FORM 2
THE PATENTS ACT, 1970
(39 OF 1970)
&
THE PATENT RULES, 2003
COMPLETE SPECIFICATION
(See section 10 and rule 13)
“METHOD AND SYSTEM FOR SCALING TARGET NETWORK FUNCTION(S) BASED ON NETWORK LOAD DATA”
We, Jio Platforms Limited, an Indian National, of Office - 101, Saffron, Nr. Centre Point, Panchwati 5 Rasta, Ambawadi, Ahmedabad - 380006, Gujarat, India.
The following specification particularly describes the invention and the manner in which it is to be performed.

5 METHOD AND SYSTEM FOR SCALING TARGET NETWORK
FUNCTION(S) BASED ON NETWORK LOAD DATA
FIELD OF THE DISCLOSURE
10
[0001] Embodiments of the present disclosure generally relate to the field of performance of network systems. More particularly, embodiments of the present disclosure relate to a method and system for scaling target network functions (NFs) based on network load data.
15
BACKGROUND
[0002] The following description of related art is intended to provide background
information pertaining to the field of the disclosure. This section may include
20 certain aspects of the art that may be related to various features of the present
disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.
25 [0003] Wireless communication technology has rapidly evolved over the past few
decades, with each generation bringing significant improvements and advancements. The first generation of wireless communication technology was based on analog technology and offered only voice services. However, with the advent of the second generation (2G) technology, digital communication and data
30 services became possible, and text messaging was introduced. The third generation
(3G) technology marked the introduction of high-speed internet access, mobile video calling, and location-based services. The fourth generation (4G) technology revolutionized wireless communication with faster data speeds, better network coverage, and improved security. Currently, the fifth generation (5G) technology is
2

5 being deployed, promising even faster data speeds, low latency, and the ability to
connect multiple devices simultaneously. With each generation, wireless communication technology has become more advanced, sophisticated, and capable of delivering more services to its users.
10 [0004] In a 5G structure, numerous distinct Network Functions (NF) coexist
concurrently with one another, wherein each NF is associated with plurality of instances. It is important to monitor these NFs continuously to detect and troubleshoot issues in real time, ensuring seamless operation. However, due to the sheer volume of NFs in a network, managing them is challenging. Therefore, there
15 is a need for a method and system for effectively handling load of NF
instances/services.
[0005] Thus, there exists an imperative need in the art to provides real time analysis
for NFs based on their instance/service-based load on a UI making it easier to
20 monitor based on which user can take actions such as scale-in/scale-out, which the
present disclosure aims to address.
OBJECTS OF THE DISCLOSURE
25 [0006] Some of the objects of the present disclosure, which at least one
embodiment disclosed herein satisfies, are listed herein below.
[0007] It is an object of the present disclosure to provide a system and a method to
provide real time analysis for NFs based on their instance/service-based load on a
30 UI making it easier to monitor based on which user can take actions such as scale-
in/scale-out.
SUMMARY
3

5 [0008] This section is provided to introduce certain aspects of the present disclosure
in a simplified form that are further described below in the detailed description. This summary is not intended to identify the key features or the scope of the claimed subject matter.
10 [0009] The aspect of the present disclosure may relate to a method for scaling of
one or more target network functions (NFs) based on network load data. The method comprises receiving, by a transceiver unit, at a Network Data Analytic Function (NWDAF), in a network from a user, a load analytics request associated with the one or more network functions (NFs) of the network, wherein the one or
15 more NFs is associated with at least one of: a set of network instances and a set of
network services. The method further comprises fetching, by the transceiver unit at the NWDAF, from a storage unit, a set of pre-stored target data associated with the one or more NFs based on the load analytics request. The method further comprises receiving, by the transceiver unit at the NWDAF, real time network data associated
20 with the one or more NFs. The method further comprises generating, by a
processing unit at the NWDAF, a set of analyzed network load data associated with the one or more NFs based on at least one of: the set of pre-stored target data and the real time network data. The method further comprises identifying in real-time, by the processing unit at the NWDAF, at least one of: one or more target network
25 instances from the set of network instances and one or more target network services
from the set of network services, based on the set of analyzed network load data. The method further comprises transmitting, by the transceiver unit, from the NWDAF via a Network Data Analytic Function user interface (NWDAF UI), one or more target analyzed network load data from the set of analyzed network load
30 data based on at least one of: identified the one or more target network instances
and identified the one or more target network services. The method also comprises scaling, by the processing unit, at the NWDAF UI, of the one or more target network functions, based on the one or more target analyzed network load data.
4

5 [0010] In an exemplary aspect of the present disclosure, the one or more target
network instances comprises at least one of: one or more overloaded network
instances and one or more underloaded network instances; and the one or more
target network services comprises at least one of: one or more overloaded network
services and one or more underloaded network services.
10
[0011] In an exemplary aspect of the present disclosure, the identifying, the one or
more target network instances from the set of network instances and one or more
target network services from the set of network services, by the processing unit
[305] at the NWDAF [303] is performed in real-time.
15
[0012] In an exemplary aspect of the present disclosure, the scaling of the one or
more target network functions is at least one of: a scaling out and a scaling in.
[0013] In an exemplary aspect of the present disclosure, the one or more target
20 network instance from the set of network instances is identified based on a pre-
defined network instance threshold value associated with each network instance
from the set of network instances, and wherein the one or more target network
services from the set of network services is identified based on a pre-defined
network service threshold value associated with each network service from the set
25 of network services.
[0014] In an exemplary aspect of the present disclosure, the pre-stored target data associated with the one or more NFs is at least one of: a service based pre-stored data associated with each network function from the one or more NFs and an
30 instance based pre-stored data associated with said each network function from the
one or more NFs, and wherein the real time network data associated with the one or more NFs is at least one of: a service based real-time data associated with each network function from the one or more NFs and an instance based real-time data associated with said each network function from the one or more NFs.
35
5

5 [0015] In an exemplary aspect of the present disclosure, the set of analyzed network
load data associated with the one or more NFs is generated based on at least one of:
the service based pre-stored data associated with each network function from the
one or more NFs, the instance based pre-stored data associated with said each
network function from the one or more NFs, the service based real-time data
10 associated with said each network function from the one or more NFs and the
instance based real-time data associated with said each network function from the one or more NFs.
[0016] Another aspect of the present disclosure may relate to a system for scaling
15 one or more target network functions (NFs) based on network load data. The system
comprises a transceiver unit, wherein the transceiver unit is configured to receive, at a Network Data Analytic Function (NWDAF) in a network from a user, a load analytics request associated with the one or more network functions (NFs) of the network, wherein the one or more NFs is associated with at least one of a set of
20 network instances and a set of network services. The transceiver unit is further
configured to fetch, at the NWDAF from a storage unit, a set of pre-stored target data associated with the one or more NFs based on the load analytics request. The transceiver unit is further configured to receive, at the NWDAF, a real time network data associated with the one or more NFs. The system further comprises a
25 processing unit is configured to generate, at the NWDAF, a set of analyzed network
load data associated with the one or more NFs based on at least one: of the set of pre-stored target data and the real time network data. The processing unit is further configured to identify in real-time, at the NWDAF, at least one of: one or more target network instances from the set of network instances and one or more target
30 network services from the set of network services, based on the set of analyzed
network load data. In the system, the transceiver unit is further configured to transmit from the NWDAF via a Network Data Analytic Function user interface (NWDAF UI), one or more target analyzed network load data from the set of analyzed network load data based on at least one of: identified the one or more
6

