ARLS: A MapReduce-based output analysis tool for large-scale simulations. (May 2016) - Lee, Kangsun; Jung, Kwanghoon; Park, Joonho; Kwon, Dongseop
Highlights: Hadoop and MapReduce solutions can speed up the analysis of large scale simulation datasets with unstructured format. A Hadoop and MapReduce-based tool has been proposed to semi-automatically analyze large-scale simulation datasets in any format. A Surface-To-Air Missile simulator has been developed to illustrate the viability of Hadoop and MapReduce platforms in processing large scale simulation datasets with unstructured format. Abstract: As simulations are becoming popular in the analysis of the complex behavior of large-scale systems with immense inputs and outputs, there is an increasing demand to efficiently store, manage, and analyze massive simulation outputs. Hadoop and MapReduce have been used in various applications to speed up the process of analyzing large amounts of datasets. In this paper, we present ARLS (After-action Reviewer for Large-scale Simulations), a MapReduce-based output analysis tool for simulation outputs. ARLS clusters distributed storages using Hadoop and automatically composes Map and Reduce functions to process the simulation outputs. ARLS has been applied to our SAM (Surface-to-Air Missile) simulator. The SAM simulator has been developed to analyze the dynamics of a missile in designing air-defense systems. ARLS takes a large amount of unstructured simulation outputs from SAM simulator, automatically generates Map and Reduce functions to analyze the missile and the aircraft component of SAM simulator, and executes Map and Reduce jobs in parallel. The results of our experiments show that ARLS can efficiently analyze a large amount of unstructured simulation datasets by distributing datasets and computations over the cluster of commodity machines.