Print Email Facebook Twitter Using Pattern Recognition Techniques for Server Overload Detection Title Using Pattern Recognition Techniques for Server Overload Detection Author Bezemer, C.P. Cheplygina, V. Zaidman, A. Faculty Electrical Engineering, Mathematics and Computer Science Department Software Technology Date 2011-12-31 Abstract 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. To reference this document use: http://resolver.tudelft.nl/uuid:00073401-145f-4972-9f51-47109021ac53 Publisher Delft University of Technology, Software Engineering Research Group ISSN 1872-5392 Source Technical Report Series TUD-SERG-2011-009 Part of collection Institutional Repository Document type report Rights (c) 2011 The authors. Software Engineering Research Group, Department of Software Technology, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology. Files PDF TUD-SERG-2011-009.pdf 2.82 MB Close viewer /islandora/object/uuid:00073401-145f-4972-9f51-47109021ac53/datastream/OBJ/view