The existing diference between computing and I/O times has originated the so-called \I/O crisis". As long as this diference is not decreased, the I/O system will constitute the bottleneck of most computing systems. Parallel _le systems constitute a solution to this problem and they are often used in diferent kinds of environments. In spite of the proliferation of cluster environments and the improvements of parallel cluster _le systems, a large number of data analysis problems cannot be tackled. In these scenarios, the concept of parallelism should be adapted to new technologies. Grid technology enables resource sharing across wide area networks, increasing the computational power and the storage capacity, which allows the scientic community to solve formerly unachievable problems. However, grid is mainly focused on increasing the availability of resources instead of improving the performance of applications. Parallelism could optimize data access in this kind of environments and, therefore, enhance applications performance. It would be advisable to extend diferent solutions used in clusters of workstations to a grid infrastructure with the aim of overcoming I/O problems in grid applications. Nevertheless, the complexity and the dynamism of grid environments make the management and administration of such data access systems di_cult. With the aim of managing this high complexity, the system must be able to self-manage, focusing on improving the performance of I/O operations. In an I/O system, it is necessary to take into account that data is not usually required at the same time when it is produced. In this sense, performance improvements are related to both current and future states of grid elements since the actual access to data will be made later on. Prediction methods can be used to know the future behavior of the system and making decisions aimed at enhancing the performance of current and later I/O operations. As a summary, this work proposes the analysis, design and implementation of an architecture that solves the I/O problem in an efficient way managing the high complexity of a heterogeneous environment. Keywords: Parallel I/0; Grid computing; Data grid; Autonomic computing; Autonomic storage; Long term prediction; Heterogeneous environment.