Reconfiguration of optical-NFV network architectures based on cloud resource allocation and QoS degradation cost-aware prediction techniques

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. 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 under-provisioning 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 veried a cost savings of 40% compared to solutions that provide a minimization of the mean square error

Proposal and investigation of an artificial intelligence (Ai)-based cloud resource allocation algorithm in network function virtualization architectures

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

Cross-species analysis of viral nucleic acid interacting proteins identifies TAOKs as innate immune regulators

The cell intrinsic antiviral response of multicellular organisms developed over millions of years and critically relies on the ability to sense and eliminate viral nucleic acids. Here we use an affinity proteomics approach in evolutionary distant species (human, mouse and fly) to identify proteins that are conserved in their ability to associate with diverse viral nucleic acids. This approach shows a core of orthologous proteins targeting viral genetic material and species-specific interactions. Functional characterization of the influence of 181 candidates on replication of 6 distinct viruses in human cells and flies identifies 128 nucleic acid binding proteins with an impact on virus growth. We identify the family of TAO kinases (TAOK1, -2 and -3) as dsRNA-interacting antiviral proteins and show their requirement for type-I interferon induction. Depletion of TAO kinases in mammals or flies leads to an impaired response to virus infection characterized by a reduced induction of interferon stimulated genes in mammals and impaired expression of srg1 and diedel in flies. Overall, our study shows a larger set of proteins able to mediate the interaction between viral genetic material and host factors than anticipated so far, attesting to the ancestral roots of innate immunity and to the lineage-specific pressures exerted by viruses. Whether there are conserved nucleic acid (NA) binding proteins across species is not fully known. Using data from human, mouse and fly, the authors identify common binders, implicate TAOKs and show that these kinases bind NAs across species and promote virus defence in mammalian cells.We further thank Korbinian Mayr, Igor Paron, and Gaby Sowa for maintaining mass spectrometers and the MPI-B core facility, especially Judith Scholz, Leopold Urich, Sabine Suppmann, and Stephan Uebel, for support..

Association of kidney disease measures with risk of renal function worsening in patients with type 1 diabetes

Background: Albuminuria has been classically considered a marker of kidney damage progression in diabetic patients and it is routinely assessed to monitor kidney function. However, the role of a mild GFR reduction on the development of stage 653 CKD has been less explored in type 1 diabetes mellitus (T1DM) patients. Aim of the present study was to evaluate the prognostic role of kidney disease measures, namely albuminuria and reduced GFR, on the development of stage 653 CKD in a large cohort of patients affected by T1DM. Methods: A total of 4284 patients affected by T1DM followed-up at 76 diabetes centers participating to the Italian Association of Clinical Diabetologists (Associazione Medici Diabetologi, AMD) initiative constitutes the study population. Urinary albumin excretion (ACR) and estimated GFR (eGFR) were retrieved and analyzed. The incidence of stage 653 CKD (eGFR < 60 mL/min/1.73 m2) or eGFR reduction > 30% from baseline was evaluated. Results: The mean estimated GFR was 98 \ub1 17 mL/min/1.73m2 and the proportion of patients with albuminuria was 15.3% (n = 654) at baseline. About 8% (n = 337) of patients developed one of the two renal endpoints during the 4-year follow-up period. Age, albuminuria (micro or macro) and baseline eGFR < 90 ml/min/m2 were independent risk factors for stage 653 CKD and renal function worsening. When compared to patients with eGFR > 90 ml/min/1.73m2 and normoalbuminuria, those with albuminuria at baseline had a 1.69 greater risk of reaching stage 3 CKD, while patients with mild eGFR reduction (i.e. eGFR between 90 and 60 mL/min/1.73 m2) show a 3.81 greater risk that rose to 8.24 for those patients with albuminuria and mild eGFR reduction at baseline. Conclusions: Albuminuria and eGFR reduction represent independent risk factors for incident stage 653 CKD in T1DM patients. The simultaneous occurrence of reduced eGFR and albuminuria have a synergistic effect on renal function worsening

