DESC:
FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENTS RULES, 2003

COMPLETE SPECIFICATION
(See section 10 and rule 13)
1. TITLE OF THE INVENTION
A PROVISIONING UNIT FOR PROVIDING REAL-TIME NETWORK SLICE USAGE METRICS
2. APPLICANT(S)
NAME NATIONALITY ADDRESS
JIO PLATFORMS LIMITED INDIAN Office-101, Saffron, Nr. Centre Point, Panchwati 5 Rasta, Ambawadi, Ahmedabad - 380006, Gujarat, India
3. PREAMBLE TO THE DESCRIPTION
The following specification particularly describes the invention and the manner in which it is to be performed.

RESERVATION OF RIGHTS
[0001] A portion of the disclosure of this patent document contains material, which is subject to intellectual property rights such as, but are not limited to, copyright, design, trademark, Integrated Circuit (IC) layout design, and/or trade dress protection, belonging to Jio Platforms Limited (JPL) or its affiliates (hereinafter referred as owner). The owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all rights whatsoever. All rights to such intellectual property are fully reserved by the owner.
TECHNICAL FIELD
[0002] The present disclosure relates generally to the field of wireless communication systems. More particularly, the present disclosure relates to a provisioning unit configured to generate a plurality of network slice usage metrics in real-time and efficiently use network slicing.
DEFINITION
[0003] As used in the present disclosure, the following terms are generally intended to have the meaning as set forth below, except to the extent that the context in which they are used indicate otherwise.
[0004] The expression ‘network slicing’ used hereinafter in the specification refers to a process of slicing (dividing) a single physical network into multiple logical and independent networks that are configured to effectively meet the various services requirements.
[0005] The expression ‘network slice usage metrics’ used hereinafter in the specification refers to metrics that quantify how each network slice is utilized. These metrics include parameters such as data usage, bandwidth consumption, latency performance, availability, and other relevant indicators. These metrics help network operators understand the efficiency and effectiveness of each network slice.
[0006] The expression ‘network slice usage data’ used hereinafter in the specification refers to information that includes detailed specifications and configurations of each network slice. The network slice usage data includes parameters like service requirements of a slice, quality of service (QoS) parameters, security policies, resource allocation (bandwidth, computing resources), and any other specific attributes defining how the slice operates and what it offers to its users or applications.
[0007] The term “UE registration” as used herein in this specification denotes the process whereby a user equipment (UE), such as a mobile phone or other wireless device, connects to and registers with a network in order to obtain services and establish communication capabilities.
[0008] The term “PDU session establishment” as used herein in this specification refers to a process of initiating and configuring a Packet Data Unit (PDU) session within the network. A PDU session represents a logical association between the user equipment (UE) and a data network, facilitating the transmission of user data and supporting various services such as internet browsing, video streaming, and Internet of Things (IoT) applications.
[0009] The term “Network slice thresholds” as used herein in this specification refer to predefined limits or criteria that dictate the behavior or resource allocation within a network slice. These thresholds are instrumental in managing and optimizing quality of service (QoS), performance, and resource utilization for the applications or services operating within the network slice.
[00010] The term “3rd Generation Partnership Project (3GPP) access type” as used herein in this specification denotes the specific mobile network technology or generation utilized by a User Equipment (UE) or device to connect to the network. 3GPP access types encompass technologies such as 2G (GSM), 3G (UMTS/HSPA), 4G (LTE), and 5G (5G NR).
[00011] The term “Non-3GPP access type” as used herein in this specification refers to alternative access technologies or networks that do not conform to the standards established by the 3rd Generation Partnership Project (3GPP). Non-3GPP access types include Wi-Fi, Bluetooth, satellite networks, fixed-line broadband networks, and local area networks (LANs).
[00012] The term “Network Slice Admission Control Function (NSACF)” as used herein in this specification refers to a specific network element (server) responsible for managing the admission of network slices into the network. The NSACF operates based on defined criteria and policies to ensure efficient and controlled integration of network slices into the overall network infrastructure.
[00013] These definitions are in addition to those expressed in the art.
BACKGROUND
[00014] The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include 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.
[00015] In fifth generation (5G) communication system, a user equipment (UE) and a Next Generation NodeB (gNB) use next generation application protocol (ngap) to transfer nas (non-access stratum) messages, requesting a new session via N1 or N2 interface. Access and mobility management function (AMF) receives these requests, handles all access and mobility management-related tasks, and forwards session management requirements to session management function (SMF). The AMF determines which SMF to be selected for processing the session request. The SMF is responsible for interacting with a separate data plane, creating, updating, and deleting packet data unit (PDU) sessions, and managing session connections (session context) with the user plane function (UPF).
[00016] Data is communicated between the UE and the data network through various components along its path. In 5G, resource allocation and data path configuration are performed either statically or semi-statically. To ensure optimization for each individual UE or use case (such as rush hour traffic, regular hour traffic, enhanced mobile broadband (eMBB), Internet of Things (IoT), etc.), all these components are to be optimized. The resource allocation and parameters of these components can be configured dynamically through automation, or a set of parameters of all components along the data path for specific UEs or use cases may also be defined by a network operator. This set of parameters assigned for UEs or use cases is referred to as a “network slice”. Network slice is a logical concept that splits the resources along the data path into multiple sets that are optimized for specific UEs or use cases. Each network slice is an isolated end-to-end network that can be customized for different applications, services, or customers. Different network slices can be dedicated to different purposes, such as ensuring a specific application or service gets priority access to capacity and delivery or isolating traffic for specific users or device classes. Slicing networks enables a network operator to maximize the use of network resources and service flexibility.
[00017] In the 5G architecture, the network slice admission control function (NSACF) server (service) enables multiple independent network slices to coexist and interoperate on the same physical infrastructure. The NSACF server is used to monitor the number of registered terminals (UEs) and the number of PDU sessions on the network slice.
[00018] When multiple SMFs and AMFs are deployed in the network, it is a cumbersome task for the NSACF server to obtain various information in real-time corresponding to the network slicing and perform efficient load balancing across SMFs. For example, the information includes the current number of registered UEs in the slice and the number of currently active PDU sessions. In such a scenario, network efficiency, stability, and performance of the communication system are drastically reduced. Efficient load balancing is essential, as overloaded network slices can cause hindrances in UE admissions and PDU session creations and unloaded overloaded network slices cause cost and inefficient use of infrastructure. A network operator may be able to drive all the data at his end and may perform a number of analytics tasks on the data to get the relevant information; however, this approach is complex and time-consuming.
[00019] Hence, there is a need for a system that is configured to provide a real-time comprehensive view of the network load for effective and efficient allocation of resources.
OBJECTIVES
[00020] Some of the objectives of the present disclosure, which at least one embodiment herein satisfies, are as follows:
[00021] An objective of the present disclosure is to provide a provisioning unit that provides real-time network slice usage metrics.
[00022] Another objective of the present disclosure is to provide a provisioning unit that provides real-time network slice usage metrics corresponding to the network slice access types.
[00023] Yet another objective of the present disclosure is to provide a provisioning unit that informs which segment of the network slices is overloaded.
[00024] Still another objective of the present disclosure is to provide a provisioning unit that uses the network slice usage metrics to monitor and improve the utilization of the resources of each network slice in real-time.
[00025] Other objectives and advantages of the present disclosure will be more apparent from the following description, which is not intended to limit the scope of the present disclosure.
SUMMARY
[00026] The present disclosure envisages a provisioning unit for providing a plurality of network slice usage metrics in real-time. The provisioning unit includes a receiving unit and a processing unit. The receiving unit is configured to receive a request from a computing device for providing network slice usage metrics corresponding to a network slice. The processing unit is in communication with the receiving unit configured to receive the request. The processing unit is configured to establish a connection with at least one network slice admission control function (NSACF) server and at least one data source associated with the at least one NSACF server. The provisioning unit is configured to extract network slice usage data corresponding to the network slice from the at least one data source based on the received request. The provisioning unit is configured to process the extracted network slice usage data to generate the plurality of network slice usage metrics corresponding to the network slice. The provisioning unit is configured to categorize the generated plurality of network slice usage metrics slices based on one or more distinct access types.
[00027] In an embodiment, the one or more distinct access types include a regular access type, a third-generation partnership project (3GPP) access type, and a non-3GPP access type.
[00028] In an embodiment, the provisioning unit includes a communication unit configured to communicate the plurality of generated network slice usage metrics to the NSACF server.
[00029] In an embodiment, the NSACF server is configured to periodically update the at least one data source by retrieving the network slice usage data from at least one network function.
[00030] In an embodiment, the at least one network slice admission control function (NSACF) server is configured to determine the one or more distinct access types by extracting a number of information associated with a session request received from the at least one network function.
