Mapping of some soil properties at catchment scale

[Abstract] The spatial structure of gravel content and three textural fractions (sand, silt, clay) was investigated in a 19.8 ha mixed, agricultural and forest catchment through of geostatistical techniques. Three different depths (0-15 cm, 15-30 cm and 30-45 cm) were sampled in order to describe the spatial variability from 0 to about 300 m. It was shown a spatial structure for all the studied variables, which could be described by different types of semivariograms (sphericals,exponentials an gaussians) with nugget effect component and a spatial component ranging from 3,5 to 365 m. Maps were performed using the information contained in the semivariograms by block kriging, so that contour maps were drawn for the different texture fractions and also showing the kriging errors. It was found greater spatial dependence of the studied variables in the first 15 cm than in the other depths

Flame-MR: An event-driven architecture for MapReduce applications

[Abstract] Nowadays, many organizations analyze their data with the MapReduce paradigm, most of them using the popular Apache Hadoop framework. As the data size managed by MapReduce applications is steadily increasing, the need for improving the Hadoop performance also grows. Existing modifications of Hadoop (e.g., Mellanox Unstructured Data Accelerator) attempt to improve performance by changing some of its underlying subsystems. However, they are not always capable to cope with all its performance bottlenecks or they hinder its portability. Furthermore, new frameworks like Apache Spark or DataMPI can achieve good performance improvements, but they do not keep compatibility with existing MapReduce applications. This paper proposes Flame-MR, a new event-driven MapReduce architecture that increases Hadoop performance by avoiding memory copies and pipelining data movements, without modifying the source code of the applications. The performance evaluation on two representative systems (an HPC cluster and a public cloud platform) has shown experimental evidence of significant performance increases, reducing the execution time by up to 54% on the Amazon EC2 cloud.Ministerio de Economía y Competititvidad; TIN2013-42148-PMinisterio de Educación; FPU14/0280

Determinación de valores séricos y factores asociados en Caninos Domésticos (canis familiaris) en el Barrio San Pedro de Teneria de la Parroquia San Juan de Pastocalle

The present investigation was carried out in Barrio San Pedro de Teneria, in Ecuador there are very few studies on serum values and associated factors, This research was carried out in 75 canines of the neighborhood to determine the serum values and risk factors in (canis familiaris) for which the serum values and associated factors were scientifically based, established the associated factors of the domestic canines (canis familiaris) applying questionnaire, the factors associated with the serum values in the domestic canines (canis familiaris) were determined and the influence of the results on the serum values vs the associated factors was determined. The investigation was scientific and documentary for which the researchers applied a survey to the owners of the domestic canines (canis familiaris), then the researchers made the count of the number of leukocytes, erythrocytes, urea, creatinine, glucose, ALT, AST, etc. In domestic canines (canis familiaris) according to race and age, to determine the prevalence of diseases, the age range with the most inconveniences was 1 to 5 years, with 29.92% of problems with hematocrit, 22, 22% in hemoglobin, 18% in erythrocytes and 35.19% of problems with eosinophils, the research will have a great impact on society, since by means of the socialization of the results obtained, the population will be encouraged to hold pets responsibly.La presente investigación se realizó en el Barrio San Pedro de Teneria, en el Ecuador existe muy pocos estudios sobre los valores séricos y factores asociados, por lo que la investigación se realizó en 75 caninos del Barrio para determinar los valores séricos y factores de riesgo en (canis familiaris) para lo cual se fundamentó científicamente los valores séricos y factores asociados, estableció los factores asociados de los caninos domésticos (canis familiaris) aplicando cuestionario, se relacionó los factores asociados con los valores séricos en los caninos domésticos (canis familiaris) y se determinó la influencia de los resultados en los valores séricos vs los factores asociados, la investigación fue científica y documental para lo cual se realizó una encuesta a los dueños de los caninos domésticos (canis familiaris), luego se dtermino el número de leucocitos, eritrocitos, urea, creatinina, glucosa, ALT, AST, etc. En caninos domésticos (canis familiaris) de acuerdo a la edad, el rango con más alteración en los niveles séricos fue el de 1 a 5 años con 54 caninos, teniendo elevado el hematocrito en un 59,02%, disminuido el hematocrito en un 33,2%, la hemoglobina elevada en un 62,75%, disminuida en un 40%, los eritrocitos se encuentran elevados en un 90% y disminuidos en un 50%, la investigación causa impacto en la sociedad ya que mediante la socialización de los resultados obtenidos se incentivará a la población a la tenencia responsable de las mascotas

