Abstract: A system performs joint placement and chaining of virtual network functions (VNFs) based on a genetic algorithm in response to a request for virtual network services, including an in-line service. The request includes a description of a virtual network of VNFs and virtual links connecting the VNFs. A description of a physical network including servers and physical links is provided. Each chromosome in a population encodes a mapping between the virtual links enumerated to form a locus and a corresponding sequence of server pairs. Each chromosome is evaluated against objective functions subject to constraints to identify a chromosome as a solution. The VNFs are placed on the servers according to the mapping encoded in the identified chromosome. According to the mapping, each VNF is mapped to one of the servers and each virtual link is mapped to a path composed of one or more of the physical links.

Abstract: Network policies for providing a network service are managed in a Network Function Virtualization (NFV) environment. The policies are obtained from service requests and specify placement and lifecycle management of Virtual Network Functions (VNFs). Attribute values are extracted from each policy. The extracted attribute values include at least a policy type value which specifies an action performed by the policy. The policies are grouped based on the attribute values. The policies in the same group share at least a common policy type value. The attribute values are compared within each group to detect conflicts and redundancies before deployment of the network service and in real-time after the network service is deployed. One or more of the obtained policies are removed to produce a policy repository free of the conflicts and the redundancies.

Abstract: Network policies for providing a network service are managed in a Network Function Virtualization (NFV) environment. The policies are obtained from service requests and specify placement and lifecycle management of Virtual Network Functions (VNFs). Attribute values are extracted from each policy. The extracted attribute values include at least a policy type value which specifies an action performed by the policy. The policies are grouped based on the attribute values. The policies in the same group share at least a common policy type value. The attribute values are compared within each group to detect conflicts and redundancies before deployment of the network service and in real-time after the network service is deployed. One or more of the obtained policies are removed to produce a policy repository free of the conflicts and the redundancies.

Abstract: Virtual Network Functions (VNFs) are placed in a substrate network and the placement is readjusted based on dynamic resource availability and dynamic resource utilization in the substrate network. A predetermined number of servers is selected sequentially as cluster-heads based on a set of metrics which measure the efficiency of the servers in different aspects. The servers are partitioned into the predetermined number of disjoint clusters with different efficiency aspects. Each cluster includes one of the cluster-heads which performs the placement and readjustment of the VNFs for the cluster. An incoming VNF is placed at a given server in a given cluster by the cluster-head of the given cluster, which optimizes an objective function subject to a set of constraints. The objective function is optimized with respect to a subset of the metrics which excludes one or more metrics in which the given cluster is efficient.

Abstract: Virtual Network Functions (VNFs) are placed in a substrate network and the placement is readjusted based on dynamic resource availability and dynamic resource utilization in the substrate network. A predetermined number of servers is selected sequentially as cluster-heads based on a set of metrics which measure the efficiency of the servers in different aspects. The servers are partitioned into the predetermined number of disjoint clusters with different efficiency aspects. Each cluster includes one of the cluster-heads which performs the placement and readjustment of the VNFs for the cluster. An incoming VNF is placed at a given server in a given cluster by the cluster-head of the given cluster, which optimizes an objective function subject to a set of constraints. The objective function is optimized with respect to a subset of the metrics which excludes one or more metrics in which the given cluster is efficient.

Abstract: A system performs joint placement and chaining of virtual network functions (VNFs) based on a genetic algorithm in response to a request for virtual network services, including an in-line service. The request includes a description of a virtual network of VNFs and virtual links connecting the VNFs. A description of a physical network including servers and physical links is provided. Each chromosome in a population encodes a mapping between the virtual links enumerated to form a locus and a corresponding sequence of server pairs. Each chromosome is evaluated against objective functions subject to constraints to identify a chromosome as a solution. The VNFs are placed on the servers according to the mapping encoded in the identified chromosome. According to the mapping, each VNF is mapped to one of the servers and each virtual link is mapped to a path composed of one or more of the physical links.

Abstract: A method for real-time prediction of resource consumption by a system is provided that includes determining a real-time prediction of resource demand by the system. A Genetic Algorithm (GA) is used to dynamically determine an optimal size of a sliding window and an optimal number of predicted data within the real-time prediction of the resource demand. The data within the real-time prediction of the resource demand is adjusted based on an estimated probability of prediction errors and a variable padding, which is based on a mean of at least one previous standard deviation of the predicted data within the real-time prediction of the resource demand.

Abstract: There is provided a method an apparatus and an application instance, for estimating workload. The method comprises generating a workload model from observed workload data; generating at least two intermediate workload predictions using the workload model; and applying a genetic algorithm (GA) to the at least two intermediate workload predictions to generate a final workload prediction. The workload model may be generated by applying Hull-White modelling to a workload dataset derived from the observed workload data. Applying the GA may comprise generating mean and standard deviation values for each data of the workload dataset; generating uniform and non-uniform splines for curve fitting the mean and standard deviation values; and generating fixed and variable theta values at given times by applying stochastic differential equation (SDE) to the observed workload values, mean splines values, standard deviation spline values and Weiner process at the given times.