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The Contaminant Source Characterization (CSC) problem in a Water Distributed System (WDS) exhibits a compute-intensive challenge that requires highly reliable and high performance computing resources in order to achieve near real-time processing. Traditional solution to the CSC problem with MPI via Grid/cluster computing cannot fulfill CSC&#x2019;s QoS requirements, such as, reliability, scalability and flexibility. To address the aforementioned research issues, we have developed a parallel solution to the CSC problem using MapReduce in Clouds, which mainly includes 1) parallelization of the process of evaluating individuals in the Genetic Algorithm for CSC with MapReduce, and 2) developing an advanced cyberinfrastructure in an academic Cloud computing test bed (the FutureGrid test bed). We have carried out performance evaluation and discussion on our solution. Test results and performance evaluation show that parallel GA with MapReduce in a dynamic cyberinfrastructure can deliver a high performance, fault tolerance and flexible solution for the CSC problem.