BigDataSDNSim: A Simulator for Analyzing Big Data Applications in Software-Defined Cloud Data Centers

TL;DR

BigDataSDNSim is a simulation tool designed to model and analyze big data applications and SDN-enabled networks in cloud data centers, enabling cost-effective performance evaluation.

Contribution

The paper introduces BigDataSDNSim, a novel simulation platform for modeling big data systems and SDN networks within cloud environments, addressing the need for scalable testing tools.

Findings

SDN networks outperform legacy networks in performance.

SDN networks consume less energy.

Simulation results validate the effectiveness of BigDataSDNSim.

Abstract

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.