Distributed and Parallel Delaunay Triangulation Construction with Balanced Binary-tree Model in Cloud

Delaunay triangulation (D-TIN) is an important graphic tool in computational geometry, which is not only widely used in many real applications, but also very significant for many spatial data mining algorithms. However, constructing Delaunay triangulation is time-consuming for most practical applications. Distributed and parallel computing mechanism is becoming a good choice to solve large scale and compute-intensive D-TIN applications. This paper proposes a novel hybrid algorithm (HA) for D-TIN construction in cloud computing environment, which is based on a balanced binary-tree model and an elegant data structure called quad-edge. HA combines the divide & conquer approach and the incremental method. Moreover, a distributed and parallel version of Delaunay triangulation computing service in cloud is designed and implemented. The hybrid algorithm performed in both centralised and in cloud environments are compared. Experimental results showed that the hybrid D-TIN service outperforms both the the divide & conquer one and the incremental one, and it can effectively provide higher data mining services with fundamental D-TIN construction function in cloud.