5 target network instances and identified the one or more target network services. The
processing unit is further configured to scale, at the NWDAF UI, the one or more target network functions, based on the one or more target analyzed network load data.
10 [0017] Another aspect of the present disclosure may relate to a non-transitory
computer readable storage medium, storing instructions for scaling of one or more target network functions (NFs) based on network load data, the instructions include executable code which, when executed by a one or more units of a system, causes: a transceiver unit to receive, at a Network Data Analytic Function (NWDAF) in a
15 network from a user, a load analytics request associated with the one or more
network functions (NFs) of the network, wherein the one or more NFs is associated with at least one of a set of network instances and a set of network services. The instructions, which when executed, further cause the transceiver unit to fetch, at the NWDAF from a storage unit, a set of pre-stored target data associated with the one
20 or more NFs based on the load analytics request. The instructions, which when
executed, further cause the transceiver unit to receive, at the NWDAF, real time network data associated with the one or more NFs. The instructions further cause a processing unit to generate, at the NWDAF, a set of analyzed network load data associated with the one or more NFs based on at least one: of the set of pre-stored
25 target data and the real time network data. The instructions, which when executed,
further cause the processing unit to identify in real-time, at the NWDAF, at least one of: one or more target network instances from the set of network instances and one or more target network services from the set of network services, based on the set of analyzed network load data. The instructions, which when executed, further
30 cause the transceiver unit to transmit from the NWDAF via a Network Data
Analytic Function user interface (NWDAF UI), one or more target analyzed network load data from the set of analyzed network load data based on at least one of: identified the one or more target network instances and identified the one or more target network services. The instructions, which when executed, further cause
7

5 the processing unit to scale, at the NWDAF UI, the one or more target network
functions, based on the one or more target analyzed network load data.
[0018] Yet another aspect of the present disclosure may relate to scaling of one or more target network functions (NFs) based on network load data. The user
10 equipment (UE) is configured to send, to a Network Data Analytic Function
(NWDAF), in a network from a user, a load analytics request associated with the one or more network functions (NFs) of the network. It is to be noted that the one or more NFs is associated with at least one of: a set of network instance and a set of network services. The UE is further configured to transmit from the NWDAF via
15 a Network Data Analytic Function user interface (NWDAF UI), one or more target
analyzed network load data from a set of analyzed network load data based on at least one of: identified one or more target network instances and one or more target network services. The UE is also configured to scale, at the NWDAF UI, the one or more target network functions, based on the one or more target analyzed network
20 load data.
DESCRIPTION OF DRAWINGS
[0019] The accompanying drawings, which are incorporated herein, and constitute
25 a part of this disclosure, illustrate exemplary embodiments of the disclosed methods
and systems in which like reference numerals refer to the same parts throughout the
different drawings. Components in the drawings are not necessarily to scale,
emphasis instead being placed upon clearly illustrating the principles of the present
disclosure. Some drawings may indicate the components using block diagrams and
30 may not represent the internal circuitry of each component. It will be appreciated
by those skilled in the art that disclosure of such drawings includes disclosure of electrical components, electronic components or circuitry commonly used to implement such components.
8

5 [0020] FIG.1 illustrates an exemplary block diagram representation of a 5th
generation core (5GC) network architecture [100].
[0021] FIG. 2 illustrates an exemplary block diagram of a computing device [200]
upon which the features of the present disclosure may be implemented in
10 accordance with exemplary implementation of the present disclosure.
[0022] FIG. 3 illustrates an exemplary block diagram of a system [300] for scaling of one or more target network functions (NFs) [301] based on network load data, in accordance with exemplary implementations of the present disclosure. 15
[0023] FIG. 4 illustrates an exemplary method [400] flow diagram for scaling of one or more target network functions (NFs) [301] based on network load data, in accordance with the exemplary embodiments of the present disclosure.
20 [0024] FIG. 5 illustrates a process [500] for scaling of one or more target network
functions (NFs) [301] based on network load data.
[0025] FIG. 6 illustrates an exemplary system architecture [600] for scaling of a target network function named NRF based on network load data. 25
[0026] The foregoing shall be more apparent from the following more detailed description of the disclosure.
DETAILED DESCRIPTION
30
[0027] In the following description, for the purposes of explanation, various specific details are set forth to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features
9

5 described hereafter can each be used independently of one another or with any
combination of other features. An individual feature may not address any of the
problems discussed above or might address only some of the problems discussed
above. Some of the problems discussed above might not be fully addressed by any
of the features described herein. Example embodiments of the present disclosure
10 are described below, as illustrated in various drawings in which like reference
numerals refer to the same parts throughout the different drawings.
[0028] The ensuing description provides exemplary embodiments only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather,
15 the ensuing description of the exemplary embodiments will provide those skilled in
the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.
20
[0029] It should be noted that the terms "mobile device", "user equipment", "user device", “communication device”, “device” and similar terms are used interchangeably for the purpose of describing the disclosure. These terms are not intended to limit the scope of the disclosure or imply any specific functionality or
25 limitations on the described embodiments. The use of these terms is solely for
convenience and clarity of description. The disclosure is not limited to any particular type of device or equipment, and it should be understood that other equivalent terms or variations thereof may be used interchangeably without departing from the scope of the disclosure as defined herein.
30
[0030] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other
10

5 components may be shown as components in block diagram form in order not to
obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the embodiments.
10 [0031] Also, it is noted that individual embodiments may be described as a process
which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process
15 is terminated when its operations are completed but could have additional steps not
included in a FIG.
[0032] The word “exemplary” and/or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the
20 subject matter disclosed herein is not limited by such examples. In addition, any
aspect or design described herein as “exemplary” and/or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms
25 “includes,” “has,” “contains,” and other similar words are used in either the detailed
description or the claims, such terms are intended to be inclusive—in a manner similar to the term “comprising” as an open transition word—without precluding any additional or other elements.
30 [0033] As used herein, an “electronic device”, or “portable electronic device”, or
“user device” or “communication device” or “user equipment” or “device” refers to any electrical, electronic, electromechanical and computing device. The user device is capable of receiving and/or transmitting one or parameters, performing function/s, communicating with other user devices and transmitting data to the
11

