5G Service-Based Architecture (SBA) Communications Based on Machine Learning

Abstract

A method, system, and storage medium are provided. The method may include receiving, by a network repository function (NRF), registration information for each of a plurality of network functions (NFs), determining, by the NRF, load balancing prioritization information for the plurality of NFs based on the received registration information, and transmitting, by the NRF, the load balancing prioritization information to a service mesh control plane. The service mesh control plane may be configured to perform load balancing for the plurality of NFs based on the load balancing prioritization information.

Description

[Technical Field]

[0001] The present disclosure relates to systems and methods for communication forwarding and control, load balancing, and fault management in a 5G service-based architecture (SBA). [Background technology]

[0002] Fifth-generation (5G) core networks are being standardized by the Third Generation Partnership Project (3GPP) based on a service-based architecture (SBA), which means that each element (e.g., each network function (NF)) provides a standard service and may be called a "producer." Services may be consumed by other NFs, called "consumers." Improved load balancing and fault management in SBA are needed. Summary of the Invention [Means for solving the problem]

[0003] According to aspects of the present disclosure, a method may include receiving, by a network repository function (NRF), registration information for each of a plurality of network functions (NFs), determining, by the NRF, load balance prioritization information for the plurality of NFs based on the received registration information, and transmitting, by the NRF, the load balance prioritization information to a service mesh control plane. The service mesh control plane may be configured to perform load balancing for the plurality of NFs based on the load balance prioritization information.

[0004] According to aspects of the present disclosure, a system may include a plurality of NFs, an NRF, and a service mesh control plane. The NRF may be configured to receive registration information for each of the plurality of NFs, determine load balancing prioritization information for the plurality of NFs based on the received registration information, and transmit the load balancing prioritization information to the service mesh control plane. The service mesh control plane may be configured to perform load balancing for the plurality of NFs based on the load balancing prioritization information.

[0005] According to aspects of the present disclosure, a non-transitory computer-readable storage medium may be coupled to one or more processors and may store instructions that, when executed, cause the one or more processors to receive, by an NRF, registration information for each of a plurality of NFs, determine, by the NRF, load balancing prioritization information for the plurality of NFs based on the received registration information, and transmit, by the NRF, the load balancing prioritization information to a service mesh control plane or service communication proxy (SCP). The service mesh control plane or SCP may be configured with machine learning to perform communication message forwarding and control and load balancing for the plurality of NFs based on the load balancing prioritization information.

[0006] Additional aspects will be set forth in part in the description that follows, and in part will be obvious from the description, or may be learned by practice of the presented embodiments of the present disclosure. [Brief explanation of the drawings]

[0007] These and other aspects, features, and aspects of embodiments of the present disclosure will become apparent from the following description taken in conjunction with the accompanying drawings.

[0008] [Figure 1] FIG. 1 is a diagram of a device of a system according to an embodiment. [Figure 2] FIG. 2 is a diagram of components of the device of FIG. 1 according to an embodiment. [Figure 3]FIG. 1 is a diagram of a fifth generation (5G) core 3rd Generation Partnership Project (3GPP®) system architecture according to an embodiment. [Figure 4] Diagram of 5G core 3GPP service-based architecture (SBA). [Figure 5] Diagram of 5G core 3GPP SBA integrated with service mesh. [Figure 6] FIG. 1 is a diagram of a 5G core 3GPP SBA with an integrated service mesh, according to an embodiment. [Figure 7] FIG. 1 illustrates an example implementation of a service mesh control plane with a network repository function (NRF), according to an embodiment. [Figure 8] FIG. 1 is a diagram of an example machine learning (ML) implementation of a service mesh control plane with an NRF, according to an embodiment. [Figure 9] FIG. 1 is a diagram of a 5G core 3GPP SBA with an integrated service mesh, according to an embodiment. [Figure 10] 1 is a flowchart of a method for communication forwarding and control and load balancing in an SBA according to an embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0009] The following detailed description of the exemplary embodiments refers to the accompanying drawings, in which the same reference numbers in different drawings may identify the same or similar elements.

