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. In fact if the predicted traffic is higher than the real one then an over allocation cost, referred to as over-provisioning cost, will be paid by the network operator; conversely, in the opposite case, Quality of Service degradation cost, referred to as under-provisioning cost, will be due to compensate the users because of the resource under allocation. In this paper we propose and investigate a resource allocation strategy based on a Long Short Term Memory algorithm in which the training operation is based on the minimization of an asymmetric cost function that differently weighs the positive and negative prediction errors and the corresponding over-provisioning and under-provisioning costs. In a typical traffic and network scenario, the proposed solution allows for a cost saving by 30% with respect to the case of solution with symmetric cost function.
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 traffic 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. In case the predicted values are higher than the real ones, the resource allocation algorithms will allocate more resources than necessary with the consequent introduction of an over-provisioning cost. Conversely, when the predicted values are lower than the real values, the allocation of fewer resources will lead to a degradation of QoS and the introduction of an under-provisioning cost. When over-provisioning and under-provisioning costs are different, most of the prediction algorithms proposed in the literature are not adequate because they are based on minimizing the mean square error or symmetric cost functions. For this reason we propose and investigate a forecasting methodology in which it is introduced an asymmetric cost function capable of weighing the costs of over-provisioning and underprovisioning differently. We have applied the proposed forecasting methodology for resource allocation in a Network Function Virtualization architectures where the Network Function Virtualization Infrastructure Point-of-Presences are interconnected by an elastic optical network. We have verified a cost savings of 40% compared to solutions that provide a minimization of the mean square error.
Network Function Virtualization is a new technology allowing for a elastic cloud and bandwidth resource allocation. The technology requires an orchestrator whose role is the service and resource orchestration. It receives service requests, each one characterized by a Service Function Chain, which is a set of service functions to be executed according to a given order. It implements an algorithm for deciding where both to allocate the cloud and bandwidth resources and to route the SFCs. In a traditional orchestration algorithm, the orchestrator has a detailed knowledge of the cloud and network infrastructures and that can lead to high computational complexity of the SFC Routing and Cloud and Bandwidth resource Allocation (SRCBA) algorithm. In this paper, we propose and evaluate the effectiveness of a scalable orchestration architecture inherited by the one proposed within the European Telecommunications Standards Institute (ETSI) and based on the functional separation of an NFV orchestrator in Resource Orchestrator (RO) and Network Service Orchestrator (NSO). Each cloud domain is equipped with an RO whose task is to provide a simple and abstract representation of the cloud infrastructure. These representations are notified of the NSO that can apply a simplified and less complex SRCBA algorithm. In addition, we show how the segment routing technology can help to simplify the SFC routing by means of an effective addressing of the service functions. The scalable orchestration solution has been investigated and compared to the one of a traditional orchestrator in some network scenarios and varying the number of cloud domains. We have verified that the execution time of the SRCBA algorithm can be drastically reduced without degrading the performance in terms of cloud and bandwidth resource costs.
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