Probability, statistics, and queueing theory with computer science applications
Probability, statistics, and queueing theory with computer science applications
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Adaptive algorithms for managing a distributed data processing workload
IBM Systems Journal
On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
A signal analysis of network traffic anomalies
Proceedings of the 2nd ACM SIGCOMM Workshop on Internet measurment
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Pinpoint: Problem Determination in Large, Dynamic Internet Services
DSN '02 Proceedings of the 2002 International Conference on Dependable Systems and Networks
A unifying framework for detecting outliers and change points from non-stationary time series data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Eigenspace-based anomaly detection in computer systems
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
On-line EM Algorithm for the Normalized Gaussian Network
Neural Computation
Anomaly detection in IP networks
IEEE Transactions on Signal Processing
High speed and robust event correlation
IEEE Communications Magazine
IBM Journal of Research and Development
Dependency detection using a fuzzy engine
DSOM'07 Proceedings of the Distributed systems: operations and management 18th IFIP/IEEE international conference on Managing virtualization of networks and services
Monere: monitoring of service compositions for failure diagnosis
ICSOC'11 Proceedings of the 9th international conference on Service-Oriented Computing
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We introduce a network-based problem detection framework for distributed systems, which includes a data-mining method for discovering dynamic dependencies among distributed services from transaction data collected from network, and a novel problem detection method based on the discovered dependencies. From observed containments of transaction execution time periods, we estimate the probabilities of accidental and non-accidental containments, and build a competitive model for discovering direct dependencies by using a model estimation method based on the online EM algorithm. Utilizing the discovered dependency information, we also propose a hierarchical problem detection framework, where microscopic dependency information is incorporated with a macroscopic anomaly metric that monitors the behavior of the system as a whole. This feature is made possible by employing a network-based design which provides overall information of the system without any impact on the performance.