Self-adaptive approximate queries for large-scale information aggregation

Self-adaptation enables distributed software to modify its behaviour based on changes in the operating environment. In large-scale information systems for cloud computing that use hierarchical data aggregation, self-adaption may be used to respond to an approximate query, thereby reducing use of network bandwidth and retrieval time. We present a novel algorithm that uses an Analytic Hierarchical Process (AHP) in order to apply self-adaption to approximate queries based on network-awareness. The AHP-based algorithm provides a trade-off among network usage, retrieval time and the accuracy of the retrieved results. Simulations show that the number of needed messages reduces with AHP to a constant upper bound. The retrieval time reduces to a constant factor under an increasing number of nodes. Our results demonstrate that the algorithm is able to provide responses with the required accuracy, primarily by adapting the depth of the query based on the number of messages and the network conditions.

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