This feature supports the derivation and exposure of application layer analytics to provide insight on the operation and performance of an application (VAL server or EAS, application session), and in particular statistics or prediction on parameters related to e.g. VAL server number of connections for a given time and area, VAL server rate of connection requests, connection probability failure rates, RTT and deviations for a VAL server or VAL UE session, packet loss rates etc. This feature also supports the collection of service experience information from the ADAE clients (as described in clause 8.9) to support application performance analytics.

This feature introduces application layer analytics to provide insight on the performance of the VAL applications when using a given network slice (from a list of subscribed slices for the VAL customer). Such capability provides an analytics service to a consumer who can be either the VAL server (for helping to identify what slice it will use for its applications) or for other consumers such as SEAL NSCE to support on providing analytics (since NSCE doesn't contain an analytics engine for providing analytics on top of NWDAF [4] /MDAS [5]).

This feature supports the derivation and exposure of application layer analytics to predict the performance of an application session among two or more VAL UEs within a service or group. Such prediction relates to application QoS attributes prediction for a given time horizon and area. This can be requested by the VAL server during the session, or the VAL server can subscribe to receive predicted application QoS downgrade indication for an ongoing session. Such analytics will help improving the application service experience and allow the VAL layer to pro-actively adapt to predicted application QoS changes.

This feature supports application layer analytics enablement to allow a VAL server to be notified based on analytics whether the accuracy of a location can be met for a given application and optionally for a given UE/group route. For example, a VAL server may request the ADAE server to provide analytics whether the accuracy of a location for the UEs within a VAL application is predicted to be sustainable or is expected to downgrade in a specific area or for an expected route from location A to location B.

This feature introduces service API analytics to allow a VAL server or any other consumer (e.g. API provider) to be notified on the predicted /statistic availability and service level for the requested service API analytics. Such analytics may be utilized by the API provider to perform actions to avoid service API invocation failures or other actions like throttling/rate limitations. Also, such analytics will support the VAL server to identify if/when to perform an API invocation request based on the API expected status at the given area and time horizon.

Slice usage pattern analytics provides network slice usage pattern analytics based on collected network slice performance and analytics, historical network slice status, and network performance to help the analytics consumer manage the network slice.

Edge load analytics provide insight on the operation and performance of an EDN and in particular statistics or prediction on parameters related to:

• the EAS / EES load for one or more EAS/EES
• edge platform load parameters, which include the aggregated load per EDN or per DNAI due to the edge support services and e.g., load level of edge computational resources.

Such analytics can improve edge support services by allowing the pro-active edge service operation changes to deal with possible edge overload scenarios. For example, this can trigger EAS migration to a different EDN / central DN, or pro-active EAS reselection for a target UE or group of UEs.

This feature introduces exposure of edge computing preparation analytics of the EAS, EES, and/or ECS to the analytics consumer (e.g., the VAL server, ECS, EES). The ADAE server provides the edge computing preparation analytics based on collected edge deployment time information, historical edge computing preparation analytics, instantiation triggering time and registration time from the EDN.

This feature supports server-to-server performance analytics to allow an analytics consumer (such as VAL server or EES) to be notified on QoS analytics or predictions between two or more servers. Such prediction relates to QoS attributes prediction for a given time horizon and area. Such analytics allow the VAL layer to pro-actively adapt to predicted QoS changes.

This feature supports collision detection analytics to allow an analytics consumer (such as VAL Server, LM server, UAE server, UAS application specific server) to be notified on analytics for collision detection between any target VAL UEs, collision detection between any UEs and target VAL UEs, or collision detection between any UE within the Area of Interest.

This feature supports location-related UE group analytics to allow an analytics consumer (such as LMS) to be notified on analytics for UE group route or UE group member deviation. Such analytics can be used, e.g. UE group route prediction can be used to formulate application group profile with Expected Group Geographical Service Area as described in clause 8.2.11 of TS 23.558. UE group member deviation prediction can be used for VAL to know which UE group member falls behind other group members or target group member (then VAL can send warning/reminder to the group members).

This feature supports Application Layer AI/ML Member Capability Analytics to allow an analytics consumer (such as e.g. VAL Server, AIMLE Server) to be notified on analytics for application layer AI/ML Member capability. Such analytics can be used to support application layer AI/ML services, e.g. supporting FL member selection and reselection.

The common identities for SEAL refer to TS 23.434. The following clauses list the additional identities and commonly used values for Application Data Analytics Enablement Service.

The ADAE server ID uniquely identifies the application data analytics enablement server, and each ADAE server ID is unique within PLMN domain.

The ADAE client ID uniquely identifies the application data analytics enablement client.

The A-ADRF ID uniquely identifies the application data analytics repository function.

The A-DCCF ID uniquely identifies the application data collection and coordination function.

The Data Producer ID uniquely identifies the data producer / source which is used as input for application data analytics enablement services. Data Producer based on the analytics event, can be either a network function or a management domain function/service or an application server or client or an edge / cloud service.

The ADAE service area is the area where the Application Data Analytics Enablement server owner provides its analytics services. It is equal to the coverage area for which analytics apply. The ADAE service area can be expressed as a Topological Service Area (e.g. a list of TA), a Geographical Service Area (e.g. geographical coordinates) or both.

The analytics ID (or analytics event ID) identifies the application layer analytics event which corresponds to the specified ADAE analytics services.