A Novel Oppositional Chaotic Flower Pollination Optimization Algorithm for Automatic Tuning of Hadoop Configuration...

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.