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9.18.3.2 Network Slice Allocation
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Table 9.18.3.2-1 and Table 9.18.3.2-2 describe information elements for Network Slice Allocation request and response between the VAL server and the NSCE server.
Table 9.18.3.2-1: Network Slice Allocation request
Information element
Status
Description
VAL server ID
M
The identifier of the VAL server
VAL service ID
M
Identifier of the application service
VAL UE's ID List
O
The list of VAL UE IDs for which the request applies
Area of interest
M
The service area for which the application service profile applies, which can be expressed as a geographical area (e.g. geographical coordinates), or a topological area (e.g. a list of TA).
Network slice related Identifier(s)
O
The slice identifier(s) which can be in a prioritized order.
Network Slice requirements
O
The properties of network slice related requirement. If Service Profile is known by the VAL server, it can be provided to the NSCE server. The GST defined by GSMA (see clause 2.2 in [5]) and the performance requirements defined in clause 7 TS 22.261 [6] are all considered as input for it.
Slice adjustment requirements
O
Indicates the adjustment for slice requirements
Notification endpoint
O
Indicates the VAL server endpoint to receive notification.
Table 9.18.3.2-2: Network Slice Allocation response
Information element
Status
Description
VAL server ID
M
The identifier of the VAL server
VAL service ID
M
Identifier of the application service to be supported by the created slice related communication service.
Result
M
Indicates the success or failure of the slice related communication service creation
Network slice info List
O
(see NOTE 1)
The list of the network slice info allocated by NSCE
> Network slice info
O
(see NOTE 1)
The network slice info which includes the attributes and the corresponding values of network slice
>>S-NSSAI
O
(see NOTE 1)
The identifier of network slice
>>attributes of network slice
O
(see NOTE 1)
The list of attributes of the serviceProfile e.g, dLtThptPerSlice or latencies of network slice as defined in serviceProfile TS 28.541[10]
>>AttributeValues
O
(see NOTE 1)
The corresponding values of the attributes of the service profiles that determined by the NSCE server
Cause
O
(see NOTE 2)
Indicates the cause of creation failure
>Failed Network Slice Info List
O
(see NOTE 2)
Indicates the list of network slice info which cannot be allocated by NSCE server.
NOTE 1: Shall be present if the result is success and shall not be present otherwise
NOTE 2: Shall be present if the result is failure and shall not be present otherwise
Table 9.18.3.2-3: Network Slice Allocation notification
Information element
Status
Description
VAL server ID
M
The identifier of the VAL server
VAL service ID
M
Identifier of the application service to be supported by the created slice related communication service.
Result
M
Indicates the success or failure of the slice related communication service creation
Network slice info List
O
(see NOTE 1)
The list of the network slice info allocated by NSCE
> Network slice info
O
(see NOTE 1)
The network slice info which includes the attributes and the corresponding values of network slice
>>S-NSSAI
O
(see NOTE 1)
The identifier of network slice
>>attributes of network slice
O
(see NOTE 1)
The list of attributes of the serviceProfile e.g, dLtThptPerSlice or latencies of network slice as defined in serviceProfile TS 28.541[10]
>>AttributeValues
O
(see NOTE 1)
The corresponding values of the attributes of the service profiles that determined by the NSCE server
Cause
O
(see NOTE 2)
Indicates the cause of creation failure
>Failed Network Slice Info List
O
(see NOTE 2)
Indicates the list of network slice info which cannot be allocated by NSCE server.
NOTE 1: Shall be present if the result is success and shall not be present otherwise
NOTE 2: Shall be present if the result is failure and shall not be present otherwise
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9.18.4 APIs
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9.18.4.1 General
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Table 9.18.4.1-1 illustrates the API for Network Slice Allocation.
Table 9.18.4.1-1: SS_NSCE_NSAllocation
API Name
API Operations
Operation
Semantics
Consumer(s)
SS_NSCE_NSAllocation
NSAllocation_Request /Response
Request /Response
VAL server
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9.18.4.2 SS_NSCE_NSAllocation_Request /Response operation
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API operation name: SS_NSCE_NSAllocation_Request /Response
Description: The consumer requests the network slice allocation.
Inputs: See table 9.18.3.2-1.
Outputs: See table 9.18.3.2-2.
See clause 9.18.2.1 for details of usage of this operation.
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9.19 Authorization and authentication
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VAL server authorization and authentication are specified in 3GPP TS 33.434 [22], clause 5.1.1.8. Annex A (informative): Deployment models A.1 Deployment scenarios A.1.1 General Based on the network slicing capability of the S-NSSAI granularity provided by SA2 and the network slicing capability of the NSI/S-NSSAI granularity provided by SA5, the NSCE service is provides network slicing management and control capabilities in the S-NSSAI granularity for vertical industries. A network slice can have only one owner and one NSCE service provider. NSCE service provider and slice owner can be different. For example the slice owner is VAL server, but the NSCE service provider is MNO. This clause describes examples of deployment models with respect to different deployment scenarios as follows. A.1.2 Centralized NSCE deployment Figure A.1.2 provides a example of centralized deployment of NSCE server whose service area covering the whole PLMN. It is also possible slice coverage area to be smaller than the NSCE service area. The network slice capability enablement service is provided with the view of whole PLMN in this scenario. Figure A.1.2: Illustration of centralized NSCE deployment A.1.3 Distributed NSCE deployment The distributed deployment refers to the deployment model in which multiple NSCE servers are deployed by same provider, whose service area only covers some specific areas as shown below (based on geographical coordinates or TA list(s)). Figure A.1.3: Illustration of distributed NSCE deployment When there are multiple NSCE servers managed by same provider, NSCE server(s) can be subscribed for providing the network slice statistics to another NSCE server to provide a global view. There can be two use cases to provide the NSCE service in the distributed deployment: One use case is that the distributed deployed NSCE is about a slice service area which is equivalent to the edge/NPN’s service area. For this scenario, if the distributed deployed NSCE wants to access the NEF/NWDAF/NSACF services or to receive policies from OAM, it needs to interact to the global NSCE. A further use case could be that some NSCE services (e.g. MnS discovery) are locally provided to VAL servers (for example as a micro-service), whereas other capabilities are provided for the whole PLMN area. So, the distributed NSCE includes a subset of capabilities which are edge native. The local deployment of such capabilities can allow for more efficient services to the VAL servers (e.g. for QoS verification, the edge deployed NSCE can receive more timely KQI/QoE measurements and can process them locally before triggering an event). A.1.4 NPN NSCE deployment The NSCE architecture supports the deployment that NSCE server is deployed in NPN. Figure A.1.4 shows a deployment example of NSCE server deployment in the NPN. This case is valid if a geographical match between slice coverage area, NPN coverage area and NSCE service area is pre-configured. The matching may be pre-configured in the NSCE server by network operator based on the TA list or geographical coordinates. The NSCE server is deployed in Non-public network to provide the network slice capabilities exposure application service based on the interaction with NPN-5GC and NPN management system. Figure A.1.4: Illustration of NPN NSCE deployment A.1.5 Edge NSCE deployment The NSCE architecture supports the depolyment that the NSCE server is deployed in EDN as an EAS to provide the network slice capabilities exposure application service, based on the interaction with 5GS pertaining the network slice, and edge computing management system. Figure A.1.5 shows the edge NSCE deployment cases when the NSCE server is deployed in the EDN using LADNs as described in Annex A.2.4 of TS 23.558 [22]. This case is valid if a geographical match between slice coverage area, LADN service area (which is EDN service area) and NSCE service area is pre-configured in the NSCE server. The matching