Performance evaluation of nested-loop join processing on networks of workstations

Networks of workstations (NOWs) are attractive for parallel processing due to their cost advantage. This paper investigates the performance issues in processing join operations and the inherent tradeoff in the networked workstation environment. Specifically, we look at the performance of the nested-loop join algorithm. Since NOWs are heterogeneous in nature, load sharing is important for their performance. We evaluated the performance of three load sharing methods: static equal, static proportional and dynamic scheduling with fixed-chunk size. The three scheduling methods are evaluated on an experimental heterogeneous network of workstations with non-query background loads. Our experimental result suggest that, when there is no background load, dynamic scheduling outperforms static equal scheduling (up to 40%) and marginally better (about 10% better speedup) than the static proportional scheduling. When there is dynamic background load on nodes, dynamic scheduling provides substantial performance improvement over the static proportional scheduling (up to 50%) and static equal scheduling (up to about 100%). In all cases, selection of an appropriate chunk size is important in dynamic scheduling