Ricerc@Sapienza

The high time needed to reconfigure cloud resources in Network Function Virtualization network environments has led to the proposal of solutions in which a prediction based-resource allocation is performed. All of them are based on traffic or needed resource prediction with the minimization of symmetric loss functions like Mean Squared Error. When inevitable prediction errors are made, the prediction methodologies are not able to differently weigh positive and negative prediction errors that could impact the total network cost.

The high time required for the deployment of cloud resources in Network Function Virtualization network architectures has led to the proposal and investigation of algorithms for predicting trafc or the necessary processing and memory resources. However, it is well known that whatever approach is taken, a prediction error is inevitable. Two types of prediction errors can occur that have a different impact on the increase in network operational costs.

The paper proposes and investigates solutions to the computing and bandwidth resource allocation problem in Multi-Provider Network Function Virtualization (NFV) environment. The scenario is characterized by Cloud Infrastructures (CI) managed by different providers and interconnected by an Elastic Optical Networks (EON).

We propose an algorithm for the cloud and bandwidth resource allocation in Multi-Provider NFV environments. The resources are allocated so as to take into account the different costs charged by the cloud Infrastructure Providers (InP). The effectiveness of the proposed algorithm is confirmed from the comparison with the results of the optimal problem. Its application in medium and large networks has shown that it can lead to cost saving as high as 65% with respect to algorithms that allocate resources without taking into account the cost differences charged by the InPs.

This paper proposes and investigates a reconfiguration policy of cloud and bandwidth resources in network function virtualization environments in which the network function virtualization infrastructure-point of presences are interconnected by an elastic optical network. The proposed policy is applied in dynamic traffic scenarios, it is based on virtual network function instances migrations and reconfiguration of optical circuits. The policy aims at minimizing the sum of three cost components: the cloud resource cost, the bandwidth cost, and the reconfiguration cost.