[0010] Figure 1 is a diagram of a system according to an embodiment, including a user device 110, a server device 120, and a network 130. The user device 110 and the server device 120 may be interconnected via a wired connection, a wireless connection, or a combination of wired and wireless connections.

[0011] The user device 110 may include a computing device (e.g., a desktop computer, a laptop computer, a tablet computer, a handheld computer, a smart speaker, a server device, etc.), a mobile phone (e.g., a smartphone, a wireless phone, etc.), a camera device, a wearable device (e.g., smart glasses or a smart watch), or a similar device.

[0012] Server device 120 may include one or more devices. For example, server device 120 may be a server device, a computing device, etc.

[0013] Network 130 may include one or more wired and / or wireless networks. For example, network 130 may include a cellular network (e.g., a fifth-generation (5G) network, a long-term evolution (LTE) network, a third-generation (3G) network, a code division multiple access (CDMA) network, etc.), a public land mobile network (PLMN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a telephone network (e.g., a public switched telephone network (PSTN)), a private network, an ad hoc network, an intranet, the Internet, an optical fiber-based network, etc., and / or a combination of these or other types of networks.

[0014] According to an embodiment, client device 110 may correspond to user equipment (UE), network 130 may correspond to a radio access network (RAN), and server device 120 may correspond to a core network (e.g., a 5G core network) for a telecommunications system (or one or more devices of the core network).

[0015] The number and arrangement of devices and networks shown in Figure 1 are provided as an example. In practice, there may be additional, fewer, different, or differently arranged devices and / or networks. Furthermore, two or more devices shown in Figure 1 may be implemented within a single device, or a single device shown in Figure 1 may be implemented as multiple distributed devices. Additionally or alternatively, a set of devices (e.g., one or more devices) may perform one or more functions described as being performed by another set of devices.

[0016] 2 is a diagram of components of one or more devices of FIG. 1 according to an embodiment. Device 200 may correspond to user device 110 and / or server device 120.

[0017] As shown in FIG. 2, device 200 may include a bus 210, a processor 220, a memory 230, a storage component 240, an input component 250, an output component 260, and a communication interface 270.

[0018] Bus 210 includes components that enable communication between components of device 200. Processor 220 is implemented in hardware, firmware, or a combination of hardware and software. Processor 220 may be a central processing unit (CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), a microprocessor, a microcontroller, a digital signal processor (DSP), a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), or another type of processing component. Processor 220 includes one or more processors that can be programmed to perform functions.

[0019] Memory 230 may include random access memory (RAM), read-only memory (ROM), and / or another type of dynamic or static storage device (e.g., flash memory, magnetic memory, and / or optical memory) that stores information and / or instructions for use by processor 220.

[0020] Storage component 240 stores information and / or software related to the operation and use of device 200. For example, storage component 240 may include a hard disk (e.g., a magnetic disk, optical disk, magneto-optical disk, and / or solid-state disk), a compact disk (CD), a digital versatile disk (DVD), a floppy disk, a cartridge, a magnetic tape, and / or another type of non-transitory computer-readable medium along with a corresponding drive.

[0021] Input component 250 includes components that enable device 200 to receive information, such as through user input (e.g., a touchscreen display, a keyboard, a keypad, a mouse, a button, a switch, and / or a microphone). Input component 250 may include sensors for sensing information (e.g., a global positioning system (GPS) component, an accelerometer, a gyroscope, and / or an actuator).

[0022] Output components 260 include components that provide output information from device 200 (eg, a display, a speaker, and / or one or more light emitting diodes (LEDs)).

[0023] Communications interface 270 includes transceiver-like components (e.g., a transceiver and / or a separate receiver and transmitter) that enable device 200 to communicate with other devices via a wired connection, a wireless connection, or a combination of wired and wireless connections, etc. Communications interface 270 may enable device 200 to receive information from another device and / or provide information to another device. For example, communications interface 270 may include an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, a radio frequency (RF) interface, a universal serial bus (USB) interface, a Wi-Fi interface, a cellular network interface, etc.