can based on the TA list or geographical coordinates. Figure A.1.5: Illustration of edge NSCE deployment A.2 Deployment of NSCE server(s) in relation to VAL server and 3GPP system To support the centralized/distributed deployment, the NSCE server(s) will have different deployment models and different relation with VAL server and 3GPP system. A.2.1 Centralized NSCE deployment The NSCE server can be deployed in single PLMN operator domain (as a SEAL server as specified in Figure 8.2.1-1 TS 23.434), deployed in VAL service provider domain by vertical (as a SEAL server as specified in Figure 8.2.2-1 TS 23.434), or deployed outside of both the VAL service provider domain and PLMN operator domain i.e. in 3rd party domain (as a SEAL server as specified in Figure 8.2.3-1 TS 23.434). The deployment of NSCE server(s), with connections to 3GPP network systems in multiple PLMN operator domains (as a SEAL server as specified in Figure 8.2.2-2) is also supported. When the vertical consumer wants to get NSCE services in two countries which are operated by two different MNOs, the NSCE service provider has to interact with two 3GPP network systems. The NSCE server is either deployed in the VAL service provider domain or deployed separately in the 3rd party domain. A.2.2 Distributed NSCE deployment The NSCE servers can be distributed in multiple PLMN domains (as a SEAL server as specified in TS 23.434 Figure 8.2.1-2, Figure 8.2.1-3), or distributed in single PLMN operator domain (as a SEAL server as specified in TS 23.434 Figure 8.2.1-4). The NSCE servers can also distributed in VAL service provider domain by vertical (as a SEAL server as specified in TS 23.434 Figure 8.2.2-3), or distributed deployed in 3rd party domain by 3rd party. The VAL server can communicate with multiple NSCE servers via NSCE-S as long as other NSCE servers are discovered and accessible. Or, the VAL server can communicate with other NSCE servers via NSCE-E if needed. A.3 Deployment of NSCE server(s) in relation to SEAL The NSCE server(s) supports standalone deployment independent with other SEAL services, it can interact with other SEAL service(s) via SEAL-X interface as specified in Clause 6.2 TS 23.434, . The NSCE service(s) supports combined deployment with other SEAL services, it can interact with other SEAL service via service API as specified in clause 15 TS 23.434. Annex B (informative): Change history Change history Date Meeting TDoc CR Rev Cat Subject/Comment New version 2022-06 SA6#49-bis-e TS skeleton 0.0.0 2022-07 SA6#49-bis-e TS Skeleton agreed in SA6#49-bis-e: S6-221640, Implemented pCRs approved in SA6#49-bis-e: S6-221641, S6-221642, S6-221643, S6-221822, S6-221818. 0.1.0 2022-09 SA6#50-e Implemented pCRs approved in SA6#50: S6-222146, S6-222197, S6-222407 Editorial changes by the rapporteur. 0.2.0 2022-10 SA6#51-e Implemented pCRs approved in SA6#51: S6-223036, S6-223035, S6-222901, S6-222764. Editorial changes by the rapporteur. 0.3.0 2022-11 SA6#52 Implemented pCRs approved in SA6#51: S6-223449, S6-223517, S6-223595, S6-223557, S6-223207, S6-223477 Editorial changes by the rapporteur. 0.4.0 2023-01 SA6#52-bis-e Implemented pCRs approved in SA6#52-bis-e: S6-230053, S6-230054, S6-230353, S6-230467, S6-230355, S6-230359, S6-230379, S6-230397, S6-230459, S6-230360, S6-230460, S6-230352, S6-230403, S6-230136, S6-230417, S6-230419, S6-230420, S6-230421, S6-230422, S6-230461 Editorial changes by the rapporteur. 0.5.0 2023-03 SA6#53 Implemented pCRs approved in SA6#53: S6-230960, S6-230961, S6-230616, S6-231027, S6-231028, S6-230997, S6-231069, S6-230967, S6-231009, S6-230968, S6-230598Editorial changes by the rapporteur. 0.6.0 2023-03 SA#99 SP-230272 Presentation for information at SA#99 1.0.0 2023-03 SA#99 SP-230346 Correction of implementation of pCR S6-231009 and presentation for information at SA#99 1.1.0 2023-04 SA6#54 Implemented pCRs approved in SA6#54: S6-231499, S6-231616, S6-231617, S6-231618, S6-231619, S6-231467, S6-231259, S6-231258, S6-231620, S6-231338, S6-231445, S6-231621 Editorial changes by the rapporteur. 1.2.0 2023-05 SA6#55 Implemented pCRs approved in SA6#55: S6-232092, S6-231800, S6-231802, S6-231803, S6-232093, S6-231805, S6-231806, S6-232094, S6-232095, S6-232096. Editorial changes by the rapporteur. 1.3.0 2023-06 SA#100 SP-230686 Presentation for approval at SA#100 2.0.0 2023-06 SA#100 SP-230686 MCC Editorial update for publication after TSG SA approval (SA#100) 18.0.0 2023-12 SA#102 SP-231561 0002 2 F Solve the EN in registration 18.1.0 2023-12 SA#102 SP-231571 0003 1 B Solve EN in clause 9.2 19.0.0 2023-12 SA#102 SP-231571 0004 3 B Enhancements to Network slice allocation procedure in NSaaS model 19.0.0 2023-12 SA#102 SP-231571 0005 3 B Enhancements to Slice requirement verification and alignment capability 19.0.0 2023-12 SA#102 SP-231571 0006 1 F Correction of reference 19.0.0 2024-03 SA#103 SP-240307 0009 1 A Fault diagnosis subscription request 19.1.0 2024-03 SA#103 SP-240307 0011 3 A Add late notification to the network slice adaptation procedures 19.1.0 2024-03 SA#103 SP-240319 0017 3 B Enhancements to Slice requirement verification and alignment capability 19.1.0 2024-03 SA#103 SP-240307 0018 1 A Correction of Area of interest 19.1.0 2024-03 SA#103 SP-240307 0018 1 A IE Name and Reference Corrections 19.1.0 2024-06 SA#104 SP-240760 0021 1 A Correction of Procedure name 19.2.0 2024-06 SA#104 SP-240774 0022 1 B Enhancements to Slice requirement verification and alignment capability 19.2.0 2024-06 SA#104 SP-240760 0024 3 A Update on predictive slice modification in Inter-PLMN based slice service continuity 19.2.0 2024-06 SA#104 SP-240760 0026 1 A UE IP address preservation indicator deletion 19.2.0 2024-09 SA#105 SP-241215 0030 2 A Adding a note on predictive slice modification in inter-PLMN based slice service continuity 19.3.0
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1 Scope
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The present document specifies the procedures, information flows and APIs necessary for Application Data Analytics Enablement SEAL Service.
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2 References
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The following documents contain provisions which, through reference in this text, constitute provisions of the present document.
- References are either specific (identified by date of publication, edition number, version number, etc.) or non‑specific.
- For a specific reference, subsequent revisions do not apply.
- For a non-specific reference, the latest version applies. In the case of a reference to a 3GPP document (including a GSM document), a non-specific reference implicitly refers to the latest version of that document in the same Release as the present document.
[1] 3GPP TR 21.905: "Vocabulary for 3GPP Specifications".
[2] 3GPP TS 23.434: "Service Enabler Architecture Layer for Verticals (SEAL); Functional architecture and information flows".
[3] 3GPP TS 26.531: "Data Collection and Reporting; General Description and Architecture"
[4] 3GPP TS 23.288: "Architecture enhancements for 5G System (5GS) to support network data analytics services".
[5] 3GPP TS 28.104: "Management and orchestration; Management Data Analytics".
[6] 3GPP TS 23.435: "Procedures for Network Slice Capability Exposure for Application Layer Enablement Service".
[7] 3GPP TS 28.552: "Management and orchestration; 5G performance measurements".
[8] 3GPP TS 23.222: "Common API Framework for 3GPP Northbound APIs".
[9] 3GPP TS 23.501: “System architecture for the 5G System”.
[10] GSMA NG.116 - Generic Network Slice Template.
[11] 3GPP TS 22.261: “Service requirements for the 5G system”.
[12] 3GPP TS 28.545: "Management and orchestration; Fault Supervision (FS)".
[13] 3GPP TS 23.433: "Service Enabler Architecture Layer for Verticals (SEAL); Data Delivery enabler for vertical applications".
[14] 3GPP TS 23.558: "Architecture for enabling Edge Applications".
[15] 3GPP TS 28.623: "Telecommunication management; Generic Network Resource Model (NRM) Integration Reference Point (IRP); Solution Set (SS) definitions".
[16] 3GPP TS 23.401: "General Packet Radio Service (GPRS) enhancements for Evolved Universal Terrestrial Radio Access Network (E-UTRAN) access".
[17] 3GPP TS 23.303: "Proximity-based services (ProSe); Stage 2".
[18] 3GPP TS 23.273: "5G System (5GS) Location Services (LCS); Stage 2".
[19] 3GPP TS 23.482: “Functional architecture and information flows for AIML Enablement Service”.
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3 Definitions of terms and abbreviations
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3.1 Terms
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For the purposes of the present document, the terms given in 3GPP TR 21.905 [1] and the following apply. A term defined in the present document takes precedence over the definition of the same term, if any, in 3GPP TR 21.905 [1].
