Multi-tenant Pub/Sub processing for real-time data streams

Devices and sensors generate streams of data across a diversity of locations and protocols. That data usually reaches a central platform that is used to store and process the streams. Processing can be done in real time, with transformations and enrichment happening on-the-fly, but it can also happen after data is stored and organized in repositories. In the former case, stream processing technologies are required to operate on the data; in the latter batch analytics and queries are of common use. This paper introduces a runtime to dynamically construct data stream processing topologies based on user-supplied code. These dynamic topologies are built on-the-fly using a data subscription model defined by the applications that consume data. Each user-defined processing unit is called a Service Object. Every Service Object consumes input data streams and may produce output streams that others can consume. The subscription-based programing model enables multiple users to deploy their own data-processing services. The runtime does the dynamic forwarding of data and execution of Service Objects from different users. Data streams can originate in real-world devices or they can be the outputs of Service Objects. The runtime leverages Apache STORM for parallel data processing, that combined with dynamic user-code injection provides multi-tenant stream processing topologies. In this work we describe the runtime, its features and implementation details, as well as we include a performance evaluation of some of its core components.This work is partially supported by the European Research Council (ERC) un- der the EU Horizon 2020 programme (GA 639595), the Spanish Ministry of Economy, Industry and Competitivity (TIN2015-65316-P) and the Generalitat de Catalunya (2014-SGR-1051).Peer ReviewedPostprint (author's final draft

Changes in hydrodynamic, structural and geochemical properties in carbonate rock samples due to reactive transport

Reactive transport plays an important role in the development of a wide range of both anthropic and natural processes affecting geological media. To predict the consequences of reactive transport processes on structural and hydrodynamic properties of a porous media at large time and spatial scales, numerical modeling is a powerful tool. Nevertheless, such models, to be realistic, need geochemical, structural and hydrodynamic data inputs representative of the studied reservoir or material. Here, we present an experimental study coupling traditional laboratory measurements and percolation experiments in order to obtain the parameters that define rock heterogeneity, which can be altered during the percolation of a reactive fluid. In order to validate the experimental methodology and identify the role of the initial heterogeneities on the localization of the reactive transport processes, we used three different limestones with different petrophysical characteristics. We tracked the changes of geochemical, structural and hydrodynamic parameters in these samples induced by the percolation of an acid fluid by measuring, before and after the percolation experiment, petrophysical and hydrodynamic properties of the rocks.Peer ReviewedPostprint (published version

ALOJA: A benchmarking and predictive platform for big data performance analysis

The main goals of the ALOJA research project from BSC-MSR, are to explore and automate the characterization of cost-effectivenessof Big Data deployments. The development of the project over its first year, has resulted in a open source benchmarking platform, an online public repository of results with over 42,000 Hadoop job runs, and web-based analytic tools to gather insights about system's cost-performance1. This article describes the evolution of the project's focus and research lines from over a year of continuously benchmarking Hadoop under dif- ferent configuration and deployments options, presents results, and dis cusses the motivation both technical and market-based of such changes. During this time, ALOJA's target has evolved from a previous low-level profiling of Hadoop runtime, passing through extensive benchmarking and evaluation of a large body of results via aggregation, to currently leveraging Predictive Analytics (PA) techniques. Modeling benchmark executions allow us to estimate the results of new or untested configu- rations or hardware set-ups automatically, by learning techniques from past observations saving in benchmarking time and costs.This work is partially supported the BSC-Microsoft Research Centre, the Span- ish Ministry of Education (TIN2012-34557), the MINECO Severo Ochoa Research program (SEV-2011-0067) and the Generalitat de Catalunya (2014-SGR-1051).Peer ReviewedPostprint (author's final draft

Si elimino el examen, ¿cómo evalúo?: Una discusión sobre las actividades sustitutivas del examen y su escalabilidad

Se ha discutido mucho sobre los inconvenientes de los exámenes, siendo criticados en ocasiones por fomentar el aprendizaje superficial. Aunque muchos profesores estemos de acuerdo, nos preguntamos ¿por qué actividades se pueden sustituir? Existen experiencias para eliminar el uso de exámenes, pero la mayoría son criticadas por realizarse sobre grupos pequeños con muchas actividades evaluadoras. Este trabajo presenta diversas estrategias y actividades realizadas en una asignatura del grado de Informática para poder eliminar los exámenes consiguiendo al mismo tiempo un alto nivel de aprendizaje. La experiencia se ha realizado sobre un grupo pequeño, pero ante la presión de nuestro centro por aumentar la matrícula, se están diseñando las actividades para seguir garantizando una buena experiencia educativa sin incrementar el trabajo del profesor.Peer ReviewedPostprint (published version

