IP Library Granted Patent US 11,689,968
Granted Patent B2
US 11,689,968 · App. 17/184,060 · Granted Jun 27, 2023

Method and apparatus for executing virtualized network function

Inventors: Seonjun Park (Suwon-si, KR); Youngsun Seo (Suwon-si, KR); Byonggon Chun (Suwon-si, KR); Jihun Ha (Suwon-si, KR)
Assignee: SAMSUNG ELECTRONICS CO., LTD.
H04W28/12G06F9/45558H04L43/0882G06F2009/45595
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Quick Facts
Patent No.
US 11,689,968
App. No.
17/184,060
Granted
Jun 27, 2023
Kind
B2
Abstract

Provided is a technology for executing a virtualized network function in a wireless communication system. According to an embodiment of the disclosure, a server may obtain traffic processing information and mobile edge computing (MEC) service usage information regarding user equipments (UEs) that have accessed a plurality of base stations connected to the server, obtain, based on the traffic processing information and the MEC service usage information, information about traffic to occur due to UEs accessing the plurality of base stations and MEC services to be used by the UEs, and determine, based on the obtained information about the traffic and usage of the MEC services, at least one hardware component on which a software component virtualizing a network function in the server is to be executed.

Claims (54)

1. A method, performed by a server, of executing a virtualized network function in a wireless communication system, the method comprising:

obtaining traffic processing information and mobile edge computing (MEC) service usage information regarding a plurality of user equipments (UEs) that have accessed to the server via a plurality of base stations, wherein the traffic processing information includes traffic information indicating an amount of traffic occurred on a virtualized random access network, vRAN, function and resource information indicating physical resources including hardware components used for processing the traffic;

identifying an amount of MEC service usage based on the traffic processing information and the MEC service usage information;

predicting an amount of traffic to occur and an amount of MEC service usage to occur based on the amount of the traffic occurred on the vRAN function and the amount of the MEC service usage; and

determining, based on the predicted amount of the traffic and the predicted amount of the MEC service usage, a type of the physical resource on which each of a software component for the vRAN function and an MEC function in the server is to be executed.

2. The method of claim 1 , wherein identifying the amount of the MEC service usage comprises:

identifying, based on the traffic processing information and the MEC service usage information, the amount of MEC service usage for each preset time unit.

3. The method of claim 1 , wherein the determining the type of the physical resource comprises:

comparing each of the amount of traffic and the amount of MEC service usage predicted to occur at particular time point with at least one preset threshold; and

determining, based on a result of the comparing, the type of the physical resources on which each of the software component for the vRAN function and the MEC function is to be executed.

4. The method of claim 3 , wherein the determining the type of the physical resources comprises:

based on a same hardware component beings determined for each of the vRAN function and the MEC function, determining priorities of the vRAN function and the MEC function based on required performance in the wireless communication system; and

determining, based on the determined priorities, the type of the physical resource on which the vRAN function or the MEC function is to be executed.

5. The method of claim 4 , further comprising, when a hardware component determined for each of the vRAN function and the MEC function is a virtualized hardware component, determining, based on the priorities, a ratio of the hardware component in which each of the vRAN function or the MEC function is to be executed.

6. The method of claim 1 , wherein the determining the type of the physical resources comprises:

Based on at least one of an amount of traffic or an amount of MEC service usage predicted to occur being in a first range, determining a hardware component on which the software component is to be executed as a graphics processing unit (GPU); and

based on at least one of the amount of traffic or the amount of MEC service usage predicted to occur being in a second range, determining a hardware component on which the software component is to be executed as a field programmable gate array (FPGA).

7. The method of claim 1 , further comprising, when at least one of the amount of traffic or the amount of MEC service usage predicted to occur at a particular time point is identified to exceed a threshold, performing hardware offloading before the particular time point so that the software component executed on a first hardware component is to be executed on a second hardware component.

8. The method of claim 1 , further comprising obtaining information about a plurality of events causing a change in the traffic or the MEC service usage, predicted to occur,

wherein predicting the traffic to occur and the MEC service usage to occur comprises:

identifying an amount of traffic and an amount of MEC service usage previously predicted for each event; and

determining, based on the identified amount of traffic and amount of MEC service usage previously predicted for each event, an amount of traffic and an amount of MEC service usage to occur for one of the events.

9. The method of claim 1 , wherein the hardware components includes at least one of a central processing unit (CPU), a GPU, an FPGA, or a network interface controller (NIC).