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3.2 Abbreviations
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For the purposes of the present document, the abbreviations given in 3GPP TR 21.905 [1] and the following apply. An abbreviation defined in the present document takes precedence over the definition of the same abbreviation, if any, in 3GPP TR 21.905 [1].
ADAES Application Data Analytics Enabler Server
ADAEC Application Data Analytics Enabler Client
A-ADRF Application layer - Analytical Data Repository Function
A-DCCF Application layer - Data Collection and Coordination Function
ASP Application Service Provider
DNAI Data Network Access Identifier
EAS Edge Application Server
EEL Edge Enabler Layer
EES Edge Enabler Server
FLS Fused Location Server
LMS Location Management Server
MDAS Management Domain Analytics Service
NSCE Network Slice Capability Enablement
NWDAF Network Data Analytics Function
OAM Operation, Administration and Maintenance
RNIS Radio Network Information Service
RTT Round-Trip Time
VAL Vertical Application Layer
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4 Architectural requirements
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4.1 General Description
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The following clauses specify the requirements for application data analytics enablement service.
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4.2 General Requirements
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[AR-4.2-a] The ADAE client and the ADAE server shall support one or more VAL applications.
[AR-4.2-b] Supported ADAE capabilities shall be offered as APIs to the VAL applications.
[AR-4.2-c] The ADAE shall support interaction with 3GPP network system to consume network and management data analytics services.
[AR-4.2-d] The ADAE client shall be capable to communicate with one or more ADAE servers of the same ADAE service provider.
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4.3 ADAE internal architecture requirements
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[AR-4.3-a] The ADAE layer shall be able to provide a data collection coordination functionality to enable the collection from diverse data sources (OAM, 5GC, UE) per application data analytics event type.
[AR-4.3-b] The ADAE layer shall include a data analytics repository function to store application data analytics.
[AR-4.3-c] The data collection coordination and repository capabilities may be offered as APIs to ADAE server.
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4.4 ADAE capability related requirements
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[AR-4.4-a] The ADAE server shall be capable of providing data analytics for the VAL server performance.
[AR-4.4-b] The ADAE server shall be capable of providing data analytics for the VAL application sessions (for both Uu-based and PC5-based sessions).
[AR-4.4-c] The ADAE server shall be able to collect application performance measurements and analytics from one or more ADAE clients.
[AR-4.4-d] The ADAE server shall be capable of collecting edge data from one or more edge platforms
[AR-4.4-e] The ADAE server shall enable the exposure of edge data analytics to the VAL applications
[AR-4.4-f] The ADAE server shall be capable of providing data analytics for the VAL server or VAL session performance for a requested slice or slice instance.
[AR-4.4-g] The ADAE server shall be capable of providing data analytics for the location accuracy of one or more VAL UEs.
[AR-4.4-h] The ADAE server shall be capable of providing data analytics related to the availability and status of one or more service APIs.
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5 Application architecture for ADAES
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5.1 General
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This clause provides the functional architecture for ADAE. This includes the on-network and off-network functional models which are provided in detail in clause 5.2.
In addition, the ADAE internal architecture is described in 5.3, which aligns with the 3GPP data analytics framework (specified in TS 23.288 [4]) and introduces new logical entities within ADAE framework, such as the A-DCCF and A-ADRF.
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5.2 Functional architecture
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5.2.1 General
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The functional architecture for the application data analytics enablement is based on the generic functional model specified in clause 6.2 of 3GPP TS 23.434 [2]. It is organized into functional entities to describe a functional architecture which addresses the support for application data analytics enablement aspects for vertical applications.
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5.2.2 On-network Functional Architecture
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For the on-network functional architecture, both service-based representation and reference point representation are provided.
Figure 5.2.2-1 depicts the application data analytics enablement architecture in the non-roaming case, using the reference point representation showing how various entities interact with each other.
Figure 5.2.2-1: Architecture for application data analytics enablement – reference points representation
The application data analytics enablement client communicates with the application data analytics enablement server over the ADAE-UU reference point. The application data analytics enablement client provides the support for application data analytics enablement functions to the VAL client(s) over ADAE‑C reference point. The VAL server(s) communicates with the application data analytics enablement server over the ADAE-S reference point. The application data analytics enablement server, acting as AF, may communicate with the 5G Core Network functions (over N33 reference point to NEF and N6 reference point to UPF) and OAM (over ADAE-OAM interface).
Figure 5.2.2-2 exhibits the service-based interfaces for providing and consuming application data analytics enablement services. The application data analytics enablement server could provide service to VAL server and ADAE client through interface SAdae.
Figure 5.2.2-2: Architecture for application data analytics enablement – Service based representation
Figure 5.2.2-3 illustrates the service-based representation for utilization of the 5GS network services based on the 5GS SBA specified in 3GPP TS 23.501 [9].
Figure 5.2.2-3: Architecture for application data analytics enablement utilizing the 5GS network services based on the 5GS SBA – Service based representation
Figure 5.2.2-4 illustrates the architecture for inter-service communication between ADAES server and other SEAL server.
Figure 5.2.2-4: Inter-service communication between ADAES server and other SEAL server
The ADAE server interacts with another SEAL server for inter-service communication over SEAL-X reference point.
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5.2.3 Off-network Functional Architecture
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Figure 5.2.3-1 illustrates the generic off-network functional model for ADAE.
Figure 5.2.3-1: Generic off-network functional model
In the vertical application layer, the VAL client of UE1 communicates with VAL client of UE2 over VAL-PC5 reference point. An application data analytics enablement client of UE1 interacts with the corresponding application data analytics enablement client of UE2 over ADAE-PC5 reference points. The UE1, if connected to the network via Uu reference point, can also act as a UE-to-network relay, to enable UE2 to access the VAL server(s) over the VAL-UU reference point.
The service-based interface representation is specified in clause 15 of 3GPP TS 23.434 [2].
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5.2.4 Functional Architecture for supporting interactions with SEAL AIMLE
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Figure 5.2.4-1 illustrates the architecture representation including AIMLE (as specified in 3GPP TS 23.482 [19]) for supporting ML-enabled analytics in ADAES. In this representation, the AIML support capabilities serve ADAES to enhance its analytics services. Based on the VAL request to provide ML-enabled analytics, ADAES may consume AIMLE services (e.g., for ML model training for a given analytics ID) to derive application layer data analytics.
Figure 5.2.4-1: Architecture representation for supporting AIML-enabled ADAE analytics.
For the interaction between AIMLE server and ADAES, AIML-X is introduced to support consuming AIMLE services for deriving ADAE analytics (e.g. VAL server performance analytics).
The ML repository is specified in 3GPP TS 23.482 [19] as a repository for the ML model and registry for the ML-related information (such as ML/FL members). This repository may be utilized by ADAES via AIMLE server (via AIML-R) for fetching ML-related information (e.g., trained ML model, ML/FL members) which is used for a given ADAE analytics event.
Further details on the AIMLE capabilities and architecture, where the ADAES is a consumer of the AIMLE services, are specified in 3GPP TS 23.482 [19].
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5.3 ADAE internal architecture
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In ADAE framework, A-DCCF and A-ADRF can be defined as functionalities within the internal ADAE architecture and can offer the following functionalities:
- Application layer - Data Collection and Coordination Function (A-DCCF) coordinates the collection and distribution of data requested by the consumer (ADAE server). Data Collection Coordination is supported by a A-DCCF. ADAE server can send requests for data to the A-DCCF rather than directly to the Data Sources. A-DCCF may also perform data processing/abstraction and data preparation based on the VAL server requirements.
- Application layer – Analytics and Data Repository Function (A-ADRF) stores historical data and/or analytics, i.e., data and/or analytics related to past time period that has been obtained by the consumer (e.g. ADAE server). After the consumer obtains data and/or analytics, consumer may store historical data and/or analytics in an A-ADRF. Whether the consumer directly contacts the A-ADRF or goes via the A-DCCF is based on configuration.
Figure 5.3-1 illustrates the generic functional model for ADAE when re-using the 3GPP network data analytics model.
Figure 5.3-1: ADAE internal functional architecture
In this model, an A-DCCF is used to fetch data or put data into an application-level entity (e.g. A-ADRF, Data Source). Such A-DCCF coordinates the collection and distribution of data requested by ADAE server (over ADCCF-1, ADAE-X). ADAE server can also directly interact with the Data Sources via ADAE-Y.