5 other user devices. The user equipment may have a processor, a display, a memory,
a battery and an input-means such as a hard keypad and/or a soft keypad. The user equipment may be capable of operating on any radio access technology including but not limited to IP-enabled communication, Zig Bee, Bluetooth, Bluetooth Low Energy, Near Field Communication, Z-Wave, Wi-Fi, Wi-Fi direct, etc. For
10 instance, the user equipment may include, but not limited to, a mobile phone,
smartphone, virtual reality (VR) devices, augmented reality (AR) devices, laptop, a general-purpose computer, desktop, personal digital assistant, tablet computer, mainframe computer, or any other device as may be obvious to a person skilled in the art for implementation of the features of the present disclosure.
15
[0034] Further, the user device and/or a system as described herein to implement technical features as disclosed in the present disclosure may also comprise a “processor” or “processing unit”, wherein processor refers to any logic circuitry for processing instructions. The processor may be a general-purpose processor, a
20 special purpose processor, a conventional processor, a digital signal processor, a
plurality of microprocessors, one or more microprocessors in association with a Digital Signal Processor (DSP) core, a controller, a microcontroller, Application Specific Integrated Circuits, Field Programmable Gate Array circuits, any other type of integrated circuits, etc. The processor may perform signal coding data
25 processing, input/output processing, and/or any other functionality that enables the
working of the system according to the present disclosure. More specifically, the processor is a hardware processor.
[0035] As used herein, “a user equipment”, “a user device”, “a smart-user-device”,
30 “a smart-device”, “an electronic device”, “a mobile device”, “a handheld device”,
“a wireless communication device”, “a mobile communication device”, “a communication device” may be any electrical, electronic and/or computing device or equipment, capable of implementing the features of the present disclosure. The user equipment/device may include, but is not limited to, a mobile phone, smart

5 phone, laptop, a general-purpose computer, desktop, personal digital assistant,
tablet computer, wearable device or any other computing device which is capable
of implementing the features of the present disclosure. Also, the user device may
contain at least one input means configured to receive an input from at least one of
a transceiver unit, a processing unit, a storage unit, a detection unit and any other
10 such unit(s) which are required to implement the features of the present disclosure.
[0036] As used herein, “storage unit” or “memory unit” refers to a machine or computer-readable medium including any mechanism for storing information in a form readable by a computer or similar machine. For example, a computer-readable
15 medium includes read-only memory (“ROM”), random access memory (“RAM”),
magnetic disk storage media, optical storage media, flash memory devices or other types of machine-accessible storage media. The storage unit stores at least the data that may be required by one or more units of the system to perform their respective functions.
20
[0037] As used herein “interface” or “user interface” refers to a shared boundary across which two or more separate components of a system exchange information or data. The interface may also be referred to a set of rules or protocols that define communication or interaction of one or more modules or one or more units with
25 each other, which also includes the methods, functions, or procedures that may be
called.
[0038] All modules, units, components used herein, unless explicitly excluded
herein, may be software modules or hardware processors, the processors being a
30 general-purpose processor, a special purpose processor, a conventional processor,
a digital signal processor (DSP), a plurality of microprocessors, one or more microprocessors in association with a DSP core, a controller, a microcontroller, Application Specific Integrated Circuits (ASIC), Field Programmable Gate Array circuits (FPGA), any other type of integrated circuits, etc.
13

5
[0039] As used herein the transceiver unit include at least one receiver and at least one transmitter configured respectively for receiving and transmitting data, signals, information or a combination thereof between units/components within the system and/or connected with the system.
10
[0040] As discussed in the background section, the current known solutions have several shortcomings. The present disclosure aims to overcome the above-mentioned and other existing problems in this field of technology by providing method and system of scaling of one or more target network functions (NFs) [301]
15 based on network load data.
[0041] FIG. 1 illustrates an exemplary block diagram representation of 5th generation core (5GC) network architecture [100], in accordance with exemplary implementation of the present disclosure. As shown in FIG. 1, the 5GC network
20 architecture [100] includes a user equipment (UE) [102], a radio access network
(RAN) [104], an access and mobility management function (AMF) [106], a Session Management Function (SMF) [108], a Service Communication Proxy (SCP) [110], an Authentication Server Function (AUSF) [112], a Network Slice Specific Authentication and Authorization Function (NSSAAF) [114], a Network Slice
25 Selection Function (NSSF) [116], a Network Exposure Function (NEF) [118], a
Network Repository Function (NRF) [120], a Policy Control Function (PCF) [122], a Unified Data Management (UDM) [124], an application function (AF) [126], a User Plane Function (UPF) [128], a data network (DN) [130], a Network Data Analytics Function (NWDAF) [303], wherein all the components are assumed to
30 be connected to each other in a manner as obvious to the person skilled in the art
for implementing features of the present disclosure.
[0042] The Radio Access Network (RAN) [104] is the part of a mobile telecommunications system that connects the user equipment (UE) [102] to the core

5 network (CN) and provides access to different types of networks (e.g., 5G network).
The RAN [104] consists of radio base stations and the radio access technologies that enable wireless communication.
[0043] The Access and Mobility Management Function (AMF) [106] is the 5G core
10 network function responsible for managing access and mobility aspects, such as UE
registration, connection, and reachability. The RAN [104] also handles mobility management procedures like handovers and paging.
[0044] The Session Management Function (SMF) [108] is the 5G core network
15 function responsible for managing session-related aspects, such as establishing,
modifying, and releasing sessions. The SMF [108] coordinates with the User Plane Function (UPF) [128] for data forwarding and handles IP address allocation and QoS enforcement.
20 [0045] The Service Communication Proxy (SCP) [110] is a network function in the
5G core network that facilitates communication between other network functions by providing a secure and efficient messaging service. The SCP [110] acts as a mediator for service-based interfaces.
25 [0046] The Authentication Server Function (AUSF) [112] is the network function
in the 5G core responsible for authenticating UEs during registration and providing security services. The AUSF [112] generates and verifies authentication vectors and tokens.
30 [0047] The Network Slice Specific Authentication and Authorization Function
(NSSAAF) [114] is the network function that provides authentication and authorization services specific to network slices. The NSSAAF [114] ensures that UEs can access only the slices for which they are authorized.

5 [0048] The Network Slice Selection Function (NSSF) [116] is the network function
responsible for selecting the appropriate network slice for a UE based on factors such as subscription, requested services, and network policies.
[0049] The Network Exposure Function (NEF) [118] is the network function that
10 exposes capabilities and services of the 5G network to external applications,
enabling integration with third-party services and applications.
[0050] The Network Repository Function (NRF) [120] is the network function that
acts as a central repository for information about available network functions and
15 services. The NRF [120] facilitates the discovery and dynamic registration of
network functions.
[0051] The Policy Control Function (PCF) [122] is the network function
responsible for policy control decisions, such as QoS, charging, and access control,
20 based on subscriber information and network policies.
[0052] The Unified Data Management (UDM) [124] is the network function that centralizes the management of subscriber data, including authentication, authorization, and subscription information. 25
[0053] The Application Function (AF) [126] is the network function that represents external applications interfacing with the 5G core network to access network capabilities and services.
30 [0054] The User Plane Function (UPF) [128] is the network function responsible
for handling user data traffic, including packet routing, forwarding, and QoS enforcement.
16

