Network function virtualization foresees the virtualization of service functions and their execution on virtual machines. 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 in general are software modules executed on virtual machines. The virtualization challenges include: 1) where to instantiate VNFIs; ii) how many resources to allocate to each VNFI; iii) how to route SFC requests to the appropriate VNFIs in the right sequence; and iv) when and how to migrate VNFIs in response to changes to SFC request intensity and location. We develop an approach that uses three algorithms that are used back-to-back resulting in VNFI placement, SFC routing, and VNFI migration in response to changing workload. The objective is to first minimize the rejection of SFC bandwidth and second to consolidate VNFIs in as few servers as possible so as to reduce the energy consumed. The proposed consolidation algorithm is based on a migration policy of VNFIs that considers the revenue loss due to QoS degradation that a user suffers due to information loss occurring during the migrations. The objective is to minimize the total cost given by the energy consumption and the revenue loss due to QoS degradation. We evaluate our suite of algorithms on a test network and show performance gains that can be achieved over using other alternative naive algorithms.

Network virtualization techniques allow for the coexistence of many virtual networks hosted in the same substrate network. Virtual router migration allows for resource consolidation with the consequence to reduce the power consumption in low traffic periods. Unfortunately, virtual router migration has the effect of degrading the quality of service during the downtime in which the router is not able to carry on its forwarding function. For this reason, despite the advantages in power consumption saving, the migration technique should be applied with parsimony to avoid an excessive QoS degradation. The objective of this paper is to study virtual network embedding (VNE) problems aware of both operation and reconfiguration costs that are characterized by the energy consumption and the revenue loss due to QoS degradation. Both embedding optimization problem formulation and feasible solutions to the problem in cycle-stationary traffic scenario and when the possible embeddings are determined a priori will be given. Two migration policies will be introduced and compared. The first one, referred to as “global,” is based on the knowledge of the entire daily traffic profile and it is determined by solving a Markov decision process. The second one, referred to as “local,” is only based on the knowledge of the current traffic. The results achieved show how the application of the global policy allows better performance than the local one and its application can lead to a cost reduction in the order of 35% with respect to traditional migration policies.