Abstract

Performance modeling has become increasingly important in the design, engineering and optimization of information technology (IT) infrastructures and applications. However, modeling work itself is time consuming and requires a good knowledge not only of the system, but also of modeling techniques. One of the biggest challenges in modeling complex IT systems consists in the calibration of model parameters, such as the service requirements of various job classes. We present an approach for solving this problem in the queueing network framework using inference techniques. This is done through a mathematical programming formulation, for which we propose an efficient and robust solution method. The necessary input data are end-to-end measurements which are usually easy to obtain. The robustness of our method means that the inferred model performs well in the presence of noisy data and further, is able to detect and remove outlying data sets. We present numerical experiments using data from real IT practice to demonstrate the promise of our framework and algorithm.

Keywords: Parameter inference; Queueing model; IT system

Article Outline

1. Introduction

2. The modeling framework

2.1. Background on IT systems

2.2. Queueing model

2.3. The inference problem

3. Queueing dynamics

4. A quadratic program

4.1. Notation

4.2. Single experiment

4.3. Multiple experiments: general formulation

4.4. Properties of the model

5. The self-adjusting nested optimization method

5.1. Basic idea of the method

5.2. Steps of the self-adjusting nested estimation method

5.3. Further uses of the characterization of the nested optimality set

6. Analysis and numerical experience

6.1. Comparative results on synthetic data

6.2. Validation results from a real IT system

7. Conclusions and future research directions

References