Performance models for desktop grids

Main characteristics of desktop grids are the large number of nodes and their heterogeneity. Application speedup on a large-scale desktop grid is limited by the heterogeneous computational capabilities of each node, which increase the synchronization overhead, and by the large number of nodes, that results in the serial fraction dominating performance. In this paper we present an innovative technique which may outperform the throughput of traditional grid applications by merging job partitioning and job replication. We utilize ordered statistics analytical models for the performance analysis of desktop–based grid applications. The models describe the effects of resource heterogeneity, serial fraction and synchronization overheads on the application– level performance. Using the models we show how the proposed policies can be tuned with respect to the size of the grid in order to optimize the grid throughput.