Network function Virtualization (NFV) is expected to accelerate service deployment and enable service agility for Telco operators. NFV builds on virtualization technology and off-the-shelf general purpose hardware, shifting previously dedicated Telco appliances towards the cloud ecosystem. This paradigm shift brings new concepts such as dynamic service chaining that necessitates to rethinking the Network Management approach. In this paper we present CogSLA, a data-driven solution for SLA enforcement in an NFV deployment. CogSLA uses Deep Feedforward Neural Network or Multi-Layer perceptron (MLP) for achieving proactive identification of SLA violations. The key contribution of this work is to proactively change the network state, anticipating and avoiding foreseeable SLA violation.
A Deep learning based SLA management for NFV-based services / Bendriss, Jaafar; Grida Ben Yahia, Imen; Riggio, Roberto; Zeghlache, Djamal. - (2018). (Intervento presentato al convegno 21st International Conference on Innovation in Clouds, Internet and Networks, ICIN 2018 tenutosi a Paris, France nel 19 February 2018 to 22 February 2018) [10.1109/ICIN.2018.8401592].
A Deep learning based SLA management for NFV-based services
Roberto Riggio;
2018-01-01
Abstract
Network function Virtualization (NFV) is expected to accelerate service deployment and enable service agility for Telco operators. NFV builds on virtualization technology and off-the-shelf general purpose hardware, shifting previously dedicated Telco appliances towards the cloud ecosystem. This paradigm shift brings new concepts such as dynamic service chaining that necessitates to rethinking the Network Management approach. In this paper we present CogSLA, a data-driven solution for SLA enforcement in an NFV deployment. CogSLA uses Deep Feedforward Neural Network or Multi-Layer perceptron (MLP) for achieving proactive identification of SLA violations. The key contribution of this work is to proactively change the network state, anticipating and avoiding foreseeable SLA violation.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