Also, Application layer – Analytics and Data Repository Function (A-ADRF) can be used to store historical data and/or analytics, i.e., data and/or analytics related to past time period that has been obtained by the ADAE server (via AADRF-1) or other NFs/NWDAF. ADAE server can also fetch historical data from A-ADRF. Whether the ADAE server directly contacts the A-ADRF or goes via the A-DCCF is based on configuration.
Data Sources can be 5GS data sources (5GC, OAM) or enablement layer data sources (SEAL, EEL) or external data sources at the DN side (VAL server/ EAS) and VAL UEs. A-DCCF and A-ADRF can be used only for interacting with certain data sources (e.g., 5GC, OAM) based on configuration, and can be hidden from the VAL layer.
NOTE: If the Data Source is the VAL UE, then the data collection mechanism shall reuse the SA4 mechanism based on EVEX study (TS 26.531 [3]).
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5.4 Functional entities description
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5.4.1 General
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The functional entities for ADAE service are described in the following subclauses.
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5.4.2 Application Data Analytics Enablement client
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The application data analytics enablement and provides client side functionalities for the functionalties provided by the application data analytics enablement server. The application data analytics enablement client interacts with the application data analytics enablement server.
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5.4.3 Application Data Analytics Enablement server
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The application data analytics enablement server functional entity provides application layer analytics to support the VAL applications. The application data analytics enablement server acts as CAPIF's API exposing function as specified in 3GPP TS 23.222 [8]. The application data analytics enablement server also supports interactions with the corresponding application data analytics enablement server in distributed SEAL deployments. The ADAE server also interacts with 3GPP core network over N33 or N6 interface to subscribe to changes in configuration or other application server specific events. The ADAE server also acts as a co-ordinating entity to collect data from different sources and perform necessary actions to provide required analytics.
The ADAE server provides following server side functionalities:
- monitoring performance of an application (VAL server or EAS, application session) and providing support for application performance analytics;
- monitoring performance of a given network slice (from a list of subscribed slices for the VAL customer) and also usage pattern, and providing support for slice-specific application performance analytics and slice usage pattern analytics;
- monitoring performance of an application session among two or more VAL UEs within a service or group, and providing support for UE-to-UE application performance analytics;
- monitoring accuracy of a location and providing support for location accuracy analytics;
- monitoring availability and service level for service APIs and providing support for service API analytics;
- monitoring edge load parameters and providing support for edge load analytics;
- monitoring ranging/SL positioning data and location information, and providing support for collision detection analytics.
- monitoring location information and providing support for location-related UE group analytics.
- monitoring application layer AI/ML member capability data and providing support for Application Layer AI/ML Member Capability Analytics.
To support ML-enabled analytics services, the ADAE server may provide the above server-side functionalities by consuming SEAL AIMLE services (as specified in 3GPP TS 23.482 [19]).
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5.5 Reference points description
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5.5.1 General
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The reference points for the functional model for application data analytics enablement are described in the following subclauses.
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5.5.2 ADAE-UU
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The interactions related to application data analytics enablement functions between the application data analytics enablement client and the application data analytics enablement server are supported by ADAE-UU reference point. This reference point utilizes Uu reference point as described in 3GPP TS 23.401 [16] and 3GPP TS 23.501 [9].
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5.5.3 ADAE-PC5
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The interactions related to application data analytics enablement functions between the application data analytics enablement clients located in different VAL UEs are supported by the ADAE-PC5 reference point. This reference point utilizes PC5 reference point as described in 3GPP TS 23.303 [17].
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5.5.4 ADAE-C
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The interactions related to application data analytics enablement functions between the VAL client(s) and the application data analytics enablement client within a VAL UE are supported by the ADAE-C reference point.
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5.5.5 ADAE-S
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The interactions related to application data analytics enablement functions between the VAL server(s) and the application data analytics enablement server are supported by the ADAE-S reference point. This reference point is an instance of CAPIF-2 reference point as specified in 3GPP TS 23.222 [8].
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5.5.4 ADAE-X
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The interactions related to application data analytics enablement functions between the application data analytics enablement server and the Application-layer DCCF (A-DCCF) for data coordination aspects are supported by the ADAE-X reference point.
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5.5.5 ADAE-Y
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The interactions related to application data analytics enablement functions between the application data analytics enablement server and the data producers (or data sources) for collecting data to be used for the ADAE analytics services (if A-DCCF is not used) are supported by the ADAE-Y reference point.
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5.5.6 ADCCF-1
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The interactions related to application data analytics enablement functions between the application layer data collection and coordination entity and the data sources for data coordination aspects are supported by the ADCCF-1 reference point.
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5.5.7 AADRF-1
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The interactions related to application data analytics enablement functions between the application data analytics enablement server (or the A-DCCF) and the application layer - analytics and data repository function (A-ADRF) for storing data and analytics related to the ADAE analytics services (if A-DCCF is not used) are supported by the AADRF-1 reference point.
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5.5.8 SEAL-X
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The interactions between the NSCE servers and other SEAL servers are generically referred to as SEAL‑X reference point. The specific SEAL server interactions corresponding to SEAL-X are described in 3GPP TS 23.434 [2].
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5.5.9 AIML-X
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The interactions between the SEAL AIMLE server and ADAES for supporting ML-enabled analytics is generically referred to as AIML‑X reference point. AIML-X is an instance of SEAL-X as described 3GPP TS 23.434 [2].
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6 ADAE layer Functional Description
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6.1 Support for application performance analytics
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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.
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6.2 Support for slice-specific application performance analytics
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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]).
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6.3 Support for UE-to-UE application performance analytics
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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.
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6.4 Support for location accuracy analytics
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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.
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6.5 Support for service API analytics
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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.
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6.6 Slice usage pattern analytics
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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.
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6.7 Support for edge load analytics
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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.
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6.8 Edge computing preparation analytics
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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.
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6.9 Support for server-to-server performance analytics
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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.
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6.10 Support for collision detection analytics
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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.
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6.11 Support for location-related UE group analytics
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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 3GPP TS 23.558 [14] clause 8.2.11. 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).
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6.12 Support for Application Layer AI/ML Member Capability Analytics
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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.
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6.13 Support for VAL performance analytics for tethered UEs
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This feature supports a new ADAES analytics functionality on tethered VAL connectivity performance. The tethered VAL connectivity performance can be defined as the application session performance corresponding to either only the tethered link (tethered UE and host UE) or the end-to-end VAL performance including the tethered link (as extension of VAL session performance analytics).
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6.14 Support for DN Energy Analytics
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This feature supports a logical functionality at the ADAES to provide analytics on the energy consumption /efficiency of an edge platform (including the EESs / EASs). The DN energy analytics is performed per DNN/ DNAI and may be used to trigger the application server migration to different cloud. The analytics are based on NWDAF analytics and UPF/DN measurements on user plane load as well as edge/app side measurements on the energy consumption.
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6.15 Support for ML Model Performance Degradation Detection
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This feature supports ML model performance degradation detection to allow a consumer (such as e.g. AIMLE Server) to be notified on performance degradation of an ML model. Such detection can be used to support application layer AI/ML operations, e.g. supporting decision making on retrain an ML model.
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7 Identities and commonly used values
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7.1 General
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The common identities for SEAL refer to TS 23.434[2]. The following clauses list the additional identities and commonly used values for Application Data Analytics Enablement Service.
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7.2 ADAE Server ID
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The ADAE server ID uniquely identifies the application data analytics enablement server, and each ADAE server ID is unique within PLMN domain.
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7.3 ADAE client ID
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The ADAE client ID uniquely identifies the application data analytics enablement client.
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7.4 A-ADRF ID
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The A-ADRF ID uniquely identifies the application data analytics repository function.
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7.5 A-DCCF ID
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The A-DCCF ID uniquely identifies the application data collection and coordination function.
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7.6 Data Producer ID
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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.
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7.7 ADAE service area
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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.
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7.8 Analytics ID
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The analytics ID (or analytics event ID) identifies the application layer analytics event which corresponds to the specified ADAE analytics services.
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8 Procedures and information flows
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8.1 General
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This clause describes the procedures and the information flows related to the ADAE capabilities, as introduced in clause 6.
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8.2 Procedure on support for application performance analytics
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8.2.1 General
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In this functionality, two procedures are described in more detail in clause 8.2.2 and 8.2.3 accordingly:
- one procedure for VAL server related analytics where an example in provided for VAL server performance,
- one procedure for VAL session/UE related analytics.