[00031] In an embodiment, the processing unit is configured to update an allocation of resources associated with the network slice based upon the categorized the plurality of generated network slice usage metrics.
[00032] In an embodiment, the network slice usage data includes a number of registered user equipments (UEs), a number of active packet data unit (PDU) sessions, a number of UEs connected to a plurality of network slices, a ratio of the number of registered UEs on a specific network slice to the maximum number of registered UEs allowed, and a ratio of the number of established PDU sessions to the maximum allowed number of established PDU sessions.
[00033] In an embodiment, the provisioning unit is configured to allocate the network slices based upon a plurality of use cases including an ultra-high bandwidth use case, a very low-latency use case, an ultra-reliable low-latency use case, a high-bandwidth use case, and a massive IoT use case.
[00034] In an embodiment, the plurality of network slice usage metrics includes latency, connection success rate, registered user equipment, and packet data unit (PDU) created sessions.
[00035] The present disclosure envisages a method for providing a plurality of network slice usage metrics in real-time. The method includes receiving, by a receiving unit, a request from a computing device for providing the network slice usage metrics corresponding to a network slice. The method includes establishing, by a processing unit, a connection with a at least one data source associated with at least one network slice admission control function (NSACF) server. The method includes extracting, by the processing unit, network slice usage data corresponding to the network slice from the at least one data source based on the received request. The method includes processing, by the processing unit, the extracted network slice usage data to generate the plurality of network slice usage metrics corresponding to the network slice. The method includes categorizing the plurality of generated plurality of network slice usage metrics slices based on one or more distinct access types.
[00036] In an embodiment, the method includes updating, by the NSACF server, periodically the at least one data source by retrieving the network slice usage data from at least one network function.
[00037] In an embodiment, the method includes determining, by the NSACF server, the one or more distinct access types by extracting a number of information associated with a session request received from the at least one network function.
[00038] In an embodiment, the method includes updating, by the processing unit, an allocation of resources associated with the network slice based upon the categorized plurality of generated network slice usage metrics.
[00039] In an embodiment, the present disclosure envisages a user equipment (UE) communicatively coupled with a provisioning unit. The coupling comprises steps of receiving a connection request, sending an acknowledgment of the connection request to the provisioning unit, and transmitting a plurality of signals in response to the connection request. The provisioning unit is configured to provide a plurality of network slice usage metrics in real-time. The provisioning unit includes a receiving unit and a processing unit. The receiving unit is configured to receive a request from a computing device for providing network slice usage metrics corresponding to a network slice. The processing unit is configured to cooperate with the receiving unit to receive the request. The processing unit is configured to establish a connection with at least one network slice admission control function (NSACF) server and at least one data source associated with the at least one NSACF server. The provisioning unit is configured to extract network slice usage data corresponding to the network slice from the at least one data source based on the received request. The provisioning unit is configured to process the extracted network slice usage data to generate the plurality of network slice usage metrics corresponding to the network slice. The provisioning unit is configured to categorize the generated plurality of network slice usage metrics slices based on one or more distinct access types.
BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWING
[00040] The accompanying drawings, which are incorporated herein, and constitute 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 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.
[00041] FIG. 1A illustrates an exemplary network architecture for implementing a provisioning unit for providing a plurality of network slice usage metrics, in accordance with an embodiment of the present disclosure.
[00042] FIG. 1B illustrates a high level architecture for implementing the provisioning unit, in accordance with an embodiment of the present disclosure.
[00043] FIG. 2 illustrates a block diagram of the provisioning unit, in accordance with an embodiment of the present disclosure.
[00044] FIG. 3A illustrates an exemplary flow diagram of a method for providing the plurality of network slice usage metrics, in accordance with an embodiment of the present disclosure.
[00045] FIG. 3B illustrates another exemplary flow diagram of a method for providing the plurality of network slice usage metrics, in accordance with an embodiment of the present disclosure.
[00046] FIG. 4 illustrates a computer system in which or with which the embodiments of the present disclosure may be implemented.
[00047] The foregoing shall be more apparent from the following more detailed description of the disclosure.
LIST OF REFERENCE NUMERALS
100A - Network Architecture
102 - Users
104 - Computing devices/User equipment (UE)
106 - Network
100B - High Level Architecture
110 - 5G Radio Access Network (RAN)
115 - Next Generation NodeB (gNB)
120 - Core Network
125 - Access And Mobility Management Function (AMF)
130 - Session Management Function (SMF)
135 - User Plane Function (UPF)
140 - Data Network
145 - Network Slice Admission Control Function (NSACF) Server
150 – Data Source
200 - Provisioning Unit
202 – Receiving Unit
204 – Memory
206 – A Plurality of Interfaces
208 – Processing Unit
212 – Database
410 – External Storage Device
420 – Bus
430 – Main Memory
440 – Read Only Memory
450 – Mass Storage Device
460 – Communication Port
470 – Processor
DETAILED DESCRIPTION
[00048] In the following description, for the purposes of explanation, various specific details are set forth in order 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 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 are described below, as illustrated in various drawings in which like reference numerals refer to the same parts throughout the different drawings.
[00049] The ensuing description provides exemplary embodiments only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, 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.
[00050] 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 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 in order to avoid obscuring the embodiments.
[00051] Also, it is noted that individual embodiments may be described as a process that 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 is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.
[00052] The word “exemplary” and/or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the 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 “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive like the term “comprising” as an open transition word without precluding any additional or other elements.
[00053] Reference throughout this specification to “one embodiment” or “an embodiment” or “an instance” or “one instance” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
[00054] The terminology used herein is to describe particular embodiments only and is not intended to be limiting the disclosure. As used herein, the singular forms “a”, “an”, and “the” are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term “and/or” includes any combinations of one or more of the associated listed items. 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 invention. These terms are not intended to limit the scope of the invention or imply any specific functionality or limitations on the described embodiments. The use of these terms is solely for convenience and clarity of description. The invention 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 invention as defined herein.
[00055] 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 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 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.
[00056] Further, the user device may also comprise a “processor” or “processing unit” includes processing unit, wherein processor refers to any logic circuitry for processing instructions. The processor may be a general-purpose processor, a special purpose processor, a conventional processor, a digital signal processor, a plurality of microprocessors, one or more microprocessors in association with a 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 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.
[00057] As portable electronic devices and wireless technologies continue to improve and grow in popularity, the advancing wireless technologies for data transfer are also expected to evolve and replace the older generations of technologies. In the field of wireless data communications, the dynamic advancement of various generations of cellular technology are also seen. The development, in this respect, has been incremental in the order of second generation (2G), third generation (3G), fourth generation (4G), and now fifth generation (5G), and more such generations are expected to continue in the forthcoming time.
[00058] Radio Access Technology (RAT) refers to the technology used by mobile devices/ user equipment (UE) to connect to a cellular network. It refers to the specific protocol and standards that govern the way devices communicate with base stations, which are responsible for providing the wireless connection. Further, each RAT has its own set of protocols and standards for communication, which define the frequency bands, modulation techniques, and other parameters used for transmitting and receiving data. Examples of RATs include GSM (Global System for Mobile Communications), CDMA (Code Division Multiple Access), UMTS (Universal Mobile Telecommunications System), LTE (Long-Term Evolution), and 5G. The choice of RAT depends on a variety of factors, including the network infrastructure, the available spectrum, and the mobile device's/device's capabilities. Mobile devices often support multiple RATs, allowing them to connect to different types of networks and provide optimal performance based on the available network resources.
[00059] While considerable emphasis has been placed herein on the components and component parts of the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiment as well as other embodiments of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the disclosure and not as a limitation.
[00060] Network slicing is a prominent feature of 5G networks that allows a single physical network to be divided (or sliced) into multiple logical and independent networks that are configured to effectively meet various service requirements. Each virtual network may be referred to as a network slice. Each network slice is an isolated end-to-end network that can be customized for different applications, services, or customers. This allows a single physical network to support a wide range of use cases, from IoT devices with low-data requirements to high-bandwidth applications like video streaming, by allocating resources as needed to each network slice. It is required that the data traffic be distributed efficiently across multiple network slices so that resource utilization and user experience can be improved, and latency can be reduced. Load balancing across the network slices allows the network operators (service providers) to optimize the performance of their networks by allocating traffic to the most appropriate network slice based on factors such as traffic type, network congestion, and user priority. Overloaded network slices can indeed cause issues with UE (User Equipment) admissions and PDU (Packet Data Unit) session creations.
[00061] The present disclosure provides a real-time overview of network load across the network slices for efficient resource allocation and optimization.
