Fault and performance management in multi-cloud based NFV using shallow and deep predictive structures

• Published in: Journal of reliable intelligent environments 2017-12, Vol.3 (4), p.221-231
• Main Authors: Gupta, Lav, Samaka, M, Jain, Raj, Erbad, Aiman, Bhamare, Deval, Chan, H Anthony
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Computer Science; Configuration management; Fault detection; Faults; Health Informatics; Localization; Machine learning; Markov processes; Original Article; Performance and Reliability; Performance management; Simulation and Modeling; Software Engineering/Programming and Operating Systems; Support vector machines; System effectiveness; User Interfaces and Human Computer Interaction; Virtual networks
• ISSN: 2199-4668; 2199-4676
• DOI: 10.1007/s40860-017-0053-y

Deployment of network function virtualization (NFV) over multiple clouds accentuates its advantages such as 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 such as 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.