Lightweigth Adaptive fault-tolerant data storage system (AFTSYS)

Research group ARCOS of Universidad Carlos III de Madrid (Spain) have been working on flexible and adaptive data storage systems for several years. The storage systems developed are featured by software governance, making them portable across different hardware storage resources, and their dynamic adaptativy to the different circumstances of computer systems following the autonomic system paradigm. They also allow getting high performance storage by using data distribution or striping across multiple devices. One of the group’s technologies y the AFTSYS system. A fault-tolerant storage system for persistent distributed objects, user configurable and adaptive to system behaviour

Almacenamiento de datos ligero adaptativo y tolerante a fallos (AFTSYS)

El grupo de investigación ARCOS de la Universidad Carlos III de Madrid (España), lleva varios años trabajando en sistemas de almacenamiento de datos flexibles y adaptativos. Sus sistemas de almacenamiento se caracterizan porque se gobiernan mediante software, lo que permite implementarlos sobre distintas plataformas hardware asegurando su portabilidad, se adaptan dinámicamente a las circunstancias de los sistemas siguiendo el paradigma de los sistemas autónomos y permiten obtener partido de sistemas con almacenamiento de datos distribuidos o repartidos entre múltiples dispositivos. Una de las tecnologías del grupo es el sistema AFTSYS. Un sistema de almacenamiento tolerante a fallos a nivel de objetos persistentes distribuidos, configurable por el usuario y adaptable al comportamiento del sistema

Creación automática de aplicaciones seguras para wireless sensors networks usando MDA

El grupo de investigación ARCOS de la Universidad Carlos III de Madrid (España), lleva varios años trabajando en sistemas de almacenamiento de datos flexibles y adaptativos. Sus sistemas de almacenamiento se caracterizan porque se gobiernan mediante software, lo que permite implementarlos sobre distintas plataformas hardware asegurando su portabilidad, se adaptan dinámicamente a las circunstancias de los sistemas siguiendo el paradigma de los sistemas autónomos y permiten obtener partido de sistemas con almacenamiento de datos distribuidos o repartidos entre múltiples dispositivos. Una de las tecnologías del grupo es el sistema AFTSYS. Un sistema de almacenamiento tolerante a fallos a nivel de objetos persistentes distribuidos, configurable por el usuario y adaptable al comportamiento del sistema

Optimal railway infrastructure maintenance and repair policies to manage risk under uncertainty with adaptive control

The aim of this paper is to apply two adaptive control formulations under uncertainty, say open-loop and closed-loop, to the process of developing maintenance and repair policies for railway infrastructures. To establish the optimal maintenance and repair policies for railway lines, we use a previous design of risk model based on two factors: the criticality and the deterioration ratios of the facilities. Thus, our theory benefits from the Reliability Centered Management methodology application, but it also explicitly models uncertainty in characterizing a facility deterioration rate to decide the optimal policy to maintain the railway infrastructures. This may be the major contribution of this work. To verify the models presented, a computation study has been developed and tested for a real scenario: the railway line Villalba-Cercedilla in Madrid (Spain). Our results demonstrate again that applying any adaptive formulation, the cost of the railway lines maintenance shown is decreased. Moreover applying a Closed Loop Formulation the cost associated to the risk takes smaller values (40% less cost for the same risk than the deterministic approach), but with an Open Loop formulation the generated risk in the railway line is also smaller

Combining malleability and I/O control mechanisms to enhance the execution of multiple applications

This work presents a common framework that integrates CLARISSE, a cross-layer runtime for the I/O software stack, and FlexMPI, a runtime that provides dynamic load balancing and malleability capabilities for MPI applications. This integration is performed both at application level, as libraries executed within the application, as well as at central-controller level, as external components that manage the execution of different applications. We show that a cooperation between both runtimes provides important benefits for overall system performance: first, by means of monitoring, the CPU, communication and I/O performances of all executing applications are collected, providing a holistic view of the complete platform utilization. Secondly, we introduce a coordinated way of using CLARISSE and FlexMPI control mechanisms, based on two different optimization strategies, with the aim of improving both the application I/O and overall system performance. Finally, we present a detailed description of this proposal, as well as an empirical evaluation of the framework on a cluster showing significant performance improvements at both application and wide-platform levels. We demonstrate that with this proposal the overall I/O time of an application can be reduced by up to 49% and the aggregated FLOPS of all running applications can be increased by 10% with respect to the baseline case. (C) 2018 Elsevier Inc. All rights reserved.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work has been partially supported by the Spanish “Ministerio de Economia y Competitividad” under the project grant TIN2016-79637-P “Towards Unification of HPC and Big Data paradigms” and EU under the COST Program Action IC1305, Network for Sustainable Ultrascale Computing (NESUS)