[00062] A preferred embodiment of a provisioning unit for providing network slice usage metrics of the present disclosure is now being described in detail with reference to FIG. 1A – FIG. 4.
[00063] FIG. 1A illustrates an exemplary network architecture (100A) for implementing the provisioning unit (200) for providing a plurality of network slice usage metrics, in accordance with an embodiment of the present disclosure.
[00064] As illustrated in FIG. 1A, the network architecture (100A) may include one or more computing devices or user equipments (UEs) (104-1, 104-2, …. 104-N) associated with one or more users (102-1, 102-2, …., 102-N) in an environment. A person of ordinary skill in the art will understand that one or more users (102-1, 102-2, …102-N) may be individually referred to as the user (102) and collectively referred to as the users (102). Similarly, a person of ordinary skill in the art will understand that one or more UEs (104-1, 104-2, ….104-N) may be individually referred to as the UE (104) and collectively referred to as the UEs (104). A person of ordinary skill in the art will appreciate that the terms “computing device(s)” and “user equipment” may be used interchangeably throughout the disclosure. Although three UEs (104) are depicted in FIG. 1A, however any number of the user equipments (104) may be included without departing from the scope of the ongoing description. In an embodiment, each of the UE (104) may have a unique identifier attribute associated therewith. In an embodiment, the unique identifier attribute may be indicative of at least one of a Mobile Station International Subscriber Directory Number (MSISDN), International Mobile Equipment Identity (IMEI) number, an International Mobile Subscriber Identity (IMSI), a Subscriber Permanent Identifier (SUPI), and the like.
[00065] In an embodiment, the UE (104) may include smart devices operating in a smart environment, for example, an Internet of Things (IoT) system. In such an embodiment, the UE (104) may include, but is not limited to, smart phones, smart watches, smart sensors (e.g., mechanical, thermal, electrical, magnetic, etc.), networked appliances, networked peripheral devices, a networked lighting system, communication devices, networked vehicle accessories, networked vehicular devices, smart accessories, tablets, smart television (TV), computers, a smart security system, a smart home system, other devices for monitoring or interacting with or for the users (102) and/or entities, or any combination thereof. A person of ordinary skill in the art will appreciate that the UE (104) may include, but is not limited to, intelligent, multi-sensing, network-connected devices, which can integrate seamlessly with each other and/or with a central server or a cloud-computing system or any other device that is network-connected.
[00066] In an embodiment, the UE (104) may include, but is not limited to, a handheld wireless communication device (e.g., a mobile phone, a smart phone, a phablet device, and so on), a wearable computer device (e.g., a head-mounted display computer device, a head-mounted camera device, a wristwatch computer device, and so on), a Global Positioning System (GPS) device, a laptop computer, a tablet computer, or another type of portable computer, a media playing device, a portable gaming system, and/or any other type of computer device with a wireless communication capabilities, and the like. In an embodiment, the UE (104) may include, but is not limited to, any electrical, electronic, electro-mechanical, or an equipment, or a combination of one or more of the above devices such as virtual reality (VR) devices, augmented reality (AR) devices, a laptop, a general-purpose computer, a desktop, a personal digital assistant, a tablet computer, a mainframe computer, or any other computing device. In addition, the UE (104) may include one or more in-built or externally coupled accessories including, but not limited to, a visual aid device such as a camera, an audio aid, a microphone, a keyboard, and input devices for receiving input from the user (102) or an entity such as touch pad, a touch enabled screen, an electronic pen, and the like. A person of ordinary skill in the art will appreciate that the UE (104) may not be restricted to the mentioned devices and various other devices may be used.
[00067] In FIG. 1A, the UE (104)/computing device (104) may communicate with the provisioning unit (200) via a network (106) to enable the provisioning unit (200) to generate the plurality of network slice usage metrics in real-time.
[00068] In an aspect, the plurality of network slice usage metrics may include resource utilization metrics, service level agreements (SLAs) metrics, quality of service (QoS) metrics, traffic patterns metrics, dynamic scaling and elasticity metrics, fault management metrics, and operational efficiency metrics. In an example, the resource utilization metrics include CPU usage, memory usage, network bandwidth consumption, and storage utilization within each network slice. The SLAs metrics related to SLAs agreed upon with customers or service providers, such as latency, throughput, availability, and reliability. Monitoring SLA compliance helps ensure that network slices meet performance expectations. The QoS metrics measure a level of service quality experienced by users or applications within a network slice. QoS metrics can include packet loss rate, jitter, delay, and overall network responsiveness. The traffic patterns metrics includes traffic characteristics within each network slice, such as peak usage times, volume of data transferred, and types of applications generating traffic (e.g., video streaming, IoT data). The dynamic scaling and elasticity metrics show how well network slices adapt to changes in demand or traffic load, and the efficiency of scaling resources up or down dynamically. Customer Satisfaction metrics are related to customer satisfaction and user experience within each network slice, which may involve surveys, user ratings, or direct feedback from subscribers. The fault management metrics are related to detecting, isolating, and resolving faults or issues within network slices, ensuring minimal disruption to services. The operational efficiency metrics evaluate the efficiency of network slice management processes, such as provisioning, configuration management, and lifecycle management. Each of the plurality of network slice usage metrics provides a different perspective on how well network slices are performing and being utilized within a telecommunications network. Operators and service providers use these metrics to optimize resource allocation, improve service delivery, and ensure a positive experience for end-users and applications.
[00069] In an embodiment, the network (106) may include at least one of a second generation (2G), third generation (3G), fourth generation (4G), fifth generation (5G) network, a sixth generation (6G) network, or the like. The network (106) may enable the UEs (104) to communicate with other devices in the network architecture (100A) and/or with the provisioning unit (200). The network (106) may include a wireless card or some other transceiver connection to facilitate this communication. In another embodiment, the network (106) may be implemented as, or include any of a variety of different communication technologies such as a wide area network (WAN), a local area network (LAN), a wireless network, a mobile network, a Virtual Private Network (VPN), the Internet, the Public Switched Telephone Network (PSTN), or the like. In an embodiment, the network (106) may include, by way of example but not limitation, at least a portion of one or more networks having one or more nodes that transmit, receive, forward, generate, buffer, store, route, switch, process, or a combination thereof, etc. one or more messages, packets, signals, waves, voltage or current levels, some combination thereof, or so forth. In an embodiment, the UE (104) may be communicatively coupled with the network (106). The network (106) may receive a connection request from the UE (104). The network (106) may send an acknowledgment of the connection request to the UE (104). The UE (104) may transmit several signals in response to the connection request.
[00070] FIG. 1B illustrates a high level architecture (100B) for implementing the provisioning unit (200), in accordance with an embodiment of the present disclosure.
[00071] As shown in FIG. 1B, the high level architecture (100B) includes the computing device (104), a 5G radio access network (RAN) (110), a core network (120), and the provisioning unit (200). In an example, the architecture (100B) may include any number of computing devices (104)being used by any number of users. The example of a single computing device (104) is merely provided for illustrative purposes. The users (102) may be configured to communicate with one or more networks. In the example, the users (102) may wirelessly communicate with the 5G RAN (110). In an aspect, the users (102) may also communicate with other types of networks (e.g., 5G cloud RAN, a next generation RAN (NG-RAN), long-term evolution (LTE) RAN, a cellular network, a wireless local area network (WLAN), etc.) and the users (102)/ computing device (104) may also communicate with networks over a wired connection.
[00072] To establish a connection (Packet Data Unit (PDU) session), the user (102) using the UE is required to establish the connection with a data network (140) via the core network (120). For example, the user (102)) may set up a connection to a Next Generation NodeB (gNB) (115). The user (102) performs the random access procedure to initiate communication with the gNB (115) and sends a radio resource control (RRC) connection request message to the gNB (115). After receiving the RRC connection request message, the gNB (115) performs network-attached storage (NAS) level authentication and initiates ciphering for the NAS messages with the core network (120). The core network (120) completes the security procedure with the gNB (115) and handles the RRC reconfiguration from the gNB (115). The UE responds with a channel state information (CSI) report as requested by the gNB (115), and the connection is established.
[00073] The 5G RAN (110) may include, for example, cells or base stations (Node Bs, evolved Node Bs (eNodeBs), macrocells, microcells, small cells, femtocells, etc.) that are configured to send and receive traffic from UEs. In the architecture (100B), the 5G RAN (110) is shown with the gNB (115). However, in real-time scenario, the architecture (100B) may include any number of different types of base stations or cells deployed by any number of RANs.
