Abstract: Described is centralized scheduling of baseband unit resources of a hub, including allocating and deallocating baseband unit resources of a distributed unit instance based on anticipated and/or actual demand for the resources. For example, when user equipment transitions to a connected state, a corresponding message can be detected and used to determine whether sufficient baseband unit resources exist to handle the traffic of the newly connecting user equipment. If not, additional baseband unit resources are allocated, coupled to a node (cell), and the node scheduled to handle the user equipment traffic. When user equipment transitions to an inactive state, the corresponding command can be detected and used to determine whether the baseband unit resources are still needed for other traffic. If not, the baseband unit resources are deallocated.

Abstract: Described is pooling baseband units into a hub and mapping radio units associated with the hub to baseband units of the group based on respective resource capacity data of the respective baseband units and an estimated resource usage data of a device that couples to a baseband unit. In one alternative, estimated peak resource usage data of a radio unit over an operation interval is used to select a baseband unit, generally based on which baseband unit of the pool has the least remaining resource capacity, to which the radio unit is mapped for the operation interval. In another alterative, estimated peak resource usage data resource (usage and duration) of a user equipment session is used to dynamically map the user equipment session to a baseband unit, generally selected by which baseband unit of the pool has the least remaining resource capacity.

Abstract: Described is allocating and deallocating baseband unit resources of a distributed unit based on anticipated and/or actual demand for the resources. For example, when user equipment transitions to a connected state, a corresponding message can be detected and used to determine whether sufficient baseband unit resources are needed to handle the traffic of the newly connecting user equipment. If not, additional baseband unit resources are allocated. When user equipment transitions to an inactive state, the corresponding command can be detected and used to determine whether the baseband unit resources are still needed for other traffic. If not, baseband unit resources are deallocated; that is, if a distributed unit's resources are no longer needed, the distributed unit's resources are deactivated to reduce resource consumption. Deallocation can be performed by changing the resources to a power conservation (e.g., sleep) state, with allocation performed by changing such resources back to an active state.

Abstract: The described technology is generally directed towards intelligent dual-connectivity for non-standalone network nodes. Network nodes can report state information to a central controller, such as a radio access network intelligent controller. The controller can determine, based on the state information reported by multiple network nodes, network nodes to cooperate in non-standalone mode. The controller can provide the network nodes with instructions to implement the controller's non-standalone relationship determinations.

Abstract: The described technology is generally directed towards a cellular network area optimizer. The area optimizer observes cellular network conditions at multiple radio access network (RAN) nodes within a target area. Based on observed conditions, the area optimizer applies a set of parameter values at the multiple RAN nodes. The set of parameter values enhances the overall throughput, while maintaining or improving connection retainability and accessibility, of the multiple RAN nodes under the observed conditions. The area optimizer learns different sets of parameter values to apply in response to different observed conditions by making parameter value adjustments and observing the effect of the adjustments on overall throughput of the RAN nodes in the target area.

Abstract: The described technology is generally directed towards intelligent dual-connectivity for non-standalone network nodes. Network nodes can report state information to a central controller, such as a radio access network intelligent controller. The controller can determine, based on the state information reported by multiple network nodes, network nodes to cooperate in non-standalone mode. The controller can provide the network nodes with instructions to implement the controller's non-standalone relationship determinations.

Abstract: The described technology is generally directed towards data clustering for network traffic modeling. Cellular network measurement data from different geographic areas can be separated into clusters based on similarities in network performance indicators, cell traffic load data, their changing pattern over time, and/or other metrics. A machine learning model can then be assigned to each cluster, and the machine learning models can be trained to make network traffic control decisions under conditions exhibited in their respective clusters. If the error rate of the trained machine learning models is acceptable, then the machine learning models can be deployed for use at network equipment. If the overall error rate is not acceptable, then the cellular network measurement data can be re-separated into a larger number of clusters, and machine learning models can again be trained for each cluster.

Abstract: The described technology is generally directed towards a cellular network area optimizer. The area optimizer observes cellular network conditions at multiple radio access network (RAN) nodes within a target area. Based on observed conditions, the area optimizer applies a set of parameter values at the multiple RAN nodes. The set of parameter values enhances the overall throughput, while maintaining or improving connection retainability and accessibility, of the multiple RAN nodes under the observed conditions. The area optimizer learns different sets of parameter values to apply in response to different observed conditions by making parameter value adjustments and observing the effect of the adjustments on overall throughput of the RAN nodes in the target area.

Abstract: The described technology is generally directed towards a cellular network area optimizer. The area optimizer observes cellular network conditions at multiple radio access network (RAN) nodes within a target area. Based on observed conditions, the area optimizer applies a set of parameter values at the multiple RAN nodes. The set of parameter values enhances the overall throughput, while maintaining or improving connection retainability and accessibility, of the multiple RAN nodes under the observed conditions. The area optimizer learns different sets of parameter values to apply in response to different observed conditions by making parameter value adjustments and observing the effect of the adjustments on overall throughput of the RAN nodes in the target area.

Abstract: The described technology is generally directed towards intelligent dual-connectivity for non-standalone network nodes. Network nodes can report state information to a central controller, such as a radio access network intelligent controller. The controller can determine, based on the state information reported by multiple network nodes, network nodes to cooperate in non-standalone mode. The controller can provide the network nodes with instructions to implement the controller's non-standalone relationship determinations.

