Planning large data transfers in institutional grids

In grid computing, many scientific and engineering applications require access to large amounts of distributed data. The size and number of these data collections has been growing rapidly in recent years. The costs of data transmission take a significant part of the global execution time. When communication streams flow concurrently on shared links, transport control protocols have issues allocating fair bandwidth to all the streams, and the network becomes sub-optimally used. One way to deal with this situation is to schedule the communications in a way that will induce an optimal use of the network. We focus on the case of large data transfers that can be completely described at the initialization time. In this case, a plan of data migration can be computed at initialization time, and then executed. However, this computation phase must take a small time when compared to the actual execution of the plan. We propose a best effort solution, to compute approximately, based on the uniform random sampling of possible schedules, a communication plan. We show the effectiveness of this approach both theoretically and by simulations.