[0024] Device 200 may perform one or more processes described herein. Device 200 may perform operations based on processor 220 executing software instructions stored by a non-transitory computer-readable medium, such as memory 230 and / or storage component 240. A computer-readable medium is defined herein as a non-transitory memory device. A memory device includes memory space within a single physical storage device or memory space across multiple physical storage devices.

[0025] The software instructions may be loaded into memory 230 and / or storage component 240 from another computer-readable medium or from another device via communication interface 270. When executed, the software instructions stored in memory 230 and / or storage component 240 may cause processor 220 to perform one or more processes described herein.

[0026] Additionally or alternatively, hardwired circuitry may be used in place of or in combination with software instructions to perform one or more processes described herein. Thus, the embodiments described herein are not limited to any specific combination of hardware circuitry and software.

[0027] 3 is a diagram of a 5G Core 3G Partnership Project (3GPP) system architecture according to an embodiment. The architecture may include multiple network functions (NFs), such as a Network Slice Selection Function (NSSF) 302 connected via an Nnssf interface, a Network Publication Function (NEF) 304 connected via an Nnef interface, a Network Repository Function (NRF) 306 connected via an Nnrf interface, a Policy Control Function (PCF) 308 connected via an Npcf interface, a Unified Data Management (UDM) Function 310 connected via an Nudm interface, an Application Function (AF) 312 connected via an Naf interface, a Network Slice Specific Authentication and Authorization Function (NSSAAF) 314 connected via an Nnssaaf interface, an Authentication Server Function (AUSF) 316 connected via an Nausf interface, an Access and Mobility Management Function (AMF) 318 connected via an Namf interface, and a Session Management Function (SMF) 320 connected via an Nsmf interface. As will be understood by one of ordinary skill in the art from the disclosure herein, the NFs shown are exemplary and not exclusive, as other NFs may be included in the SBA.

[0028] The architecture may also include a service communication proxy (SCP) 322, which may be configured to intercept communications with other NFs and NRFs and may have standard communications with the NRFs to perform network control, load balancing, and fault detection. The user equipment (UE) 330 may be connected to the AMF 318 by an N1 interface and may generally be connected to the system by an access network such as a radio access network (RAN) 332. The AMF 318 may be connected to the RAN 332 by an N2 interface. The user plane function (UPF) 334 may be connected to the RAN 332 by an N3 interface, to the data network (DN) by an N5 interface, and to the SMF 320 by an N4 interface. The UPF 334 may include an N9 interface.

[0029] 4 is a diagram of a 5G core 3GPP service-based architecture (SBA). The SBA 402 may include multiple NFs, such as an NSSF 404, an AUSF 405, a UDM 406, a PCF 408, a charging function (CHF) 410, a NEF 411, an AMF 412, and an SMF 414. It will be understood by those skilled in the art that the examples disclosed herein are not limited to the specific illustrated NFs, as the SBA may include additional or alternative NFs. The SMF 414 may communicate with a UPF 420, and a UE 422 may communicate with the SBA 402 via a RAN 424. The SBA 402 includes an NRF 416.

[0030] The NRF operates such that each network element or NF within the 5G core SBA registers with the NRF before initiating its respective operation (e.g., the NF sends registration information). The registration may include information about the NF, including capabilities, NF Frequency Domain Name (FQDN) or Internet Protocol (IP) address, NF location, NF priority, NF capacity, NF loading capabilities, NF services, and / or other information as would be understood by one of ordinary skill in the art from the disclosure herein.

[0031] When a consumer NF attempts to use one of the services, it sends an inquiry (e.g., a discovery message) to the NRF regarding which producer NFs are available and can provide the service. The NRF responds with the producer NFs including their FQDNs and / or IP addresses, and the NRF may select a producer NF based on predetermined rules of operator selection, such as capacity, priority, locality, etc. The consumer NF may then initiate communication with the producer NF accordingly. If there are multiple producer NFs, the NRF may provide the consumer NF with a list of available and capable producer NFs, and the consumer NF may select which producer NF to communicate with.

