A data scheduling algorithm for autonomous distributed real-time applications in grid computing

Large-scale data intensive applications with real-time requirements are currently emerging in many disciplines of science and engineering. Such applications can benefit from a grid environment provided that an efficient solution to the following data scheduling problem can be found: schedule the transfer of a set of large-scale data objects of distributed applications in a grid environment so as to meet real-time constraints associated with these data transfers. Based on this premise, This work focuses on the aforementioned problem and proposes a new effective path-selection based scheduling algorithm. The algorithm performs its optimization based on a schedule reflection model; a new cost criterion that takes into account the satisfiability of each application as a whole. We show, by simulation, that our algorithm improves the performance of data intensive real-time applications.