Machine Learning for Cognitive Network Management

Ayoubi, Sara; Limam, Noura; Salahuddin, Mohammad A.; Shahriar, Nashid; Boutaba, Raouf; Estrada-Solano, Felipe; Rendón, Óscar Mauricio Caicedo

doi:10.1109/mcom.2018.1700560

Cited by 174 publications

(119 citation statements)

References 11 publications

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“…Finally, communication networks are moving towards being fully autonomic and self-managing [101,102]. To this end, machine learning and deep learning algorithms have been proposed to determine configurations of wireless parameters [103,104].…”

Section: Future Workmentioning

confidence: 99%

Efficient placement of edge computing devices for vehicular applications in smart cities

Premsankar

Ghaddar

Francesco

et al. 2018

NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium

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The author of this dissertation contributed to the development of the heuristic algorithms, the implementation of the simulator, the evaluation through simulation, the analysis of results, and writing the article.Publication IV empirically evaluates the impact of edge computing on emerging interactive applications and services. At the time of writing, there was no clear consensus on what specifically constitutes the edge of the network in edge computing. Thus, we first survey the state of the art in edge computing architectures and determine different deployment options for edge devices. Next, we experimentally evaluate edge computing when available at different physical locations and with different virtualization technologies. Specifically, we characterize the end-to-end latency (including

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Section: Future Workmentioning

confidence: 99%

Efficient placement of edge computing devices for vehicular applications in smart cities

Premsankar

Ghaddar

Francesco

et al. 2018

NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium

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“…There are also studies presenting implementations of ML techniques to leverage autonomy and self-management capabilities while exploiting the available of networking data [5]- [9]. These studies focus on improving ML techniques for network management but do not provide a system that is concentrating specifically on network services.…”

Section: A Related Workmentioning

confidence: 99%

“…These studies focus on improving ML techniques for network management but do not provide a system that is concentrating specifically on network services. For example, the article in [5] considers the usage of ML for cognitive network management, and provides a discussion on how to defeat the bottlenecks and limiting factors for the deployment of autonomic systems with ML. Similarly, the study in [6] proposes a cognitive management framework with unsupervised deep learning and probabilistic generative models for network optimization.…”

Section: A Related Workmentioning

confidence: 99%

A Machine Learning Based Management System for Network Services

Türk¹,

Zeydan²,

Bilgin³

2019

2019 International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)

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Providing high quality and uninterrupted network service is becoming crucial for service providers. In this paper, we present an approach to quantify and indicate service quality based on the topology state transitions from the perspective of network service provider. Building our model as a Finite State Machine (FSM), we show novel application of machine learning (ML) classification algorithms to classify appropriate states for undefined input alphabets in FSM. In other words, we implement ML algorithms to extract both service states and possible root causes of service degradation only from measured certain Key Performance Indicator (KPI) values that are observed directly through network elements. We have implemented our network topology state classification approach using the dataset obtained in Graphical Network Simulator 3 (GNS3) simulation environment, and performed measurements to evaluate classification accuracy of different algorithms. Additionally, we have identified priority of relevant KPIs impacting the service quality. Our results indicate that network topology state changes can be classified up to 88% accuracy and F1 scores using ensemble learning methods such as Gradient Boosting Classifiers.

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“…Still, because new features continue to appear over time, anomaly detection systems should be flexible enough to accommodate new conditions, instead of being restricted to a steady set of predefined anomalies. One of the approaches that have been used to cope with this scenario is the use of machine learning-based classifiers [2].…”

Section: Introductionmentioning

confidence: 99%

Improved Network Traffic Classification Using Ensemble Learning

Possebon

Silva

Granville

et al. 2019

2019 IEEE Symposium on Computers and Communications (ISCC)

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Despite the large number of research efforts that applied specific machine learning algorithms for network traffic classification, recent work has highlighted limitations and particularities of individual algorithms that make them more suitable to specific types of traffic and scenarios. As such, an important topic in this area is how to combine individual algorithms using meta-learning techniques in order to obtain more robust traffic classification metrics. This paper presents a comparative analysis among meta-learning approaches and individual classifiers to classify network traffic. We investigate and evaluate a range of meta-learning techniques, including Voting, Stacking, Bagging and Boosting. We then propose a new experimental analysis of different meta-learning techniques -also known as ensemble learners -and compare them with their own base classifiers when used individually. Finally, considering the emerging popularity of Neural Networks, we analyze this scenario using the Multi-layer Perceptron classifier. The experiments were performed with data provided by the UCI Machine Learning Repository. The best performance was obtained by an ensemble technique (Bagging), which obtained accuracy of 99.972% and false positive rate of 0.00018%.

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Machine Learning for Cognitive Network Management

Cited by 174 publications

References 11 publications

Efficient placement of edge computing devices for vehicular applications in smart cities

Efficient placement of edge computing devices for vehicular applications in smart cities

A Machine Learning Based Management System for Network Services

Improved Network Traffic Classification Using Ensemble Learning

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