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BigDataSDNSim: A simulator for analyzing big data applications in software‐defined cloud data centers
Software: Practice and Experience ( IF 3.5 ) Pub Date : 2020-10-27 , DOI: 10.1002/spe.2917
Khaled Alwasel 1, 2 , Rodrigo N. Calheiros 3 , Saurabh Garg 4 , Rajkumar Buyya 5 , Mukaddim Pathan 6 , Dimitrios Georgakopoulos 7 , Rajiv Ranjan 1
Affiliation  

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.

中文翻译:

BigDataSDNSim:用于分析软件定义云数据中心大数据应用的模拟器

云数据中心中大数据和软件定义网络 (SDN) 的新兴范式引起了业界和学术界的极大关注。需要大数据与SDN的融合协调,提升大数据应用的应用和网络性能。虽然对大数据和 SDN 的实证评估和分析可以作为观察提出的解决方案的一种方式,但由于多种原因,例如成本高昂、耗时和复杂性,通常不切实际或难以应用;此外,它超出了大多数人的能力范围。因此,仿真工具是以可控、可重复和可配置的方式执行具有成本效益、可扩展的实验的首选选项。为了填补这一空白,我们提出了一个新的,一个名为 BigDataSDNSim 的自包含仿真工具,可以在云计算环境中对大数据管理系统 (YARN)、大数据编程模型 (MapReduce) 和支持 SDN 的网络进行建模和仿真。为了展示 BigDataSDNSim 的功效、有效性和特性,我们提供了一个用例,在性能和能耗方面比较了支持 SDN 的网络与传统网络。
更新日期:2020-10-27
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