PaaS Optimization of Apache Applications Using System Parameter Tuning of Big Data Platforms in Distributed Computing

Abstract

Widely used data processing platforms use distributed systems to process huge data efficiently. The aim of this article is to optimize the platform services by tuning only the relevant, tunable, system parameters and to identify the relation between the software quality metrics. The system parameters of data platforms based on the service level agreements can be defined and customized. In the first stage, the most significant parameters are identified and shortlisted using various feature selection approaches. In the second stage, the iterative runs of applications are executed for tuning these shortlisted parameters to identify the optimal value and to understand the impact of individual input parameters on the system output parameter. The empirical results imply significant improvement in performance and with which it is possible to render the proposed work optimizing the services offered by these data platforms.