Best path in mountain environment based on parallel A* algorithm and Apache Spark.

Pathfinding problem has several applications in our life and widely used in virtual environments. It has different goals such as shortest path, secure path, or optimal path. Pathfinding problem deals with a large amount of data since it considers every point located in 2D or 3D scenes. The number of possibilities in such a problem is huge. Moreover, it depends on determining standards of best path definition. In this paper, we introduce a parallel A* algorithm to find the optimal path using Apache Spark. The proposed algorithm is evaluated in terms of runtime, speedup, efficiency, and cost on a generated dataset with different sizes (small, medium, and large). The generated dataset considers real terrain challenges, such as the slope and obstacles. Hadoop Insight cluster provided by Azure has been used to run the application. The proposed algorithm reached a speedup up to 4.85 running on six worker nodes. [ABSTRACT FROM AUTHOR]