Web-based Spatial Decision Support Systems (WebSDSS): Evolution, Architecture, Examples and Challenges

Spatial Decision Support Systems (SDSS), which support spatial analysis and decision making, are currently receiving much attention. Research on SDSS originated from two distinct sources, namely, the GIS community and the DSS community. The synergy between these two research groups has lead to the adoption of state of the art technical solutions and the development of sophisticated SDSS that satisfy the needs of geographers and top-level decision makers. Recently, the Web has added a new dimension to SDSS and Web-based SDSS (WebSDSS) that are being developed in a number of application domains. This article provides an overview of the emergence of SDSS, its architecture and applications, and discusses some of the enabling technologies and research challenges for future SDSS development and deployment

Plataformes avançades en el Núvol per a la reproductibilitat d'experiments computacionals

Tesis por compendio[ES] La tesis presentada se enmarca dentro del ámbito de la ciencia computacional. Dentro de esta, se centra en el desarrollo de herramientas para la ejecución de experimentación científica computacional, el impacto de la cual es cada vez mayor en todos los ámbitos de la ciencia y la ingeniería. Debido a la creciente complejidad de los cálculos realizados, cada vez es necesario un mayor conocimiento de las técnicas y herramientas disponibles para llevar a cabo este tipo de experimentos, ya que pueden requerir, en general, una gran infraestructura computacional para afrontar los altos costes de cómputo. Más aún, la reciente popularización del cómputo en la Nube ofrece una gran variedad de posibilidades para configurar nuestras propias infraestructuras con requisitos específicos. No obstante, el precio a pagar es la complejidad de configurar dichas infraestructuras en este tipo de entornos. Además, el aumento en la complejidad de configuración de los entornos en la nube no hace más que agravar un problema ya existente en el ámbito científico, y es el de la reproducibilidad de los resultados publicados. La falta de documentación, como las versiones de software que se han usado para llevar a cabo el cómputo, o los datos requeridos, provocan que una parte significativa de los resultados de experimentos computacionales publicados no sean reproducibles por otros investigadores. Como consecuencia, se produce un derroche de recursos destinados a la investigación. Como respuesta a esta situación, existen, y continúan desarrollándose, diferentes herramientas para facilitar procesos como el despliegue y configuración de infraestructura, el acceso a los datos, el diseño de flujos de cómputo, etc. con el objetivo de que los investigadores puedan centrarse en el problema a abordar. Precisamente, esta es la base de los trabajos desarrollados en la presente tesis, el desarrollo de herramientas para facilitar que el cómputo científico se beneficie de entornos de computación en la Nube de forma eficiente. El primer trabajo presentado empieza con un estudio exhaustivo de las prestaciones d'un servicio relativamente nuevo, la ejecución serverless de funciones. En este, se determinará la conveniencia de usar este tipo de entornos en el cálculo científico midiendo tanto sus prestaciones de forma aislada, como velocidad de CPU y comunicaciones, como en conjunto mediante el desarrollo de una aplicación de procesamiento MapReduce para entornos serverless. En el siguiente trabajo, se abordará una problemática diferente, y es la reproducibilidad de experimentos computacionales. Para conseguirlo, se presentará un entorno, basado en Jupyter, donde se encapsule tanto el proceso de despliegue y configuración de infraestructura computacional como el acceso a datos y la documentación de la experimentación. Toda esta información quedará registrada en el notebook de Jupyter donde se ejecuta el experimento, permitiendo así a otros investigadores reproducir los resultados simplemente compartiendo el notebook correspondiente. Volviendo al estudio de las prestaciones del primer trabajo, teniendo en cuenta las medidas y bien estudiadas fluctuaciones de éstas en entornos compartidos, como el cómputo en la Nube, en el tercer trabajo se desarrollará un sistema de balanceo de carga diseñado expresamente para este tipo de entornos. Como se mostrará, este componente es capaz de gestionar y corregir de forma precisa fluctuaciones impredecibles en las prestaciones del cómputo en entornos compartidos. Finalmente, y aprovechando el desarrollo anterior, se diseñará una plataforma completamente serverless encargada de repartir y balancear tareas ejecutadas en múltiples infraestructuras independientes. La motivación de este último trabajo viene dada por los altos costes computacionales de ciertos experimentos, los cuales fuerzan a los investigadores a usar múltiples infraestructuras que, en general, pertenecen a diferentes organizaciones.[CA] La tesi presentada a aquest document s'emmarca dins de l'àmbit de la ciència computacional. Dintre d'aquesta, es centra en el desenvolupament d'eines per a l'execució d'experimentació científica computacional, la qual té un impacte cada vegada major en tots els àmbits de la ciència i l'enginyeria. Donada la creixent complexitat dels càlculs realitzats, cada vegada és necessari un major coneixement sobre les tècniques i eines disponibles per a dur a terme aquestes experimentacions, ja que poden requerir, en general, una gran infraestructura computacional per afrontar els alts costos de còmput. Més encara, la recent popularització del còmput en el Núvol ofereix una gran varietat de possibilitats per a configurar les nostres pròpies infraestructures amb requisits específiques. No obstant, el preu a pagar és la complexitat de configurar les esmenades infraestructures a aquest tipus d'entorns. A més, l'augment de la complexitat de configuració dels entorns de còmput no ha fet més que agreujar un problema ja existent a l'àmbit científic, i és la reproductibilitat de resultats publicats. La manca de documentació, com les versions del programari emprat per a dur a terme el còmput, o les dades requerides ocasionen que una part no negligible dels resultats d'experiments computacionals publicats no siguen reproduïbles per altres investigadors. Com a conseqüència, es produeix un malbaratament dels recursos destinats a la investigació. Com a resposta a aquesta situació, existeixen, i continuen desenvolupant-se, diverses eines per facilitar processos com el desplegament i configuració d'infraestructura, l'accés