Nuevo algoritmo de relajación para motores de cuerpos sólidos

Con el fin de conocer mejor a las bacterias, en la actualidad se han desarrollado aplicaciones que permite simular el comportamiento de las colonias formadas por este tipo de organismos. Una de las piezas más importantes que tienen estos simuladores es el motor de físicas. Éste es el encargado de resolver todas las fuerzas producidas entre las bacterias y conseguir que todas queden correctamente colocadas y distribuidas a lo largo de la colonia, tratando de asemejarse lo más posible a la realidad. En una simulación de éstas características, todas las bacterias, además de estar en contacto entre sí, crecen en un pequeño porcentaje durante cada fotograma. Ello produce una gran cantidad de solapamiento a lo largo de toda la colonia que el motor de físicas tiene que resolver. El trabajo que se describe en este documento surge de la ineficiencia del proceso actual para distribuir el solapamiento originado en el interior de la colonia, hasta su exterior. Es importante señalar que la física se lleva el 99% del tiempo de procesado de la simulación de una colonia, con lo que una mejora en el motor de físicas conseguiría incrementar en gran medida la capacidad de simulación. El objetivo no es otro que poder simular más cantidad de bacterias en menos tiempo, facilitando el estudio de esta área tan reciente como es la biología sintética. ---ABSTRACT---In order to better understand bacteria, new applications have been developed to simulate the behavior of colonies formed by these organisms. One of the most important parts of these simulators is the physics engine. This module is responsible for solving all the forces produced between bacteria and ensure that they are properly located and distributed throughout the colony, trying to be as close as possible to reality. In a simulation with these features, all bacteria, besides being in contact with each other, grow in a small percentage at each frame. This produces a large amount of overlap along the entire colony that the physics engine must solve. The work described in this document arises from the inefficiency of the current process to distribute the overlap originated at the core of the colony outwards. Importantly, physics takes up 99% of the processing time of the simulation of a colony. Therefore, improving the physics engine would translate in a drastic increase in the throughput of the simulation. The goal is simply to be able to simulate more bacteria in less time, making the study of the recent area, synthetic biology, much easier.

ARGoS: a modular, multi-engine simulator for heterogeneous swarm robotics

We present ARGoS, a novel open source multi-robot simulator. The main design focus of ARGoS is the real-time simulation of large heterogeneous swarms of robots. Existing robot simulators obtain scalability by imposing limitations on their extensibility and on the accuracy of the robot models. By contrast, in ARGoS we pursue a deeply modular approach that allows the user both to easily add custom features and to allocate computational resources where needed by the experiment. A unique feature of ARGoS is the possibility to use multiple physics engines of different types and to assign them to different parts of the environment. Robots can migrate from one engine to another transparently. This feature enables entirely novel classes of optimizations to improve scalability and paves the way for a new approach to parallelism in robotics simulation. Results show that ARGoS can simulate about 10,000 simple wheeled robots 40% faster than real-time.

An Autonomous Engine for Services Configuration and Deployment.

The runtime management of the infrastructure providing service-based systems is a complex task, up to the point where manual operation struggles to be cost effective. As the functionality is provided by a set of dynamically composed distributed services, in order to achieve a management objective multiple operations have to be applied over the distributed elements of the managed infrastructure. Moreover, the manager must cope with the highly heterogeneous characteristics and management interfaces of the runtime resources. With this in mind, this paper proposes to support the configuration and deployment of services with an automated closed control loop. The automation is enabled by the definition of a generic information model, which captures all the information relevant to the management of the services with the same abstractions, describing the runtime elements, service dependencies, and business objectives. On top of that, a technique based on satisfiability is described which automatically diagnoses the state of the managed environment and obtains the required changes for correcting it (e.g., installation, service binding, update, or configuration). The results from a set of case studies extracted from the banking domain are provided to validate the feasibility of this proposa

The Learning of the subject Biology in a Master in Biomedical Physics

BIOLOGY is a dynamic and fascinating science. The study of this subject is an amazing trip for all the students that have a first contact with this subject. Here, we present the development of the study and learning experience of this subject belonging to an area of knowledge that is different to the training curriculum of students who have studied Physics during their degree period. We have taken a real example, the “Elements of Biology” subject, which is taught as part of the Official Biomedical Physics Master, at the Physics Faculty, of the Complutense University of Madrid, since the course 2006/07. Its main objective is to give to the student an understanding how the Physics can have numerous applications in the Biomedical Sciences area, giving the basic training to develop a professional, academic or research career. The results obtained when we use new virtual tools combined with the classical learning show that there is a clear increase in the number of persons that take and pass the final exam. On the other hand, this new learning strategy is well received by the students and this is translated to a higher participation and a decrease of the giving the subject up