[00074] In an example, the core network (120) includes several components, including but not limited to an access and mobility management function (AMF) (125), a session management function (SMF) (130), a user plane function (UPF) (135), a network slice admission control function (NSACF) server (145), and at least one data source (150). The gNB (115) is connected to the AMF (125). The AMF (125) is configured to perform connection and mobility management in the 5G RAN (110). In an example, the AMF (125) is configured to perform operations related to registration procedure management between the UE and the SMF (130). An actual communication network may include any appropriate number of AMFs. The AMF is configured to determine which SMF is best suited to handle the requirements of the UE.
[00075] The UPF (135) handles the forwarding and routing of the user data packets. The NSACF server (145) is designed to oversee the admission of network slices into the network based on predefined criteria and policies. The at least one data source (150) is used for storing network-related information and configurations. The core network (120) manages the network resources and provides connectivity between different UEs.
[00076] In an operative aspect, the AMF (125) may perform operations related to registration procedure management between the UE and the SMF. The AMF (125) is configured to determine which SMF is best suited to handle the requirements of the UE. Bases upon the connection request received (having a requested network slice) from the UE, the AMF (125) queries the NRF (Network Repository Function) to find the SMF for the requested network slice. The selection of the SMF is based on various parameters like service type (S-NSSAI), network data (DNN), and operator policies. The selected SMF sets up the PDU session according to the defined network slice, ensuring the UE connects to the desired service efficiently.
[00077] In an aspect, the UE sends a PDU session establishment request to the SMF (130) after authentication. In an example, the PDU session establishment request may include various parameters such as session type (Type of PDU session (e.g., IPv4, IPv6), requested QoS (quality of service requirements for the session), and network slice information (identifies the network slice for the session).
[00078] The SMF (130) performs operations related to session management, such as, but not limited to, session establishment, session release, IP address allocation, policy and QoS enforcement, etc. When the UE registration request is successfully completed with the AMF, the determined SMF initiates the PDU establishment request for a specific network slice. The SMF processes the request, checks resource availability, and interacts with other functions (e.g., Policy Control Function, PCF) for policy enforcement. Upon successful validation, the SMF instructs the UPF (135) to establish the data path, and a confirmation is sent back to the UE. Once established, the session allows user data transmission between the UE and the network.
[00079] In an operative aspect, the SMF (130) may configured to transmit multiple PDU session establishment requests pertaining to a designated network slice. For instance, in the context of a network slice P, a plurality of SMFs (130) initiate the transmission of PDU session establishment requests concurrently. Further, a plurality of AMFs (125) may be configured to communicate with the NSACF server. The communication between the AMF and the Network Slice Selection Assistance Function (NSACF) is essential for efficiently managing UE connections in the network. When the UE initiates registration, the AMF collects detailed information regarding the device's capabilities and the specific network slice requirements it may have, such as service types (e.g., enhanced Mobile Broadband, ultra-reliable low latency). The AMF (125) then formulates a Slice Selection Request, which includes UE identification, location, and requested service levels. This request is sent to the NSACF, which assesses available network slices based on current load, performance metrics, and pre-defined policies. The NSACF evaluates this information to determine the most suitable slice and generates a Slice Selection Response, providing the AMF with the selected slice identifier (S-NSSAI) and any specific parameters needed for configuration.
[00080] Once the AMF (125) receives the response from the NSACF, the AMF proceeds to establish a connection to the designated network slice for the UE. This involves configuring the appropriate Quality of Service (QoS) settings and potentially coordinating with other network functions like the User Plane Function (UPF) to ensure proper data routing and service delivery. The communication does not end here; ongoing monitoring is vital. The AMF (125) may periodically send updates to the NSACF regarding changes in the UE's status or location, allowing for dynamic adjustments to the slice allocation as needed.
[00081] When network slices reach their capacity limits, new UEs may experience delays or denials during the admission process, while ongoing sessions might suffer from degraded performance or instability. To enhance operational efficiency a comprehensive view of network load across all network slices is necessary. This system would continuously analyze various metrics, such as slice utilization, traffic patterns, and QoS requirements, allowing the AMF (125) to allocate resources where they are most needed dynamically. For instance, if a particular slice approaches its capacity threshold, the AMF (125) may preemptively redirect new UE admissions to less congested slices or initiate scaling mechanisms to balance the load more effectively. Further, if a particular access type uses a network slice, the system may be able to update resources assigned to the network slice by analyzing the network usage data metrics based on the access type. Additionally, the NSACF server (145) is able to provide insights on the best available slice for each UE based on real-time data, enabling quicker and more efficient PDU session creation. By doing so, the system effectively contributes to the optimization of resource utilization, fostering a balanced operational environment and enhancing overall network performance.
[00082] The AMF (125) and the SMF (130) are also connected to the NSACF server (145). The NSACF server (145) is configured to perform operations related to controlling the number of UEs and/or sessions registered per network slice. During operation, the NSACF server (145) is configured to check the count of registered UEs and/or PDU sessions associated with each network slice. This monitoring is essential to maintain an accurate and up-to-date representation of the real-time PDU load for each network slice, enabling effective load balancing across the 5G network infrastructure. It is a requirement that the NSACF server (145) possesses detailed insights into the operational metrics and performance indicators of each network slice. This data is critical to ensure optimal resource allocation and to facilitate dynamic adjustments in response to varying traffic conditions and user demands. The operational capabilities of the NSACF server (145) are fundamental to achieving a balanced distribution of network resources, thereby enhancing overall network performance and user experience within the network.
[00083] As shown in FIG. 1B, the provisioning unit (200) is commutatively coupled to the at least one data source (150). In an aspect, the provisioning unit (200) is configured to communicate with the NSACF server (145). In another aspect, the provisioning unit (200) may be embedded within the NSACF server (145).
[00084] The NSACF server (145) is operatively coupled with the at least one data source (150). The at least one data source (also known as a database, or a context data store) (150) is configured to store a plurality of details (representing network slice usage data) corresponding to each network slice. In an example, the network slice usage data includes a number of registered UEs through the plurality of AMFs, a number of connected UEs along with their corresponding Public land mobile network (PLMN) IDs through the plurality of SMFs, a predefined list of SMFs having a unique number corresponding to each SMF of the plurality of SMFs, the number of active PDU sessions, the number of UEs connected to each of the network slice(s), a predefined threshold value corresponding to the UE registration for each network slice, a value corresponding to PDU session establishments for SMF of the plurality of SMFs, a value corresponding to PDU session establishments for each network slice, a list of identifiers having a unique number for each network slice, types of network slice(s), network slice selection assistance information (NSSAI), and a list of service providers. NSSAI refers to the set of information elements that assist in selecting and identifying the appropriate network slice instance for a particular service or user equipment (UE). In an example, the plurality of details may include reference count data of the SMF serving a particular network slice in the PLMN.
[00085] To maintain an up-to-date understanding of network performance, the NSACF server (145) may be configured to update the at least one data source by retrieving the network slice usage data from at least one network function (such as the AMF and the SMF). To retrieve the network slice usage data, the NSACF is configured with specific parameters that dictate the frequency of these network slice usage data retrievals. During each retrieval cycle, the NSACF server (145) sends one or more slice usage query requests to the AMF and SMF using efficient communication protocols like REST(Representational State Transfer)ful Hypertext Transfer Protocol (HTTP) methods (GET, POST, PUT, DELETE). The AMF provides real-time data on active user equipment (UE), ongoing sessions, and resource consumption, while the SMF offers insights into PDU session performance, including throughput and QoS adherence. This data allows the NSACF server (145) to assess the current load and performance of each network slice, enabling it to make informed decisions about slice allocations for incoming UE requests. Continuous monitoring facilitates effective load balancing; for instance, if a specific slice is heavily utilized due to increased IoT device activity, the NSACF may recommend reallocating resources or suggest alternative slices. This real-time data-driven approach enhances operational efficiency and ensures that network slices can meet varying service demands, ultimately leading to improved user experiences and network reliability.
[00086] Although FIG. 1A and FIG. 1B show exemplary components of the network architecture (100A, 100B), in other embodiments, the network architecture (100A, 100B) may include fewer components, different components, differently arranged components, or additional functional components than depicted in FIG. 1A and FIG. 1B. Additionally, or alternatively, one or more components of the architectures (100A, 100B) may perform functions described as being performed by one or more other components of the architectures (100A, 100B).
[00087] FIG. 2 illustrates an exemplary block diagram of the provisioning unit (200), in accordance with embodiments of the present disclosure.
[00088] In an embodiment, the provisioning unit (200) includes a receiving unit (202), a memory (204), a processing unit (210), and a communication unit (212).
