knowledge, most of the work on SRS have been applied to non-university educational levels and for assessment purposes and very few of them have applied SRS to undergraduate engineering studies. The novelty of this work lies in introducing new methodologies supported by SRS in university engineering studies

Seasonal thermodynamic prediction of the performance of a hybrid solar gas-turbine power plant

[EN] An entirely thermodynamic model is developed for predicting the performance records of a solar hybrid gas turbine power plant with variable irradiance and ambient temperature conditions. The model considers a serial solar hybridization in those periods when solar irradiance is high enough. A combustion chamber allows to maintain an approximately constant inlet temperature in the turbine ensuring a stable power output. The overall plant thermal efficiency is written as a combination of the thermal efficiencies of the involved subsystems and the required heat exchangers. Numerical values of model input parameters are taken from a central tower installation recently developed near Seville, Spain. Real data for irradiance and external temperature are taken in hourly terms. The curves of several variables are obtained for representative days of all seasons: overall plant efficiency, solar subsystem efficiency, solar share, fuel conversion rate, and power output. The fuel consumption assuming natural gas fueling is calculated and the reduction in greenhouse emissions is discussed. The model can be applied to predict the daily and seasonal evolution of the performance of real installations in terms of a reduced set of parameters.MINECO of Spai

Desarrollo de actividades online por medio de WIRIS para la asignatura Mecánica y Termodinámica

Memoria ID12-0214. Ayudas de la Universidad de Salamanca para la innovación docente, curso 2012-2013

Compartmental Learning versus Joint Learning in Engineering Education

[EN]Sophomore students from the Chemical Engineering undergraduate Degree at the University of Salamanca are involved in a Mathematics course during the third semester and in an Engineering Thermodynamics course during the fourth one. When they participate in the latter they are already familiar with mathematical software and mathematical concepts about numerical methods, including non-linear equations, interpolation or differential equations. We have focused this study on the way engineering students learn Mathematics and Engineering Thermodynamics. As students use to learn each matter separately and do not associate Mathematics and Physics, they separate each matter into different and independent compartments. We have proposed an experience to increase the interrelationship between different subjects, to promote transversal skills, and to make the subjects closer to real work. The satisfactory results of the experience are exposed in this work. Moreover, we have analyzed the results obtained in both courses during the academic year 2018–2019. We found that there is a relation between both courses and student’s final marks do not depend on the course