[0032] However, the behavior of a consumer NF may be vendor-dependent. For some vendors, a consumer NF is designated to be responsible for achieving load balancing and distributing traffic among all producer NFs. However, in this case, all NFs must be integrated with this load balancing feature. Furthermore, if any of the producer NFs fails, communication continues to fail until the consumer NF detects the failure. When the consumer NF detects the failure, it sends a message to another NF, which adds more latency to the 5G core network. Furthermore, other vendors may not include any load balancing feature, and communication fails completely without attempting to communicate with another producer NF. Provided herein are systems, methods, and devices that improve communication message forwarding and control, load balancing, and fault management between NFs in an SBA.

[0033] 5 is a diagram of a 5G core 3GPP SBA incorporating a service mesh. The SBA 502 may include multiple NFs, such as an NSSF 504, an AUSF 505, a UDM 506, a PCF 508, a CHF 510, a NEF 511, an AMF 512, and an SMF 514. Those skilled in the art will appreciate that the examples disclosed herein are not limited to the specific illustrated NFs, as the SBA may include additional or alternative NFs. The SMF 514 may communicate with a UPF 520, and a UE 522 may communicate with the SBA 502 via a RAN 524. The SBA 502 includes an NRF 516.

[0034] The SBA 502 in FIG. 5 orchestrates a service mesh, which is a software component within a cloud infrastructure. A service mesh includes two basic architectural components: a data plane, which is a set or mesh of interconnected proxies deployed as a separate proxy or as a sidecar with each service deployment (e.g., each NF) and through which traffic is forwarded; and a control plane 530 configured to control the proxies and provide policies and configurations to all of the running data planes in the mesh. The SBA 502 in FIG. 5 orchestrates the service mesh by modifying or incorporating proxies in each NF and by orchestrating the service mesh control plane 530 to control the operation of proxies across the mesh. Additionally, the service mesh may be configured to perform security encryption, load balancing, and other network management functions. However, the service mesh control plane 530 may perform load balancing between NFs according to the state of the NFs within the cloud infrastructure, regardless of any other factors or parameters provided to the NRF 516, such as capacity, load, priority, etc. For example, the service mesh control plane 530 may not consider any information regarding whether NFs have equal capacity or whether NFs are loaded.

[0035] 6 is a diagram of a 5G core 3GPP SBA 602 with an integrated service mesh according to an embodiment. The SBA 602 may include multiple NFs, such as an NSSF 604, an AUSF 605, a UDM 606, a PCF 608, a CHF 610, a NEF 611, an AMF 612, and an SMF 614. Those skilled in the art will appreciate that the examples disclosed herein are not limited to the specific NFs shown, as the SBA may include additional or alternative NFs. The SMF 614 may communicate with a UPF 620, and a UE 622 may communicate with the SBA 602 via a RAN 624. The SBA 602 includes an NRF 616.

[0036] A service mesh control plane 630 is provided and integrated with the NRF 616 to perform load balancing among NFs. For example, when the NRF 616 receives registration information (e.g., information about the capabilities of NFs, such as capacity, load, priority, etc.), the NRF 616 may perform load balancing prioritization for the NFs and generate load balancing prioritization information based on the registration information. The load balancing prioritization information may include information about priority and instructions about how to load balance among NFs. The NRF 616 may send the load balancing prioritization information to the service mesh control plane 630 so that the service mesh control plane 630 may perform load balancing for the NFs based on the load balancing prioritization information (e.g., via a proxy). When the NRF 616 detects any failure, the NRF 616 may update the load balancing prioritization information and send the updated load balancing prioritization information to the service mesh control plane 630, so that the service mesh control plane 630 may adjust load balancing based on the known failure, thereby avoiding any extra latency in load balancing corrections.

