A Simulation Study of Data Partitioning Algorithms for Multiple Clusters
Yu Chen, Dan Cristian Marinescu, Howard Jay Siegel, John P. Morrison · 2007
Recently we proposed algorithms for concurrent execution on multiple clusters [11]. In this case, data partitioning is done at two levels; first, the data is distributed to a collection of heterogeneous parallel systems with different resources and startup time, then, on each system the data is evenly partitioned to the available nodes. In this paper, we report on a simulation study of the algorithms.