5 [0055] The Data Network (DN) [130] refers to a network that provides data
services to user equipment (UE) [102] in a telecommunications system. The data services may include but are not limited to Internet services, private data network related services.
10 [0056] The NWDAF (Network Data Analytics Function) [303] is a network
function component within the 5G network. The NWDAF [303] may provide analytics information for different events of analytics to NF consumers. The NWDAF [303] may allow NF consumers to subscribe to and unsubscribe from one¬time, periodic notification or notification when an event is detected.
15
[0057] The present disclosure can be implemented on a computing device [200] as
shown in FIG. 2. The computing device [200] implements the present disclosure in
accordance with the 5G communication network architecture [100] (as shown in
FIG. 1).
20
[0058] FIG. 2 illustrates an exemplary block diagram of the computing device
[200] upon which the features of the present disclosure may be implemented in
accordance with exemplary implementation of the present disclosure. In an
implementation, the computing device [200] may also implement a method [400]
25 (shown in FIG. 4) for scaling of one or more target network functions (NFs) [301]
(shown in FIG. 3) based on network load data utilising a system [300]. In another implementation, the computing device [200] itself implements the method [400] for scaling of the one or more target network functions (NFs) [301] based on network load data using one or more units configured within the computing device [200],
30 wherein said one or more units are capable of implementing the features as
disclosed in the present disclosure.
[0059] The computing device [200] may include a bus [202] or other
communication mechanism for communicating information, and a hardware
35 processor [204] coupled with bus [202] for processing information. The hardware
5 processor [204] may be, for example, a general-purpose microprocessor. The
computing device [200] may also include a main memory [206], such as a random-access memory (RAM), or other dynamic storage device, coupled to the bus [202] for storing information and instructions to be executed by the processor [204]. The main memory [206] also may be used for storing temporary variables or other
10 intermediate information during execution of the instructions to be executed by the
processor [204]. Such instructions, when stored in non-transitory storage media accessible to the processor [204], render the computing device [200] into a special-purpose machine that is customized to perform the operations specified in the instructions. The computing device [200] further includes a read only memory
15 (ROM) [208] or other static storage device coupled to the bus [202] for storing static
information and instructions for the processor [204].
[0060] A storage device [210], such as a magnetic disk, optical disk, or solid-state drive is provided and coupled to the bus [202] for storing information and
20 instructions. The computing device [200] may be coupled via the bus [202] to a
Display [212], such as a cathode ray tube (CRT), Liquid crystal Display (LCD), Light Emitting Diode (LED) Display, Organic LED (OLED) Display, etc. for displaying information to a computer user. An input device [214], including alphanumeric and other keys, touch screen input means, etc. may be coupled to the
25 bus [202] for communicating information and command selections to the processor
[204]. Another type of user input device may be a cursor controller [216], such as a mouse, a trackball, or cursor direction keys, for communicating direction information and command selections to the processor [204], and for controlling cursor movement on the Display [212]. The input device [214] typically has two
30 degrees of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that
allow the device to specify positions in a plane.
[0061] The computing device [200] may implement the techniques described herein using customized hard-wired logic, one or more ASICs or FPGAs, firmware

5 and/or program logic which in combination with the computing device [200] causes
or programs the computing device [200] to be a special-purpose machine. According to one implementation, the techniques herein are performed by the computing device [200] in response to the processor [204] executing one or more sequences of one or more instructions contained in the main memory [206]. Such
10 instructions may be read into the main memory [206] from another storage medium,
such as the storage device [210]. Execution of the sequences of instructions contained in the main memory [206] causes the processor [204] to perform the process steps described herein. In alternative implementations of the present disclosure, hard-wired circuitry may be used in place of or in combination with
15 software instructions.
[0062] The computing device [200] also may include a communication interface [218] coupled to the bus [202]. The communication interface [218] provides a two-way data communication coupling to a network link [220] that is connected to a
20 local network [222]. For example, the communication interface [218] may be an
integrated services digital network (ISDN) card, cable modem, satellite modem, or a modem to provide a data communication connection to a corresponding type of telephone line. As another example, the communication interface [218] may be a local area network (LAN) card to provide a data communication connection to a
25 compatible LAN. Wireless links may also be implemented. In any such
implementation, the communication interface [218] sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information.
30 [0063] The computing device [200] can send messages and receive data, including
program code, through the network(s), the network link [220] and the communication interface [218]. In the Internet example, a server [230] might transmit a requested code for an application program through the Internet [228], the ISP [226], the local network [222], the host [224] and the communication interface

5 [218]. The received code may be executed by the processor [204] as it is received,
and/or stored in the storage device [210], or other non-volatile storage for later execution.
[0064] The present disclosure is implemented by a system [300] (as shown in FIG.
10 3). The computing device [200] (as shown in FIG. 2) may reside in the system [300].
In an implementation, the computing device [200] may be connected to the system [300] to perform the present disclosure. Referring to FIG. 3, an exemplary block diagram of the system [300] for scaling of one or more target network functions (NFs) [301] based on network load data, is shown, in accordance with the
15 exemplary implementations of the present disclosure. The system [300] comprises
at least one transceiver unit [302], at least one storage unit [304] and at least one processing unit [305]. The system [300] is in connection with at least one Network Data Analytic Function (NWDAF) [303] via at least one Network Data Analytic Function User Interface (NWDAF UI) [306]. The at least one Network Data
20 Analytic Function (NWDAF) [303] is also in connection with one or more network
function (NFs) [307]. Also, all the components/ units of the system [300] are assumed to be connected to each other unless otherwise indicated below. As shown in the FIG.3, all units shown within the system [300] should also be assumed to be connected to each other. Also, in FIG. 3 only a few units are shown, however, the
25 system [300] may comprise multiple such units or the system [300] may comprise
any such numbers of said units, as required to implement the features of the present disclosure. Further, in an implementation, the system [300] may be present in a user device to implement the features of the present disclosure. The system [300] may be a part of the user device / or may be independent of but in communication with
30 the user device (may also referred herein as a UE). In another implementation, the
system [300] may reside in a server or a network entity. In yet another implementation, the system [300] may reside partly in the server/ network entity and partly in the user device.

5 [0065] The system [300] is configured for scaling of one or more target network
functions (NFs) [301] based on network load data, with the help of the interconnection between the components/units of the system [300]. The one or more target NFs [301] refers to the network function (NF) which may be scaled in or scaled out based on the network load data.
10
[0066] The transceiver unit [302] is configured to receive, at the Network Data Analytic Function (NWDAF) [303] in a network from a user, a load analytics request associated with the one or more network functions (NFs) [307] of the network. The load analytics request refers to a request for collection and analysing
15 data related to load or traffic on the network. For instance, the load analytics request
may identify if the traffic on the network is high or low. It is to be noted that the one or more NFs [307] is associated with at least one of a set of network instances and a set of network services. It is important to note that the network instances are virtual network functions (VNFs) that are assigned to handle specific tasks such as
20 but not limited to firewall operations or load balancing. It is to be noted that the
network instances may be adjusted dynamically. While the network services are integrated versions of the network instances (also known as network slice) that caters to specific use. For example, a virtual firewall for securing data becomes a network instance while home service for internet is where the network instance of
25 firewall is used for protecting data from threats. The transceiver unit [302] is further
configured to fetch, at the NWDAF [303] from a storage unit [304], a set of pre-stored target data associated with the one or more NFs [301] based on the load analytics request. The set of pre-stored target data contains valuable information about the performance and load of the one or more network instances and the one
30 or more network services. The transceiver unit [302] is further configured to
receive, at the NWDAF [303], real time network data associated with the one or more target NFs [301]. It is to be noted that the set of pre-stored data may include the instance/ service-based load data.
21

