Root Cause Analysis of Network Failures Using Machine Learning and Summarization Techniques

González, Joaquín Miguel Alonso; Jiménez, Javier; López, Juan Carlos Dueñas; Parada, Adrián González

doi:10.1109/mcom.2017.1700066

Cited by 31 publications

(24 citation statements)

References 10 publications

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“…The opportunity is then crucial for operators to perform some proactive operation that quickly detects the signs of critical failures and prevents future defects. To perform fault prediction, most efforts [74,75,78,96], proposed ML solutions deployed in large data or logs. Sauvanaud et al [75], applied a random forest algorithm to predict the anomalous VMs.…”

Section: Fault Prediction and Proactive Recoverymentioning

confidence: 99%

“…Sauvanaud et al [75], applied a random forest algorithm to predict the anomalous VMs. Papers [74,78], proposed an offline methods to extract trends in network data that can enable the operator to proactively tackle similar faults in the future. Gonzalez et al [78], used random forest enhanced with summarization and operations techniques for the automatic identification of dependencies between system events, to help network operators predict the root causes of error.…”

Section: Fault Prediction and Proactive Recoverymentioning

confidence: 99%

“…Papers [74,78], proposed an offline methods to extract trends in network data that can enable the operator to proactively tackle similar faults in the future. Gonzalez et al [78], used random forest enhanced with summarization and operations techniques for the automatic identification of dependencies between system events, to help network operators predict the root causes of error. Furthermore, Cotroneo et al [74], proposed a dependability benchmark to support NFV providers making decisions about which management solutions can achieve the best dependability.…”

Section: Fault Prediction and Proactive Recoverymentioning

confidence: 99%

See 2 more Smart Citations

A Survey of Fault Management in Network Virtualization Environments: Challenges and Solutions

Cherrared

Imadali

Fabre

et al. 2019

IEEE Trans. Netw. Serv. Manage.

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The advent of 5G and the ever increasing stringent requirements in terms of bandwidth, latency, and quality of service pushes the boundaries of what is feasible with legacy Mobile Network Operators' technologies. Network Functions Virtualization (NFV) is a promising attempt at solving some of these challenges that is widely adopted by the industry and specified by the standardization bodies. In essence, NFV is about running Network Functions (NFs) as virtualized workloads on commodity hardware. This may optimize deployment costs and simplify the lifecycle management of NFs, but it introduces new fault management challenges and issues. In this paper, we propose a comprehensive state of the art of fault management techniques. We address the impact of virtualization in fault management. We propose a new classification of the recent fault management research achievements in the network virtualization environments and compare their major contributions and shortcomings.

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Section: Fault Prediction and Proactive Recoverymentioning

confidence: 99%

Section: Fault Prediction and Proactive Recoverymentioning

confidence: 99%

Section: Fault Prediction and Proactive Recoverymentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Fault Management in Network Virtualization Environments: Challenges and Solutions

Cherrared

Imadali

Fabre

et al. 2019

IEEE Trans. Netw. Serv. Manage.

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“…Bayesian networks are a natural candidate to perform inference, and case‐based reasoning is another valuable technique that helps to exploit previous expert knowledge on the domain. Both techniques have been proposed for RCA …”

Section: Machine Learning For Anomaly Detection and Rcamentioning

confidence: 99%

“…Both techniques have been proposed for RCA. 4,46 Figure 8 represents a possible structure for a Bayesian network representing a toy communication network (we limit the analysis to the Phy, MAC, and IP layer for clarity). In the figure we show how in each node the physical layer influences the MAC, which in turn influences the IP.…”

Section: Root Cause Analysismentioning

confidence: 99%

A Big Data and machine learning approach for network monitoring and security

Maccari

Passerini

2018

Security and Privacy

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In the last decade the performances of 802.11 (Wi‐Fi) devices skyrocketed. Today it is possible to realize gigabit wireless links spanning across kilometers at a fraction of the cost of the wired equivalent. In the same period, mesh network evolved from being experimental tools confined into university labs, to systems running in several real world scenarios. Mesh networks can now provide city‐wide coverage and can compete on the market of Internet access. Yet, being wireless distributed networks, mesh networks are still hard to maintain and monitor. This paper explains how today we can perform monitoring, anomaly detection and root cause analysis in mesh networks using Big Data techniques. It first describes the architecture of a modern mesh network, it justifies the use of Big Data techniques and provides a design for the storage and analysis of Big Data produced by a large‐scale mesh network. While proposing a generic infrastructure, we focus on its application in the security domain.

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Root Cause Analysis of Reduced Accessibility in 4G Networks

Ferreira¹,

Senna²,

Salvador³

et al. 2020

Machine Learning for Networking

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The increased programmability of communication networks makes them more autonomous, and with the ability to actuate fast in response to users and networks' events. However, it is usually a difficult task to understand the root cause of the network problems, so that autonomous actuation can be provided in advance. This paper analyzes the probable root causes of reduced accessibility in 4G networks, taking into account the information of important Key Performance Indicators (KPIs), and considering their evolution in previous time-frames. This approach resorts to interpretable machine learning models to measure the importance of each KPI in the decrease of the network accessibility in a posterior time-frame. The results show that the main root causes of reduced accessibility in the network are related with the number of failure handovers, the number of phone calls and text messages in the network, the overall download volume and the availability of the cells. However, the main causes of reduced accessibility in each cell are more related to the number of users in each cell and its download volume produced. The results also show the number of PCA components required for a good prediction, as well as the best machine learning approach for this specific use case.

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Root Cause Analysis of Network Failures Using Machine Learning and Summarization Techniques

Cited by 31 publications

References 10 publications

A Survey of Fault Management in Network Virtualization Environments: Challenges and Solutions

A Survey of Fault Management in Network Virtualization Environments: Challenges and Solutions

A Big Data and machine learning approach for network monitoring and security

Root Cause Analysis of Reduced Accessibility in 4G Networks

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