Applying intelligent data traffic adaptation to high-performance multiple big data analytics platforms

This paper introduces the integration of two data processing platforms, RHhadoop and SparkR, to carry out rapid big data retrieval and analytics using R programming, which can serve as part of business intelligence. Besides, it has developed the job scheduling optimization called Memory-Sensitive Heterogeneous Earliest Finish Time algorithm to enhance system throughput. However, the bottleneck of system throughput is definitely relevant to data traffic problem over network, especially a large amount of data exchange between distributed computing nodes within a cluster. The objective of this paper is to propose an intelligence approach to tackle the crucial problems of inefficient data traffic flow. Adaptive network-based fuzzy inference system along with particle swarm optimization has employed to tune the network-related parameters at computing nodes for improving network QoS and speed up data transportation significantly. In order to examine the computing efficiency, performance index has been evaluated for all of treatments in the experiment.