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8.2.2 Procedure on VAL server performance analytics
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Figure 8.2.2-1 illustrates the procedure where the VAL server performance analytics are performed based on data collected from the ongoing VAL sessions as well as data from the DN (VAL server, DN database or networking stack at the DN).
Pre-conditions:
1. ADAE Client (ADAEC) is connected to ADAES.
2. Data producers (e.g. A-ADRF, VAL Client) may be pre-configured with data producer profiles for the data they can provide. ADAES and ADAEC have discovered available data producers and their data producer profiles.
Figure 8.2.2-1: ADAES support for VAL server performance analytics
1. The consumer of the ADAES analytics service sends a VAL performance analytics subscription request to ADAES and provides the analytics event ID e.g. "VAL server performance analytics".
2. The ADAES sends a subscription response as a positive or negative acknowledgement to the consumer of the analytics service.
3. The ADAES maps the analytics event ID to a list of data collection event identifiers, and a list of data producer IDs. Such mapping may be preconfigured by OAM or may be determined by ADAES based on the analytics event type / vertical type and/or data producer profile.
4. The ADAES sends a data collection subscription request to the Data Producers (at the DN side or UE side) with the respective Data Collection Event ID and the requirement for data collection. Such data producers include the A-ADRF, the A-DCCF, the VAL server, SEALDD server, or the VAL UEs.
5. The Data Producer(s) sends a subscription response as a positive or negative acknowledgement to the ADAES.
NOTE: The ADAES acting as AF may also subscribe to NEF/SMF/PCF/NWDAF to monitor network/UE situation or network data analytics required for the application data analytics event.
6. The ADAES based on subscription, may receive offline stats/data from A-ADRF on the VAL server performance based on the analytics/data collection event ID. Such offline data can be average/peak throughput, average/maximum e2e delay, jitter, average application layer PER, availability, VAL server load, number of failed transactions, and can be for a given area and time of the day (based on the time/area of the request).
A session starts between the VAL server #1 and a UE (this could happen for more than one UEs).
7. The Data Producer at DN side, starts collecting data from the data generating entities, e.g. real-time networking or application data (from networking start at DN or VAL server itself), such as RTT, application layer PER, throughput.
8a. The Data Producer sends the real-time data to the ADAES, where the data correspond to the data collection ID or the analytics event ID for which the ADAES subscribed.
8b. The ADAES may receive also data (periodically or if a threshold is reached based on configuration) from the application of the UE within the ongoing session (via ADAEC). Such data can be about the RTT, average/peak throughput, jitter, QoE measurements (MOS, stalling events, stalling ratios, etc), QoS profile load, VAL server load, etc.
9. When the VAL UE session with VAL server finishes, the ADAEC notifies the ADAES of the completion of the reporting.
10. The ADAES abstracts or correlates the data based on the analytics event and the data collection configuration. Such correlation can be filtering of data for the same metrics but with different granularities or be combining/aggregating the data of segments of the end-to-end path (end to end is between VAL client and server). The outcome is an abstracted/correlated/filtered set of data.
11. The ADAES derives application layer analytics on VAL server #1 performance, based on the analytics ID and type of request. Such analytics can be stats or prediction for a given area/time and based on the event type for a given network configuration.
12. The ADAES sends the analytics to the consumer, where these analytics include the VAL server #1 predicted or statistic performance for a given area and time horizon, including also the confidence level.
NOTE: If the Data Producer in steps 4-5 and 8a is SEALDD server, procedure in clause 9.7.2.1 of 3GPP TS 23.433 [13] is used for the collection of the E2E transmission quality measurement results to ADAES.
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8.2.3 Procedure on VAL session performance analytics
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Figure 8.2.3-1 illustrates the procedure where the VAL session performance analytics are performed based on data collected from the ongoing VAL sessions.
Pre-conditions:
1. ADAEC is connected to ADAES.
2. Data producers (e.g. A-ADRF, VAL Client) may be pre-configured with data producer profiles for the data they can provide. ADAES and ADAEC have discovered available data producers and their data producer profiles.
Figure 8.2.3-1: ADAES support for VAL session performance analytics
1. The consumer of the ADAES analytics service sends a VAL performance analytics subscription request to ADAES and provides the analytics event ID e.g. "VAL session performance analytics", the target VAL UE ID, VAL server ID/VAL application ID, the time validity and area of the request, the required confidence level, exposure level for providing UE analytics. If the consumer is the VAL server, the VAL server can provide to ADAEC application data related to the UE expected route/trajectory and VAL application traffic schedule / expected session time.
2. The ADAES sends a subscription response as an ACK to the consumer.
3. The ADAES selects the corresponding ADAEC of the VAL UE for which the local analytics need to be performed.
4a. The ADAES sends a subscription request to the ADAEC with the analytics event ID and the configuration of the reporting required (e.g., periodic, based on event with threshold).
4b. The ADAEC sends a subscription response to ADAES.
5. The ADAEC maps the analytics event ID to a list of data collection event identifiers or data collected IDs at the VAL UE or other UEs within the service and in proximity (in group-based communications). The ADAEC also determines the data producers using the analytics event ID, target data producer profile and optional preconfigured policies.
6. The ADAEC subscribes to the VAL clients and/or requests UE local data based on the respective Data Collection Event ID (or the analytics event ID if they already know the mapping). This data may come from the PDU layer of the UE (via listening the traffic), or via VAL client of one or more UEs (if an application consists of a group of UEs).
A session starts between the VAL UE #1 and a VAL server.
7. The ADAEC (after being aware from the VAL client that the session started) sends a notification to ADAES that a session started, and it could be possible to provide real-time data analytics for VAL UE performance in the target area.
8. The ADAEC starts collecting data from the corresponding data producers based on subscription. Such data can be about the RTT, throughput, jitter, QoE measurements, QoS profile load, etc. It can be also possible that VAL client provides to ADAEC application data related to the UE expected route/trajectory and VAL application traffic schedule / expected session time.
9. The ADAEC filters or correlates the data based on the analytics event and the data collection configuration.
10. When the VAL UE session finishes, the ADAEC (optionally) derives VAL session analytics to ADAES on VAL UE #1 performance, based on the analytics ID and type of request. Such analytics (if performed at the ADAEC can be stats or predictions on the RTT or RTT deviation, average/peak throughput, jitter, QoE measurements (MOS, stalling events, buffer related events), QoS profile load, VAL application traffic load etc. In case of prediction, a confidence level shall be also present and a time horizon for the predicted parameters.
11. The ADAEC sends the data of step 9 or the analytics of step 10 (if ADAEC performs analytics) to the ADAES.
12. The ADAES derives application layer analytics on VAL session performance (based on the data or analytics received by the ADAEC), based on the analytics ID and type of request. Such analytics can be stats or prediction for a given area/time and based on the event type for a given network configuration. Such analytics (if no analytics is performed at ADAEC) at ADAES can be stats or predictions on the RTT or RTT deviation, average/peak throughput, jitter, QoE measurements, QoS profile load, VAL application traffic load etc. In case of prediction, a confidence level shall be also present and a time horizon for the predicted parameters.
13. The ADAES sends the analytics to the consumer, where these analytics include the VAL UE #1 session predicted or statistic performance for a given area and time horizon, including also the confidence level.
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8.2.4 Information flows
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8.2.4.1 General
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The following information flows are specified for VAL performance analytics based on 8.2.2 and 8.2.3.
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8.2.4.2 VAL performance analytics subscription request
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Table 8.2.4.2-1 describes information elements for the VAL performance analytics subscription request from the consumer (e.g. VAL server, NF, AF) to the ADAE server or from ADAE server to ADAE client.
Table 8.2.4.2-1: VAL performance analytics subscription request
Information element
Status
Description
Consumer ID
M
The identifier of the analytics consumer.
Analytics ID
M
The identifier of the analytics event. This ID can be for example “VAL server performance analytics” for procedure in 8.2.2, or “VAL session performance analytics” for procedure in 8.2.3.
Analytics type
M
The type of analytics for the event, e.g. statistics or predictions.
VAL service ID
M
The identifier of the VAL service for which analytics subscription applies.
Target VAL UE ID(s)
O
The VAL UE identifier(s) for which the analytics subscription applies.