5 [0067] The processing unit [305] is configured to generate, at the NWDAF [303],
a set of analyzed network load data associated with the one or more NFs [307] based on at least one of: the set of pre-stored target data and the real time network data. The set of analyzed network load data refers to data generated based on the analysis performed by the NSDAF [303]. The analysis may be related to load testing, load
10 balancing, and the like. The processing unit [305] is further configured to identify
in real-time, at the NWDAF [303], at least one of: one or more target network instances from the set of network instances and one or more target network services from the set of network services, based on the set of analyzed network load data. Real-time refers to a scenario where the identification process at the NWDAF [303]
15 may happen immediately as the set of analysed network load data becomes
available.
[0068] The transceiver unit [302] is further configured to transmit, from the
NWDAF [303] via a Network Data Analytic Function user interface (NWDAF UI)
20 [306], one or more target analyzed network load data from the set of analyzed
network load data based on at least one of: identified the one or more target network instances and identified the one or more target network services.
[0069] The processing unit [305] is further configured to scale, at the NWDAF UI
25 [306], the one or more target network functions [301], based on the one or more
target analyzed network load data. Scale refers to adjusting the one or more target network functions [301] dynamically to meet the one or more target analysed network load data. Scaling may include adding or removing network instances from the one or more target functions. 30
[0070] In an exemplary aspect of the present disclosure, in the disclosed system [300], the one or more target network instances comprises at least one of: one or more overloaded network instances and one or more underloaded network instances; and the one or more target network services comprises at least one of:

5 one or more overloaded network services and one or more underloaded network
services.
[0071] In an exemplary aspect of the present disclosure, in the disclosed system
[300], the scaling of the one or more target network functions [301] is at least one
10 of: a scaling out and a scaling in.
[0072] In an exemplary aspect of the present disclosure, in the disclosed system [300], the one or more target network instance is identified based on a pre-defined network instance threshold value associated with each network instance from the
15 set of network instances. The one or more target network services from the set of
network services is identified based on a pre-defined network service threshold value associated with each network service from the set of network services. The predefined network service threshold refers to when the predefined limit exceeds, it may trigger an action by the user or consumer to ensure normal operation of the
20 network service(s). The predefined network service threshold may be defined by a
user. It is to be noted that the predefined network service threshold value is indicative of the current instance or service-based load in the network service in normal characteristics (such as ideal state). When a deviation from the threshold value is detected, the user or consumers take action to ensure normal operation of
25 the network service(s). Further, scaling out of the network functions takes place
when the network service load is above the pre-defined threshold value.
[0073] In an exemplary aspect of the present disclosure, in the disclosed system
[300], the pre-stored target data associated with the one or more NFs [307] is at
30 least one of: a service based pre-stored data associated with each network function
of the one or more target NFs [301] and an instance based pre-stored data associated with said each network function of the one or more NFs [307]. The real time network data associated with the one or more NFs [307] is at least one of: a service based real-time data associated with each network function of the one or more NFs
23

5 [307] and an instance based real-time data associated with said each network
function of the one or more NFs [307]. The service based pre-stored data refers to
data stored in a particular network service. For example, a service-based load
balancer may collect real-time data on overall user engagement, such as the total
number of incoming requests and average response time in the whole system. The
10 instance based pre-stored data refers to data stored in a particular network instance.
For example, a load balancer instance monitors incoming requests, response time, etc. in real-time to identify anomalies, and ensure efficient distribution of traffic in the particular instance.
15 [0074] In an exemplary aspect of the present disclosure, the set of analyzed network
load data associated with the one or more NFs [307] is generated based on at least
one of: the service based pre-stored data associated with each network function
from the one or more NFs [307], the instance based pre-stored data associated with
said each network function from the one or more NFs [307], the service based real-
20 time data associated with said each network function from the one or more NFs
[307] and the instance based real-time data associated with said each network
function from the one or more NFs [307]. The process of generating may include
performing data analysis on the service based pre-stored data and the instance based
pre-stored data. The data analysis may provide information of the one or more NF’s
25 [307] in detail. Only the instance based pre-stored data may have varying data. The
NWDAF [303] may analyse the data both, in real-time and for historical time
period. The NWDAF [303] may use the data to historical time period data and the
real-time data to generate the set of analysed network load data.
30 [0075] The NWDAF [303] receives network function data from the one or more
NFs [307]. The NWDAF [303] performs load analytics function on the one or more NFs [307]. The NWDAF [303] generates a load analytics visualization of the load analytics associated with the network function data. The NWDAF [303] is also responsible to display the load analytics visualization to the user. The NWDAF
24

5 [303] will now be explained in detail hereinafter. A load analytics request
associated with the one or more network functions (NFs) [307], is received from the user.
[0076] In an implementation of the present disclosure, the system [300] is
10 configured for auto-scaling of the one or more target NFs [301] based on load
identification, with the help of a NWDAF AI/ML module (not shown) residing inside the NWDAF [303].
[0077] The NWDAF [303] is a crucial component of the system [300] responsible
15 for scaling of the one or more target NFs [301] based on network load data. The
NWDAF [303] is configured to harness the power of advanced AI and ML techniques to perform sophisticated data analysis, pattern recognition, and forecasting. When the system [300] receives the load analytics requests from the user associated with the one or more network functions (NFs) [307] of the network,
20 it collects the set of pre-stored target data associated with the one or more NFs
[307], such as a Network Resource Functions (NRF). This set of pre-stored target data contains valuable information about the performance and load of the one or more network instances and the one or more network services. The NWDAF [303] utilizes machine learning techniques and models to process and analyse the set of
25 pre-stored data. Thus, the NWDAF [303] can identify patterns, trends, and
anomalies in the set of pre-stored data. From such analysis, the NWDAF [303] generates the set of analysed network load data that is capable of recognizing changes in the load of the one or more network instances and the one or more network services over the time. The NWDAF is configured to perform load
30 identification, forecasting, alerts triggering, and real time analysis as explained
below:
a) Load Identification: The NWDAF [303] is configured to identify overloaded or underloaded network instances and services in the network. By analysing the pre-stored data, the NWDAF [303] may determine if certain network
5 functions [301] are experiencing excessive load or if some resources are
underutilized.
b) Forecasting: The NWDAF [303] performs forecasting based on historical data
and current trends. The NWDAF [303] may predict potential load issues in the
one or more target NFs [301], enabling proactive scaling and load management.
10 This predictive capability helps in preventing performance bottlenecks and
ensuring smooth network operations.
c) Alerts Triggering: When the NWDAF [303] detects overloaded network
instances or network services, it sends triggers to inform the NWDAF UI [306]
about the identified load-related issues and associated location of the one or
15 more target network functions [301]. This information is crucial for taking
appropriate actions, such as scaling out to handle the increased load.
d) Real-time Analysis: Apart from historical data analysis, the NWDAF [303] is
configured to perform real-time analysis. The NWDAF [303] continuously
monitors the incoming service-based data and makes instantaneous
20 assessments of load changes, allowing for rapid response and adaptive scaling.
[0078] It would be appreciated by the person skilled in the art that by integrating
AI/ML capabilities into the system [300], the scaling process becomes more
intelligent and dynamic. This integration of the AI/ML capabilities enables the
25 network to optimize resource allocation, improve overall performance, and enhance
user experience by efficiently managing the one or more target NFs [301] in real time.
[0079] Referring to FIG. 4, an exemplary method flow diagram [400] for scaling
30 of the one or more target network functions (NFs) [301] based on network load data
is shown, in accordance with exemplary implementations of the present disclosure. In an implementation the method [400] is performed by the system [300]. Further, in an implementation, the system [300] may be present in a server device to
5 implement the features of the present disclosure. Also, as shown in FIG. 4, the
method [400] starts at step [402].
[0080] At step [404], the method [400] comprises receiving, by a transceiver unit
[302], at a Network Data Analytic Function (NWDAF) [303], in a network from a
10 user, a load analytics request associated with the one or more network functions
(NFs) [307] of the network, wherein the one or more NFs [307] is associated with at least one of: a set of network instances and a set of network services.
[0081] In an implementation of the present disclosure, the load analytics request
15 received from the user. The load analytics request may be a monitoring request of
one or more Network functions (NFs) [307]. The load analytics may be based on service-based load for more efficient handling of load.
[0082] In an implementation of the present disclosure, the load analytics request
20 from the user may be a management request of one or more NFs [307] on the basis
of their service-based load for more efficient handling of load. For example, the PCF [122] (as shown in FIG. 1) may be managed based upon the retrieved analytics of the network resources usage as per the network policies.
25 [0083] At step [406], the method [400] comprises fetching, by the transceiver unit
[302] at the NWDAF [303], from a storage unit [304], a set of pre-stored target data associated with the one or more target NFs [301] based on the load analytics request.
30 [0084] In an implementation of the present disclosure, the set of pre-stored target
data based on the load analytics request from the user.
[0085] At step [408], the method [400] comprises comprises receiving, by the transceiver unit [302] at the NWDAF [303], a real time network data associated
27