10. A server for executing a virtualized network function in a wireless communication system, the server comprising:

a memory storing one or more instructions; and

at least one processor configured to execute the one or more instructions stored in the memory to:

obtain traffic processing information and mobile edge computing (MEC) service usage information regarding user equipments (UEs) that have accessed to the server via a plurality of base stations, wherein the traffic processing information includes traffic information indicating an amount of traffic occurred on a virtualized random access network, vRAN, function and resource information indicating physical resources including hardware components used for processing the traffic,

identify an amount of MEC service usage based on the traffic processing information and the MEC service usage information,

predict, an amount of traffic to occur and an amount of MEC service usage to occur based on the amount of the traffic occurred on the vRAN function and the amount of the MEC service usage, and

determine, based on the predicted amount of the traffic and the predicted amount of the MEC service usage, a type of the physical resources on which each of a software component for the vRAN function and an MEC function in the server is to be executed.

11. The server of claim 10 , wherein the at least one processor is further configured to execute the one or more instructions to:

identify, based on the traffic processing information and the MEC service usage information, the amount of MEC service usage for each preset time unit.

12. The server of claim 10 , wherein the at least one processor is further configured to execute the one or more instructions to:

compare each of the amount of traffic and the amount of MEC service usage predicted to occur at a particular time point with at least one preset threshold; and

determine, based on a result of the comparing, the type of the physical resources on which each of the software component for the vRAN function and the MEC function is to be executed.

13. The server of claim 12 , wherein the at least one processor is further configured to execute the one or more instructions to:

when a same hardware component is determined for each of the vRAN function and the MEC function, determine priorities of the vRAN function and the MEC function based on required performance in the wireless communication system; and

determine, based on the determined priorities, the type of the physical resource on which the vRAN function or the MEC function is to be executed.

14. The server of claim 13 , wherein the at least one processor is further configured to execute the one or more instructions to, when a hardware component determined for each of the vRAN function and the MEC function is a virtualized hardware component, determining, based on the priorities, a ratio of the hardware component in which each of the vRAN function or the MEC function is to be executed.

15. The server of claim 10 , wherein the at least one processor is further configured to execute the one or more instructions to:

based on at least one of an amount of traffic or an amount of MEC service usage predicted to occur being in a first range, determine a hardware component on which the software component is to be executed as a graphics processing unit (GPU); and

based on at least one of the amount of traffic or the amount of MEC service usage predicted to occur being in a second range, determine a hardware component on which the software component is to be executed as a field programmable gate array (FPGA).

16. The server of claim 10 , wherein the at least one processor is further configured to execute the one or more instructions to, when at least one of the amount of traffic or the amount of MEC service usage predicted to occur at a particular time point is identified to exceed a threshold, perform hardware offloading before the particular time point so that the software component executed on a first hardware component is executed on a second hardware component.

17. The server of claim 10 , wherein the at least one processor is further configured to execute the one or more instructions to:

obtain information about a plurality of events causing a change in the traffic or the MEC service usage, which is predicted to occur;

identify an amount of traffic and an amount of MEC service usage previously generated for each event; and

determine, based on the identified amount of traffic and amount of MEC service usage previously generated for each event, an amount of traffic and an amount of MEC service usage predicted to occur for one of the events.

18. The server of claim 10 , wherein the hardware components include at least one of a central processing unit (CPU), a GPU, an FPGA, or a network interface controller (NIC).

19. A computer program product comprising a non-transitory computer-readable recording medium having stored therein a program which causes a server to perform a method of executing a virtualized network function, the method comprising:

obtaining traffic processing information and mobile edge computing (MEC) service usage information regarding a plurality of user equipments (UEs) that have accessed to the server via a plurality of base stations, wherein the traffic processing information includes traffic information indicating an amount of traffic occurred on a virtualized random access network, vRAN, function and resource information indicating physical resources including hardware components used for processing the traffic,

identifying an amount of MEC service usage based on the traffic processing information and the MEC service usage information;

predicting an amount of traffic to occur and an amount of MEC service usage to occur based on the amount of the traffic occurred on the vRAN function and the amount of the MEC service usage; and

determining, based on the predicted amount of the traffic and the predicted amount of the MEC service usage a type of the physical resources on which each of a software component for the vRAN function and an MEC function

in the server is to be executed.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 24, 2021
From: PARK, SEONJUN; SEO, YOUNGSUN; CHUN, BYONGGON; HA, JIHUN
To: SAMSUNG ELECTRONICS CO., LTD.
Reel/Frame 055393/0882 →
Priority Claims (1)
KR 10-2020-0023847 · Feb 26, 2020 · national
Continuity (1)
Related Publication 20210266792A1 · Aug 26, 2021
Cited By (1)
US 12,439,283