Target VAL server ID
O
If consumer is different from the VAL server, this identifier shows the target VAL server for which the analytics subscription applies (for procedure in 8.2.2).
Target data producer profile criteria
O
Characteristics of the data producers to be used.
ADAE client application data
O
Represent ADAE client application data (e.g. related to the UE expected route/trajectory and VAL application traffic schedule/expected session time) that the consumer can provide, if the consumer is VAL server.
Preferred confidence level
O
The level of accuracy for the analytics service (in case of prediction).
Area of Interest
O
The geographical or service area for which the subscription request applies.
Time validity
O
The time validity of the subscription request.
Exposure level requirement
O
The level of exposure requirement (e.g. condition on providing UE analytics like threshold is reached) for the UE analytics to be exposed.
Reporting requirements
O
It describes the requirements for analytics reporting. This requirement may include e.g. the type and frequency of reporting (periodic or event triggered), the reporting periodicity in case of periodic, and reporting thresholds in case of event triggered.
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8.2.4.3 VAL performance analytics subscription response
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Table 8.2.4.3-1 describes information elements for the VAL performance analytics subscription response from the ADAE server to the consumer (e.g. VAL server, NF, AF) or from ADAE client to ADAE server.
Table 8.2.4.3-1: VAL performance analytics subscription response
Information element
Status
Description
Result
M
The result of the analytics subscription request (positive or negative acknowledgement).
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23.436
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8.2.4.4 Data collection subscription request
|
Table 8.2.4.4-1 describes information elements for the Data collection subscription request from the ADAE server to the Data Producer (e.g., A-DCCF, A-ADRF, VAL server, SEALDD server, or VAL UE via ADAE client).
Table 8.2.4.4-1: Data collection subscription request
Information element
Status
Description
ADAE server ID
M
The identifier of the ADAE server.
Data Collection Event ID
M
The identifier of the data collection event.
Data Collection requirements
M
The requirements for data collection, including the format of data, frequency of reporting, level of abstraction of data, level of accuracy of data.
Analytics ID
O
The identifier of the analytics event, for which the data collection is needed.
List of Data Producer IDs
O
In case when this request is performed via A-DCCF, then the list of Data Producer IDs is needed.
Target VAL UE ID(s) and address(es)
O
The VAL UE identifier(s) and IP address(es) for which the data collection subscription applies.
Target VAL server ID
O
This identifier shows the target VAL server for which the data collection subscription applies.
Target data producer profile criteria
O
Characteristics of the data producers to be used.
Area of Interest
O
The geographical or service area for which the requirement request applies.
Interest time period
O
Interested time period for which the requirement request applies (e.g. time of the day).
Time validity
O
The time validity of the request
|
20479eb37624e17cc85fa37e0dbf82f7
|
23.436
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8.2.4.5 Data collection subscription response
|
Table 8.2.4.5-1 describes information elements for the Data collection subscription response from the Data Producer (e.g., A-DCCF, A-ADRF, VAL server, SEALDD server, or VAL UE via ADAE client) to the ADAE server.
Table 8.2.4.5-1: Data collection subscription response
Information element
Status
Description
Result
M
The result of the data collection subscription request (positive or negative acknowledgement).
|
20479eb37624e17cc85fa37e0dbf82f7
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23.436
|
8.2.4.6 Data Notification
|
Table 8.2.4.6-1 describes information elements for the Data Notification from the Data Producer to the ADAE server.
Table 8.2.4.6-1: Data notification
Information element
Status
Description
Data Collection Event ID
M
The identifier of the data collection event.
Target VAL UE ID and address(es)
M (NOTE)
The VAL UE identifier(s) and IP address(es) for which the data apply.
Target VAL server ID
M (NOTE)
This identifier of the target VAL server for which the data applies.
Analytics ID
O
The identifier of the analytics event. This ID can be for example “VAL server performance analytics” for procedure in 8.2.2, or “VAL session performance analytics” for procedure in 8.2.3.
Data Type
O
The type of reported data samples which can be UE data, network data, application data, edge data, or different granularities / abstraction of data (e.g. real time, non real time).
Data Output
M
The reported data, which can be inform of measurements or offline/historical data on the requested parameter based on subscription. For example:
• offline stats/data from A-ADRF on the VAL server performance based on the analytics/data collection event ID. Such offline data can be average/peak throughput, average/maximum e2e delay, jitter, average application layer PER, availability, VAL server load, number of failed transactions, and can be for a given area and time of the day (based on the time/area of the request).
• from the application of the UE within the ongoing session (via ADAEC). Such data can be about the RTT, average/peak throughput, jitter, QoE measurements (MOS, stalling events, stalling ratios, etc), QoS profile load, VAL server load, etc.
NOTE: One of these shall be present based on the data collection event
|
20479eb37624e17cc85fa37e0dbf82f7
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23.436
|
8.2.4.7 Analytics Notification
|
Table 8.2.4.7-1 describes information elements for the Analytics Notification from the ADAE server to the consumer (e.g. VAL server, NF, AF).
Table 8.2.4.7-1: Analytics notification
Information element
Status
Description
Analytics ID
M
The identifier of the analytics event. This ID can be for example “VAL server performance analytics” for procedure in 8.2.2, or “VAL session performance analytics” for procedure in 8.2.3.
Analytics Output
M
The analytics outputs, which can be predictive or statistical parameter.
> VAL server performance analytics output
O
(see NOTE)
Statistics or predictions of the VAL server performance, such as RTT, average/peak throughput, jitter, QoE measurements, QoS profile load, VAL server load, VAL server predicted or expected performance change for the requesting consumer.
> VAL session performance analytics output
O
(see NOTE)
Statistics or predictions of the VAL session performance, such as RTT, average/peak throughput, jitter, QoE measurements, QoS profile load, VAL application traffic load, VAL session predicted or expected performance change.
Applicable area
M
The service area or geographical area for which the analytics output applies to.
Confidence level
O
(see NOTE)
The achieved confidence level.
Time horizon
O
(see NOTE)
The time horizon for predictive analytics.
> Start time
O
The start time point of predictive validity. If omitted, the default value is the current time.
> End time
M
The end time point of predictive validity.
NOTE: One of the IEs shall be present based on the Analyitcs ID provided in the subscription request.
|
20479eb37624e17cc85fa37e0dbf82f7
|
23.436
|
8.2.4.8 Data producer profile
|
The data producer profile IE includes information about the data generation/production capability of the data producer to support data collection for data analytics service and the availability/accessibility of the generated/produced data, as defined in Table 8.2.4.8-1.
Table 8.2.4.8-1: Data producer profile
Information element
Status
Description
Data Producer ID
M
ID of the data producer.
Data producer type (NOTE)
M
Specifies the type of the data producer, e.g., ADAEC, A-DCCF, A-ADRF, VAL server, SEAL server, SEAL client, EES, EAS.
Data type (NOTE)
M
Type of information that can be provided by the data producer, e.g., performance indicators, reproducer usage data, server load data, application performance, edge load.
Data producer role (NOTE)
O
Role of the data producer, e.g., generating entity, original producer, repository.
Original producer ID (NOTE)
O
If the data producer role is not “original producer” or “generating entity”, specifies the Producer ID of the original data producer for the data provided by this data producer.
If the data producer type is A-DCCF, this is a list of Data Producer IDs.
Data freshness (NOTE)
O
If the data producer role is not “original producer” or “generating entity”, length of time elapsed after the data is generated until is available at the data producer. Alternatively, the data collection rate supported by the producer is provided.
Data producer capability (NOTE)
O
Indicates data producer capabilities for this data type, e.g. how long the data can be stored, support for anonymization, data generation rate and schedule.
NOTE: When the Data producer profile IE is used for Target data producer profile criteria (e.g. Table 8.2.4.4-1), this IE may be a list of values.
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20479eb37624e17cc85fa37e0dbf82f7
|
23.436
|
8.3 Procedure on support for slice-specific application performance analytics
| |
20479eb37624e17cc85fa37e0dbf82f7
|
23.436
|
8.3.1 General
|
This clause describes the procedure for supporting slice-specific application performance analytics. The ADAES service consumer can subscribe and receive notifications about slice specific application performance analytics events. In case that the ADAES consumer needs information about historical data, the procedure in 8.7.3 can be used for retrieving of slice-specific application performance metrics data about a specific area and time window in the past.
|
20479eb37624e17cc85fa37e0dbf82f7
|
23.436
|
8.3.2 Procedure
|
Figure 8.3.2-1 illustrates the procedure where the VAL server performance analytics are performed based on data collected from the ongoing VAL sessions as well as data from the DN (VAL server, DN database or networking stack at DN) for a specific slice.
