Hora: Architecture-aware online failure prediction

Pitakrat, Teerat; Okanović, Dušan; Hoorn, André van; Grunske, Lars

doi:10.1016/j.jss.2017.02.041

Cited by 44 publications

(23 citation statements)

References 21 publications

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“…We filtered potential works by manually reading title, abstract, and keywords in order to include the context as opposed to applying an automated keyword-based mechanism. Summarizing, we included a total of 67 scientific papers, eight theses [81][82][83][84][85][86][87][88], three tech-reports [89][90][91], and two US patents [92,93] for the derivation of the taxonomy presented in Section 4. After refining the taxonomy, we re-classified all works to see if each work is still in the right category and then continued to classify all remaining works by reading their approach in detail.…”

Section: Methodsmentioning

confidence: 99%

“…Third, the last group focuses on the definition of failure as defined by Pitakrat et al [87] and Avizienis et al [94]: A service failure is defined as an event that occurs when the service deviates from the correct behavior. This includes an increase in response time, a service outage, or an incorrect return result.…”

Section: Prediction Targetmentioning

confidence: 99%

“…Pitakrat et al [76,87] develop a technique for predicting failures in an online environment. Their technique is based on two components.…”

Section: Architectural Modelsmentioning

confidence: 99%

See 2 more Smart Citations

A Taxonomy of Techniques for SLO Failure Prediction in Software Systems

Grohmann

Herbst

Chalbani³

et al. 2020

Computers

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Failure prediction is an important aspect of self-aware computing systems. Therefore, a multitude of different approaches has been proposed in the literature over the past few years. In this work, we propose a taxonomy for organizing works focusing on the prediction of Service Level Objective (SLO) failures. Our taxonomy classifies related work along the dimensions of the prediction target (e.g., anomaly detection, performance prediction, or failure prediction), the time horizon (e.g., detection or prediction, online or offline application), and the applied modeling type (e.g., time series forecasting, machine learning, or queueing theory). The classification is derived based on a systematic mapping of relevant papers in the area. Additionally, we give an overview of different techniques in each sub-group and address remaining challenges in order to guide future research.

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Section: Methodsmentioning

confidence: 99%

Section: Prediction Targetmentioning

confidence: 99%

See 1 more Smart Citation

A Taxonomy of Techniques for SLO Failure Prediction in Software Systems

Grohmann

Herbst

Chalbani³

et al. 2020

Computers

View full text Add to dashboard Cite

show abstract

“…Embedding textual information has been in fact made easier by the advancements in Natural Language Processing (NLP) research [2]. Few existing works also consider the need of assisting human operators in conducting root-cause analysis to be a highly desirable feature of anomaly detection systems [14,28].…”

Section: Related Workmentioning

confidence: 99%

Using Self-Organizing Maps for the Behavioral Analysis of Virtualized Network Functions

Lanciano

Ritacco

Brau

et al. 2021

Communications in Computer and Information Science

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Detecting anomalous behaviors in a network function virtualization infrastructure is of the utmost importance for network operators. In this paper, we propose a technique, based on Self-Organizing Maps, to address such problem by leveraging on the massive amount of historical system data that is typically available in these infrastructures. Indeed, our method consists of a joint analysis of system-level metrics, provided by the virtualized infrastructure monitoring system and referring to resource consumption patterns of the physical hosts and the virtual machines (or containers) that run on top of them, and application-level metrics, provided by the individual virtualized network functions monitoring subsystems and related to the performance levels of the individual applications. The implementation of our approach has been validated on real data coming from a subset of the Vodafone infrastructure for network function virtualization, where it is currently employed to support the decisions of data center operators. Experimental results show that our technique is capable of identifying specific points in space (i.e., components of the infrastructure) and time of the recent evolution of the monitored infrastructure that are worth to be investigated by human operators in order to keep the system running under expected conditions.

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“…Embedding textual information has been in fact made easier by the advancements in Natural Language Processing (NLP) research field (Bertero et al, 2017). Few ex-isting works also consider the need of assisting human operators in conducting root-cause analysis to be a highly desirable feature of anomaly detection systems (Gupta et al, 2017;Pitakrat et al, 2018).…”

Section: Related Workmentioning

confidence: 99%

Behavioral Analysis for Virtualized Network Functions: A SOM-based Approach

Cucinotta

Lanciano

Ritacco

et al. 2020

Proceedings of the 10th International Conference on Cloud Computing and Services Science

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In this paper, we tackle the problem of detecting anomalous behaviors in a virtualized infrastructure for network function virtualization, proposing to use self-organizing maps for analyzing historical data available through a data center. We propose a joint analysis of system-level metrics, mostly related to resource consumption patterns of the hosted virtual machines, as available through the virtualized infrastructure monitoring system, and the application-level metrics published by individual virtualized network functions through their own monitoring subsystems. Experimental results, obtained by processing real data from one of the NFV data centers of the Vodafone network operator, show that our technique is able to identify specific points in space and time of the recent evolution of the monitored infrastructure that are worth to be investigated by a human operator in order to keep the system running under expected conditions.

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Hora: Architecture-aware online failure prediction

Cited by 44 publications

References 21 publications

A Taxonomy of Techniques for SLO Failure Prediction in Software Systems

A Taxonomy of Techniques for SLO Failure Prediction in Software Systems

Using Self-Organizing Maps for the Behavioral Analysis of Virtualized Network Functions

Behavioral Analysis for Virtualized Network Functions: A SOM-based Approach

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