On-line bayesian context change detection in web service systems

In real-life situations characteristics of Web service systems evolve in time. Therefore, change detection techniques become substantial elements of adaptive procedures for Web service systems management, such as resource allocation and anomaly detection methods. In this paper, we propose an on-line change detector which uses the Bayesian inference. We define two models which describe situations with one change and no change within data. Next we apply Bayesian model comparison for change detection. In order to obtain analytical expressions of model evidences used in the model comparison we provide a coherent framework of change detection which focuses on an approximation of the Bayes factor. The proposed solution, contrary to state-of-the-art methods, works in an on-line fashion and the algorithm's computational complexity is proportional to the constant size of the shifting window. Low computational complexity of the change detector enables its application in complex computer networks. At the end of the research paper, the quality of the proposed algorithm is examined using simulated Web service system.

[1]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[2]  A. Raftery,et al.  Bayesian analysis of a Poisson process with a change-point , 1986 .

[3]  A. Raftery.  Extreme Value Analysis of Environmental Time Series: An Application to Trend Detection in Ground-Level Ozone: Comment: Are Ozone Exceedance Rates Decreasing? , 1989 .

[4]  J. Hartigan,et al.  Product Partition Models for Change Point Problems , 1992 .

[5]  Michèle Basseville,et al.  Detection of abrupt changes: theory and application , 1993 .

[6]  William J. Fitzgerald,et al.  Recursive Bayesian location of a discontinuity in time series , 1994, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing.

[7]  V. Paxson,et al.  Wide-area traffic: the failure of Poisson modeling , 1994, SIGCOMM.

[8]  P. Green.  Reversible jump Markov chain Monte Carlo computation and Bayesian model determination , 1995 .

[9]  S. Chen,et al.  Speaker, Environment and Channel Change Detection and Clustering via the Bayesian Information Criterion , 1998 .

[10]  Chung-Bow Lee,et al.  Bayesian analysis of a change-point in exponential families with applications , 1998 .

[11]  S. L. Shishkin,et al.  Application of the change-point analysis to the investigation of the brain’s electrical activity , 2000 .

[12]  Fredrik Gustafsson,et al.  Adaptive filtering and change detection , 2000 .

[13]  Robert D. van der Mei,et al.  Web Server Performance Modeling , 2001, Telecommun. Syst..

[14]  David E. Culler,et al.  SEDA: an architecture for well-conditioned, scalable internet services , 2001, SOSP.

[15]  Louis Anthony Cox,et al.  Data Mining and Causal Modeling of Customer Behaviors , 2002, Telecommun. Syst..

[16]  Marina Thottan,et al.  Anomaly detection in IP networks , 2003, IEEE Trans. Signal Process..

[17]  Mike P. Papazoglou,et al.  Introduction: Service-oriented computing , 2003, CACM.

[18]  Claude Sammut,et al.  Extracting Hidden Context , 1998, Machine Learning.

[19]  Y. Selen,et al.  Model-order selection: a review of information criterion rules , 2004, IEEE Signal Processing Magazine.

[20]  Shai Ben-David,et al.  Detecting Change in Data Streams , 2004, VLDB.

[21]  Miroslav Kubat.  Induction in Time-Varying Domains: Motivation, Origins, and Encouragements , 2004, ECBS.

[22]  Manuel Davy,et al.  An online kernel change detection algorithm , 2005, IEEE Transactions on Signal Processing.

[23]  ADAM GRZECH.  ANOMALY DETECTION IN DISTRIBUTED COMPUTER COMMUNICATION SYSTEMS , 2006, Cybern. Syst..

[24]  Paul Fearnhead,et al.  Exact and efficient Bayesian inference for multiple changepoint problems , 2006, Stat. Comput..

[25]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[26]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[27]  Sung-Hyuk Cha.  Comprehensive Survey on Distance/Similarity Measures between Probability Density Functions , 2007 .

[28]  Ryan P. Adams,et al.  Bayesian Online Changepoint Detection , 2007, 0710.3742.

[29]  Abdelkamel Tari,et al.  A dual-layered model for web services representation and composition , 2008, Journal of Intelligent Information Systems.

[30]  VARUN CHANDOLA,et al.  Anomaly detection: A survey , 2009, CSUR.

[31]  Grigorios Tsoumakas,et al.  Tracking recurring contexts using ensemble classifiers: an application to email filtering , 2009, Knowledge and Information Systems.

[32]  Carl E. Rasmussen,et al.  Gaussian Process Change Point Models , 2010, ICML.

[33]  Takafumi Kanamori,et al.  Statistical outlier detection using direct density ratio estimation , 2011, Knowledge and Information Systems.

[34]  Ananth Ranganathan,et al.  PLISS: Detecting and Labeling Places Using Online Change-Point Detection , 2010, Robotics: Science and Systems.

[35]  Maciej Zieba,et al.  The Proposal of Service Oriented Data Mining System for Solving Real-Life Classification and Regression Problems , 2011, DoCEIS.

[36]  Ana Pont,et al.  Key factors in web latency savings in an experimental prefetching system , 2011, Journal of Intelligent Information Systems.

[37]  Roland Siegwart,et al.  Bayesian on-line learning of driving behaviors , 2011, 2011 IEEE International Conference on Robotics and Automation.

[38]  Leonid V. Bogachev,et al.  Modelling threshold exceedances of air pollution concentrations via non-homogeneous Poisson process with multiple change-points , 2011 .

[39]  Pawel Swiatek,et al.  Complex Services Availability in Service Oriented Systems , 2011, 2011 21st International Conference on Systems Engineering.

[40]  Piotr Rygielski,et al.  Context Change Detection for Resource Allocation in Service-Oriented Systems , 2011, KES.

[41]  Jakub M. Tomczak.  On-Line Change Detection for Resource Allocation in Service-Oriented Systems , 2012, DoCEIS.

[42]  Jakub M. Tomczak,et al.  Gaussian process regression as a predictive model for Quality-of-Service in Web service systems , 2012, ArXiv.