Performance tuning of Java EE application servers with multi-objective differential evolution

This paper presents an empirical approach for the performance tuning of Java EE application servers (ASs) using a multi-objective differential evolution algorithm. It features multi-objective black-box optimization of selected AS´s configuration parameters. The proposed approach is used for performance tuning of the AS GlassFish and Java EE test application DayTrader. The obtained results improve the objectives´ values of referenced configuration (AS´s default settings) individually, as well as a whole. We also find a Pareto front approximation which entirely dominates the referenced configuration objectives´ values. Comparison with existing approaches is made based on previously found relations between certain configuration parameters´ values. Results from multi-objective performance tuning offer a choice of alternative configurations and thus enable the attainment of business goals even within an environment where the objectives´ priorities may change.