Pre-conditions:
1. ADAEC is connected to ADAES.
Figure 8.3.2-1: ADAES support for slice-related performance analytics
1. The consumer of the ADAES analytics service sends a subscription request to ADAES and provides the analytics event ID e.g. "slice-specific application performance analytics ", the target S-NSSAI, DNN, NSI ID, the time validity of the request, the required confidence level, area and time horizon, etc.
2. The ADAES sends a subscription response as an ACK to the consumer.
3. The ADAES subscribes to the Data Sources with the respective Data Collection Event ID and the requirement for data collection related to the request slice(s). Such requests may be towards:
- OAM for providing PM data related to the requested slice / NSI. Alternatively, if the interaction to OAM happens via NSCE layer (see TS 23.435 [6]), such subscription can be performed to NSCE (where ADAES is acting as VAL server).
- NWDAF for providing slice related analytics for the given area and time horizon (indicated in step 1). Such analytics can be the slice load level related network data analytics, or the service experience related network data analytics for a given slice.
4. The ADAES based on subscription, receives PM data notification from OAM or from NSCE server (via OAM APIs or NSCE-S APIs)
5. The ADAES based on subscription, receives the requested NWDAF analytics outputs. Such analytics can be:
- network slice or NSI statistics or predictions (clause 6.3.3A of TS 23.288 [4])
- per slice instance service experience stats or predictions (clause 6.4.3 of TS 23.288 [4])
6. The ADAES can also provide analytics on the VAL session performance (based on the procedure of clause 8.2.2 step 11 or clause 8.2.3 step 12) and filters the analytics only for the sessions which are connected to that requested slice for the area of interest.
7. The ADAES abstracts or correlates the data/analytics from steps 4-6 and provides analytics on the slice or NSI performance for the target VAL application/server. For example, such analytics can be about the min/average/max predicted RTT / end to end latency for the VAL application/server if this server uses a given slice/NSI (or for a list of given slices) within an area of interest.
8. The ADAES sends the analytics to the consumer, as a slice-specific performance analytics notification message.
|
20479eb37624e17cc85fa37e0dbf82f7
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23.436
|
8.3.3 Information flows
| |
20479eb37624e17cc85fa37e0dbf82f7
|
23.436
|
8.3.3.1 General
|
The following information flows are specified for slice-specific application performance analytics based on 8.3.2.
|
20479eb37624e17cc85fa37e0dbf82f7
|
23.436
|
8.3.3.2 Slice-specific performance analytics subscription request
|
Table 8.3.3.2-1 describes information elements for the slice-specific performance analytics subscription request from the consumer (VAL server / NSCE server) to the ADAE server.
Table 8.3.3.2-1: Slice-specific performance analytics subscription request
Information element
Status
Description
Consumer ID
M
The identifier of the analytics consumer.
Analytics ID
O
The identifier of the analytics event. This ID can be for example “slice-specific application performance analytics”.
Analytics type
M
The type of analytics for the event, e.g. statistics or predictions.
Slice identifier(s)
M
The identifier(s) of the target slice(s) or slice instance(s), i.e. S-NSSAI, NSI ID or ENSI.
DNN
O
The target DNN for which the request applies.
Target VAL UE ID(s)
O
The VAL UE(s) for which the analytics subscription applies.
Target VAL server ID
O
If consumer is different from the VAL server, this identifier shows the target VAL server for which the analytics subscription applies (for procedure in clause 8.3.2).
Target VAL service ID
O
The identifier of the VAL service for which the analytics applies.
Preferred confidence level
O
The required level of accuracy for the analytics service (in case of prediction).
Area of Interest
O
The geographical or service area for which the subscription request applies.
Time validity
O
The time validity of the subscription request.
Time horizon
O
The required time horizon for predictive analytics.
> Start time
O
The start time point of predictive validity. If omitted, the default value is the current time.
> End time
M
The end time point of predictive validity.
Reporting requirements
O
It describes the requirements for analytics reporting. This requirement may include e.g. the type and frequency of reporting (periodic or event triggered), the reporting periodicity in case of periodic, and reporting thresholds.
|
20479eb37624e17cc85fa37e0dbf82f7
|
23.436
|
8.3.3.3 Slice-specific performance analytics subscription response
|
Table 8.3.3.3-1 describes information elements for the slice-specific performance analytics subscription response from the ADAE server to the consumer (VAL/NSCE server).
Table 8.3.3.3-1: Slice-specific performance analytics subscription response
Information element
Status
Description
Result
M
The result of the analytics subscription request (positive or negative acknowledgement).
|
20479eb37624e17cc85fa37e0dbf82f7
|
23.436
|
8.3.3.4 Slice-specific performance analytics notification
|
Table 8.3.3.4-1 describes information elements for the slice-specific performance analytics notification from the ADAE server to the Consumer.
Table 8.3.3.4-1: Slice-specific performance analytics notification
Information element
Status
Description
Analytics ID
O
The identifier of the analytics event. This ID can be for example “slice-specific application performance analytics”.
Analytics Output
M
The predictive or statistical parameter on performance for the target VAL application/server, with the target slice or slice instance (e.g. the min/average/max predicted RTT / end to end latency for the VAL application/server if this server uses a given slice/NSI (or for a list of given slices) in the area of interest).
Confidence level
O
(NOTE)
For predictive analytics, the achieved confidence level.
Time horizon
O
(NOTE)
The time horizon for predictive analytics.
> Start time
O
The start time point of predictive validity. If omitted, the default value is the current time.
> End time
M
The end time point of predictive validity.
NOTE: These information elements shall be provided for the predictive analytics.
|
20479eb37624e17cc85fa37e0dbf82f7
|
23.436
|
8.4 Procedure on support for UE-to-UE application performance analytics
| |
20479eb37624e17cc85fa37e0dbf82f7
|
23.436
|
8.4.1 General
|
This clause describes the procedure for supporting UE-to-UE application performance analytics.
|
20479eb37624e17cc85fa37e0dbf82f7
|
23.436
|
8.4.2 Procedure
|
Figure 8.4.2-1 illustrates the procedure where the VAL session performance analytics are performed based on data collected from the ongoing VAL UE-to-UE sessions.
Pre-conditions:
1. ADAECs are connected to ADAES.
Figure 8.4.2-1: ADAES support for UE-to-UE application performance analytics
1. The consumer of the ADAES analytics service sends a subscription request to ADAES and provides the analytics event ID e.g. "UE-to-UE session performance analytics", the target VAL UE ID or group of UE IDs, the VAL service ID, the time validity and area of the request, the required confidence level, exposure level for providing UE to UE analytics. Such request can also include whether the analytics notification shall be periodic or based on an expected application QoS change (in that case also the thresholds can be provided at the request)
2. The ADAES sends a subscription response as an ACK to the consumer.
3. The ADAES selects the corresponding ADAEC #1 of the VAL UE 1 where the session performance analytics need to be performed. Such UE can be for example a capable and authorized UE from the involved VAL UE(s) within the service or group, e.g. a group lead.
4. The ADAES sends a UE-to-UE analytics request to the ADAEC #1 with the analytics ID e.g. "UE-to-UE analytics" and the configuration of the reporting required (e.g., periodic, event triggered based on threshold(s)). Such request also includes the application QoS attributes to be analyzed (latency, bitrate, jitter, application layer PER). A session starts between the VAL UE #1 and a VAL UE #2 (or more VAL UEs).
5. The ADAEC #1 starts collecting data from the corresponding VAL UE(s) based on the request. Such data can be about the latency, throughput, jitter, QoE measurements, PQI load, etc. The data can be collected by ADAEC #1 from other ADAECs via ADAE-C interface, or from the VAL clients (VAL client to VAL client interaction is out of scope).
6. The ADAEC either detects or predicts an application QoS change (depending on the authorization of ADAEC to perform analytics). Such change can be for example an application QoS downgrade related to the UE-to-UE session latency, or the application layer PER/channel losses higher than a predefined threshold, for a given time horizon with a certain confidence level.
7. The ADAEC sends the analytics to the ADAES in a UE-to-UE analytics response message.
8. The ADAES based on the received response, confirms/verifies the analytics received or provides analytics (in case that data were reported) for the UE-to-UE session. Such analytics can be about predicting the application QoS change for the UE-to-UE session.