[00089] The receiving unit (202) is configured to receive a request from the user, via the computing device (104), for providing the slice usage metrics corresponding to a network slice. In an example, the network slice may be an Enhanced Mobile Broadband (eMBB) slice, a Massive Machine Type Communication (mMTC) slice, and an Ultra-Reliable Low Latency Communication (URLLC) slice. The eMBB slice provides high data rates and improved capacity for applications requiring substantial bandwidth, such as video streaming and virtual reality. The mMTC slice supports a vast number of low-power devices, enabling efficient communication for applications like smart sensors and IoT devices that transmit small data packets infrequently. The URLLC slice ensures extremely low latency and high reliability, making it ideal for critical applications such as autonomous driving and remote surgery, where timely data transmission is essential. For example, the receiving unit (202) is a user interfacing unit, facilitating interaction between the user and the unit (200).
[00090] In an aspect, the provisioning unit (200) is configured to transfer the real-time network slice usage metrics segmented by one more access types of the network slices to the computer device periodically over a predefined time. In an aspect, the computer device may configure to define the network slice (user-defined network slice) along with its corresponding slice ID number and set a configuration in which whenever an update regarding these specific network slices occurs, the provisioning unit (200) automatically transfers updated metrics for these specific network slices to the computing device. In some examples, the computing device may be implemented as any computing device for hosting a webpage or website accessible via the network, such as, but without limitation, a web server, application server, cloud server, or other host. For example, the computing device acts as a management server capable of communicating data with respect to the device(s). The management server provides access to the hardware resources that are required for establishing network connectivity. In an example, the provisioning unit (200) is configured to supply real-time network slice metrics to the user periodically after a predefined time, thereby performing as a self-sufficient unit.
[00091] The processing unit (210) is configured to cooperate with the receiving unit (202) and receive the request. Upon receiving the request, the processing unit (210) establishes a connection with the at least one data source (150) coupled with the at least one NSACF server (145). For example, when the network operator submits the request via the receiving unit (202) for real-time slice usage metrics for the user-defined network slice, the processing unit (210) may perform the following steps:
• Collaboration with the receiving unit: The processing unit (210) actively collaborates with the receiving unit to confirm the details of the request, ensuring that all necessary parameters (such as the target network slice) are accurately specified.
• Connection establishment: Upon verification, the processing unit (210) establishes a secure connection with the at least one data source that is associated or integrated with the NSACF server. In an example, the processing unit (210) may utilize protocols such as RESTful APIs (Application Programming Interfaces) or gRPC (gRPC Remote Procedure Call) to facilitate efficient data retrieval.
[00092] The processing unit (210) is configured to extract the network slice usage data from the at least one data source (150). In an example, the data collected from the various network functions and stored in the at least one data source (150) is known as context data. In an aspect, the at least one data source may be configured to store the data received from the at least one network function along with a time stamp. In an example, the at least one data source is configured to store historical data associated with the AMF and the SMF. To extract the network slice usage data from the context data, an extraction process involves several systematic steps is performed. the extraction process ensures accurate and timely access to relevant data. First, upon receiving the request, the processing unit (210) constructs a specific query tailored to extract network slice usage data for the user-defined network slice. For example, the query may include parameters such as, a network slice identifier (to specify which slice’s load details are required) and a timeframe (to define the period for which the network slice usage data is being requested (e.g., real-time, last hour, last 24 hours)). The processing unit (210) executes the formulated query against the at least one data source. This involves sending the request to the at least one data source (150) and waiting for a response containing the requested data.
[00093] For example, in an operational scenario, the network administrator (network operator) utilizes the receiving unit (202) (user interfacing unit) to submit the request that specifies a particular network slice, such as an IoT network slice designated for massive machine-type communications. In an example, the request may include a specific network slice identifier, a time interval, and one or more performance metrics. The specific network slice identifier helps the processing unit (210) to locate and retrieve the relevant data pertaining to that particular network slice. The user may request data for a specific time period or interval (e.g., the last hour, day, or week), which aids in analyzing trends and performance over time. Users can also specify the particular metrics or indicators they are interested in, such as CPU utilization, memory usage, network traffic, latency, or throughput. These metrics provide insights into how resource utilization is within the network slice. Additionally, users may specify how they want the data to be presented, such as in graphical charts, tables, or reports, which aids in understanding trends and anomalies more effectively. Furthermore, users can set thresholds for certain metrics, triggering alerts if performance metrics exceed or fall below-specified levels. This helps in proactive monitoring and management of network slice performance. Lastly, users may request comparative data, such as comparing the current performance with historical data or benchmarking against other network slices or predefined standards. Upon receipt of this request, the receiving unit engages with the processing unit to retrieve the current network slice usage data across the various network slices associated with the specified network slice. In an example, the network slice usage data may include a total count of active PDU sessions managed by each network slice within the specified network slice, a percentage of available resources being utilized by each network slice, indicating the operational capacity, and an average response time for session management requests handled by the network slices, reflecting the efficiency of the network slice. By providing this real-time network slice usage data, the receiving unit enables the user to make informed decisions regarding resource allocation, performance optimization, and potential scaling of network resources to ensure efficient operation of the user-defined network slice.
[00094] The processing unit (210) may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that process data based on operational instructions. Among other capabilities, the processing unit (210) may be configured to fetch and execute computer-readable instructions stored in the memory (204) of the provisioning unit (200). The memory (204) may be configured to store one or more computer-readable instructions or routines in a non-transitory computer readable storage medium, which may be fetched and executed to create or share data packets over a network service. The memory (204) may comprise any non-transitory storage device including, for example, volatile memory such as random-access memory (RAM), or non-volatile memory such as erasable programmable read only memory (EPROM), flash memory, and the like.
[00095] In an embodiment, the provisioning unit (200) may include an interface(s) (206). The interface(s) (206) may comprise a variety of interfaces, for example, interfaces for data input and output devices (I/O), storage devices, and the like. The interface(s) (206) may facilitate communication through the provisioning unit (200). The interface(s) (206) may also provide a communication pathway for one or more components of the provisioning unit (200). Examples of such components include, but are not limited to, a processing engine and a database.
[00096] In an embodiment, the processing unit (210) may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing unit (210). In the examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing unit (210) may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processing unit (210) may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the processing unit (210). In such examples, the system may comprise the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to the system and the processing resource. In other examples, the processing unit (210) may be implemented by electronic circuitry.
[00097] In an embodiment, the database (220) may store data that may be generated as a result of functionalities implemented by any of the components of the provisioning unit (200). In an embodiment, the database (220) may be indicative of including, but not limited to, a relational database, a distributed database, a cloud-based database, or the like. In an exemplary embodiment, the processing unit (210) may include one or more units having functions that may include, but are not limited to, testing, storage, and peripheral functions, such as a wireless communication unit for remote operation and the like.
[00098] The processing unit (210) is configured to process the extracted network slice usage data to generate the plurality of network slice usage metrics. In an aspect, the plurality of network slice usage metrics includes latency, connection success rate, registered user equipment, and packet data unit (PDU) created sessions. The processing unit (210) is further configured to categorize the plurality of generated network slice usage metrics slices based on one or more distinct access types. In an aspect, the one or more distinct access types include a regular access type, a third-generation partnership project (3GPP) access type, and a non-3GPP access type. The regular access type refers to standard methods of connecting to a network that do not fall under specialized categories such as 3GPP or non-3GPP access types. This category typically includes conventional connectivity options commonly utilized by users, such as traditional mobile network connections based on GSM (Global System for Mobile Communications), UMTS (Universal Mobile Telecommunications System), and LTE (Long-Term Evolution). Regular access types are designed to be broadly compatible with various devices and applications, ensuring a seamless user experience without the need for specialized configurations or additional hardware. They are generally sufficient for everyday use cases, such as voice calls, SMS, and basic data services, making them suitable for the average consumer. The 3GPP access type may related to traditional mobile network standards, such as LTE and 5G, which provide robust and standardized connectivity for mobile devices. In contrast, the non-3GPP access type refers to alternative connectivity options, allowing diverse service access scenarios extending beyond traditional mobile networks. Non-3GPP service access can be requested from various sources, including interworking wireless local area networks (I-WLANs), code division multiple access (CDMA) networks, Wi-Fi networks, WiMAX networks, and fixed networks. This flexibility enables users to leverage existing infrastructure to connect to the network, ensuring broader accessibility and improved user experience. Furthermore, the non-3GPP service access can be categorized into two types: trusted non-3GPP access and untrusted non-3GPP access. Trusted non-3GPP access refers to connections established through secure and verified channels, such as enterprise Wi-Fi networks or partner networks that comply with security standards. In contrast, untrusted non-3GPP access pertains to connections that may lack stringent security controls, such as public Wi-Fi networks. This classification allows the system to apply appropriate security and resource management measures tailored to the specific access type, enhancing overall network integrity and user safety.
