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 implements a network slicing controller to manage network slicing instances in an edge cloud platform. The method includes receiving at least one policy change from an artificial intelligence powered smart traffic controller (APSTC) or an artificial intelligence powered edge traffic controller (APETC), determining whether the at least one policy change is valid based on local monitoring information, and sending the at least one policy change to a common control network function in a 5G mobile network.

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: The disclosure relates to a method, network node and service instance, the method being executed by a service provider, for optimizing resource allocation according to the needs of at least one client and according to an SLA between the client and the service provider. The method comprises obtaining a statistical behavior of a workload forecast function associated with a service of the client, obtaining a forecast value for a future workload from the workload forecast function associated with the service of the client, obtaining at least one service-level objective associated with the SLA between the client and the service provider, obtaining a modified forecast value based on the forecast value, the statistical behavior of the workload forecast function and service-level objective associated with the SLA, and adjusting resources allocated to the service based on the modified forecast thereby optimizing resource allocation for the at least one client.

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: The disclosure relates to a method, network node and service instance, the method being executed by a service provider, for optimizing resource allocation according to the needs of at least one client and according to an SLA between the client and the service provider. The method comprises obtaining a statistical behavior of a workload forecast function associated with a service of the client, obtaining a forecast value for a future workload from the workload forecast function associated with the service of the client, obtaining at least one service-level objective associated with the SLA between the client and the service provider, obtaining a modified forecast value based on the forecast value, the statistical behavior of the workload forecast function and service-level objective associated with the SLA, and adjusting resources allocated to the service based on the modified forecast thereby optimizing resource allocation for the at least one client.

Abstract: According to some embodiments, a virtual machine manager is operable to manage a virtual machine (VM) that spans a plurality of hardware appliances. The virtual machine manager receives a request to provide a service to a device, selects an application instance to provide the service to the device, and forwards the request for the service to the selected application instance. The selected application instance is running in an instance of the virtual machine associated with a first hardware appliance. The virtual machine manager determines session data associated with the service and provides the session data associated with the service to a second hardware appliance of the VM in order to enable the VM to provide high availability of the service to the device.

Abstract: According to some embodiments, a virtual machine manager is operable to manage a virtual machine (VM) that spans a plurality of hardware appliances. The virtual machine manager receives a request to provide a service to a device, selects an application instance to provide the service to the device, and forwards the request for the service to the selected application instance. The selected application instance is running in an instance of the virtual machine associated with a first hardware appliance. The virtual machine manager determines session data associated with the service and provides the session data associated with the service to a second hardware appliance of the VM in order to enable the VM to provide high availability of the service to the device.

Abstract: A high availability management system for machine-to-machine (M2M) gateways is provided. A cluster of mobile stations, which act as M2M gateways for a number of M2M devices, form a virtual high availability management pool which provides access to a telecommunications network. A network messaging center detects the failure or unavailability of an M2M gateway and initiates a failover procedure to transfer the gateway responsibility for the devices connected to the unavailable gateway. An M2M management server identifies a failover mobile station associated with the unavailable mobile station and initiates the transfer of the gateway responsibilities from the unavailable mobile station to the identified failover mobile station.