[0037] By integrating the service mesh control plane 630 with the NRF 616, the system may be implemented at reduced cost because the load balancing function is performed by the service mesh control plane 630 and, therefore, vendors of 5G NFs are not required to include load balancing features in their NFs. Furthermore, by offloading load balancing from the NFs to a service mesh integrated with the NRF, the NFs can achieve uniformity in terms of capabilities regardless of vendor. Furthermore, this implementation provides better performance by reducing latency in the event of communication failures. Furthermore, by integrating or communicatively connecting the NRF 616 with the service mesh control plane 630, load balancing is simplified and optimized (it can take into account additional factors known or maintained by the NRF, such as capacity, load, priority, etc.).

[0038] 7 is a diagram of an example implementation of a service mesh control plane integrated with an NRF, according to an embodiment. The system of FIG. 7 may include an NRF 702, an integration component 704, and a service mesh control plane 706 that communicates with the NRF 702 via the integration component 704. The integration component 704 may include a proxy interface 710, an NRF interface 712, a computation engine 714, a user input component 716, and a database 718.

[0039] Once the NRF 706 generates the load balance prioritization information, the NRF 702 provides the load balance prioritization information to the integration component 704 via the NRF interface 712. The calculation engine 714 may receive the load balance prioritization information and may determine load balancing parameters based on the load balance prioritization information. The calculation engine 714 may further determine the load balancing parameters based on information stored in the database 718 as well as settings received from user input via the user input component 716. Once the integration component 704 generates the load balancing parameters, the integration component sends the load balancing parameters to the service mesh control plane 706 via the proxy interface 710 so that load balancing can be performed.

[0040] FIG. 8 is a diagram of an example machine learning (ML) implementation of a service mesh control plane integrated with an NRF, according to an embodiment. Referring to FIG. 8, an SBA may include an NRF 802 and multiple NFs 804-814 as described above. In one embodiment, the NFs 804 and 806 are UDM NFs. At block 820, the system may collect network information from the NRF 802. At block 822, the system may send a state and a reward to an ML agent. At block 824, the ML agent may determine a reward and a Q-value. At block 826, the system may send an action with the highest reward or based on other conditions. At block 828, the system may communicate a control message to a proxy of the NFs 804-814 via an application programming interface (API). Blocks 822, 824, and 826 may be implemented by a compute engine, such as compute engine 714 of FIG. 7.

[0041] The ML agent may control the system to set actions based on the state collected from the environment. The state (S) may refer to information describing the characteristics of the destination NF, such as function name, locality, priority, utilization (i.e., system load exceeding capacity) availability, etc. The reward (R) may represent the quality of the action taken by the ML agent (i.e., whether the action is good or bad). The reward may be a function of the state and the action, and the role of the ML agent may be to select the action that maximizes the reward. The action (A) represents all possible valid actions given the state S.

[0042] FIG. 9 is a diagram of a 5G core 3GPP SBA 902 with an integrated service mesh according to an embodiment. The SBA 902 may include multiple NFs, such as an NSSF 904, an AUSF 905, a UDM 906, a PCF 908, a CHF 910, a NEF 911, an AMF 912, and an SMF 914. Those skilled in the art will appreciate that the examples disclosed herein are not limited to the specific illustrated NFs, as the SBA may include additional or alternative NFs. The SMF 914 may communicate with a UPF 920, and a UE 922 may communicate with the SBA 902 via a RAN 924. The SBA 902 includes an NRF 916, which is integrated with the service mesh control plane 930, as described above. The SBA 902 also includes an SCP 940, which includes a compute engine. The compute engine may include the ML capabilities described above with respect to FIG. 8, which may be implemented as part of the SCP 940.

[0043] FIG. 10 is a flowchart of a method for communication forwarding and control and load balancing in an SBA according to an embodiment. Referring to FIG. 10 , at operation 1002, the system receives, by an NRF, registration information for each of a plurality of NFs. At operation 1004, the system determines, by the NRF, load balance prioritization information for the plurality of NFs based on the received registration information. At operation 1006, the system transmits, by the NRF, the load balance prioritization information to a service mesh control plane or SCP. The service mesh control plane may be configured to perform load balancing for the plurality of NFs based on the load balance prioritization information. The service mesh control plane or SCP may include an integrated ML agent for determining message forwarding actions using machine learning.

