Issues in Bottleneck Detection in Multi-Tier Enterprise Applications

Abstract

In this work, the performance of various machine learning classifiers with regard to bottleneck detection in enterprise, multi-tier applications governed by service level objectives is described. Specifically, in this paper, it demonstrates the effectiveness of three classifiers, a tree-augmented Naive Bayesian network, a J48 decision tree, and LogitBoost, using our bottleneck detection process, which delves into a new area of performance analysis based on the trends of metrics (first order derivative) rather than the metric value itself. Furthermore, the efficiency of each classifier by measuring the convergence speed, or the number of staging trials required in order to provide positive results is illustrated. Finally, the effectiveness of the classifiers used in the bottleneck detection process as each classifier strongly identifies the enterprise system bottleneck