User plane function (UPF) load balancing based on network data analytics to predict load of user equipment
Embodiments are directed towards systems and methods for user plane function (UPF) and network slice load balancing within a 5G network. Example embodiments include systems and methods for load balancing based on current UPF load and thresholds that depend on UPF capacity; UPF load balancing using predicted throughput of new UE on the network based on network data analytics; UPF load balancing based on special considerations for low latency traffic; UPF load balancing supporting multiple slices, maintaining several load-thresholds for each UPF and each slice depending on the UPF and network slice capacity; and UPF load balancing using predicted central processing unit (CPU) utilization and/or predicted memory utilization of new UE on the network based on network data analytics.
1. A system, comprising:
a memory that stores computer instructions; and
a processor that executes the computer instructions to perform actions, the actions including:
maintaining a plurality of load thresholds for each user plane function (UPF) of a plurality of UPFs in a cellular telecommunication network, wherein:
the plurality of UPFs serve as anchor points between user equipment (UE) in the cellular telecommunication network and a data network (DN);
each UPF of the plurality of UPFs is a virtual network function responsible for interconnecting packet data unit (PDU) sessions between the user equipment (UE) and the DN by anchoring the PDU sessions on individual UPFs; and
the plurality of load thresholds for each UPF depend on a respective capacity of each UPF to have PDU sessions anchored thereon;
receiving a request to anchor on a UPF a PDU session of a new UE newly appearing on the cellular telecommunication network;
selecting a UPF of the plurality of UPFs on which to anchor the PDU session based on predicted throughput of the new UE based on network data analytics; and
anchoring the PDU session of the new UE to the selected UPF.
2. The system of claim 1 , wherein selecting a UPF of the plurality of UPFs on which to anchor the PDU session based on predicted throughput based on network data analytics includes:
using the network data analytics to predict throughput of the UE and load on a UPF of the new UE appearing on the cellular telecommunication network based on the predicted throughput; and
selecting a UPF of the plurality of UPFs on which to anchor the PDU session based on the predicted load of the new UE on a UPF.
3. The system of claim 2 , wherein the network data analytics is provided via a network data analytics function (NWDAF) of a 5 th generation (5G) mobile network of which the cellular telecommunication network is comprised.
4. The system of claim 2 , wherein the using the network data analytics to predict throughput of the new UE and load on a UPF of the new UE appearing on the cellular telecommunication network includes:
using artificial intelligence (AI) or machine learning (ML) algorithms to perform predictive analysis of throughput of the new UE and resulting load on a UPF of the new UE appearing on the cellular telecommunication network based on historical activity of the new UE appearing on the cellular telecommunication network.
5. The system of claim 2 , wherein the selecting a UPF of the plurality of UPFs includes:
weighting selection of a particular UPF of the plurality of UPFs based on the predicted throughput of the new UE and resulting predicted load on a UPF of the new UE by using credit/token-based weighted scheduling or probability-based weighted scheduling.
6. The system of claim 5 , wherein the weighting selection of a particular UPF based on the predicted throughput of the new UE and predicted load on a UPF of the new UE includes:
weighting selection of the particular UPF to not overload other UPFs of the plurality of UPFs as compared to the particular UPF in response to the predicted load being at a particular level.
7. The system of claim 5 , wherein the weighting selection of a particular UPF based on the predicted throughput of the new UE and predicted load on a UPF of the new UE includes:
weighting selection of the particular UPF in the plurality of UPFs to not overload the particular UPF beyond a threshold amount compared to other UPFs in the plurality of UPFs based on the predicted load by using credit/token-based weighted scheduling or probability-based weighted scheduling based on the predicted load.
8. A method, comprising:
electronically maintaining a plurality of load thresholds for each user plane function (UPF) of a plurality of UPFs in a cellular telecommunication network, wherein:
the plurality of UPFs serve as anchor points between user equipment (UE) in the cellular telecommunication network and a data network (DN);
each UPF of the plurality of UPFs is a virtual network function responsible for interconnecting packet data unit (PDU) sessions between the user equipment (UE) and the DN by anchoring the PDU sessions on individual UPFs; and
the plurality of load thresholds for each UPF depend on a respective capacity of each UPF to have PDU sessions anchored thereon;
electronically receiving a request to anchor on a UPF a PDU session of a new UE newly appearing on the cellular telecommunication network;
electronically selecting a UPF of the plurality of UPFs on which to anchor the PDU session based on predicted throughput of the new UE based on network data analytics; and
electronically anchoring the PDU session of the new UE to the selected UPF.
