In network security, Network Function Virtualization can be exploited to implement flexible security services tailored to specific user needs. However, in practice this is hard to achieve due to the limitations of reference software platforms, such as Kubernetes, which are designed to orchestrate cloud-native services. In this work, we complement Kubernetes with a state-of-the-art algorithm for application-aware provisioning of security services. We demonstrate that the proposed solution improves basic provisioning mechanisms, such as the default Kubernetes scheduler, in terms of Quality of Service and security guarantees for the users.

An innovative resource allocation framework for virtualized network environments and based on the application of Artificial Intelligence techniques is proposed and investigated. It integrates the needed processing capacity prediction procedure and the allocation one which determines the capacity under-allocation or over-allocation needed to minimize the resource allocation and Quality of Service degradation costs. The proposed solution is based on: i) a monitoring procedure in which the processing capacities required by virtual instances are periodically monitored; ii) an integrated allocation/prediction procedure in which the processing capacities to be allocated to the virtual instances are evaluated in time intervals successive to the monitoring periods. This second procedure uses a Convolutional/Long Short Term Memory neural network whose loss function is defined so as to minimize an overall cost dependent on both the cloud resource allocation and Quality of Service degradation costs. We evaluate the proposed solution in backbone and metropolitan traffic and network scenario. We show how in the traffic scenario of an Italian Mobile Operator in Milan zone, the proposed solution far outperforms the not integrated classical solution in which the capacity prediction and allocation procedures are separately performed. Furthermore its performance are very near to the one of an oracle that optimizes the capacity over/under dimensioning parameter.

The high reconfiguration time of cloud resources in Network Function Virtualization architecture has led to make ineffective the reactive cloud resource allocation procedures whose application lead to over-allocate resources or to degrade Quality of Service in decreasing/increasing traffic scenario. Recently many Artificial Intelligence (AI)-based allocation procedures have been proposed to pre-allocate cloud resource according to required processing capacity predictions. In this paper we illustrate how an ETSI NFV architecture can support these predictions procedures. Furthermore they aim to exactly predict the processing capacity to be allocated and they are all based on the minimization of a symmetric loss function of the neural network. For this reason we propose a resource allocation procedure with a asymmetric loss function whose parameters are dependent on an overall cost expressed in terms of allocation and QoS degradation costs. We prove that the proposed solution allows for a cost reduction in the order of 30% in a typical NFV traffic and network scenario.