Load-Balancing in Distributed Selective Search

Yubin Kim, Jamie Callan, J. Shane Culpepper, Alistair Moffat

Abstract: Simulation and analysis have shown that selective search can reduce the cost of large-scale distributed information retrieval. By partitioning the collection into small topical shards, and then using a resource ranking algorithm to choose a subset of shards to search for each query, fewer postings are evaluated. Here we extend the study of selective search using a fine-grained simulation investigating: selective search efficiency in a parallel query processing environment; the difference in efficiency when term-based and sample-based resource selection algorithms are used; and the effect of two policies for assigning index shards to machines. Results obtained for two large datasets and four large query logs confirm that selective search is significantly more efficient than conventional distributed search. In particular, we show that selective search is capable of both higher throughput and lower latency in a parallel environment than is exhaustive search.