5 with the one or more NFs [307]. For e.g., the one or more NFs [307] may include
at least a Network Resource Function (NRF) [120] (as shown in FIG. 1).
[0086] In an exemplary embodiment of the present disclosure, the real time network data from the NRF [120] is received via a heartbeat message.
10
[0087] At step [410], the method [400] comprises generating, by a processing unit [305] at the NWDAF [303], a set of analyzed network load data associated with the one or more NFs [307] based on at least one of: the set of pre-stored target data and the real time network data.
15
[0088] At step [412], the method [400] comprises identifying in real-time, by the processing unit [305] at the NWDAF [303], at least one of: one or more target network instances from the set of network instances and one or more target network services from the set of network services, based on the set of analyzed network load
20 data.
[0089] In an exemplary implementation of the present disclosure, the one or more
target network instances may include at least one of an overloaded NF instances
and an overload NF services, based on the set of analyzed network load data. For
25 e.g., if the load on one or more NFs [307] (let’s say NRF) changes from 1000
requests per second to 2000 requests per second, the present disclosure identifies the change in load at the NRF based on the generated set of analyzed network load data.
30 [0090] In an implementation of the present disclosure, the method [400] may
employ an AI/ML Module (not shown) of the NWDAF [303] to identify the overload or underload one or more network instances and one or more network services at the one or more NFs [307].

5 [0091] In an implementation of the present disclosure, the NWDAF [303] may send
a trigger associated with at least one of: the overloaded one or more network instances and the overloaded one or more network services.
[0092] In an implementation of the present disclosure, the NWDAF [303] may be
10 configured to forecasting at least one of the overload instances and services at the
one or more NFs [307] and underload instances and services at the one or more NFs [307].
[0093] At step [414], the method [400] comprises transmitting, by the transceiver
15 unit [302], from the NWDAF [303] via a Network Data Analytic Function user
interface (NWDAF UI) [306], to the user, the one or more target analyzed network
load data from the set of analyzed network load data. The one or more target
analyzed network load data is transmitted based on at least one of: identified the
one or more target network instances and identified the one or more target network
20 services.
[0094] At step [416], the method [400] comprises transmitting scaling, by the
processing unit [305], at the NWDAF UI [306], to the user, of the one or more target
network functions [301], based on the one or more target analyzed network load
25 data.
[0095] Thereafter, the method [400] terminates at step [418].
[0096] In an exemplary aspect of the present disclosure, in the disclosed method
30 [400], the one or more target network instances comprises at least one of: one or
more overloaded network instances and one or more underloaded network instances; and the one or more target network services comprises at least one of: one or more overloaded network services and one or more underloaded network services.
29

5
[0097] In an exemplary aspect of the present disclosure, in the disclosed method
[400], the scaling of the one or more target network functions [301] is at least one
of: a scaling out and a scaling in. For e.g., the scaling out information is based on
the at least one of the overload one or more network instances and the overload one
10 or more network services. The scaling out information is associated with the
location of the overload/underload one or more NFs [307].
[0098] In an exemplary aspect of the present disclosure, in the disclosed method [400], the one or more target network instance from the set of network instances is
15 identified based on a pre-defined network instance threshold value associated with
each network instance from the set of network instances, and wherein the one or more target network services from the set of network services is identified based on a pre-defined network service threshold value associated with each network service from the set of network services. It is to be noted that the predefined network service
20 threshold value is indicative of the current instance or service-based load in the
network service in normal characteristics. If a deviation is seen from the threshold value, the network service(s) are acted upon by the user NFs/ consumer to perform normally. For e.g., the scaling out of the network function takes place when the network service load is above the pre-defined threshold value.
25
[0099] In an exemplary aspect of the present disclosure, in the disclosed method [400], the pre-stored target data associated with the one or more target NFs [301] is at least one of: a service based pre-stored data associated with each network function from the one or more NFs [307] and an instance based pre-stored data
30 associated with said each network function from the one or more NFs [307], and
wherein the real time network data associated with the one or more NFs [307] is at least one of: a service based real-time data associated with each network function from the one or more NFs [307] and an instance based real-time data associated with said each network function from the one or more NFs [307].
30

5
[0100] In an exemplary aspect of the present disclosure, in the disclosed method
[400], the set of analyzed network load data associated with the one or more NFs
[307] is generated based on at least one of: the service based pre-stored data
associated with each network function from the one or more NFs [307], the instance
10 based pre-stored data associated with said each network function from the one or
more NFs [307], the service based real-time data associated with said each network function from the one or more NFs [307] and the instance based real-time data associated with said each network function from the one or more NFs [307].
15 [0101] Referring to FIG. 5, an exemplary process [500] for scaling of one or more
target network functions (NFs) [301] based on network load data is shown, in accordance with the present disclosure. The process [500] initiates at step S1.
[0102] At step S1, the process [500] includes sending a load analytics request sent
20 by the user to a Network Data Analytic Function (NWDAF) [303] (as shown in
FIG. 3) via a Network Data Analytic Function user Interface (NWDAF UI) [306] (as shown in FIG. 3).
[0103] At step S2, the process [500] incudes performing analysis of the NWDAF
25 [303] on the network service(s)/ network instance(s).
[0104] At step S3, the process [500] includes identification of the overloaded network service(s)/ network instance(s).
30 [0105] In addition, at step S4, the NWDAF [303] provides analysed network load
data to the user on the NWDAF UI [306].
[0106] Referring to FIG. 6, an exemplary system architecture [600] for scaling of
a target network function (NFs) [301] (as shown in FIG. 3) named NRF [301] based
35 on network load data. Further, the system architecture [600] in conjunction with
31

