No description available.

No description available.

"The fast pace of global urbanization is drastically changing the population distributions over the world, which leads to significant changes in geographical population densities. Such changes in turn alter the underlying geographical power demand over time, and drive power substations to become over-supplied (demand ≪ capacity) or under-supplied (demand ≈ capacity). In this work, we make the first attempt to investigate the problem of power substation/user assignment by analyzing large scale power grid data. We develop a Scalable Power User Assignment (SPUA) framework, that takes large-scale spatial power user/substation distribution data and temporal user power consumption data as input, and assigns users to substations, in a manner that minimizes the maximum substation utilization among all substations. To evaluate the performance of SPUA framework, we conduct evaluations on real power consumption data and user/substation location data collected from Xinjiang Province in China for 35 days in 2015. The evaluation results demonstrate that our SPUA framework can achieve a 20%–65% reduction on the maximum substation utilization, and 2 to 3.7 times reduction on total transmission loss over other baseline methods."

Tese de mestrado. Redes e Serviços de Comunicações (Área de especialização de Comunicações). 2006. Faculdade de Engenharia. Universidade do Porto

The problem of edge disjoint path routing arises from applications in distributed memory parallel computing. We examine this problem in both the directed hypercube and two-dimensional grid topologies. Complexity results are obtained for these problems where the routing must consist entirely of shortest length paths. Additionally, approximation algorithms are presented for the case when the routing request is of a special form known as a permutation. Permutations simply require that no vertex in the graph may be used more than once as either a source or target for a routing request. Szymanski conjectured that permutations are always routable in the directed hypercube, and this remains an open problem.

A numerical method for the simulation of compressible turbulent flows is presented. This method includes a novel hybrid grid generation for airfoil cascades and an unstructured mesh flow solver. The mesh tool incorporates a mapping technique and a grid smoothing method. The mapping technique is used to build an initial volume mesh and the grid smoothing method is used to improve the quality of the initial mesh. The grid smoothing is based on the optimization of mesh-quality parameters. The further improvement of the smoothed mesh is achieved by an edge-swapping and node-insertion technique. The unstructured flow solver is developed for a hybrid grid. This flow solver uses a rotational frame of reference. The convective and viscous fluxes are numerically solved by an upwind scheme and an averaged nodal gradient. A higher-order spatial accuracy is achieved by a piece-wise linear reconstruction. An explicit multi-stage method is employed for integration in time. The Menter’s k −τ model is implemented to simulate the turbulence effects. The flow solver is validated against the analytical and experimental results. A parametric study is performed for a high speed centrifugal compressor.