[0044] The foregoing disclosure provides illustration and description, but is not intended to be exhaustive or to limit implementations to the precise form disclosed. Modifications and variations are possible in light of the above disclosure or may be acquired from practice of implementations.

[0045] Some embodiments may relate to systems, methods, and / or computer-readable media at any possible level of technical detail. The computer-readable media may include a computer-readable non-transitory storage medium having computer-readable program instructions for causing a processor to perform operations.

[0046] A computer-readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. A non-exhaustive list of more specific examples of computer-readable storage media includes portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital versatile disc (DVD), memory stick, floppy disk, mechanically encoded devices such as punch cards or raised structures with instructions recorded in grooves, and any suitable combination of the above. As used herein, computer-readable storage media should not be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., light pulses passing through a fiber optic cable), or electrical signals transmitted through wires.

[0047] The computer-readable program instructions described herein can be downloaded to each computing / processing device from a computer-readable storage medium or via an external computer or external storage device over a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing / processing device.

[0048] The computer-readable program code / instructions for carrying out operations may be either source code or object code written in any combination of one or more programming languages, including assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, configuration data for integrated circuits, or object-oriented programming languages such as Smalltalk, C++, and procedural programming languages such as the "C" programming language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer, partially on a remote computer, or entirely on a remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry including, for example, a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA) may execute computer-readable program instructions by utilizing state information of the computer-readable program instructions to personalize the electronic circuitry to perform aspects or operations.

[0049] These computer-readable program instructions may be provided to a processor of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus to manufacture a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in one or more blocks of the flowcharts and / or block diagrams. These computer-readable program instructions may also be stored on a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and / or other device to function in a particular manner, such that the computer-readable storage medium having instructions stored thereon includes a product containing instructions that implement aspects of the functions / acts specified in one or more blocks of the flowcharts and / or block diagrams.

[0050] The computer-readable program instructions may also be loaded onto a computer, other programmable apparatus, or other device to cause the computer, other programmable apparatus, or other device to perform a series of operational steps to generate a computer-implemented process, such that the instructions, executing on the computer, other programmable apparatus, or other device, implement the functions / acts specified in one or more blocks of the flowcharts and / or block diagrams.

[0051] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer-readable media according to various embodiments. In this regard, each block in the flowcharts or block diagrams may represent a module, segment, or portion of instructions, including one or more executable instructions for implementing the specified logical function(s). The methods, computer systems, and computer-readable media may include additional, fewer, different, or differently arranged blocks than those shown in the figures. In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example, two blocks shown in succession may actually be executed concurrently or substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending on the functionality involved. It should also be noted that each block in the block diagrams and / or flowchart diagrams, and combinations of blocks in the block diagrams and / or flowchart diagrams, can be implemented by a dedicated hardware-based system that performs the specified functions or operations or executes a combination of dedicated hardware and computer instructions.

[0052] It will be apparent that the systems and / or methods described herein may be implemented in various forms of hardware, firmware, or combinations of hardware and software. The actual specialized control hardware or software code used to implement these systems and / or methods is not intended to limit the implementation. Thus, the operation and behavior of the systems and / or methods are described herein without reference to specific software code, and it will be understood that software and hardware can be designed to implement the systems and / or methods based on the description herein.

[0053] No element, act, or instruction used herein should be construed as critical or essential unless explicitly stated as such. Also, as used herein, the articles "a" and "an" are intended to include one or more items and may be used interchangeably with "one or more." Furthermore, as used herein, the term "set" is intended to include one or more items (e.g., related items, unrelated items, combinations of related and unrelated items, etc.) and may be used interchangeably with "one or more." Where only one item is intended, the term "one" or similar term is used. Also, as used herein, terms such as "has," "have," and "having" are intended to be open-ended terms. Furthermore, the phrase "based on" is intended to mean "at least in part," unless otherwise specified.

