Device Assisted Services (DAS) for protecting network capacity is provided. In some embodiments, DAS for protecting network capacity includes monitoring a network service usage activity of the communications device in network communication; classifying the network service usage activity for differential network access control for protecting network capacity; and associating the network service usage activity with a network service usage control policy based on a classification of the network service usage activity to facilitate differential network access control for protecting network capacity.

Device group partitions and a settlement platform are provided. In some embodiments, device group partitions (e.g., partitions of devices based on associated device groups) are provided. In some embodiments, a settlement platform service is provided. In some embodiments, a settlement platform service is provided for partitioned devices. In some embodiments, collecting device generated service usage information for one or more devices in wireless communication on a wireless network; and aggregating the device generated service usage information for a settlement platform for the one or more devices in wireless communication on the wireless network is provided. In some embodiments, a settlement platform implements a service billing allocation and/or a service/transactional revenue share among one or more partners. In some embodiments, service usage information includes micro-CDRs, which are used for CDR mediation or reconciliation that provides for service usage accounting on any device activity that is desired. In some embodiments, each device activity that is desired to be associated with a billing event is assigned a micro-CDR transaction code, and a service processor of the device is programmed to account for that activity associated with that transaction code. In some embodiments, a service processor executing on a wireless communications device periodically reports (e.g., during each heartbeat or based on any other periodic, push, and/or pull communication technique(s)) micro-CDR usage measures to, for example, a service controller or some other network element for CDR mediation or reconciliation.

Device group partitions and a settlement platform are provided. In some embodiments, device group partitions (e.g., partitions of devices based on associated device groups) are provided. In some embodiments, a settlement platform service is provided. In some embodiments, a settlement platform service is provided for partitioned devices. In some embodiments, collecting device generated service usage information for one or more devices in wireless communication on a wireless network; and aggregating the device generated service usage information for a settlement platform for the one or more devices in wireless communication on the wireless network is provided. In some embodiments, a settlement platform implements a service billing allocation and/or a service/transactional revenue share among one or more partners. In some embodiments, service usage information includes micro-CDRs, which are used for CDR mediation or reconciliation that provides for service usage accounting on any device activity that is desired. In some embodiments, each device activity that is desired to be associated with a billing event is assigned a micro-CDR transaction code, and a service processor of the device is programmed to account for that activity associated with that transaction code. In some embodiments, a service processor executing on a wireless communications device periodically reports (e.g., during each heartbeat or based on any other periodic, push, and/or pull communication technique(s)) micro-CDR usage measures to, for example, a service controller or some other network element for CDR mediation or reconciliation.

An automatic service monitor in an information technology environment has its operation controlled by information that, in part, defines entities that perform services and defines key performance indicators (KPIs) that indicate measures of performance of the services. Additional information controls the operation of the service monitor with respect to identifying and adapting for KPIs based on the non-normal data caused by maintenance work or other causes. Such adaptation may include changes in how reported information appears to the user.

An automatic service monitor in an information technology environment has its operation controlled by information that, in part, defines entities that perform services and defines key performance indicators (KPIs) that indicate measures of performance of the services. Additional information controls the operation of the service monitor with respect to identifying and adapting for KPIs based on the non-normal data caused by maintenance work or other causes. Such adaptation may include changes in how reported information appears to the user.

Modifying quality of service treatment for data flows A method of transmitting a data flow via a network is disclosed where the network supports transmission of data in accordance with a plurality of Quality of Service, QoS, models. Prior to transmission of the data flow, a client system configures a first class of service for the data flow based on a first QoS model, and a first portion of the data flow is transmitted through the network in accordance with the first class of service. In response to detecting a renegotiation condition, the network communicates with the client system to configure a second class of service for the data flow based on a second QoS model, and a subsequent portion of the data flow is transmitted through the network using the second class of service.

Modifying quality of service treatment for data flows A method of transmitting a data flow via a network is disclosed where the network supports transmission of data in accordance with a plurality of Quality of Service, QoS, models. Prior to transmission of the data flow, a client system configures a first class of service for the data flow based on a first QoS model, and a first portion of the data flow is transmitted through the network in accordance with the first class of service. In response to detecting a renegotiation condition, the network communicates with the client system to configure a second class of service for the data flow based on a second QoS model, and a subsequent portion of the data flow is transmitted through the network using the second class of service.

Embodiments of this application provide a network performance assurance method and apparatus. When the network performance assurance method is applied, a second management device determines network performance assurance information. The network performance assurance information includes at least one of a network performance assurance granularity and a network performance assurance condition. The second management device sends a target network management request including the network performance assurance information to a first management device. The first management device receives the target network management request sent by the second management device, and manages a target network based on the at least one of the network performance assurance granularity and the network performance assurance condition included in the target network management request.

The embodiments herein disclose a method for managing machine learning (ML) services in a wireless communication network. The method includes: storing a plurality of ML packages, each executing a network service request; receiving a trigger based on the network service request from a server; determining a plurality of parameters corresponding to the network service request, on receiving the trigger from the server; determining an ML package based on the trigger and the plurality of parameters corresponding to the network service request; and deploying the determined at least one ML package for executing the network service request.

The embodiments herein disclose a method for managing machine learning (ML) services in a wireless communication network. The method includes: storing a plurality of ML packages, each executing a network service request; receiving a trigger based on the network service request from a server; determining a plurality of parameters corresponding to the network service request, on receiving the trigger from the server; determining an ML package based on the trigger and the plurality of parameters corresponding to the network service request; and deploying the determined at least one ML package for executing the network service request.