Constant-time approximate sliding window framework with error control

Stream Processing is a crucial element for the Edge Computing paradigm, in which large amount of devices generate data at the edge of the network. This data needs to be aggregated and processed on-the-move across different layers before reaching the Cloud. Therefore, defining Stream Processing services that adapt to different levels of resource availability is of paramount importance. In this context, Stream Processing frameworks need to combine efficient algorithms with low computational complexity to manage sliding windows, with the ability to adjust resource demands for different deployment scenarios, from very low capacity edge devices to virtually unlimited Cloud platforms. The Approximate Computing paradigm provides improved performance and adaptive resource demands in data analytics, at the price of introducing some level of inaccuracy that can be calculated. In this paper we present the Approximate and Amortized Monoid Tree Aggregator (A 2 MTA). It is, to our knowledge, the first general purpose sliding window programable framework that combines constant-time aggregations with error bounded approximate computing techniques. It is very suitable for adverse stream processing environments, such as resource scarce multi-tenant edge computing. The framework can compute aggregations over multiple data dimensions, setting error bounds on any of them, and has been designed to support decoupling computation and data storage through the use of distributed Key-Value Stores to keep window elements and partial aggregations.This project is partially supported by the European Research Council (ERC), Spain under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 639595). It is also partially supported by the Ministry of Economy of Spain under contract TIN2015-65316-P and Generalitat de Catalunya, Spain under contract 2014SGR1051, by the ICREA Academia program, and by the BSC-CNS Severo Ochoa program (SEV-2015-0493).Peer ReviewedPostprint (author's final draft

Resource management for software defined data centers for heterogeneous infrastructures

Software Defined Data Center (SDDC) provides more resource management flexibility since everything is defined as a software, including the network as Software Defined Network (SDN).Typically, cloud providers overlook the network, which is configured in static way. SDN can help to meet applications goals with dynamic network configuration and provide best-efforts for QoS. Additionally, SDDC might benefit by instead of be composed by heavy Virtual Machines, use light-weight OS Containers. Despite the advantages of SDDC and OS Containers, it brings more complexity for resource provisioning. The goal of this project is to optimize the management of container based workloads deployed on Software defined Data Centers enabled with heterogeneous network fabrics through the use of network-aware placement algorithms that are driven by performance models

Arsenic and vanadium levels in waters in the Union Department, southeast of Córdoba province, Argentina

En Argentina, existen grandes regiones que por sus características edafoclimáticas, poseen excelentes aptitudes agropecuarias, sin embargo, sufren limitaciones de desarrollo debido a la disponibilidad de agua y la calidad del recurso hídrico disponible. Uno de los elementos químicos presentes en el agua, con mayor impacto sobre la salud humana y animal, es el arsénico. La región afectada, abarca las provincias de Córdoba, La Pampa, Santiago del Estero, San Luis, Santa Fe, Buenos Aires, Chaco, Salta, Tucumán, San Juan y Mendoza. El objetivo de este trabajo es analizar la presencia y distribución del arsénico en agua superficial y subterránea en el sudeste de la provincia de Córdoba, una de las zonas más afectadas de Argentina por la presencia de arsénico en el agua.Argentina has large regions with excellent aptitude for agricultural activities. Nevertheless, some areas show development limitations as to water availability and quality. One of the chemical elements in water with great impact on human and animal health is arsenic. The As-affected region includes the provinces of Córdoba, La Pampa, Santiago del Estero, San Luis, Santa Fe, Buenos Aires, Chaco, Salta, Tucumán, San Juan and Mendoza. The aim of this work was to analyze the presence and distribution of arsenic in superficial and ground waters in south-eastern Córdoba province, one of the areas in Argentina most affected by the presence of arsenic in water. Arsenic levels in groundwater were highly variable. The highest values were reported in the phreatic aquifer where arsenic concentration was between 20 and 4600 μg.L-1. Another element found at significant levels in the phreatic aquifer was vanadium whose concentrations were between 30 and 2710 μg.L-1. The presence of vanadium associated with high levels of arsenic could pose a risk to animal health and production.Fil: Pérez Carrera, Alejo Leopoldo. Universidad de Buenos Aires. Facultad de Ciencias Veterinarias. Centro de Estudios Transdisciplinarios del Agua; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Fernandez Cirelli, Alicia. Universidad de Buenos Aires. Facultad de Ciencias Veterinarias. Centro de Estudios Transdisciplinarios del Agua; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Prospectiva tecnológica para la identificación de oportunidades de innovación en la empresa Quinsa S.A.