Analysis and evaluation of MapReduce solutions on an HPC cluster

This is a post-peer-review, pre-copyedit version of an article published in Computers & Electrical Engineering. The final authenticated version is available online at: https://doi.org/10.1016/j.compeleceng.2015.11.021[Abstract] The ever growing needs of Big Data applications are demanding challenging capabilities which cannot be handled easily by traditional systems, and thus more and more organizations are adopting High Performance Computing (HPC) to improve scalability and efficiency. Moreover, Big Data frameworks like Hadoop need to be adapted to leverage the available resources in HPC environments. This situation has caused the emergence of several HPC-oriented MapReduce frameworks, which benefit from different technologies traditionally oriented to supercomputing, such as high-performance interconnects or the message-passing interface. This work aims to establish a taxonomy of these frameworks together with a thorough evaluation, which has been carried out in terms of performance and energy efficiency metrics. Furthermore, the adaptability to emerging disks technologies, such as solid state drives, has been assessed. The results have shown that new frameworks like DataMPI can outperform Hadoop, although using IP over InfiniBand also provides significant benefits without code modifications.Ministerio de Economía y Competitividad; TIN2013-42148-

The HPS3 Service: Reduction of Cost and Transfer Time for Storing Data on Clouds

This is a post-peer-review, pre-copyedit version. The final authenticated version is available online at: http://dx.doi.org/10.1109/HPCC.2014.38[Abstract] In the past several years, organizations have been changing their storage methods as the volume of data they managed has increased. The cloud computing paradigm offers new ways of storing data based on scalability and on good conditions of reliability and accessibility. This paper proposes the design of HPS3, a service that uses compression and concurrency techniques in order to reduce storage costs and data processing times in public storage providers. Different strategies for compressing and uploading data depending on differential characteristics of the datasets are also explained. The evaluation of HPS3 shows, in comparison with the default use of the cloud storage provider (Amazon S3), an average reduction of 61.6% in data transfer volume, 85.5% in upload time and 73.2% in incurred costs. Compared to Drop box, HPS3 shows an average improvement of 27.4% in data transfer volume and 93.6% in upload time.Ministerio de Economía y Competitividad; TIN2013-42148-PXunta de Galicia; GRC2013/05

Enhancing in-memory Efficiency for MapReduce-based Data Processing

This is a post-peer-review, pre-copyedit version of an article published in Journal of Parallel and Distributed Computing. The final authenticated version is available online at: https://doi.org/10.1016/j.jpdc.2018.04.001[Abstract] As the memory capacity of computational systems increases, the in-memory data management of Big Data processing frameworks becomes more crucial for performance. This paper analyzes and improves the memory efficiency of Flame-MR, a framework that accelerates Hadoop applications, providing valuable insight into the impact of memory management on performance. By optimizing memory allocation, the garbage collection overheads and execution times have been reduced by up to 85% and 44%, respectively, on a multi-core cluster. Moreover, different data buffer implementations are evaluated, showing that off-heap buffers achieve better results overall. Memory resources are also leveraged by caching intermediate results, improving iterative applications by up to 26%. The memory-enhanced version of Flame-MR has been compared with Hadoop and Spark on the Amazon EC2 cloud platform. The experimental results have shown significant performance benefits reducing Hadoop execution times by up to 65%, while providing very competitive results compared to Spark.Ministerio de Economía, industria y Competitividad; TIN2016-75845-P, AEI/FEDER/EUMinisterio de Educación; FPU14/0280