Optimización de carga de datos en un banco de pruebas de aviónica

Ponencias de las Decimocuartas Jornadas de Paralelismo de la Universidad Carlos III de Madrid celebradas el 15, 16 y 17 de septiembre de 2003 en Leganés, MadridEste artículo presenta las técnicas de optimización utilizadas para acelerar la carga de definiciones de señales de un banco de pruebas de aviónica. La carga de datos en formato XML puede presentar problemas de rendimiento cuando se trata de grandes volumenes de datos. Bajo esas condiciones, es necesario buscar alternativas que permitan cargar los datos de forma más eficiente, sin imponer restricciones a los generadores de los datos utilizados como entrada. El artículo analiza posibles optimizaciones y valora las ventajas e inconvenientes de cada solución.Publicad

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016)

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016) Timisoara, Romania. February 8-11, 2016.The PhD Symposium was a very good opportunity for the young researchers to share information and knowledge, to present their current research, and to discuss topics with other students in order to look for synergies and common research topics. The idea was very successful and the assessment made by the PhD Student was very good. It also helped to achieve one of the major goals of the NESUS Action: to establish an open European research network targeting sustainable solutions for ultrascale computing aiming at cross fertilization among HPC, large scale distributed systems, and big data management, training, contributing to glue disparate researchers working across different areas and provide a meeting ground for researchers in these separate areas to exchange ideas, to identify synergies, and to pursue common activities in research topics such as sustainable software solutions (applications and system software stack), data management, energy efficiency, and resilience.European Cooperation in Science and Technology. COS

Performance-aware scheduling of parallel applications on non-dedicated clusters

This work presents a HPC framework that provides new strategies for resource management and job scheduling, based on executing different applications in shared compute nodes, maximizing platform utilization. The framework includes a scalable monitoring tool that is able to analyze the platform's compute node utilization. We also introduce an extension of CLARISSE, a middleware for data-staging coordination and control on large-scale HPC platforms that uses the information provided by the monitor in combination with application-level analysis to detect performance degradation in the running applications. This degradation, caused by the fact that the applications share the compute nodes and may compete for their resources, is avoided by means of dynamic application migration. A description of the architecture, as well as a practical evaluation of the proposal, shows significant performance improvements up to 20% in the makespan and 10% in energy consumption compared to a non-optimized execution.This work was partially supported by the Spanish Ministry of Economy, Industry and Competitiveness under the grant TIN2016-79637-P "Towards Unification of HPC and Big Data Paradigms"; and the European Union's Horizon 2020 research and innovation program under Grant No. 801091, project "Exascale programming models for extreme data processing" (ASPIDE)

Evaluating the impact of the weather conditions on the influenza propagation

We show that the simulation results have the same propagation shape as the weekly influenza rates asrecorded by SISSS. We perform experiments for a realistic scenario based on actual meteorological data from2010-2011, and for synthetic values assumed under simplified predicted climate change conditions. Results show thata diminishing relative humidity of 10% produces an increment of about 1.6% in the final infection rate. The effect oftemperature changes on the infection spread is also noticeable, with a decrease of 1.1% per extra degree.Conclusions: Using a tool like ours could help predict the shape of developing epidemics and its peaks, and wouldpermit to quickly run scenarios to determine the evolution of the epidemic under different conditions. We makeEpiGraph source code and epidemic data publicly availableThis work has been partially supported by the Spanish “Ministerio de Economía y Competitividad” under the project grant TIN2016-79637-P “Towards Unification of HPC and Big Data paradigms”. The work of Maria-Cristina Marinescu has been partially supported by the H2020 European project GrowSmarter under project grant ref. 646456. The role of both funders was limited to financial support and did not imply participation of any kind in the study and collection, analysis, and interpretation of data, nor in the writing of the manuscrip

LIMITLESS - Light-weight monitoring tool for large scale systems

This work was partially supported by the European Union’s Horizon 2020 ASPIDE project (grant agreement No 801091), and the Spanish Ministry of Science and innovation Project DECIDE (Ref. PID2019-107858GB-I00.