9. The ADAES sends the derived analytics notification to the consumer.
NOTE: The mechanism for analytics collection from the UE side (steps 4, 7) shall align with the SA4 mechanism for generic data collection from the UE (TS 26.531 [3]).
|
20479eb37624e17cc85fa37e0dbf82f7
|
23.436
|
8.4.3 Information flows
| |
20479eb37624e17cc85fa37e0dbf82f7
|
23.436
|
8.4.3.1 General
|
The following information flows are specified for UE-to-UE session performance analytics based on 8.4.2
|
20479eb37624e17cc85fa37e0dbf82f7
|
23.436
|
8.4.3.2 UE-to-UE session performance analytics subscription request
|
Table 8.4.3.2-1 describes information elements for the UE-to-UE session performance analytics subscription request from the consumer (VAL server) to the ADAE server.
Table 8.4.3.2-1: UE-to-UE session performance analytics subscription request
Information element
Status
Description
VAL server ID
M
The identifier of the analytics consumer (VAL server).
Analytics ID
O
The identifier of the analytics event. This ID can be equivalent to “UE-to-UE session performance analytics”.
Analytics type
M
The type of analytics for the event, e.g. statistics or predictions.
Analyitcs category
M
The category of analytics for the event, e.g. performance change, performance sustainability for given QoS parameters (e.g., latency, PER, bitrate, jitter), or both.
VAL UE ID(s) and address(es)
M
The VAL UE identifier(s) and IP address(es) for which the analytics subscription applies.
VAL service ID
O
The identifier of the VAL service for which the subscription applies.
Preferred confidence level
O
The required level of accuracy for the analytics service (in case of prediction).
Area of Interest
O
The geographical or service area for which the subscription request applies.
Time validity
O
The time validity of the subscription request.
Exposure level requirement
O
The level of exposure requirement (e.g. condition on providing the analytics like threshold is reached) for the analytics to be exposed.
Reporting requirements
O
It describes the requirements for analytics reporting. This requirement may include e.g. the type and frequency of reporting (periodic or event triggered (e.g. based on an expected application QoS change) with the reporting granularity (e.g. individual session or group of sessions), the reporting periodicity in case of periodic, and reporting thresholds in case of event triggered.
|
20479eb37624e17cc85fa37e0dbf82f7
|
23.436
|
8.4.3.3 UE-to-UE session performance analytics subscription response
|
Table 8.4.3.3-1 describes information elements for the UE-to-UE session performance analytics subscription response from the ADAE server to the VAL server.
Table 8.4.3.3-1: UE-to-UE session performance analytics subscription response
Information element
Status
Description
Result
M
The result of the analytics subscription request (positive or negative acknowledgement).
|
20479eb37624e17cc85fa37e0dbf82f7
|
23.436
|
8.4.3.4 UE-to-UE analytics request
|
Table 8.4.3.4-1 describes information elements for the UE-to-UE analytics request from the ADAE server to the ADAE client.
Table 8.4.3.4-1: UE-to-UE analytics request
Information element
Status
Description
ADAE server ID
M
The identifier of the ADAE server.
Analytics ID
O
The identifier of the analytics event (Analytics ID= UE-to-UE analytics’).
VAL UE ID(s) and address(es)
M
The VAL UE identifier(s) and IP address(es) for which the data/analytics apply.
Application QoS attributes
M
The QoS attributes (latency, bitrate, jitter, application layer PER) to be analyzed at the ADAE client.
Reporting configuration
O
The configuration for analytics reporting. This requirement may include e.g. the frequency of reporting (periodic or event triggered), the reporting periodicity in case of periodic, and reporting thresholds in case of event triggered, whether data abstraction is needed or not.
Data collection requirements
O
The requirements for data collection, including the format of data, frequency of reporting, level of abstraction of data, level of accuracy of data.
Area of Interest
O
The geographical or service area for which the request applies.
Time validity
O
The time validity of the request.
|
20479eb37624e17cc85fa37e0dbf82f7
|
23.436
|
8.4.3.5 UE-to-UE analytics response
|
Table 8.4.3.5-1 describes information elements for the UE-to-UE analytics response from the ADAE client to the ADAE server.
Table 8.4.3.5-1: UE-to-UE analytics response
Information element
Status
Description
Analytics ID
M
The identifier of the analytics event.
VAL UE ID(s) and address(es)
M
The VAL UE identifier(s) and IP address(es) for which the analytics apply.
Analytics Output
M
The reported analytics for the UE to UE sessions, which can be in form of offline stats/historical data or predictions on the requested QoS parameter based on the analytics event.
|
20479eb37624e17cc85fa37e0dbf82f7
|
23.436
|
8.4.3.6 ADAE Analytics Notification
|
Table 8.4.3.6-1 describes information elements for the ADAE Analytics Notification from the ADAE server to the consumer (VAL server).
Table 8.4.3.6-1: ADAE Analytics notification
Information element
Status
Description
Analytics ID
O
The identifier of the analytics event. This ID can be “UE-to-UE session performance analytics”.
Analytics Output
M
The analytics outputs, which can be predictive or statistical parameter.
> Performance change
O
(NOTE)
A VAL UE to UE session predicted or expected performance change.
>> Time for change
M
The predicted or expected time when the performance change happens.
>> Confidence level
O
The achieved confidence level for the predictive analytics.
> Performance sustainability
O
(NOTE)
A VAL UE to UE session performance sustainability over a given time horizon/area.
>> Time horizon
M
The time horizon for predictive analytics.
>>> Start time
O
The start time point of predictive validity. If omitted, the default value is the current time.
>>> End time
M
The end time point of predictive validity.
>> Applicable area
M
The service area or geographical area for which the analytics output applies to.
>> Confidence level
O
For predictive analytics, the achieved confidence level can be provided.
NOTE: At least one of the IEs shall be present based on the Analytics category IE provided in the subscription request.
|
20479eb37624e17cc85fa37e0dbf82f7
|
23.436
|
8.5 Procedure on support for location accuracy analytics
| |
20479eb37624e17cc85fa37e0dbf82f7
|
23.436
|
8.5.1 General
|
This clause describes the procedure for supporting location accuracy analytics.
|
20479eb37624e17cc85fa37e0dbf82f7
|
23.436
|
8.5.2 Procedure
|
Figure 8.5.2-1 illustrates the procedure for location accuracy analytics enablement solution.
Pre-conditions:
1. ADAES is connected to A-ADRF.
2. ADAES has discovered SEAL LMS or FLS.
Figure 8.5.2-1: Location accuracy analytics procedure
1. The VAL server makes a subscription request to ADAE server for location accuracy prediction/stats, including an analytics event ID (e.g. "location accuracy prediction" or "location accuracy sustainability"), an analytics request type (if not identified specifically at the event ID) which can be the location accuracy prediction for a given location X and/or for a given UE/app. The request may include also the target area, a target VAL service, or a VAL UE, or group of UEs of the VAL service, time validity, accuracy threshold and requirements. If the VAL UEs are provided by the VAL server, this request may also include the expected route or a set of waypoints for the UEs of the VAL application.
2. The ADAE server sends a location accuracy analytics subscription response as an ACK to the VAL server.
3. The ADAE server discovers and maps the Data Sources with the respective analytics event ID for collecting location data for the corresponding VAL UEs or VAL service area.
4. The ADAE server subscribes for NWDAF UE mobility analytics per VAL UE (for all the VAL UEs) and gets notification on the per UE location/mobility analytics based on TS 23.288 clause 6.7.2. Such analytics may be requested for a list of waypoints per UE route (if indicated at step 1). The ADAE server subscribes also for SEAL LMS location reports for the respective VAL UEs or location reports from all VAL UEs within the requested area.
5. The ADAE server optionally requests location accuracy historical analytics /data from A-ADRF for the corresponding VAL UEs or VAL service area.
6. Based on the request, the ADAE server receives location accuracy historical analytics /data from A-ADRF for the corresponding VAL UEs or VAL service area.
7. The ADAE server abstracts or correlates the data/analytics from steps 4-6 and provides analytics on the location accuracy for the target VAL application. Depending on the event ID in step 1, the ADAE server can indicate whether the location accuracy is sustainable or is predicted to be downgraded or can be upgraded and become more granular (e.g. from meter to decimetre).
8. The ADAE server sends the location accuracy analytics notification to the consumer.
|
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