Fault and performance management in multi-cloud based NFV using shallow and deep predictive structures
المؤلف | Gupta, Lav |
المؤلف | Samaka, M. |
المؤلف | Jain, Raj |
المؤلف | Erbad, Aiman |
المؤلف | Bhamare, Deval |
المؤلف | Chan, H. Anthony |
تاريخ الإتاحة | 2020-11-19T08:53:09Z |
تاريخ النشر | 2017 |
اسم المنشور | 2017 26th International Conference on Computer Communications and Networks, ICCCN 2017 |
المصدر | Scopus |
الملخص | Deployment of Network Function Virtualization (NFV) over multiple clouds accentuates its advantages like flexibility of virtualization, proximity to customers and lower total cost of operation. However, NFV over multiple clouds has not yet attained the level of performance to be a viable replacement for traditional networks. One of the reasons is the absence of a standard based Fault, Configuration, Accounting, Performance and Security (FCAPS) framework for the virtual network services. In NFV, faults and performance issues can have complex geneses within virtual resources as well as virtual networks and cannot be effectively handled by traditional rule-based systems. To tackle the above problem, we propose a fault detection and localization model based on a combination of shallow and deep learning structures. Relatively simpler detection has been effectively shown to be handled by shallow machine learning structures like Support Vector Machine (SVM). Deeper structure, i.e., the stacked autoencoder has been found to be useful for a more complex localization function where a large amount of information needs to be worked through to get to the root cause of the problem. We provide evaluation results using a dataset adapted from fault datasets available on Kaggle and another based on multivariate kernel density estimation and Markov sampling. |
راعي المشروع | This work has been supported under the grant ID NPRP 6 - 901 - 2 - 370 for the project entitled "Middleware Architecture for Cloud Based Services Using Software Defined Networking (SDN)," which is funded by the Qatar National Research Fund (QNRF) and by Huawei Technologies. The statements made herein are solely the responsibility of the authors. |
اللغة | en |
الناشر | Institute of Electrical and Electronics Engineers Inc. |
الموضوع | Deep learning Fault detection Fault localization FCAPS Machine learning Multi-cloud Network function virtualization NFV Service function chain Stacked autoencoder Support vector machine Virtual network function Virtual network service |
النوع | Conference Paper |
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