Gueyoung Jung

The characterization of distributed n-tier application performance is an important and challenging problem due to their complex structure and the significant variations in their workload. Theoretical models have difficulties with such wide range of environmental and workload settings. Experimental approaches using manual scripts are error-prone, time consuming, and expensive. We use code generation techniques and tools to create and run the scripts for large-scale experimental observation of n-tier benchmarking application performance measurements over a wide range of parameter settings and software/hardware combinations. Our experiments show the feasibility of experimental observations as a sound basis for performance characterization, by studying in detail the performance achieved by (up to 3) database servers and (up to 12) application servers in the RUBiS benchmark with a workload of up to 2700 concurrent users.

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