Abstract
Geographical knowledge cloud service is a typical online service that provides big spatial data analysis with the function of knowledge discovery or decision-making. The composition of geographical knowledge cloud service imposes stricter requirements for better overall QoS and execution efficiency of the service chain. In this paper, we present a data volume aware ant colony optimization approach called DVA-MOACO algorithm for geographical knowledge cloud service composition. Our algorithm utilizes a multi-index service quality evaluation model, and improves the transition probability while considering the data transfer cost and other QoS constraints simultaneously when ant finding path. Our algorithm could reach the Pareto near optimal solution rapidly with better QoS performance and lower data transfer cost from numerous candidate solutions.
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