[0054] The descriptions of various aspects and embodiments are presented for illustrative purposes but are not intended to be exhaustive or limited to the disclosed embodiments. While combinations of features are recited in the claims and / or disclosed herein, these combinations are not intended to limit the disclosure of possible implementations. Indeed, many of these features may be combined in ways not specifically recited in the claims and / or disclosed in the specification. While each dependent claim listed below may depend directly on only one claim, a disclosure of possible implementations includes each dependent claim in combination with all other claims in the claim set. Many modifications and variations will be apparent to those skilled in the art without departing from the scope of the described embodiments. The terminology used herein has been selected to best explain the principles of the embodiments, practical applications or technical improvements to technology found in the marketplace, or to enable those skilled in the art to understand the embodiments disclosed herein.

Claims

1. receiving, by a Network Repository Function (NRF), registration information for each of a plurality of Network Functions (NFs); determining, by the NRF, load balancing prioritization information for the plurality of NFs based on the received registration information; transmitting the load balancing prioritization information by the NRF to an integration component comprising a machine learning agent; determining, by the integration component, at least one load balancing parameter based on the load balancing prioritization information; sending the at least one load balancing parameter to a service mesh control plane by the integration component, wherein the service mesh control plane is integrated with the NRF; A method comprising: The method, wherein the service mesh control plane is configured to perform load balancing for the plurality of NFs based on the at least one load balancing parameter.

2. 2. The method of claim 1, wherein the plurality of NFs include at least one of an Application Management Function (AMF), a Session Management Function (SMF), a Network Slice Selection Function (NSSF), an Authentication Server Function (AUSF), or a Policy Control Function (PCF).

3. The method of claim 1 , wherein the registration information includes information regarding at least one of an Internet Protocol (IP) address, location, priority, capacity, load, or service of the NF.

4. The method of claim 1 , wherein the service mesh control plane is configured to perform load balancing for the plurality of NFs by a service mesh proxy for each of the plurality of NFs.

5. 1. A system comprising: a plurality of network functions (NFs); a Network Repository Function (NRF); a service mesh control plane; The NRF is receiving registration information for each of a plurality of NFs; determining load balancing prioritization information for the plurality of NFs based on the received registration information; configured to send the load balancing prioritization information to an integration component comprising a machine learning agent; The integration component: determining at least one load balancing parameter based on the load balancing prioritization information; configured to transmit the at least one load balancing parameter to the service mesh control plane, wherein the service mesh control plane is integrated with the NRF; The system, wherein the service mesh control plane is configured to perform load balancing for the plurality of NFs based on the at least one load balancing parameter.

6. 6. The system of claim 5, wherein the plurality of NFs include at least one of an Application Management Function (AMF), a Session Management Function (SMF), a Network Slice Selection Function (NSSF), an Authentication Server Function (AUSF), or a Policy Control Function (PCF).

7. The system of claim 5 , wherein the registration information includes information regarding at least one of an Internet Protocol (IP) address, location, priority, capacity, load, or service of the NF.

8. The system of claim 5 , wherein the service mesh control plane is configured to perform load balancing for the plurality of NFs by a service mesh proxy for each of the plurality of NFs.

9. A computer program that, when executed by one or more processors, causes the one or more processors to: receiving, by a Network Repository Function (NRF), registration information for each of a plurality of Network Functions (NFs); determining, by the NRF, load balancing prioritization information for the plurality of NFs based on the received registration information; causing the NRF to transmit the load balancing prioritization information to an integration component comprising a machine learning agent; causing the integration component to determine at least one load balancing parameter based on the load balancing prioritization information; causing the integration component to transmit the at least one load balancing parameter to a service mesh control plane, wherein the service mesh control plane is integrated with the NRF; A computer program product configured to cause the service mesh control plane to perform a process of performing load balancing on the plurality of NFs based on the at least one load balancing parameter.

10. 10. The computer program product of claim 9, wherein the plurality of NFs include at least one of an Application Management Function (AMF), a Session Management Function (SMF), a Network Slice Selection Function (NSSF), an Authentication Server Function (AUSF), or a Policy Control Function (PCF).

11. 10. The computer program product of claim 9, wherein the registration information includes information regarding at least one of an Internet Protocol (IP) address, location, priority, capacity, load, or service of the NF.

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