A dynamic load balancing strategy for parallel datacube computation

In recent years, OLAP technologies have become one of the important applications in the database industry. In particular, the datacube operation proposed in [5] receives strong attention among researchers as a fundamental research topic in the OLAP technologies. The datacube operation requires computation of aggregations on all possible combinations of each dimension attribute. As the number of dimensions increases, it becomes very expensive to compute datacubes, because the required computation cost grows exponentially with the increase of dimensions. Parallelization is very important factor for fast datacube computation. However, we cannot obtain sufficient performance gain in the presence of data skew even if the computation is parallelized. In this paper, we present a dynamic load balancing strategy, which enables us to extract the effectiveness of parallizing datacube computation sufficiently. We perform experiments based on simulations and show that our strategy performs well.