5 system [300] as depicted in FIG. 3 may perform method [400] as depicted in FIG.
4.
[0107] In an example, a network administrator notices a recent increase in network traffic in certain parts of the network during peak hours. To proactively manage the
10 resources and avoid any service degradation, the administrator sends a load
analytics request to the Network Data Analytic Function (NWDAF) [303] (as shown in FIG. 3). The NWDAF [303], in response to the load analytics request, receives service-based data from the network function such as a Network Resource Function (NRF) [301]. The NRF [301] provides details about the current load on
15 various Network Functions (NFs). The NWDAF [303] then processes the service-
based data, analysing the load patterns, traffic flow, and other relevant parameters. Let's say, for instance, the data indicates that a specific server is handling 2000 requests per second, up from the server’s usual capacity of 1000 requests per second. Using a built-in AI/ML module (not shown), the NWDAF [303] identifies
20 this server as an overloaded NF instance. The identification is based on a pre-
defined load threshold - say 1500 requests per second for this server, which is clearly exceeded in this case. Upon identifying the overload situation, the NWDAF [303] then transmits this critical information, including the location of the overloaded server, to the NWDAF User Interface (NWDAF UI) [306] (as shown in
25 FIG. 3). The network administrator, alerted via the NWDAF UI [306], can now
initiate appropriate measures like deploying additional servers or load balancing to handle the increased traffic. Additionally, the NWDAF [303] also starts forecasting future loads on the server based on current and historical data. The NWDAF UI [306] may perform closed loop reporting to the data consumers [601] in case of
30 high data traffic. Closed loop reporting refers to a system where the information
may be made available to both the consumers and the system operators. If the system [600] predicts further increase in traffic, it triggers an alert, prompting the administrator to increase the capacity in advance, thus avoiding potential service disruption.

5
[0108] In an implementation of the present disclosure, the system [600] may be
configured to perform the method [400] as depicted in FIG. 4 via the one or more
components as depicted in FIG. 3 of the system [300]. The NWDAF [303] is configured to
fetch the set of pre-stored target data of the one or more NFs [307]. The NWDAF [303]
10 then analyses the data both on real time and for historic period. It uses the fetched data
to record it past behaviour and show the output in terms of graph/reports for user’s better experience.
[0109] An another aspect of the present disclosure may relate to a non-transitory
15 computer readable storage medium, storing instructions for scaling of one or more
target network functions (NFs) [301] based on network load data, the instructions include executable code which, when executed by a one or more units of a system, causes: a transceiver unit [302] to receive, at a Network Data Analytic Function (NWDAF) [303] in a network from a user, a load analytics request associated with
20 the one or more network functions (NFs) [307] of the network, wherein the one or
more NFs [307] is associated with at least one of a set of network instances and a set of network services. The instructions further cause the transceiver unit [302] to fetch, at the NWDAF [303] from a storage unit [304], a set of pre-stored target data associated with the one or more target NFs [301] based on the load analytics
25 request. The instructions further cause the transceiver unit [302] to receive, at the
NWDAF [303], real time network data associated with the one or more target NFs [301]. The instructions further cause a processing unit [305] to generate, at the NWDAF [303], a set of analyzed network load data associated with the one or more NFs [307] based on at least one: of the set of pre-stored target data and the real time
30 network data. The instructions further cause the processing unit [305] to identify in
real-time, at the NWDAF [303], at least one of: one or more target network instances from the set of network instances and one or more target network services from the set of network services, based on the set of analyzed network load data. The instructions further cause the transceiver unit [302] to transmit, from the

5 NWDAF [303] via a Network Data Analytic Function user interface (NWDAF UI)
[306], one or more target analyzed network load data from the set of analyzed
network load data based on at least one of: identified the one or more target network
instances and identified the one or more target network services. The instructions
further cause the processing unit [305] to scale, at the NWDAF UI [306], the one
10 or more target network functions [301], based on the one or more target analyzed
network load data.
[0110] Yet another aspect of the present disclosure may relate to scaling of one or more target network functions (NFs) [301] based on network load data. The user
15 equipment (UE) [102] is configured to send to a Network Data Analytic Function
(NWDAF) [303], in a network from a user, a load analytics request associated with the one or more network functions (NFs) [307] of the network. It is to be noted that the one or more NFs [307] is associated with at least one of: a set of network instance and a set of network services. The UE is further configured to transmit,
20 from the NWDAF [303] via a Network Data Analytic Function user interface
(NWDAF UI) [306], one or more target analyzed network load data from a set of analyzed network load data based on at least one of: identified one or more target network instances and one or more target network services. The UE is also configured to scale, at the NWDAF UI [306] the one or more target network
25 functions [301], based on the one or more target analyzed network load data.
[0111] Further, in accordance with the present disclosure, it is to be acknowledged
that the functionality described for the various the components/units can be
implemented interchangeably. While specific embodiments may disclose a
30 particular functionality of these units for clarity, it is recognized that various
configurations and combinations thereof are within the scope of the disclosure. The functionality of specific units as disclosed in the disclosure should not be construed as limiting the scope of the present disclosure. Consequently, alternative arrangements and substitutions of units, provided they achieve the intended
5 functionality described herein, are encompassed within the scope of the present
disclosure.
[0112] As is evident from the above, the present disclosure provides a technically advanced solution for scaling of one or more target network functions (NFs) [301]
10 based on network load data. The present disclosure provides a technically advanced
solution by auto-scaling of the one or more target network functions (NFs) [301] based on load identification. The present disclosure provides many advantages over the previous known art because the Network Data Analytic Function (NWDAF) [303] provides real time analysis for the one or more NFs [307] based on their
15 instance/service-based load on the NWDAF UI [306] which makes it extremely
easy to monitor the one or more NFs [307], based on which user take actions such as scale-in/scale-out. It further provides analysis on historic data which helps the user in understanding the behaviour of the one or more target NFs [301] in the past.
20 [0113] While considerable emphasis has been placed herein on the disclosed
implementations, it will be appreciated that many implementations can be made and that many changes can be made to the implementations without departing from the principles of the present disclosure. These and other changes in the implementations of the present disclosure will be apparent to those skilled in the art, whereby it is to
25 be understood that the foregoing descriptive matter to be implemented is illustrative
and non-limiting.

