Data warehouse queries pose challenging performance problems that
often necessitate the use of parallel database systems (PDBS). Although
dynamic load balancing is of key importance in PDBS, to our knowledge it has
not yet been investigated thoroughly for parallel data warehouses. In this study,
we propose a scheduling strategy that simultaneously considers both processors
and disks while utilizing the load balancing potential of a Shared Disk
architecture. We compare the performance of this new method to several other
approaches in a comprehensive simulation study, incorporating skew aspects
and typical data warehouse features such as star schemas.