This paper deals with an efficient and robust parallel and distributed simulation framework for standalone Monte Carlo simulation based on a MapReduce computing framework. The Monte Carlo simulation method is inherently computing-intensive and requires many replicated simulation runs to get meaningful statistical results. Thus, it is important to reduce total simulation time by exploiting hardware and/or software as well as to reuse existing standalone simulation programs with little modification for the replicated simulations. To cope with this situation, we propose a general framework that turns a stand-alone Monte Carlo simulator into a chain of MapReduce jobs in order to run the simulation on a MapReduce framework such as Hadoop. A case study of an air defense simulation on 16-node Hadoop cluster illustrates that the proposed framework is feasible and fully utilizes the merit of the parallel and distributed computing environment.