  Emerging paradigms of big data and Software-Defined Networking (SDN) in cloud
data centers have gained significant attention from industry and academia. The
integration and coordination of big data and SDN are required to improve the
application and network performance of big data applications. While empirical
evaluation and analysis of big data and SDN can be one way of observing
proposed solutions, it is often impractical or difficult to apply for several
reasons, such as expensive undertakings, time consuming, and complexity; in
addition, it is beyond the reach of most individuals. Thus, simulation tools
are preferable options for performing costeffective, scalable experimentation
in a controllable, repeatable, and configurable manner. To fill this gap, we
present a new, self-contained simulation tool named BigDataSDNSim that enables
the modeling and simulating of big data management systems (YARN), big data
programming models (MapReduce), and SDN-enabled networks within cloud computing
environments. To demonstrate the efficacy, effectiveness, and features of
BigDataSDNSim, a use-case that compares SDN-enabled networks with legacy
networks in terms of the performance and energy consumption is presented.
