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    A Middleware for Large-scale Simulation Systems & Resource Management

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    Date
    2013-05-26
    Author
    Makkapati, Hemanth
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    Abstract
    Socially coupled systems are comprised of inter-dependent social, organizational, economic, infrastructure and physical networks. Today\'s urban regions serve as an excellent example of such systems. People and institutions confront the implications of the increasing scale of information becoming available due to a combination of advances in pervasive computing, data acquisition systems as well as high performance computing. Integrated modeling and decision making environments are necessary to support planning, analysis and counter factual experiments to study these complex systems.

    Here, we describe SIMFRASTRUCTURE -- a computational infrastructure that supports high performance computing oriented decision and analytics environments to study socially coupled systems. Simfrastructure provides a middleware with multiplexing mechanism by which modeling environments with simple and intuitive user-interfaces can be plugged in as front-end systems, and high-end computing resources -- such as clusters, grids and clouds -- can be plugged in as back-end
    systems for execution. This makes several key aspects of simulation systems such as the computational complexity, data management and resource management and allocation completely transparent to the users. The decoupling of user interfaces, data repository and computational resources from simulation execution allows users to run simulations and access the results asynchronously and enables them to add new datasets and simulation models dynamically.  Simfrastructure enables implementation of a simple yet powerful modeling environment with built-in analytics-as-aservice platform, which provides seamless access to high end computational resources, through an intuitive interface for studying socially coupled systems.

    We illustrate the applicability of Simfrastructure in the context of an integrated modeling environment to study public health epidemiology and network science.
    URI
    http://hdl.handle.net/10919/50863
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    • Masters Theses [19418]

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