[00099] In an aspect, the plurality of network slice usage metrics may be categorized into three distinct access types. The plurality of network slice usage metrics discloses percentages for total resource consumption in the network slice. For the 3GPP access type, primarily serving enhanced Mobile Broadband (eMBB) users, utilization peaks at 70% during busy hours, driven by high-definition video streaming and online gaming. Meanwhile, the non-3GPP access type, including Wi-Fi connections, accounts for 20% of resource utilization. This reflects the activity of IoT devices and users accessing home networks, though their demand is lower during peak times when mobile users dominate. Lastly, the regular access type, representing fixed broadband and legacy systems, demonstrates only 10% utilization, indicating that these users are less active than those on mobile networks. This categorization of usage metrics enables the provisioning unit to make strategic resource allocation decisions, ensuring that the eMBB slice receives sufficient resources during high-demand periods while potentially reallocating excess capacity from the lower-utilized non-3GPP and regular access types to enhance overall network performance.
[000100] In an example, the network slice usage data may include a total number of active sessions within the designated network slice. For example, the network slice usage data may include the percentage of utilized resources for each distinct access type, reflecting the operational load. For example, the network slice usage data may reveal that the 3GPP access type is utilizing 75% of the allocated resources of the network slice, the non-3GPP access type is operating at 10% utilization, and regular access type is utilizing the 15% of the resources associated with the network slice. In an example, the network slice usage metrics may include average response times for session establishment and management requests processed by each network slice. An example may indicate that the average response time, for the regular access type, to join the network slice is 120 milliseconds, while for the 3GPP access type averages 80 milliseconds. For example, the network slice usage metrics may include metrics related to the success rate of session establishment requests, illustrating the reliability of the network slice. For example, the network slice may exhibit a success rate of 98% for the 3GPP access type, while the network slice A achieves a success rate of 95% for non-3GPP access type. In another example, the network slice usage metrics may include the volume of data traffic handled by each access types in the network slice, indicating the overall network slice load. For instance, the 3GPP access type might uses 500 GB of data traffic daily, whereas the non-3GPP access type uses 800 GB. In an example, the network slice usage metrics may include peak usage times for each network slice/access type, providing insights into when network slices experience the highest demand. For example, the data could show that the 3GPP access type experiences peak loads between 6 PM and 8 PM. For example, the network slice usage metrics may include geographic information regarding which access types are being established or terminated, helping to identify regional load patterns.
[000101] In a structural aspect, the processing unit includes a parser. The parser is configured to extract the network slice usage data details from the at least one data source (150). Upon successful retrieval, the processing unit processes the incoming data to ensure it is formatted correctly and meets the user’s request. This may involve aggregating results, calculating averages, or converting data formats. After retrieving the necessary data, the processing unit (210) processes the network slice usage data (slice usage information). In an example, the processing may involve several operations, including aggregating results, calculating averages, or converting data formats. The processing unit (210) formulates a response to be sent to a display unit, enabling the user to view the requested network slice usage metrics.
[000102] In an embodiment, the display unit may be configured to allow the user to view the requested network slice usage metrics in a clear and informative manner. By employing this structured approach, the provisioning unit ensures that users receive accurate and actionable insights into network slice performance, enabling effective monitoring and decision-making regarding resource allocation and network optimization. In an embodiment, the display unit is configured to present network slice usage metrics in a visually intuitive manner, utilizing a color-coded scheme to differentiate between various access types. Each access type (such as 3GPP, non-3GPP, and regular access) is assigned a distinct color, facilitating quick identification and analysis by the user. This visual distinction allows for immediate recognition of performance trends and resource utilization across the network.
[000103] In an embodiment, the display unit is designed to present the network slice usage metrics in a user-friendly format, allowing easy interpretation of the network slice usage metrics. This may include graphical representations, such as charts or tables. The display unit is capable of dynamically updating the information in real-time, reflecting the most current network slice usage metrics as extracted from the processing unit. For instance, a user may see that 3GPP access types are utilizing a higher percentage of bandwidth compared to non-3GPP access types, which may be represented in contrasting shades. This structured approach ensures that users receive accurate and actionable insights into network slice performance, empowering effective monitoring and informed decision-making regarding resource allocation and network optimization. By leveraging color coding and clear visual representation, the system enhances user engagement and facilitates a deeper understanding of network dynamics.
[000104] In an example, the provisioning unit (200) employs an automated refresh mechanism that updates the displayed data every few seconds or upon receiving new data from the processing unit. The display unit may also include interactive features, allowing the network operator to filter, sort, or select specific network slices for detailed analysis. The display can incorporate visual indicators, such as color-coding or alerts, to highlight critical load conditions or performance thresholds that require immediate attention.
[000105] The communication unit (212) is configured to send the generated network slice usage metrics to the computer device and the NSACF server over the network. In an aspect, the processing unit (210) is configured to generate an update signal to notify the computing device of the updated metrics. In an aspect, the network slice usage metrics may update over time due to various factors, including changes in user demand, time of day, seasonal events, and the types of applications being utilized. For instance, network usage often follows predictable patterns based on the time of day; during peak hours, such as evenings, there may be increased demand for the eMBB network slice as users stream videos or engage in online gaming. Conversely, usage may drop significantly during early morning hours when fewer people are active online. For example, during a major sporting event, a surge in viewers accessing streaming services may heavily utilize eMBB network slice. Additionally, varying application demands can influence resource allocation. For instance, an increase in remote work applications or video conferencing services during a global event, like a pandemic, can elevate the demand for the URLLC network slices over the other types of slices.
[000106] By analyzing the generated network slice usage metrics over a time period, the network operators can gain insights into user behavior and dynamically adjust resource allocation to ensure optimal performance and quality of service. For example, during peak streaming hours, more resources can be allocated to the 3GPP access type while maintaining sufficient capacity for other access types, ultimately enhancing user satisfaction and network efficiency.
[000107] In an aspect, the NSACF server (145) is configured to analyze the throughput of each access type based on the received network slice usage metrics. In another aspect, the NSACF server (145) is configured to analyze the load of each access type. The NSACF server (145) is further configured to adjust a maximum number of PDU session establishment requests to be served by each SMF or distinct access type over the user-defined network slice based on the generated network slice usage metrics.
[000108] In an aspect, the NSACF server (145) is configured to utilize historical network slice usage data (stored in the database or the context data) to predict trends in utilization across various access types, specifically differentiating between 3GPP access types (e.g., LTE, 5G) and non-3GPP access types (e.g., Wi-Fi, fixed networks). The context data includes data such as active user counts, resource consumption, and performance statistics, all accompanied by time stamps to facilitate analysis.
[000109] The NSACF server (145) retrieves historical metrics from the at least one data source, filtering the data based on criteria like access type and time intervals. Once the data is filtered, the NSACF server may employ various analytical techniques to predict future trends. In an example, the NSACF server (145) may employ at least one statistical modeling method(in an example, time-series analysis) to identify patterns in the network slice usage data. By identifying the patterns, the NSACF server may respond promptly to unexpected changes in usage. Therefore, the NSACF optimizes resource allocation, ensuring that network slices can effectively meet the evolving demands of users.
[000110] In an aspect, the user is a network operator or a service provider. In an aspect, the user is configured to analyze the received network slice usage metrics in real-time. Based on the analysis, if it is found that the specific network slice or distinct access type is overloaded, the user is configured to generate a diversion request to divert the new PDU establishment request to another available network slice (s).
[000111] The NSACF server (145) is configured to determine the one or more distinct access types by extracting a number of information associated with a session request. In an aspect, the session request is received by the NSACF server (145) from the at least one network function (the AMF). In an example, the number of information may include information such as the access type preferred by the user, the access type compatible with the UE, the access type provided by the service provider in a location in which the user resides, and the access type allowed according to the plans of the user subscribed to. When a session request is initiated by the user equipment (UE), this session request is sent to the NSACF server through the AMF, containing critical information (user identity, device information, requested services, and location data) that informs the server about the user's connectivity needs. The device information provides context regarding the type of access required, while the requested services indicate necessary quality of service (QoS) and bandwidth needs. Location data may influence the access type selection, particularly concerning the availability of different network technologies in specific areas. The AMF handles initial contact with the UE and manages mobility, while the SMF focuses on session establishment and resource allocation, collectively forwarding relevant data to the NSACF server. Based on the extracted information, the NSACF server classifies the access type into two primary categories: 3GPP access types and non-3GPP access types. The 3GPP access types include methods standardized by the 3rd Generation Partnership Project (3GPP), such as GSM, UMTS, LTE, and 5G NR (New Radio). For instance, if a session request indicates high-definition video streaming from a 5G-capable device, the NSACF would classify this as a 3GPP access type. Conversely, non-3GPP access types encompass alternative connectivity options like Wi-Fi networks and interworking wireless local area networks (I-WLANs). If an IoT device connects via Wi-Fi, the NSACF server would categorize this as a non-3GPP access type.
