An adaptive model for online detection of relevant state changes in Internet-based systems
Modern Internet-based systems typically involve a large number of servers and applications and require real-time management strategies for cloning and migrating virtual machines, as well as re-distributing or re-mapping the underlying hardware. At the basis of most real-time management strategies there is the need to continuously evaluate system state behavior and to detect when a relevant state change is occurring. Modern Internet-based systems open new and interesting scenarios in the field of the research on the online state change detection models. In this paper, we propose an adaptive state change detection model that we demonstrate is suitable to analyze continuous streams of data coming from Internet-based systems characterized by high variability and non stationarity of the monitored resource measures that result in not-acceptable false alarm rates. Our model solves the limits of the traditional solutions while retaining their computational efficiency. The solution we present combines two key elements: an on-line wavelet model to denoise data streams and an adaptive detection rule. Experiments carried out using empirical and synthetic data sets confirm that the proposed method is able to signal all relevant state changes limiting the incorrect detections and to provide robust results even in non-stationary and highly variable contexts.
internet-based systems resource management online statistical algorithms