Proposal and investigation of an optical reconfiguration cost aware policy for resource allocation in network function virtualization infrastructures

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. The objective is to minimize the total cost given by the sum of three components: i) the cloud resource cost; ii) the bandwidth costs; iii) the reconfiguration costs characterized by the revenue loss of the Telecommunication Service Provider due to the degradation of the Quality of Service occurring during the reconfiguration of the optical circuits. We define and investigate a heuristic of polynomial complexity. The application of the heuristic to the large distance USNET network for typical traffic and network parameters allows for a saving by 40% in total cost with respect to the case in which a traditional policy is applied

Cost-aware and aI-based resource prediction in softwarized networks

Resource prediction algorithms have been recently proposed in Network Function Virtualization Architectures. An prediction-based resource allocation is characterized by higher operation costs due to: i) resource underestimate that leads to Quality of Service degradation; ii) used cloud resource over allocation when a resource overestimate occurs. To reduce such a cost, we propose cost-aware prediction algorithm able to minimize the sum of the two cost components previously mentioned. We compare in a real network and traffic scenario the proposed technique to the traditional one in which the Root Mean Squared Error. We show home the proposed solution allows for cost advantages in the order of 20%

Impact of the maximum number of switching reconfigurations on the cost saving in network function virtualization environments with elastic optical interconnection

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 objective of the paper is to study the impact of the maximum number of switch reconfigurations on the cost saving that the Networking Function Virtualization technology allows us to achieve. The problem is studied in the case of a scenario with an elastic optical network interconnecting datacenters in which the Virtual Machines are executed. The problem can be formulated as an Integer Linear Programming one introducing a constraint on the maximum number of switch reconfigurations but due to its computational complexity we propose a low computational complexity heuristic allowing for results close to the optimization ones. The results show how the limitation on the number of possible reconfigurations has to be taken into account to evaluate the effectiveness in terms of cost saving that the Virtual Machine migrations in Network Function Virtualization environment allows us to achieve

Proposal and investigation of a convolutional and lstm neural network for the cost-aware resource prediction in softwarized networks†

Resource prediction algorithms have been recently proposed in Network Function Virtualization architectures. A prediction-based resource allocation is characterized by higher operation costs due to: (i) Resource underestimate that leads to quality of service degradation; (ii) used cloud resource over allocation when a resource overestimate occurs. To reduce such a cost, we propose a cost-aware prediction algorithm able to minimize the sum of the two cost components. The proposed prediction solution is based on a convolutional and Long Short Term Memory neural network to handle the spatial and temporal correlations of the need processing capacities. We compare in a real network and traffic scenario the proposed technique to a traditional one in which the aim is to exactly predict the needed processing capacity. We show how the proposed solution allows for cost advantages in the order of 20%

AI-based resource prediction in network function vrtualization architectures

The high reconfiguration time of virtualised networks led to the definition of allocation procedures based on the prediction of the processing resources required. We propose an Artificial Intelligence-based resource allocation procedure in which the use of processing resources is monitored and the resources to be allocated are accordingly predicted. We evaluate the impact on the costs of the proposed allocation procedure and show that the cost increase is limited with respect to the case of exact knowledge of the needed processing resources

Study and investigation of SARIMA-based traffic prediction models for the resource allocation in NFV networks with elastic optical interconnection

The paper investigates resource allocation problems in Network Function Virtualization (NFV) network architectures in which the datacenters are interconnected by an Elastic Optical Network and the offered traffic is predicted by a Seasonal Autoregressive Integrated Moving Average (SARIMA) model. We apply a procedure for deseasonalizing, eliminating the trend, estimating the parameters of the SARIMA model and forecasting real traffic values. The procedure is able to forecast the traffic so as to minimize the network operation cost and taking into account the following cost components: i) the cloud resource costs occurring when a higher resource provisioning is accomplished due to traffic overestimation; ii) the Quality of Service (QoS) degradation cost due to the user traffic loss occurring when the traffic is underestimated and fewer resources than needed are allocated