Server Workload Classification and Analysis with Machine Learning Algorithms

Abstract

The main factor in measuring server performance is the accuracy of detection mechanisms. Sever is needed to detect server overload condition accurately. Therefore, it can be satisfied customers by reducing request drop rate. Server overload detection would be an initial step of overload control system. In order to provide such a detection mechanism, it is important to choose the best classifier which is the most suitable for our dataset. Selecting correct classifier maximize the performance of detection mechanism. In this paper, we present how server workload classification task is performed by using different machine learning classification methods and how the best classifier improve overload detection mechanism. We make a synthetic dataset by using window performance monitor tool. Many classifiers are evaluated over synthetic dataset.