La computación grid constituye uno de los campos más prometedores de los sistemas informáticos. La próxima generación de aplicaciones científicas se beneficiará de una infraestructura de gran escala y multi organizacional que ofrece más potencia de cómputo de la que puede ofrecer cualquier institicuón de forma individual. Los sistemas grid necesitan planificadores de alto nivel que gestionen de forma adecuada los recursos distribuídos en varias organizaciones. Estos sistemas de gestión de recursos grid deben tomar decisiones de planificación sin realmente poseer los recursos y sin tener control total sobre las aplicaciones que en dichos recursos se ejecutan, introduciendo nuevos desafíos a la hora de realizar la planificación de aplicaciones. Aunque los sistemas grids consisten de muchos recursos y las aplicaciones enviados a estos sistemas pueden aprovechar estos recursos usándolos de forma coordinada, la mayoría de los sistemas de gestión de recursos se han centrado en la ejecución de aplicaciones secuenciales, convirtiendo el grid en un gran sistema multi-sitio donde las aplicaciones se ejecutan de forma batch.Sin embargo, en esta tesis nos hemos centrado en un tipo de aplicaciones que ha recibido poca atención hasta el momento: paralelas e interactivas. Las aplicaciones interactivas requieren la posibilidad de iniciar en un futuro inmediato su ejecución. Además, durante su ejecución es necesario proveer de mecanismos que establezcan un canal de comunicación entre el usuario y la aplicación. En el caso de las aplicaciones paralelas, es necesaria la co-asignación, es decir garantizar la disponibilidad simultánea de los recursos cuando la aplicación necesite usarlos. En este trabajo proponemos una nueva arquitectura para la ejecución de estos tipos de aplicaciones y una implementación de la misma: el gestor de recursos CrossBroker. Esta arquitectura incluye mecanismos que permiten la co-asignación de aplicaciones paralelos y la interacción de los usuarios con las aplicaciones en ejecución. Adicionalmente, con la introducción de un mecanismo de multiprogramación, proporcionamos un inicio de aplicaciones rápido incluso en escenarios de alta ocupación de los recursos. / Grid computing constitutes one of the most promising fields in computer systems. The next generation of scientific applications can profit from a large-scale, multi-organizational infrastructure that offers more computing power than one institution alone is able to afford. Grids need high-level schedulers that can be used to manage the resources spanning different organizations. These Grid Resource Management Systems (GRMS) have to make scheduling decisions without actually owning the grid resources, or having full control over the jobs that are running there. This introduces new challenges in the scheduling process done by the GRMSs. Although grids consist of many resources, and jobs submitted to grid may benefit from using them in a coordinated way, most of the Grid Resource Management Systems have focused on the execution of sequential jobs, with the grid being a large multi-site environment where jobs run in a batch-like way.However, in this work we concentrate on a kind of jobs that have received little attention to date: interactive and parallel jobs. Interactive jobs require the possibility of starting in the immediate future and need mechanisms to establish a communication channel with the user. Parallel applications introduce the need for co-allocation, guaranteeing the simultaneous availability of the resources when they are accessed by the applications. We address the challenges of executing such jobs with a new architecture for a GRMS and an implementation of that architecture called the CrossBroker. Our architecture includes mechanisms to allow the co-allocation of parallel jobs and the interaction of users with running applications. Additionally with the introduction of a multi-programming mechanism, a fast startup of jobs even in high occupancy scenarios is provided.

En este trabajo se ha desarrollado una nueva solución para la integración de políticas de planificación de workflows en sistemas gestores de workflows (workflow engines). SchedFlow es la solución propuesta e implementada, la cual Permite que un usuario final pueda utilizar la política de planificación que desee, ya sea esta estática o dinámica, sin necesidad de modificar el sistema gestor de workflow. Además SchedFlow toma en cuenta cuando una tarea del workflow no se puede ejecutar por eventos externos, como que una máquina está siendo utilizada por el usuario principal de la misma. En este tipo de escenarios SchedFlow realiza la replanificación de tareas, siempre bajo la política de planificación definida o elegida por el usuario. Como resultado se ha obtenido una aportación novedosa ya que normalmente si el usuario desea utilizar una política de planificación específica, deberá modificar el gestor de workflow para que soporte dicha política de planificación. Un punto importante a destacar, es que esta tesis además de contener un estudio exhaustivo de la bibliografía existente en el área, y de realizar un análisis de problema y diseño de solución propuesta, implementa la solución en un entorno oportunístico real. Los resultados de esta tesis para la planificación de workflows en entornos Grid, abre una nueva vía para el estudio de políticas de planificación para este tipo de aplicaciones, que será aprovechado por futuras investigaciones en la UAB.

No description available.

Recent enthusiasm in grid computing has resulted in a tremendous amount of research in resource scheduling techniques for tasks in a workflow. Most of the work on resource scheduling is aimed at minimizing the total response time for the entire workflow and treats the estimated response time of a task running on a local resource as a constant. However in a dynamic environment such grid computing, the behavior of resources simply cannot be ensured. In this thesis, thus, we propose a probabilistic framework for resource scheduling in a grid environment that views the task response time as a probability distribution to take into consideration the uncertain factors. The goal is to dynamically assign resources to tasks so as to maximize the probability of completing the entire workflow within a desired total response time. We propose three algorithms for the dynamic resource scheduling in grid environment, namely the integer programming, the max-max heuristic and the min-max heuristic. Experimental results using synthetic data derived from a real protein annotation workflow application demonstrate that the proposed probability-based scheduling strategies have similar performance in an environment with homogeneous resources and perform better in an environment with heterogeneous resources, when compared with the existing methods that consider the response time as a constant. Of the two proposed heuristics, min-max generally yields better performance.