A Simulation Study on Urban Water Threat Detection in Modern Cyberinfrastructures

The computation of Contaminant Source Characterization (CSC) is a critical research issue in Water Distribution System (WDS) management. We use a simulation framework to identify optimized locations of sensors that lead to fast detection of contamination sources. The optimization engine is based on a Genetic Algorithm (GA) that interprets trial solutions as individuals. During the optimization process many thousands of these solutions are generated. For a large WDS, the calculation of these solutions are non-trivial and time consuming. Hence, it is a compute intensive application that requires significant compute resources. Furthermore, we strive to generate solutions quickly in order to respond to the urgency of a response. To carry out the calculations we require user-level middleware that can be supporting the workflow of the application and manages the resource assignment in an efficient and fault tolerant fashion. To do so we have prototyped the middleware framework that provides a convenient command line and portal layer of steering applications on Grids. Internally, we utilize a sophisticated workflow engine that provides the ability to access elementary fault tolerant mechanisms for job scheduling. This includes the management of job replicas and the reaction on late return of results. We report the test results of CSC problem solving on a real Grid test bed - the Tera Grid test bed. In addition, we contrast this system architecture with a Hadoop-based implementation that automatically includes fault tolerance. The later activity has been conducted on Future Grid.