Abstract: A system that determines whether a trigger has occurred within a cloud infrastructure. The system, in response to determining that a trigger has occurred, extracts characteristics from one or more virtual network functions (VNFs) of a service chain. The system, in response to extracting characteristics from the one or more VNFs, determines rehoming actions for each of the one or more VNFs. The system, in response to determining rehoming actions, predicts a rehoming delay or a chain downtime for each of the rehoming actions for each of the one or more VNFs. The system determines an optimal rehoming action from the rehoming actions for at least one of the one or more VNFs using the rehoming delay or the chain downtime for each rehoming action of the rehoming actions. The system performs the optimal rehoming action for the at least one of one or more VNFs.

Abstract: A system that determines whether a trigger has occurred within a cloud infrastructure. The system, in response to determining that a trigger has occurred, extracts characteristics from one or more virtual network functions (VNFs) of a service chain. The system, in response to extracting characteristics from the one or more VNFs, determines rehoming actions for each of the one or more VNFs. The system, in response to determining rehoming actions, predicts a rehoming delay or a chain downtime for each of the rehoming actions for each of the one or more VNFs. The system determines an optimal rehoming action from the rehoming actions for at least one of the one or more VNFs using the rehoming delay or the chain downtime for each rehoming action of the rehoming actions. The system performs the optimal rehoming action for the at least one of one or more VNFs.

Abstract: Concepts and technologies are disclosed herein for an application deployment engine. A processor that executes an application deployment engine can receive an application request. The processor can obtain network topology data that indicates availability of resources of a data center, an application template associated with the application, and a running time during which an application placement plan is to be identified out of a large number of placement scenarios within the running time. The application template can describe an application flow path associated with the application. The processor can identify the application placement plan, where the application placement plan can include an optimal placement of the application at the data center, before a given running time expires by pruning the large search space. The processor can generate a command to effect deployment of the application in accordance with the application placement plan.

Abstract: Virtual redundancy for active-standby cloud applications is disclosed herein. A virtual machine (“VM”) placement scheduling system is disclosed herein. The system can compute, for each standby VM of a plurality of available standby VMs, a minimum required placement overlap delta to meet an entitlement assurance rate (“EAR”) threshold. The system can compute a minimum number of available VM slots for activating each standby VM to meet the EAR threshold. For each standby VM of a given application, the system can filter out any server of a plurality of servers that does not meet criteria. If a given server meets the criteria, the system can add the given server to a candidate list; sort, in descending order, the candidate list by the minimum required placement overlap delta and the number of available virtual machine slots; and select, from the candidate list of servers, a candidate server from atop the candidate list.

Abstract: The concepts and technologies disclosed herein are directed to a policy-driven homing service system that can receive, from a master service orchestrator, a demand specifying a service component to be used to provide, at least in part, a service. The system can receive, from a policy system, a homing constraint. The system can determine, for the demand, an initial set of all potential candidates. The system can apply the homing constraint to each potential candidate in the initial set of all potential candidates. The system can determine a resultant set of potential solutions that satisfy the homing constraint. The system can determine a best solution from the resultant set of potential solutions. The system can send the best solution to the master service orchestrator, which instantiates the demand based upon the best solution.

Abstract: Concepts and technologies are disclosed herein for an application deployment engine. A processor that executes an application deployment engine can receive an application request. The processor can obtain network topology data that indicates availability of resources of a data center, an application template associated with the application, and a running time during which an application placement plan is to be identified out of a large number of placement scenarios within the running time. The application template can describe an application flow path associated with the application. The processor can identify the application placement plan, where the application placement plan can include an optimal placement of the application at the data center, before a given running time expires by pruning the large search space. The processor can generate a command to effect deployment of the application in accordance with the application placement plan.

Abstract: Concepts and technologies are disclosed herein for an application deployment engine. A processor that executes an application deployment engine can receive an application request. The processor can obtain network topology data that indicates availability of resources of a data center, an application template associated with the application, and a running time during which an application placement plan is to be identified out of a large number of placement scenarios within the running time. The application template can describe an application flow path associated with the application. The processor can identify the application placement plan, where the application placement plan can include an optimal placement of the application at the data center, before a given running time expires by pruning the large search space. The processor can generate a command to effect deployment of the application in accordance with the application placement plan.

Abstract: Virtual redundancy for active-standby cloud applications is disclosed herein. A virtual machine (“VM”) placement scheduling system is disclosed herein. The system can compute, for each standby VM of a plurality of available standby VMs, a minimum required placement overlap delta to meet an entitlement assurance rate (“EAR”) threshold. The system can compute a minimum number of available VM slots for activating each standby VM to meet the EAR threshold. For each standby VM of a given application, the system can filter out any server of a plurality of servers that does not meet criteria. If a given server meets the criteria, the system can add the given server to a candidate list; sort, in descending order, the candidate list by the minimum required placement overlap delta and the number of available virtual machine slots; and select, from the candidate list of servers, a candidate server from atop the candidate list.

Abstract: The concepts and technologies disclosed herein are directed to a policy-driven homing service system that can receive, from a master service orchestrator, a demand specifying a service component to be used to provide, at least in part, a service. The system can receive, from a policy system, a homing constraint. The system can determine, for the demand, an initial set of all potential candidates. The system can apply the homing constraint to each potential candidate in the initial set of all potential candidates. The system can determine a resultant set of potential solutions that satisfy the homing constraint. The system can determine a best solution from the resultant set of potential solutions. The system can send the best solution to the master service orchestrator, which instantiates the demand based upon the best solution.

Abstract: A method includes, for components of an application, identifying a plurality of groups including a first group and a second group, The method also includes nesting the first group into the second group to create a nested group, based at least on a level of the first group not exceeding a level of the second group. The method includes deploying the application by placing the components of the nested group within a network in accordance with a restriction of the nested group. The components include at least one virtual machine.