a les dades, el disseny de fluxos de còmput, etc. amb l'objectiu de que els investigadors puguen centrar-se en el problema a abordar. Precisament, aquesta és la base dels treballs desenvolupats durant la tesi que segueix, el desenvolupar eines per a facilitar que el còmput científic es beneficiar-se d'entorns de computació en el Núvol d'una forma eficient. El primer treball presentat comença amb un estudi exhaustiu de les prestacions d'un servei relativament nou, l'execució serverless de funcions. En aquest, es determinarà la conveniència d'emprar este tipus d'entorns en el càlcul científic mesurant tant les seues prestacions de forma aïllada, com velocitat de CPU i la velocitat de les comunicacions, com en conjunt a través del desenvolupament d'una aplicació de processament MapReduce per a entorns serverless. Al següent treball, s'abordarà una problemàtica diferent, i és la reproductibilitat dels experiments computacionals. Per a aconseguir-ho, es presentarà una entorn, basat en Jupyter, on s'englobe tant el desplegament i configuració d'infraestructura computacional, com l'accés a les dades requerides i la documentació de l'experimentació. Tota aquesta informació quedarà registrada al notebook de Jupyter on s'executa l'experiment, permetent així a altres investigadors reproduir els resultats simplement compartint el notebook corresponent. Tornant a l'estudi de les prestacions del primer treball, donades les mesurades i ben estudiades fluctuacions d'aquestes en entorns compartits, com en el còmput en el Núvol, al tercer treball es desenvoluparà un sistema de balanceig de càrrega dissenyat expressament per aquest tipus d'entorns. Com es veurà, aquest component és capaç de gestionar i corregir de forma precisa fluctuacions impredictibles en les prestacions de còmput d'entorns compartits. Finalment, i aprofitant el desenvolupament anterior, es dissenyarà una plataforma completament serverless per a repartir i balancejar tasques executades en múltiples infraestructures de còmput independents. La motivació d'aquest últim treball ve donada pels alts costos computacionals de certes experimentacions, els quals forcen als investigadors a emprar múltiples infraestructures que, en general, pertanyen a diferents organitzacions. Es demostrarà la capacitat de la plataforma per balancejar treballs i minimitzar el malbaratament de recursos[EN] This document is focused on computational science, specifically in the development of tools for executions of scientific computational experiments, whose impact has increased, and still increasing, in all scientific and engineering scopes. Considering the growing complexity of scientific calculus, it is required large and complex computational infrastructures to carry on the experimentation. However, to use this infrastructures, it is required a deep knowledge of the available tools and techniques to be handled efficiently. Moreover, the popularity of Cloud computing environments offers a wide variety of possible configurations for our computational infrastructures, thus complicating the configuration process. Furthermore, this increase in complexity has exacerbated the well known problem of reproducibility in science. The lack of documentation, as the used software versions, or the data required by the experiment, produces non reproducible results in computational experiments. This situation produce a non negligible waste of the resources invested in research. As consequence, several tools have been developed to facilitate the deployment, usage and configuration of complex infrastructures, provide access to data, etc. with the objective to simplify the common steps of computational experiments to researchers. Moreover, the works presented in this document share the same objective, i.e. develop tools to provide an easy, efficient and reproducible usage of cloud computing environments for scientific experimentation. The first presented work begins with an exhaustive study of the suitability of the AWS serverless environment for scientific calculus. In this one, the suitability of this kind of environments for scientific research will be studied. With this aim, the study will measure the CPU and network performance, both isolated and combined, via a MapReduce framework developed completely using serverless services. The second one is focused on the reproducibility problem in computational experiments. To improve reproducibility, the work presents an environment, based on Jupyter, which handles and simplify the deployment, configuration and usage of complex computational infrastructures. Also, includes a straight forward procedure to provide access to data and documentation of the experimentation via the Jupyter notebooks. Therefore, the whole experiment could be reproduced sharing the corresponding notebook. In the third work, a load balance library has been developed to address fluctuations of shared infrastructure capabilities. This effect has been wide studied in the literature and affects specially to cloud computing environments. The developed load balance system, as we will see, can handle and correct accurately unpredictable fluctuations in such environments. Finally, based on the previous work, a completely serverless platform is presented to split and balance job executions among several shared, heterogeneous and independent computing infrastructures. The motivation of this last work is the huge computational cost of many experiments, which forces the researchers to use multiple infrastructures belonging, in general, to different organisations. It will be shown how the developed platform is capable to balance the workload accurately. Moreover, it can fit execution time constrains specified by the user. In addition, the platform assists the computational infrastructures to scale as a function of the incoming workload, avoiding an over-provisioning or under-provisioning. Therefore, the platform provides an efficient usage of the available resources.This study was supported by the program “Ayudas para la contratación de personal investigador en formación de carácter predoctoral, programa VALi+d” under grant number ACIF/2018/148 from the Conselleria d’Educació of the Generalitat Valenciana. The authors would also like to thank the Spanish "Ministerio de Economía, Industria y Competitividad"for the project “BigCLOE” with reference number TIN2016-79951-R.Giménez Alventosa, V. (2022). Plataformes avançades en el Núvol per a la reproductibilitat d'experiments computacionals [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/184010TESISCompendi