9. The method of claim 8 , wherein selecting a UPF of the plurality of UPFs on which to anchor the PDU session based on predicted throughput based on network data analytics includes:
using the network data analytics to predict throughput of the UE and load on a UPF of the new UE appearing on the cellular telecommunication network based on the predicted throughput; and
selecting a UPF of the plurality of UPFs on which to anchor the PDU session based on the predicted load of the new UE on a UPF.
10. The method of claim 9 , wherein the network data analytics is provided via a network data analytics function (NWDAF) of a 5 th generation (5G) mobile network of which the cellular telecommunication network is comprised.
11. The method of claim 9 , wherein the using the network data analytics to predict throughput of the new UE and load on a UPF of the new UE appearing on the cellular telecommunication network includes:
using artificial intelligence (AI) or machine learning (ML) algorithms to perform predictive analysis of throughput of the new UE and resulting load on a UPF of the new UE appearing on the cellular telecommunication network based on historical activity of the new UE appearing on the cellular telecommunication network.
12. The method of claim 9 , wherein the selecting a UPF of the plurality of UPFs includes:
weighting selection of a particular UPF of the plurality of UPFs based on the predicted throughput of the new UE and resulting predicted load on a UPF of the new UE by using credit/token-based weighted scheduling or probability-based weighted scheduling.
13. The method of claim 12 , wherein the weighting selection of a particular UPF based on the predicted throughput of the new UE and predicted load on a UPF of the new UE includes:
weighting selection of the particular UPF to not overload other UPFs of the plurality of UPFs as compared to the particular UPF in response to the predicted load being at a particular level.
14. The method of claim 12 , wherein the weighting selection of a particular UPF based on the predicted throughput of the new UE and predicted load on a UPF of the new UE includes:
weighting selection of the particular UPF in the plurality of UPFs to not overload the particular UPF beyond a threshold amount compared to other UPFs in the plurality of UPFs based on the predicted load by using credit/token-based weighted scheduling or probability-based weighted scheduling based on the predicted load.
15. A non-transitory computer-readable storage medium having computer-executable instructions stored thereon that, when executed by at least one computer processor, cause actions to be performed including:
maintaining a plurality of load thresholds for each user plane function (UPF) of a plurality of UPFs in a cellular telecommunication network, wherein:
the plurality of UPFs serve as anchor points between user equipment (UE) in the cellular telecommunication network and a data network (DN);
each UPF of the plurality of UPFs is a virtual network function responsible for interconnecting packet data unit (PDU) sessions between the user equipment (UE) and the DN by anchoring the PDU sessions on individual UPFs; and
the plurality of load thresholds for each UPF depend on a respective capacity of each UPF to have PDU sessions anchored thereon;
receiving a request to anchor on a UPF a PDU session of a new UE newly appearing on the cellular telecommunication network;
selecting a UPF of the plurality of UPFs on which to anchor the PDU session based on predicted throughput of the new UE based on network data analytics; and
anchoring the PDU session of the new UE to the selected UPF.
16. The non-transitory computer-readable storage medium of claim 15 , wherein selecting a UPF of the plurality of UPFs on which to anchor the PDU session based on predicted throughput based on network data analytics includes:
using the network data analytics to predict throughput of the UE and load on a UPF of the new UE appearing on the cellular telecommunication network based on the predicted throughput; and
selecting a UPF of the plurality of UPFs on which to anchor the PDU session based on the predicted load of the new UE on a UPF.
17. The non-transitory computer-readable storage medium of claim 16 , wherein the network data analytics is provided via a network data analytics function (NWDAF) of a 5 th generation (5G) mobile network of which the cellular telecommunication network is comprised.
18. The non-transitory computer-readable storage medium of claim 16 , wherein the using the network data analytics to predict throughput of the new UE and load on a UPF of the new UE appearing on the cellular telecommunication network includes:
using artificial intelligence (AI) or machine learning (ML) algorithms to perform predictive analysis of throughput of the new UE and resulting load on a UPF of the new UE appearing on the cellular telecommunication network based on historical activity of the new UE appearing on the cellular telecommunication network.
19. The non-transitory computer-readable storage medium of claim 16 , wherein the selecting a UPF of the plurality of UPFs includes:
weighting selection of a particular UPF of the plurality of UPFs based on the predicted throughput of the new UE and resulting predicted load on a UPF of the new UE by using credit/token-based weighted scheduling or probability-based weighted scheduling.
20. The non-transitory computer-readable storage medium of claim 19 wherein the weighting selection of a particular UPF based on the predicted throughput of the new UE and predicted load on a UPF of the new UE includes:
weighting selection of the particular UPF to not overload other UPFs of the plurality of UPFs as compared to the particular UPF in response to the predicted load being at a particular level.