The growth of information technology along with the revolution of the industry and business has led to the generation of an enormous amount of data. This big data needs a platform beyond the traditional data possessing context that relies on some computational servers communicating through a network in its lower layer. One of the most important challenges in data processing is how to transfer the big batches of data between the servers to achieve fast responsiveness. Consequently, the underlying network plays a critical role in the performance of a big data analysis platform. Ideally, this network must use the shortest path that has the lowest amount of load, to transfer the large-scale data. To address this issue, we propose a software-defined networking (SDN) enabled scheduling method that uses the tabu search algorithm to schedule big data tasks. The proposed algorithm not only considers data locality but also uses the network traffic status for efficient scheduling. Our extensive simulative study in the Mininet emulator shows that the proposed scheme gives high performance and minimizes job completion time.

中文翻译：

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支持软件定义网络的大数据任务调度：一种禁忌搜索方法

信息技术的发展以及工业和商业的革命导致了海量数据的产生。这种大数据需要一个超越传统数据拥有上下文的平台，该平台依赖于一些计算服务器通过其下层网络进行通信。数据处理中最重要的挑战之一是如何在服务器之间传输大批量数据以实现快速响应。因此，底层网络对大数据分析平台的性能起着至关重要的作用。理想情况下，该网络必须使用负载量最低的最短路径来传输大规模数据。为了解决这个问题，我们提出了一种支持软件定义网络（SDN）的调度方法，该方法使用禁忌搜索算法来调度大数据任务。所提出的算法不仅考虑了数据局部性，而且还利用网络流量状态进行有效调度。我们在 Mininet 仿真器中进行的广泛模拟研究表明，所提出的方案具有高性能并最大限度地减少了作业完成时间。