Abstract
In the field of social science, a variety of high-performance computing simulations such as the Monte Carlo simulation and the Multi-agent simulation must be efficiently performed to deal with social scientific big data. To facilitate social scientists in performing their own analysis against such big data, the information infrastructure for social science must be equipped with a core technology that efficiently and effectively leverages limited resources available on the information infrastructure. From such a perspective, a new type of job management technology, which treats not only computational resources such as the Central Processing Unit (CPU) and memory, but also network resources unlike traditional job management, is investigated in this paper. A cluster system with a fat-tree topology interconnect is conventional cluster architecture these days. For this investigation, the National Aeronautics Space Administration Advanced Supercomputing, USA (NAS) Parallel Benchmarks, which contain computation patterns often observed in social scientific simulations, are used to assess the efficacy of the resource allocation by our proposed job management technology on a cluster system with a fat-tree topology interconnect.
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Watashiba, Y., Date, S., Abe, H. et al. Efficacy Analysis of a SDN-enhanced Resource Management System through NAS Parallel Benchmarks. Rev Socionetwork Strat 8, 69–84 (2014). https://doi.org/10.1007/s12626-014-0045-9
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DOI: https://doi.org/10.1007/s12626-014-0045-9