Towards efficient XML parsing through minimization of JVM parameter space

Abstract

A significant increase in the usage of Extensible Markup Language (XML) data for various protocols and standards emphasizes the development of efficient XML parsers. For the Java language, the XML DOM parser despite performing in-memory operations is unable to achieve peak execution performance on modern systems, especially for parsing large XML files. The issue of inefficient execution may be mitigated by selecting appropriate runtime parameters for the Java Virtual Machine (JVM). This entails to exploring parameter space in an exhaustive manner that is not practically feasible for rapid application development. This paper aims at performance enhancement of XML parsing through selection of optimal set of JVM runtime parameters. The proposed approach works independent of parser design. It reduces JVM parameter space through machine learning-based models which are trained using profile data. The impact of parameters is determined using linear regression and artificial neural network-based models. The subsequent computation of a location-based weight vector along with a threshold value for filtration of parameters generates a set of optimal parameters for performance enhancement. The XML parsing code using the optimal parameters achieves average speedups of 13.18% and 21.42% over the standard code on Intel Xeon and Intel Core i7-based systems, respectively.