[000112] For example, consider a scenario where a session request is received from an AMF for a smartphone. The request reveals a premium subscriber plan, identifies the device as a 5G smartphone, and indicates that the user wants to stream a 4K video while located in an area with 5G coverage. In this case, the NSACF server determines that the access type is 3GPP, facilitating the selection of a network slice optimized for the eMBB network slice. On the other hand, if the session request comes from a smart thermostat using a Wi-Fi network, the NSACF server would classify this as a non-3GPP access type, allowing for resource allocation appropriate for the mMTC network slice.
[000113] In an aspect, the provisioning unit (200) is configured to allocate the network slices based upon a plurality of use cases, including an ultra-high bandwidth use case, a very low-latency use case, an ultra-reliable low-latency use case, a high-bandwidth use case, and a massive IoT use case. In the ultra-high bandwidth use case, network slices are allocated to applications demanding extensive data throughput, such as high-definition video streaming or large-scale file transfers. For example, for the very low-latency use case, the network slices are designated for applications requiring minimal delay in data transmission, such as real-time gaming or remote surgery applications. For the ultra-reliable low-latency use case, the network slices are used for applications requiring high reliability and low latency, such as autonomous vehicles or industrial automation systems. The high-bandwidth use case is assigned for applications that require significant data capacity, such as virtual reality experiences or cloud-based data analytics. The massive IoT use case focuses on applications involving a vast number of interconnected devices with varied communication needs, such as smart city infrastructure or agricultural sensor networks.
[000114] In an example, the processing unit (210) is configured to dynamically update resource allocation for network slices based on categorized usage metrics, based upon the one or more distinct access types. For example, consider a scenario where the network slice supports various access types, including 3GPP and non-3GPP. The processing unit (210) analyzes the categorized network slice usage metrics that reveal that the 3GPP access type, particularly for video streaming services, is experiencing significant demand during evening hours, resulting in higher utilization of the eMBB (enhanced Mobile Broadband) slice. Simultaneously, the categorized network slice usage metrics show that non-3GPP access types, such as Wi-Fi connections for IoT devices, are underutilized during this peak period. In response, the processing unit can reallocate resources from the less utilized non-3GPP network slice to the eMBB network slice, ensuring that users receive a seamless and high-quality streaming experience.
[000115] In an aspect, the provisioning unit (200) is configured to monitor the plurality of network slice usage metrics and provide improved utilization of the network slice resources. In an example, the plurality of network slice usage metrics can be generated based on different type of layers such as a resource layer, a network slice layer, and a service layer. In one aspect, the resource layer evaluates the utilization of physical and virtual resources allocated to each network slice, such as CPU, memory, and bandwidth usage. Further, for the network slice layer, the metrics focus on the overall performance and operation of individual network slices, including reliability, latency, and throughput metrics specific to each slice. The service layer assesses the usage and performance of services provided by network slices to end-users or applications, such as availability, response times, and service level agreements (SLAs). For example, metrics from the resource layer might indicate the current CPU utilization of a network slice allocated to a high-bandwidth application, while network slice layer metrics could highlight the latency performance of a slice designated for ultra-reliable low-latency applications. Concurrently, service layer metrics could reflect the availability and response times experienced by users accessing services through these network slices.
[000116] In an aspect, the provisioning unit (200) is configured to use the network slice utilization metrics to monitor and improve the utilization of the resources associated with each access type in real-time. In one aspect, the provisioning unit (200) is configured to offer a comprehensive assessment of network slice utilization, specifically identifying overloaded segments or access types within the network slices. For instance, it can pinpoint whether network slices allocated for high-bandwidth applications are experiencing excessive demand compared to others allocated for low-latency or IoT applications.
[000117] By employing these capabilities, the provisioning unit enhances the efficiency and performance of network slice resource management, ensuring that resources are allocated optimally to meet the diverse demands and performance criteria across different network slice segments. This approach supports robust and adaptive network operations, accommodating fluctuating usage patterns and maintaining high-quality service delivery within the telecommunications environment.
[000118] FIG. 3A illustrates an exemplary flow diagram of a method (300) for providing the plurality of network slice usage metrics, in accordance with an embodiment of the present disclosure.
[000119] Step (302) includes receiving, by the receiving unit, the request from the computing device for providing the network slice usage metrics corresponding to the network slice. In an aspect, the plurality of network slice usage metrics includes latency, connection success rate, registered user equipment, and packet data unit (PDU) created sessions. For example, the receiving unit (202) is the user interfacing unit, facilitating interaction between the user and the system.
[000120] Step (304) includes establishing, by the processing unit, the connection with the at least one data source (150) associated with the at least one network slice admission control function (NSACF) server. The processing unit (210) is configured to cooperate with the receiving unit (202) and receive the request. Upon receiving the request, the processing unit (210) establishes a connection with the at least one data source (150) coupled with the at least one NSACF server (145). For example, when the network operator submits the request via the receiving unit (202) for real-time slice usage metrics for a user-defined network slice, the processing unit (210) may confirm the details of the request, ensuring that all necessary parameters (such as the target network slice) are accurately specified. Upon verification, the processing unit (210) establishes a secure connection with the database that is associated or integrated with the NSACF server. In an example, the processing unit (210) may utilize protocols such as RESTful APIs (Application Programming Interfaces) or gRPC (gRPC Remote Procedure Call) to facilitate efficient data retrieval.
[000121] Step (306) includes extracting, by the processing unit, network slice usage data from the at least one data source (150) based on the received request. Upon receiving the request, the processing unit (210) constructs a specific query tailored to extract network slice usage data for the user-defined network slice. For example, the query may include parameters such as, a network slice identifier (to specify which slice’s load details are required) and a timeframe (to define the period for which the network slice usage metrics is being requested (e.g., real-time, last hour, last 24 hours)). The processing unit (210) executes the formulated query against the database. This involves sending the request to the least one data source (150) and waiting for a response containing the requested data.
[000122] Step (308) includes processing, by the processing unit, the extracted network slice usage data to generate the plurality of network slice usage metrics. In an aspect, the processing unit may be configured to analyze the extracted information. In an example, the processing (analysis) involves various computational tasks such as aggregation, calculation, normalization and classification. After processing the information, the processing unit generates the plurality of network slice usage metrics. In an example, imagine a telecommunications company offering network slicing services to various clients. The extracted information may include data on each client's network slices, including bandwidth allocation, latency requirements, and security configurations. For a gaming company using a network slice, the utilization rate may be 80% (slice used 80% of the time during peak hours), bandwidth consumption may be 100 Mbps on average, and latency performance may be 15 ms on average for the 3GPP access type. For the for the non-3GPP access type, the utilization rate may be 60% (slice used less during off-peak hours), bandwidth consumption may be 50 Mbps on average and latency performance may be 5 ms on average. The generated metrics provide insights into how effectively each network slice is utilized between various access types and whether adjustments are needed. For instance, if the 3GPP access type consistently reaches bandwidth limits during peak hours, the provider might consider reallocating resources or upgrading the slice allocation corresponding to the 3GPP access type.
[000123] Step (310) categorizing, by the processing unit (210), the plurality of generated plurality of network slice usage metrics slices based on one or more distinct access types.
[000124] In an embodiment, the method includes a step of allocating of network slices based upon a plurality of use cases including an ultra-high bandwidth use case, a very low-latency use case, an ultra-reliable low-latency use case, a high-bandwidth use case, and a massive IoT use case.
[000125] In an embodiment, the method includes a step of updating, by the NSACF server (145), periodically the at least one data source by retrieving the network slice usage data from at least one network function.
[000126] In an embodiment, the method includes a step of determining, by the NSACF server (145), the one or more distinct access types by extracting a number of information associated with a session request received from the at least one network function.
[000127] In an embodiment, the method includes a step of updating, by the processing unit (210), an allocation of resources associated with the network slice based upon the categorized plurality of generated network slice usage metrics.
[000128] FIG. 3B illustrates another exemplary flow diagram of a method (350) for providing the plurality of network slice usage metrics, in accordance with an embodiment of the present disclosure.
[000129] At step (352) of the flow diagram, the provisioning unit (200) may receive the request (slice usage metric request) from the user for providing the slice usage metrics corresponding to a network slice.
