Using Pattern Recognition Techniques for Server Overload Detection

One of the key factors in customer satisfaction is application performance. To be able to guarantee good performance, it is necessary to take appropriate measures before a server overload occurs. While in small systems it is usually possible to predict server overload using a subjective human expert, an automated overload prediction mechanism is important for ultra-large scale systems, such as multi-tenant Software-as-a-Service (SaaS) systems. An automated prediction mechanism would be an initial step towards self-adaptiveness of such systems, a property which leads to less human intervention during maintenance, resulting in less errors and better quality of service. In order to provide such a prediction mechanism, it is important to have a solid overload detection approach, which is (1) a first step towards automated prediction and (2) necessary for automated testing of a prediction mechanism. In this paper we propose a number of steps which help with the design and optimization of a statistical pattern classifier for server overload detection. Our approach is empirically evaluated on a synthetic dataset