A statistical anomaly-based algorithm for on-line fault detection in complex software critical systems

Authors:
Antonio Bovenzi;Francesco Brancati;Stefano Russo;Andrea Bondavalli
Affiliations:
Dipartimento di Informatica e Sistemistica, Università degli Studi di Napoli "Federico II", Napoli, Italy;Dipartimento di Sistemi e Informatica, Università degli Studi di Firenze, Italy;Dipartimento di Informatica e Sistemistica, Università degli Studi di Napoli "Federico II", Napoli, Italy;Dipartimento di Sistemi e Informatica, Università degli Studi di Firenze, Italy
Venue:
SAFECOMP'11 Proceedings of the 30th international conference on Computer safety, reliability, and security
Year:
2011

Citing 8
Cited 0

Detection of abrupt changes: theory and application

Detection of abrupt changes: theory and application
On the Quality of Service of Failure Detectors

DSN '00 Proceedings of the 2000 International Conference on Dependable Systems and Networks (formerly FTCS-30 and DCCA-8)
Basic Concepts and Taxonomy of Dependable and Secure Computing

IEEE Transactions on Dependable and Secure Computing
A framework for dependable QoS adaptation in probabilistic environments

Proceedings of the 2008 ACM symposium on Applied computing
A survey of online failure prediction methods

ACM Computing Surveys (CSUR)
Emulation of Transient Software Faults for Dependability Assessment: A Case Study

EDCC '10 Proceedings of the 2010 European Dependable Computing Conference
Towards Identifying the Best Variables for Failure Prediction Using Injection of Realistic Software Faults

PRDC '10 Proceedings of the 2010 IEEE 16th Pacific Rim International Symposium on Dependable Computing
Operating system support to detect application hangs

VECoS'08 Proceedings of the Second international conference on Verification and Evaluation of Computer and Communication Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

The next generation of software systems in Large-scale Complex Critical Infrastructures (LCCIs) requires efficient runtime management and reconfiguration strategies, and the ability to take decisions on the basis of current and past behavior of the system. In this paper we propose an anomalybased approach for the detection of online faults, which is able to (i) cope with highly variable and non-stationary environment and to (ii) work without any initial training phase. The novel algorithm is based on Statistical Predictor and Safety Margin (SPS), which was initially developed to estimate the uncertainty in time synchronization mechanisms. The SPS anomaly detection algorithm has been experimented on a case study from the Air Traffic Management (ATM) domain. Results have been compared with an algorithm, which adopts static thresholds, in the same scenarios [5]. Experimental results show limitations of static thresholds in highly variable scenarios, and the ability of SPS to fulfill the expectations.