A Taxonomy of Anomalies in Distributed Cloud Systems: The CRI-Model

Reichert, Kim; Pokahr, Alexander; Hohenberger, Till; Haubeck, Christopher; Lamersdorf, Winfried

doi:10.1007/978-3-319-66379-1_22

Cited by 1 publication

(1 citation statement)

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“…To develop a taxonomy of non-conformances, we considered both perspectives of research and practice [25]. To narrow the wide range of literature and define the categories of the dimensions of our taxonomy we focus for anomalies on distributed (cloud) systems and for deviations on Cyber-Physical Production Systems.…”

Section: Approachmentioning

confidence: 99%

The CRI-model: A domain-independent taxonomy for non-conformance between observed and specified behaviour

Haubeck¹,

Pokahr

Reichert³

et al. 2018

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Anomaly detection is the process of identifying nonconforming behaviour. Many approaches from machine learning to statistical methods exist to detect behaviour that deviate from its norm. These non-conformances of specifications can stem from failures in the system or undocumented changes of the system during its evolution. However, no generic solutions exist for classifying and identifying these non-conformances. In this paper, we present the CRI-Model (Cause, Reaction, Impact), which is a taxonomy based on a study of anomaly types in the literature, an analysis of system outages in major cloud companies and evolution scenarios which describe and implement changes in Cyber-Physical Production Systems. The goal of the taxonomy is to be usable for different objectives like discover gaps in the detection process, determine components most often affected by a particular anomaly type or describe system evolution. While the dimensions of the taxonomy are fixed, the categories can be adapted to different domains. We show and validate the applicability of the taxonomy to distributed cloud systems using a large data set of anomaly reports and cyber-physical production systems by categorizing common changes of an evolution benchmarking plant.

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Section: Approachmentioning

confidence: 99%