5 We Claim:
1. A method [400] for scaling of one or more target network functions (NFs) [301] based on network load data, the method [400] comprising:
- receiving, by a transceiver unit [302], at a Network Data Analytic Function
(NWDAF) [303], in a network from a user, a load analytics request
10 associated with the one or more network functions (NFs) [307] of the
network, wherein the one or more NFs [307] is associated with at least one of: a set of network instances and a set of network services;
- fetching, by the transceiver unit [302] at the NWDAF [303], from a storage
unit [304], a set of pre-stored target data associated with the one or more
15 target NFs [301] based on the load analytics request;
- receiving, by the transceiver unit [302] at the NWDAF [303], a real time network data associated with the one or more NFs [307];
- generating, by a processing unit [305] at the NWDAF [303], a set of analyzed network load data associated with the one or more NFs [307] based
20 on at least one of: the set of pre-stored target data and the real time network
data;
- identifying, by the processing unit [305] at the NWDAF [303], at least one
of: one or more target network instances from the set of network instances
and one or more target network services from the set of network services,
25 based on the set of analyzed network load data;
- transmitting, by the transceiver unit [302], from the NWDAF [303] via a
Network Data Analytic Function user interface (NWDAF UI) [306], one or
more target analyzed network load data from the set of analyzed network
load data based on at least one of: the identified one or more target network
30 instances and the identified one or more target network services; and
- scaling, by the processing unit [305], at the NWDAF UI [306], of the one
or more target network functions [301], based on the one or more target
analyzed network load data.

5 2. The method [400] as claimed in claim 1, wherein the one or more target
network instances comprises at least one of: one or more overloaded network
instances and one or more underloaded network instances; and the one or more
target network services comprises at least one of: one or more overloaded
network services and one or more underloaded network services.
10 3. The method [400] as claimed in claim 1, wherein the identifying, the one or
more target network instances from the set of network instances and one or more target network services from the set of network services, by the processing unit [305] at the NWDAF [303] is performed in real-time.
4. The method [400] as claimed in claim 2, wherein the scaling of the one or
15 more target network functions [301] is at least one of: a scaling out and a
scaling in.
5. The method [400] as claimed in claim 1, wherein the one or more target
network instance from the set of network instances is identified based on a
pre-defined network instance threshold value associated with each network
20 instance from the set of network instances, and wherein the one or more target
network services from the set of network services is identified based on a pre¬defined network service threshold value associated with each network service from the set of network services.
6. The method [400] as claimed in claim 1, wherein the pre-stored target data
25 associated with the one or more target NFs [301] is at least one of: a service
based pre-stored data associated with each network function from the one or
more NFs [307] and an instance based pre-stored data associated with said
each network function from the one or more NFs [307], and wherein the real¬
time network data associated with the one or more NFs [307] is at least one
30 of: a service based real-time data associated with each network function from
the one or more NFs [307] and an instance based real-time data associated with said each network function from the one or more NFs [307].
7. The method [400] as claimed in claim 5, wherein the set of analyzed network
load data associated with the one or more NFs [307] is generated based on at

5 least one of: the service based pre-stored data associated with each network
function from the one or more NFs [307], the instance based pre-stored data
associated with said each network function from the one or more NFs [307],
the service based real-time data associated with said each network function
from the one or more NFs [307] and the instance based real-time data
10 associated with said each network function from the one or more NFs [307].
8. A system [300] for scaling of one or more target network functions (NFs) [301] based on network load data, the system [300] comprises:
- a transceiver unit [302], wherein the transceiver unit [302] is configured
to:
15 • receive, at a Network Data Analytic Function (NWDAF) [303] in a
network from a user, a load analytics request associated with the one or
more network functions (NFs) [307] of the network, wherein the one or
more NFs [307] is associated with at least one of a set of network
instances and a set of network services;
20 • fetch, at the NWDAF [303] from a storage unit [304], a set of pre-stored
target data associated with the one or more target NFs [301] based on the load analytics request;
• receive, at the NWDAF [303], a real time network data associated with
the one or more target NFs [301];
25 - a processing unit [305] connected to at least the transceiver unit [302],
wherein the processing unit [305] is configured to:
• generate, at the NWDAF [303], a set of analyzed network load data
associated with the one or more NFs [307] based on at least one: of the
set of pre-stored target data and the real time network data;
30 • identify, at the NWDAF [303], at least one of: one or more target
network instances from the set of network instances and one or more target network services from the set of network services, based on the set of analyzed network load data; wherein the transceiver unit [302] is further configured to:

5
• transmit, from the NWDAF [303] via a Network Data Analytic Function
user interface (NWDAF UI) [306], one or more target analyzed network
load data from the set of analyzed network load data based on at least
one of: the identified one or more target network instances and the
10 identified one or more target network services;
wherein the processing unit [305] is further configured to:
• scale, at the NWDAF UI [306], the one or more target network functions
[301], based on the one or more target analyzed network load data.
9. The system [300] as claimed in claim 8, wherein the one or more target
15 network instances comprises at least one of: one or more overloaded network
instances and one or more underloaded network instances; and the one or more target network services comprises at least one of: one or more overloaded network services and one or more underloaded network services.
10. The system [300] as claimed in claim 8, wherein the NWDAF [303] may
20 identify the one or more target network instances from the set of network
instances and one or more target network services from the set of network services in real-time.
11. The system [300] as claimed in claim 9, wherein the scaling of the one or more
target network functions [301] is at least one of: a scaling out and a scaling in.
25 12. The system [300] as claimed in claim 8, wherein the one or more target
network instance from the set of network instances is identified based on a
pred-defined network instance threshold value associated with each network
instance from the set of network instances, and wherein the one or more target
network services from the set of network services is identified based on a pre-
30 defined network service threshold value associated with each network service
from the set of network services.
13. The system [300] as claimed in claim 8, wherein the pre-stored target data
associated with the one or more target NFs [301] is at least one of: a service
based pre-stored data associated with each network function from the one or
5 more NFs [307] and an instance based pre-stored data associated with said
each network function from the one or more NFs [307], and wherein the real
time network data associated with the one or more NFs [307] is at least one
of: a service based real-time data associated with each network function from
the one or more NFs [307] and an instance based real-time data associated
10 with said each network function from the one or more NFs [307].
14. The system [300] as claimed in claim 12, wherein the set of analyzed network
load data associated with the one or more NFs [307] is generated based on at
least one of: the service based pre-stored data associated with each network
function from the one or more NFs [307], the instance based pre-stored data
15 associated with said each network function from the one or more NFs [307],
the service based real-time data associated with said each network function from the one or more NFs [307] and the instance based real-time data associated with said each network function from the one or more NFs [307].
15. A user equipment (UE) [102] for scaling of one or more target network
20 functions (NFs) [301] based on network load data, the user equipment (UE)
[102] being configured to:
o send, to a Network Data Analytic Function (NWDAF) [303], in
a network from a user, a load analytics request associated with
the one or more network functions (NFs) [307] of the network,
25 wherein the one or more NFs [307] is associated with at least one
of: a set of network instance and a set of network services;
o transmit, from the NWDAF [303] via a Network Data Analytic
Function user interface (NWDAF UI) [306], one or more target
30 analyzed network load data from a set of analyzed network load
data based on at least one of: identified one or more target network instances and one or more target network services; and

5 o scale, at the NWDAF UI [306] the one or more target network
functions [301], based on the one or more target analyzed network load data.