Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Performance debugging for distributed systems of black boxes
SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
Basic Concepts and Taxonomy of Dependable and Secure Computing
IEEE Transactions on Dependable and Secure Computing
SRDS '06 Proceedings of the 25th IEEE Symposium on Reliable Distributed Systems
Online Failure Forecast for Fault-Tolerant Data Stream Processing
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Adaptive system anomaly prediction for large-scale hosting infrastructures
Proceedings of the 29th ACM SIGACT-SIGOPS symposium on Principles of distributed computing
Practical online failure prediction for Blue Gene/P: Period-based vs event-driven
DSNW '11 Proceedings of the 2011 IEEE/IFIP 41st International Conference on Dependable Systems and Networks Workshops
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This paper introduces a novel approach to failure prediction for mission critical distributed systems that has the distinctive features to be black-box, non-intrusive and online. The approach combines Complex Event Processing (CEP) and Hidden Markov Models (HMM) so as to analyze symptoms of failures that might occur in the form of anomalous conditions of performance metrics identified for such purpose. The paper describes an architecture named CASPER, based on CEP and HMM, that relies on sniffed information from the communication network of a mission critical system, only, for predicting anomalies that can lead to software failures. An instance of CASPER has been implemented, trained and tuned to monitor a real Air Traffic Control (ATC) system. An extensive experimental evaluation of CASPER is presented. The obtained results show (i) a very low percentage of false positives over both normal and under stress conditions, and (ii) a sufficiently high failure prediction time that allows the system to apply appropriate recovery procedures.