Journal of Network and Systems Management
A Distributed and Reliable Platform for Adaptive Anomaly Detection in IP Networks
DSOM '99 Proceedings of the 10th IFIP/IEEE International Workshop on Distributed Systems: Operations and Management: Active Technologies for Network and Service Management
Using Time over Threshold to Reduce Noise in Performance and Fault Management Systems
DSOM '00 Proceedings of the 11th IFIP/IEEE International Workshop on Distributed Systems: Operations and Management: Services Management in Intelligent Networks
Application of a network dynamics analysis tool to mobile ad hoc networks
Proceedings of the 9th ACM international symposium on Modeling analysis and simulation of wireless and mobile systems
An online predictive control framework for designing self-managing computing systems
Multiagent and Grid Systems
Dynamic resource allocation for shared data centers using online measurements
IWQoS'03 Proceedings of the 11th international conference on Quality of service
Dolly: virtualization-driven database provisioning for the cloud
Proceedings of the 7th ACM SIGPLAN/SIGOPS international conference on Virtual execution environments
A conceptual design of knowledge-based real-time cyber-threat early warning system
ISPA'06 Proceedings of the 2006 international conference on Frontiers of High Performance Computing and Networking
HOIDS-Based detection method of vicious event in large networks
EUC'06 Proceedings of the 2006 international conference on Emerging Directions in Embedded and Ubiquitous Computing
Self-star Properties in Complex Information Systems
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Predicting network performance problems enables network operators to take corrective actions in advance of service disruptions. Typically, service problems are detected by tests that compare a metric (e.g., response time) to a threshold. Herein, we present an on-line algorithm for predicting the probability of threshold violations over a time horizon. Our algorithm uses two cascaded submodels. The first removes non-stationarities by employing a discrete time Kalman Filter in combination with analysis of variance. We derive parameters of the Kalman Filter from differential equations that describe characteristics of the data. The second sub-model estimates the probability of threshold violations by using a second order autoregressive model in combination with change-point detection. Using data from a production web server, we evaluate our approach and show that it produces average accuracies that are comparable to those of an off-line algorithm. However, our on-line algorithm produces predictions with considerably smaller variances. Further advantages of our approach are: (a) requiring much less data than the off-line technique-one day versus multiple months; and (b) adapting to changes in the system and workloads since parameters are estimated on-line.