Design of efficient Java communications for high performance computing

[Abstract] There is an increasing interest to adopt Java as the parallel programming language for the multi-core era. Although Java offers important advantages, such as built-in multithreading and networking support, productivity and portability, the lack of efficient communication middleware is an important drawback for its uptake in High Performance Computing (HPC). This PhD Thesis presents the design, implementation and evaluation of several solutions to improve this situation: (1) a high performance Java sockets implementation (JFS, Java Fast Sockets) on high-speed networks (e.g., Myrinet, InfiniBand) and shared memory (e.g., multi-core) machines; (2) a low-level messaging device, iodev, which efficiently overlaps communication and computation; and (3) a more scalable Java message-passing library, Fast MPJ (F-MPJ). Furthermore, new Java parallel benchmarks have been implemented and used for the performance evaluation of the developed middleware. The final and main conclusion is that the use of Java for HPC is feasible and even advisable when looking for productive development, provided that efficient communication middleware is made available, such as the projects presented in this Thesis.[Resumen] La tesis doctoral "Design of Efficient Java Communications for High Performance Computing" parte de la hipótesis inicial de que es posible desarrollar aplicaciones Java en computación de altas prestaciones, un ámbito en el que el rendimiento es crucial, siempre que esté disponible un middleware de comunicación eficiente. Así, se han diseñado, desarrollado y evaluado diferentes bibliotecas de comunicación en Java, desde el nivel de sockets al de paso de mensajes, obteniendo notables incrementos de eficiencia, confirmando que la hipótesis inicial es factible

On the design of architecture-aware algorithms for emerging applications

This dissertation maps various kernels and applications to a spectrum of programming models and architectures and also presents architecture-aware algorithms for different systems. The kernels and applications discussed in this dissertation have widely varying computational characteristics. For example, we consider both dense numerical computations and sparse graph algorithms. This dissertation also covers emerging applications from image processing, complex network analysis, and computational biology. We map these problems to diverse multicore processors and manycore accelerators. We also use new programming models (such as Transactional Memory, MapReduce, and Intel TBB) to address the performance and productivity challenges in the problems. Our experiences highlight the importance of mapping applications to appropriate programming models and architectures. We also find several limitations of current system software and architectures and directions to improve those. The discussion focuses on system software and architectural support for nested irregular parallelism, Transactional Memory, and hybrid data transfer mechanisms. We believe that the complexity of parallel programming can be significantly reduced via collaborative efforts among researchers and practitioners from different domains. This dissertation participates in the efforts by providing benchmarks and suggestions to improve system software and architectures.Ph.D.Committee Chair: Bader, David; Committee Member: Hong, Bo; Committee Member: Riley, George; Committee Member: Vuduc, Richard; Committee Member: Wills, Scot

QoS control of E-business systems through performance modelling and estimation

E-business systems provide the infrastructure whereby parties interact electronically via business transactions. At peak loads, these systems are susceptible to large volumes of transactions and concurrent users and yet they are expected to maintain adequate performance levels. Over provisioning is an expensive solution. A good alternative is the adaptation of the system, managing and controlling its resources. We address these concerns by presenting a model that allows fast evaluation of performance metrics in terms of measurable or controllable parameters. The model can be used in order to (a) predict the performance of a system under given or assumed loading conditions and (b) to choose the optimal configuration set-up for certain controllable parameters with respect to specified performance measures. Firstly, we analyze the characteristics of E-business systems. This analysis leads to the analytical model, which is sufficiently general to capture the behaviour of a large class of commonly encountered architectures. We propose an approximate solution which is numerically efficient and fast. By mean of simulation, we prove that its accuracy is acceptable over a wide range of system configurations and different load levels. We further evaluate the approximate solution by comparing it to a real-life E-business system. A J2EE application of non-trivial size and complexity is deployed on a 2-tier system composed of the JBoss application server and a database server. We implement an infrastructure fully integrated on the application server, capable of monitoring the E-business system and controlling its configuration parameters. Finally, we use this infrastructure to quantify both the static parameters of the model and the observed performance. The latter are then compared with the metrics predicted by the model, showing that the approximate solution is almost exact in predicting performance and that it assesses the optimal system configuration very accurately.EThOS - Electronic Theses Online ServiceGBUnited Kingdo