A tuple space implementation for large-scale infrastructures

Coordinating activities in a distributed system is an open research topic. Several models have been proposed to achieve this purpose such as message passing, publish/subscribe, workflows or tuple spaces. We have focused on the latter model, trying to overcome some of its disadvantages. In particular we have applied spatial database techniques to tuple spaces in order to increase their performance when handling a large number of tuples. Moreover, we have studied how structured peer to peer approaches can be applied to better distribute tuples on large networks. Using some of these result, we have developed a tuple space implementation for the Globus Toolkit that can be used by Grid applications as a coordination service. The development of such a service has been quite challenging due to the limitations imposed by XML serialization that have heavily influenced its design. Nevertheless, we were able to complete its implementation and use it to implement two different types of test applications: a completely parallelizable one and a plasma simulation that is not completely parallelizable. Using this last application we have compared the performance of our service against MPI. Finally, we have developed and tested a simple workflow in order to show the versatility of our service.

Urban air quality: new high-resolution modeling approaches and forecasting tools for citizens

Air pollution is one of the greatest health risks in the world. At the same time, the strong correlation with climate change, as well as with Urban Heat Island and Heat Waves, make more intense the effects of all these phenomena. A good air quality and high levels of thermal comfort are the big goals to be reached in urban areas in coming years. Air quality forecast help decision makers to improve air quality and public health strategies, mitigating the occurrence of acute air pollution episodes. Air quality forecasting approaches combine an ensemble of models to provide forecasts from global to regional air pollution and downscaling for selected countries and regions. The development of models dedicated to urban air quality issues requires a good set of data regarding the urban morphology and building material characteristics. Only few examples of air quality forecast system at urban scale exist in the literature and often they are limited to selected cities. This thesis develops by setting up a methodology for the development of a forecasting tool. The forecasting tool can be adapted to all cities and uses a new parametrization for vegetated areas. The parametrization method, based on aerodynamic parameters, produce the urban spatially varying roughness. At the core of the forecasting tool there is a dispersion model (urban scale) used in forecasting mode, and the meteorological and background concentration forecasts provided by two regional numerical weather forecasting models. The tool produces the 1-day spatial forecast of NO2, PM10, O3 concentration, the air temperature, the air humidity and BLQ-Air index values. The tool is automatized to run every day, the maps produced are displayed on the e-Globus platform, updated every day. The results obtained indicate that the forecasting output were in good agreement with the observed measurements.