Orchestrating Science DMZs for Big Data Acceleration: Challenges and Approaches

In recent years, most scientific research in both academia and industry has become increasingly data-driven. According to market estimates, spending related to supporting scientific dataintensive research is expected to increase to $5.8 billion by 2018 [1]. Particularly for dataintensive scientific fields such as bioscience, or particle physics within academic environments, data storage/processing facilities, expert collaborators and specialized computing resources do not always reside within campus boundaries. With the growing trend of large collaborative partnerships involving researchers, expensive scientific instruments and high performance computing centers, experiments and simulations produce peta-bytes of data viz., Big Data, that is likely to be shared and analyzed by scientists in multi-disciplinary areas [2]. With the United States of America (USA) government initiating a multi-million dollar research agenda on Big Data topics including networking [3], funding agencies such as National Science Foundation, Department of Energy, and Defense Advanced Research Projects Agency are encouraging and supporting cross-campus Big Data research collaborations globally.