Fluid-physics-module experiments

Some floating-liquid-zone experiments performed under reduced-gravity conditions are reviewed. Several types of instabilities are discussed, together with the relevant parameters controlling them. It is shown that the bounding values of these parameters could be increased, by orders of magnitude in several instances, by selecting appropriate liquids. Two of the many problems that a Fluid-Physics Module, devised to perform experiments on floating zones in a space laboratory, would involve are discussed: namely (i) procedures for disturbing the zoneunder controlled conditions, and (ii) visualisation of the inner flow pattern. Several topics connected with the nonisothermal nature and the phase-changes of floating zones are presented. In particular, a mode of propagation through the liquid zone for disturbances which could appear in the melting solid/liquid interface is suggested. Although most research on floating liquid zones is aimed at improving the crystal-growth process, some additional applications are suggested.

StreamCloud: An Elastic Parallel-Distributed Stream Processing Engine

In recent years, applications in domains such as telecommunications, network security or large scale sensor networks showed the limits of the traditional store-then-process paradigm. In this context, Stream Processing Engines emerged as a candidate solution for all these applications demanding for high processing capacity with low processing latency guarantees. With Stream Processing Engines, data streams are not persisted but rather processed on the fly, producing results continuously. Current Stream Processing Engines, either centralized or distributed, do not scale with the input load due to single-node bottlenecks. Moreover, they are based on static configurations that lead to either under or over-provisioning. This Ph.D. thesis discusses StreamCloud, an elastic paralleldistributed stream processing engine that enables for processing of large data stream volumes. Stream- Cloud minimizes the distribution and parallelization overhead introducing novel techniques that split queries into parallel subqueries and allocate them to independent sets of nodes. Moreover, Stream- Cloud elastic and dynamic load balancing protocols enable for effective adjustment of resources depending on the incoming load. Together with the parallelization and elasticity techniques, Stream- Cloud defines a novel fault tolerance protocol that introduces minimal overhead while providing fast recovery. StreamCloud has been fully implemented and evaluated using several real word applications such as fraud detection applications or network analysis applications. The evaluation, conducted using a cluster with more than 300 cores, demonstrates the large scalability, the elasticity and fault tolerance effectiveness of StreamCloud. Resumen En los útimos años, aplicaciones en dominios tales como telecomunicaciones, seguridad de redes y redes de sensores de gran escala se han encontrado con múltiples limitaciones en el paradigma tradicional de bases de datos. En este contexto, los sistemas de procesamiento de flujos de datos han emergido como solución a estas aplicaciones que demandan una alta capacidad de procesamiento con una baja latencia. En los sistemas de procesamiento de flujos de datos, los datos no se persisten y luego se procesan, en su lugar los datos son procesados al vuelo en memoria produciendo resultados de forma continua. Los actuales sistemas de procesamiento de flujos de datos, tanto los centralizados, como los distribuidos, no escalan respecto a la carga de entrada del sistema debido a un cuello de botella producido por la concentración de flujos de datos completos en nodos individuales. Por otra parte, éstos están basados en configuraciones estáticas lo que conducen a un sobre o bajo aprovisionamiento. Esta tesis doctoral presenta StreamCloud, un sistema elástico paralelo-distribuido para el procesamiento de flujos de datos que es capaz de procesar grandes volúmenes de datos. StreamCloud minimiza el coste de distribución y paralelización por medio de una técnica novedosa la cual particiona las queries en subqueries paralelas repartiéndolas en subconjuntos de nodos independientes. Ademas, Stream- Cloud posee protocolos de elasticidad y equilibrado de carga que permiten una optimización de los recursos dependiendo de la carga del sistema. Unidos a los protocolos de paralelización y elasticidad, StreamCloud define un protocolo de tolerancia a fallos que introduce un coste mínimo mientras que proporciona una rápida recuperación. StreamCloud ha sido implementado y evaluado mediante varias aplicaciones del mundo real tales como aplicaciones de detección de fraude o aplicaciones de análisis del tráfico de red. La evaluación ha sido realizada en un cluster con más de 300 núcleos, demostrando la alta escalabilidad y la efectividad tanto de la elasticidad, como de la tolerancia a fallos de StreamCloud.