Log File Template Detection as a Multi-Objective Optimization Problem

Abstract

There is a need for automatic log file template detection tool to find out all the log messages through search space. On the other hand, the template detection tool should cope with two constraints: (i) it could not be too general and (ii) it could not be too specific These constraints are, contradict to one another and can be considered as a multi-objective optimization problem. Thus, a novel multi-objective optimization based log-file template detection approach named LTD-MO is proposed in this paper. It uses a new multi-objective based swarm intelligence algorithm called chicken swarm optimization for solving the hard optimization issue. Moreover, it analyzes all templates in the search space and selects a Pareto front optimal solution set for multi-objective compensation. The proposed approach is implemented and evaluated on eight publicly available benchmark log datasets. The empirical analysis shows LTD-MO detects large number of appropriate templates by significantly outperforming the existing techniques on all datasets.