Time series: theory and methods
Time series: theory and methods
Kalman filtering with real-time applications
Kalman filtering with real-time applications
Congestion avoidance and control
SIGCOMM '88 Symposium proceedings on Communications architectures and protocols
A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
An introduction to wavelets
Agile application-aware adaptation for mobility
Proceedings of the sixteenth ACM symposium on Operating systems principles
Host load prediction using linear models
Cluster Computing
An Architectural Evaluation of Java TPC-W
HPCA '01 Proceedings of the 7th International Symposium on High-Performance Computer Architecture
Simulation and the Monte Carlo Method (Wiley Series in Probability and Statistics)
Simulation and the Monte Carlo Method (Wiley Series in Probability and Statistics)
Models and framework for supporting runtime decisions in Web-based systems
ACM Transactions on the Web (TWEB)
Power and Performance Management of Virtualized Computing Environments Via Lookahead Control
ICAC '08 Proceedings of the 2008 International Conference on Autonomic Computing
ACM Computing Surveys (CSUR)
Advanced Data Mining Techniques
Advanced Data Mining Techniques
Black-box and gray-box strategies for virtual machine migration
NSDI'07 Proceedings of the 4th USENIX conference on Networked systems design & implementation
Change detection with kalman filter and CUSUM
DS'06 Proceedings of the 9th international conference on Discovery Science
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Additive Change Detection in Nonlinear Systems With Unknown Change Parameters
IEEE Transactions on Signal Processing
Selective resource characterization for evaluation of system dynamics
ACM SIGMETRICS Performance Evaluation Review
Automatic virtual machine clustering based on bhattacharyya distance for multi-cloud systems
Proceedings of the 2013 international workshop on Multi-cloud applications and federated clouds
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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.