At present, due to the introduction of the big data era, numerous numbers of data are generated consistently. Many applications utilize big data platforms, namely Spark, Hadoop, Amazon web services, and so on, since these platforms use several parameters for tuning that further enhance the operating performances. It requires a long duration of time to tune the parameters because of the complex relationship and large quantity of parameters. As a result, the building of such parameters and performance optimization at a particular duration of time becomes a challenging task. Several auto-tuning approaches are developed to achieve an optimal design. However, these approaches increase the computation time and minimize the efficiency of the cluster. It is necessary to tune the parameters automatically with low computational and processing time as well as to improve the performance of the system. In this proposed approach, a novel automatic parameter tuning system named as Opt. Tuner is proposed to select the Hadoop configuration parameters with less computational time. The optimization of the proposed approach is achieved by the Flower Pollination Algorithm. Here, a chaotic mapping along with Opposition-Based Learning is introduced for population initialization to form a novel Oppositional Chaotic Flower Pollination Algorithm. The main motive of this initialization phase involves in generating better individuals and to guide the search agent more quickly. In this novel approach, 15 configuration parameters are considered for auto-tuning. Finally, the performance of the proposed approach utilizes the wordcount and sort application to investigate the exhibition and proficiency of diverse databases.

中文翻译：

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自动调整Hadoop配置参数的新型对立混沌花授粉优化算法。

当前，由于大数据时代的到来，持续生成大量数据。许多应用程序利用大数据平台，即Spark，Hadoop，Amazon Web服务等，因为这些平台使用多个参数进行调整，从而进一步提高了操作性能。由于复杂的关系和大量的参数，因此需要很长的时间来调整参数。结果，在特定时间段内建立此类参数和优化性能成为一项艰巨的任务。开发了几种自动调整方法来实现最佳设计。但是，这些方法增加了计算时间并使群集效率最小化。必须以低的计算和处理时间自动调整参数，并提高系统的性能。在这种提议的方法中，一种新颖的自动参数调整系统名为Opt。建议使用Tuner以更少的计算时间选择Hadoop配置参数。通过花授粉算法实现了所提出方法的优化。在这里，引入了基于对立的学习的混沌映射，用于种群初始化，从而形成了一种新颖的对立的混沌花授粉算法。初始化阶段的主要动机在于培养更好的人并更快地指导搜索代理。在这种新颖的方法中，考虑了15个配置参数用于自动调整。最后，