203 páginas y anexosEl estudio prospectivo tecnológico para la empresa QUINSA S.A., realizado entre enero y octubre de 2017 en la ciudad de Neiva (Huila, Colombia), está dividido en nueve apartados: primero se presenta un contexto que pretende definir el alcance del estudio (en términos del estado del arte de la empresa, las tendencias mundiales, la vigilancia tecnológica y la matriz de cambio), luego se incluyen la identificación de factores de cambio y la priorización de variables estratégicas; en seguida, se presenta una construcción preliminar de escenarios que sirve de insumo para obtener los resultados del juego de actores; luego, se definen y narran los escenarios deseables, en donde la empresa se proyecta en el futuro y se apuesta por uno de ellos, que se valida contrastándose con los escenarios probables; a continuación, se diseñan estrategias para materializar el escenario apuesta en acciones concretas y ordenadas; y finalmente, con base en la experiencia resultante del desarrollo del proceso prospectivo, se emiten algunas conclusiones, recomendaciones y comentarios finales y se respalda el contenido del informe con las referencias bibliográficas utilizadas.The technological prospective study for the company QUINSA SA, carried out between January and October 2017 in the city of Neiva (Huila, Colombia), is divided into nine sections: first, a context that aims to define the scope of the study (in terms of the state of the state of art, global trends, technological surveillance and the change matrix), then includes the identification of the change factors and the prioritization of the strategic variables; Next, a preliminary construction of scenarios is presented that serves as an input to obtain the results of the stakeholder game; then, the desirable scenarios, where the company is projected in the future, are defined and narrated, and QUINSA choose one of them, which is validated against the probable scenarios; then, strategies to materialize the bet scenario in concrete and orderly actions are designed; and finally, based on the experience resulting from the development of the prospective process, some conclusions, recommendations and final comments were issued with the bibliographic references used.Magíster en Prospectiva y Pensamiento EstratégicoMaestrí

Arsenic levels in bovine tissues in the southeast of the province Córdoba, Argentina

El arsénico es un contaminante natural de aguas subterráneas en una amplia zona de Argentina, en particular el sudeste de la provincia de Córdoba es una de las regiones más afectadas. La información a nivel mundial acerca de la transferencia de arsénico a la cadena agroalimentaria particularmente a productos cárnicos es escasa. En este trabajo, se determinaron las concentraciones de arsénico en riñón, hígado, músculo esquelético y glándula mamaria en bovinos de la zona de estudio. Los órganos donde se registraron las mayores concentraciones de arsénico fueron hígado y riñón. Los niveles hallados en hígado estuvieron entre 27,0 y 46,5 ng/g y en riñón, entre 24,0 y 73,2 ng/g. En las muestras de músculo y glándula mamaria, las concentraciones estuvieron en todos los casos por debajo del límite de detección de la técnica utilizada. Las concentraciones de arsénico en los diferentes tejidos analizados se encontraron dentro de los límites recomendados a nivel nacional.Arsenic is a groundwater contaminant widely distributed in Argentina. One of the most affected area is the southeast of Cordoba province. The information about the transfer of arsenic to the food chain and meat products is scarce. In this study, the concentrations of arsenic in kidney, liver, muscle and udder in cattle in the study area were analyzed. The highest concentrations of arsenic were found in liver and kidney. The levels found in liver ranged from 27.0 to 46.5 ng/g while in kidney, ranged between 24.0 to 73.2 ng /g. In muscle and udder samples arsenic were below the detection limit of the technique in all cases. The levels of arsenic in the analyzed tissues were within the national recommended limits.Fil: Pérez Carrera, Alejo Leopoldo. Universidad de Buenos Aires. Facultad de Ciencias Veterinarias. Centro de Estudios Transdisciplinarios del Agua; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Pérez Gardiner, M. L.. Universidad de Buenos Aires. Facultad de Ciencias Veterinarias. Centro de Estudios Transdisciplinarios del Agua; ArgentinaFil: Fernandez Cirelli, Alicia. Universidad de Buenos Aires. Facultad de Ciencias Veterinarias. Centro de Estudios Transdisciplinarios del Agua; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

ALOJA: A framework for benchmarking and predictive analytics in Hadoop deployments

This article presents the ALOJA project and its analytics tools, which leverages machine learning to interpret Big Data benchmark performance data and tuning. ALOJA is part of a long-term collaboration between BSC and Microsoft to automate the characterization of cost-effectiveness on Big Data deployments, currently focusing on Hadoop. Hadoop presents a complex run-time environment, where costs and performance depend on a large number of configuration choices. The ALOJA project has created an open, vendor-neutral repository, featuring over 40,000 Hadoop job executions and their performance details. The repository is accompanied by a test-bed and tools to deploy and evaluate the cost-effectiveness of different hardware configurations, parameters and Cloud services. Despite early success within ALOJA, a comprehensive study requires automation of modeling procedures to allow an analysis of large and resource-constrained search spaces. The predictive analytics extension, ALOJA-ML, provides an automated system allowing knowledge discovery by modeling environments from observed executions. The resulting models can forecast execution behaviors, predicting execution times for new configurations and hardware choices. That also enables model-based anomaly detection or efficient benchmark guidance by prioritizing executions. In addition, the community can benefit from ALOJA data-sets and framework to improve the design and deployment of Big Data applications.This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 639595). This work is partially supported by the Ministry of Economy of Spain under contracts TIN2012-34557 and 2014SGR1051.Peer ReviewedPostprint (published version