Special Issue: Grid Computing, High Performance and Distributed Application

In the recent decades we have witnessed a major revolution in the computer field. The major challenges posed by applications in fields of bioinformatics, earth sciences or weather forecasting, among others, have caused the proliferation of complex solutions, such as grid, cloud and highperformance computing. The common objective of all these disciplines is the sharing of hardware and software resources to provide an infrastructure in which to run efficiently these applications. Particularly, grid computing has been one of the most important computing topics in the last years. Within this context, the GADAworkshop arose in 2004 as a forum for researchers in grid computing and its application to data analysis. From then until 2008, GADA became a reference conference for researchers in grid, covering also a broader set of disciplines, although grid computing continued to play a key role in the set of main topics of the conference. This special issue includes the eight best papers presented at the International Conference on Grid Computing, High Performance and Distributed Application (GADA 2008) from a total of 31 submissions with an acceptance rate of 26%. GADA 2008 was held in conjunction with the On The Move Conferences during November 2008 in Monterrey, Mexico. Each submitted paper was reviewed by three reviewers and one of the program chairs, and a total of 70 reviewers were involved in the review process of GADA 2008. A further review stage was performed to select the papers for this special issue. Topics covered by these papers include grid modelling, performance and scalability. Three different papers address the important topic of grid modelling. Branco et al. [1] describe the experience in the development of the data management middleware DQ2, used in the ATLAS experiment for the Large Hadron Collider (HLC). From this experience, they have identified an important degree of uncertainty over the behaviour of large grid infrastructures. From the analysis of this uncertainty, they propose novel modelling and simulation techniques for Data Grids. van der Aalst et al. [2] show a formal description of the grid in terms of a Colored Petri Net (CPN). This formalism can be used as a conceptual model of a grid environment, and also allows you to perform various types of analysis, including performance analysis. The model has been validated by means of experiments in a testbed grid architecture. Montes et al. [3] present a methodology based on data mining techniques for the building of a global behaviour model of the grid. This methodology deals with the complexity of grid environments, providing a simple model that can be used for administrative purposes. The validation of the model has been performed by means of real and simulated case studies.