Contaminant source characterization (CSC) in a water distribution system (WDS) exhibits a computationally intensive problem. Traditional solutions to the CSC problem can't fulfill the CSC's quality-of-service (QoS) requirements. We present a parallel solution using the MapReduce paradigm in the cloud that can deliver a high-performance, fault-tolerant, and flexible solution.■ ■ production grid and cluster computing typically employ job-queuing systems, which will likely delay the job execution; and ■ ■ because complex software dependencies are required to install and configure the environment for CSC problem solving, there's a lack of scalability and flexibility in such a dedicated computing infrastructure.Thus, a need exists for a cyberinfrastructure that fosters a high-performance, fault-tolerant, and flexible solution to CSC. Cloud computing 1,2 is a recent and effective solution to scientific and engineering computing applications. For example, virtualization and virtual machine (VM) technologies support on-demand resource provisioning for scientific applications. The Map-Reduce paradigm 3 provides an effective solution for high-performance and reliable large-scale parallel data processing.www.computer.org/cise 9
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