Abstract

Achieving interactive response times when searching for documents on the web has become a challenge especially with the tremendous increase in the size of information available nowadays. Incorporating parallelism in search engines is one of the approaches towards achieving this aim. In this paper, we present a model for parallel query processing. Then, this model is extended particularly for usage on shared-memory and cluster parallel architectures. A special simulator, reflecting the proposed model, was developed allowing parameters concerning the data set, queries and architectures to be varied. A total of 32 experiments were conducted and the output was studied for the effect of varying different parameters. A number of performance measures such as average response time, speedup and efficiency are computed to study the effect of varying the parameters. Results show that in terms of average response time, speedup and efficiency, the proposed model for parallel query processing on shared-memory architecture outperforms that on cluster-based architecture.

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