A System Architecture for Real-time Anomaly Detection in Large-scale NFV Systems

Gulenko, Anton; Wallschläger, Marcel; Schmidt, Florian; Kao, Odej; Liu, Feng

doi:10.1016/j.procs.2016.08.076

Cited by 27 publications

(14 citation statements)

References 4 publications

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“…Gulenko et al [2] described an analysis pipeline and architecture to provide self-healing to cloud systems. The system is build upon the open-source stream processing framework Bitflow 6 in order to enable the sending of monitoring data as well as analysis results between machine learning algorithms.…”

Section: Background Self-healing Analysismentioning

confidence: 99%

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Towards AIOps in Edge Computing Environments

Becker,

Schmidt,

Gulenko

et al. 2021

Preprint

Self Cite

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Edge computing was introduced as a technical enabler for the demanding requirements of new network technologies like 5G. It aims to overcome challenges related to centralized cloud computing environments by distributing computational resources to the edge of the network towards the customers. The complexity of the emerging infrastructures increases significantly, together with the ramifications of outages on critical use cases such as self-driving cars or health care. Artificial Intelligence for IT Operations (AIOps) aims to support human operators in managing complex infrastructures by using machine learning methods. This paper describes the system design of an AIOps platform which is applicable in heterogeneous, distributed environments. The overhead of a high-frequency monitoring solution on edge devices is evaluated and performance experiments regarding the applicability of three anomaly detection algorithms on edge devices are conducted. The results show, that it is feasible to collect metrics with a high frequency and simultaneously run specific anomaly detection algorithms directly on edge devices with a reasonable overhead on the resource utilization.

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Section: Background Self-healing Analysismentioning

confidence: 99%

“…During previous work we have developed a self-healing pipeline which was extensively tested in cloud computing environments [2]. It consists of detecting autonomously anomalies, find the root cause, and plan and execute appropriate actions to resolve or mitigate the occurred problem.…”

Section: Introductionmentioning

confidence: 99%

Towards AIOps in Edge Computing Environments

Becker,

Schmidt,

Gulenko

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…For what concerns NFV applications, the existing literature reports that ML techniques have been effectively used to solve different problems. In particular, in [13] a set of ML techniques are tested for an anomaly detection application. In this case, though, only supervised methods are considered and their performance is compared on data sets containing NFV features associated to different types of faults.…”

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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“…For what concerns NFV applications, the existing literature reports that ML techniques have been effectively used to solve different problems. In particular, in (Gulenko et al, 2016b) a set of ML techniques are tested for an anomaly detection application. In this case, though, only supervised methods are considered and their performance is compared on data sets containing NFV features associated to different types of faults.…”

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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A System Architecture for Real-time Anomaly Detection in Large-scale NFV Systems

Cited by 27 publications

References 4 publications

Towards AIOps in Edge Computing Environments

Towards AIOps in Edge Computing Environments

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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