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.

Software Defined Networking (SDN) is a widely-adopted network architecture that provides high flexibility through the separation of the network logic from the forwarding functions. Researchers thoroughly analyzed SDN vulnerabilities and improved its security. However, we believe important security aspects of SDN are still left uninvestigated. In this paper, we raise the concern of the possibility for an attacker to obtain detailed knowledge about an SDN network.

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.

Network function virtualization (NFV) is a new network architecture framework that implements network functions in software running on a pool of shared commodity servers. NFV can provide the infrastructure flexibility and agility needed to successfully compete in today's evolving communications landscape. Any service is represented by a service function chain (SFC) that is a set of VNFs to be executed according to a given order. The running of VNFs needs the instantiation of VNF instances (VNFIs) that are software modules executed on virtual machines.

The introduction of Network Function Virtualization (NFV) led to a new business model in which the Telecommunication Service Provider needs to rent cloud resources to Infrastructure Provider (InP) at prices as low as possible. Lowest prices can be achieved if the cloud resources, that is long-term Virtual Machines (VM), can be rented in advance. This is in contrast with the short-term VMs that are rented on demand and have higher costs. For this reason we propose a proactive solution in which the cloud resource rent is planned in advance based on a peak traffic knowledge.

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.

Network Function Virtualization is based on the virtualization of the network functions and it is a new technology allowing for a more flexible allocation of cloud and bandwidth resources. In order to employ the flexibility of the technology and to adapt its use according to the traffic variation, reconfigurations of the cloud and bandwidth resources are needed by means of both migration of the Virtual Machines executing the network functions and reconfiguration of circuits interconnecting the Virtual Machines.

The paper proposes and investigates the problem of the reconfiguration of cloud and bandwidth resources in Multi-Provider Network Function Virtualization architectures where the Cloud Infrastructures (CI) are managed by different Providers and interconnected by an elastic optical network. The resource reconfiguration is performed by taking into account the different costs charged by the Infrastructure Providers (InP) of the CIs and by exploiting the advantages of the adaptive optical modulation.

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.