[000130] At step (354) of the flow diagram (300), upon receiving the request, the provisioning unit (200) establishes a connection with the context data (150) coupled with the at least one NSACF server (145). In an aspect, to retrieve the slice usage data based upon the request, the provisioning unit (200) may configure to send a “Get slice usage data” request to the context data (150). In an example, the “Get slice usage data” request is a GET HTTP request used to retrieve data from a specified resource on a server. The GET request begins with the request line, which includes the method (GET), the URL of the resource, and the HTTP version. the provisioning unit (200) may request a parser and/or a processor to obtain information from the context data. In an embodiment, the parser and the processor may be sub-components of the provisioning unit (200). According to an implementation, the parser and/or the processor may process the information obtained from the context data. In an aspect, the parser may be a software component that reads, interprets, and analyzes structured data from the data context, such as slice load distribution information. The parser extracts meaningful information (e.g., UE registrations, slice data) and converts it into a format that the processing unit can process. It essentially breaks down the complex data into more understandable and usable components for further operations. Additionally, the processor is a component responsible for executing operations on the parsed data. Once the parser has interpreted and extracted the necessary data, the processor performs computations, logic checks, and updates, such as determining the number of UEs in a specific slice or analyzing trends in UE registration. The processor applies business rules or algorithms to the data, driving the dynamic slice load distribution process forward.
[000131] At step (356) of the flow diagram, the NSACF server (145) may be configured to insert the slice usage data into the context data using a “Post slice NSACF information” request. The NSACF server may be configured to update the context data by retrieving the network slice usage data from the at least one network function (such as the AMF and the SMF). To retrieve the network slice usage data, the NSACF is configured with specific parameters that dictate the frequency of these network slice usage data retrievals. In an example, the POST request refers to a method in HTTP (Hypertext Transfer Protocol) used for sending data to the NSACF server (145) or the to create/update a resource. The POST request in this scenario would carry information like registration data or updates about UE, facilitating the admission and management of UEs within network slices.
[000132] In an embodiment, the NSACF (NSACF server) provides Nnsacf_NSAC service. The Nnsacf_NSAC service provides the service capability for the NF Service Consumer (e.g. AMF) to request admission control for UEs accessing a specific network slice, or for PDU sessions to be established to a specific network slice. The following are the key functionalities of this NF service: request the NSACF to control the number of UEs registered to a specific network slice, e.g. perform availability check and update the number of UEs registered to a specific network slice.
[000133] In another embodiment, for network slice admission control for controlling the number of UEs, the NF Service Consumer (e.g. AMF, combined SMF+PGW-C) shall invoke the NumOfUEsUpdate service operation to request the NSACF to perform network slice admission control procedure related to the number of UEs, by using the HTTP POST method. The NF Service Consumer (e.g. AMF, combined SMF+PGW-C) shall send a POST request to the resource representing the network slice admission control related to the number of UEs through resource URL (i.e…/slices/UEs) in the NSACF.
[000134] In an embodiment, the payload body of the POST request shall contain the input data structure (i.e. UeACRequestData) for network slice admission control, which shall contain the following information:
• the SUPI(s) of the UE(s);
• the access type, over which the UE registers to the network or deregisters from the network;
• a list of S-NSSAIs which are subject to NSAC, and for each S-NSSAI an update flag indicates the operation to that S-NSSAI
[000135] In an embodiment, the network may serve a single UE with one or more Network Slice instances simultaneously via a 5G-AN regardless of the access type(s) over which the UE is registered (i.e. 3GPP Access and/or Non-3GPP access Access).The AMF instance serving the UE logically belongs to each of the Network Slice instances serving the UE, i.e. this AMF instance is common to the Network Slice instances serving a UE.
[000136] Based on operator policies, a network slice may be defined as an on-demand slice, which requires a UE to register to the slice only when the UE intends to establish and use the corresponding PDU Session. The 5G system (5GS) supports the following methods to enforce on-demand slices as follows:
[000137] Network slice usage control is achieved in the following ways:
• Configuration of slice-specific polices to the UE
• Running a slice usage control timer,
• Running a PDU session inactivity timer
[000138] If a UE indicates support of slice-specific policies to the AMF, the AMF determines slice-specific policies for the UE and may configure slice-specific policies to the UE together with Configured NSSAI to control the usage of a network slice. The AMF may be locally configured with slice-specific policies or receive policies from the (AM-)PCF. The slice specific policies indicate whether a network slice needs to be registered only when the corresponding PDU Session is required. The UE shall follow the slice-specific policies if it is received from the AMF.
[000139] In an embodiment, the NSACF may be configured to perform per access type network slice admission control. In this case, the NSACF shall check whether the access type provided by the NF Service Consumer is configured for Network Slice Admission Control (NSAC) for the indicated S-NSSAI to control the number of UEs. If the access type is not configured for NSAC for the indicated S-NSSAI, the NSACF shall skip the above handling for increasing/decreasing the number of UEs and return successful handling for this S-NSSAI. If the access type is configured for NSAC for the indicated S-NSSAI, the NSACF shall perform the above handling taking the access type into account and record/remove the UE registration associated with the access type. If the total number of UEs will exceed the maximum number of UEs allowed to be registered to this slice, the NSACF shall record this S-NSSAI in the failed list of S-NSSAI in the response message.
[000140] If the NSACF is not configured to perform per access type network slice admission control, the NSACF may perform network slice admission control without taking access type into account. For example, the NSACF is configured with a total quota for the PLMN, but the network slice admission control is not specific to one access type. The NSACF shall record the access type(s) associated with the UE registration. The NSACF shall remove the corresponding UE registration entry when the UE deregisters from all access types.
[000141] The present disclosure is configured to provide the plurality of network slice usage metrics in real-time, offering an up-to-date view of how resources are being consumed across different access types in the network slice. By analyzing these metrics, the present disclosure may identify which segments/access types of the network slice is overloaded, enabling proactive management and optimization.
[000142] FIG. 4 illustrates an example computer system (400) in which or with which the embodiments of the present disclosure may be implemented.
[000143] As shown in FIG. 4, the computer system (400) may include an external storage device (410), a bus (420), a main memory (430), a read-only memory (440), a mass storage device (450), a communication port(s) (460), and a processor (470). A person skilled in the art will appreciate that the computer system (400) may include more than one processor and communication ports. The processor (470) may include various modules associated with embodiments of the present disclosure. The communication port(s) (460) may be any of an RS-232 port for use with a modem-based dialup connection, a 10/100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fiber, a serial port, a parallel port, or other existing or future ports. The communication ports(s) (460) may be chosen depending on a network, such as a Local Area Network (LAN), Wide Area Network (WAN), or any network to which the computer system (400) connects.
[000144] In an embodiment, the main memory (430) may be Random Access Memory (RAM), or any other dynamic storage device commonly known in the art. The read-only memory (440) may be any static storage device(s) e.g., but not limited to, a Programmable Read Only Memory (PROM) chip for storing static information e.g., start-up or basic input/output system (BIOS) instructions for the processor (470). The mass storage device (450) may be any current or future mass storage solution, which can be used to store information and/or instructions. Exemplary mass storage solutions include, but are not limited to, Parallel Advanced Technology Attachment (PATA) or Serial Advanced Technology Attachment (SATA) hard disk drives or solid-state drives (internal or external, e.g., having Universal Serial Bus (USB) and/or Firewire interfaces).
[000145] In an embodiment, the bus (420) may communicatively couple the processor(s) (470) with the other memory, storage, and communication blocks. The bus (420) may be, e.g., a Peripheral Component Interconnect (PCI)/PCI Extended (PCI-X) bus, Small Computer System Interface (SCSI), Universal Serial Bus (USB), or the like, for connecting expansion cards, drives, and other subsystems as well as other buses, such a front side bus (FSB), which connects the processor (470) to the computer system (400).
[000146] In another embodiment, operator and administrative interfaces, e.g., a display, keyboard, and cursor control device may also be coupled to the bus (420) to support direct operator interaction with the computer system (400). Other operator and administrative interfaces can be provided through network connections connected through the communication port(s) (460). Components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system (400) limit the scope of the present disclosure.
[000147] The present disclosure further discloses a user equipment (UE) communicatively coupled with a network or the provisioning unit (200). The coupling comprises steps of receiving a connection request, sending an acknowledgment of the connection request to the provisioning unit, and transmitting a plurality of signals in response to the connection request. The provisioning unit is configured to provide a plurality of network slice usage metrics in real-time. The provisioning unit includes a receiving unit and a processing unit. The receiving unit is configured to receive a request from a computing device for dispatching network slice usage metrics. The processing unit is configured to cooperate with the receiving unit to receive the request. The processing unit is configured to establish a connection with at least one network slice admission control function (NSACF) server and a database associated with the at least one NSACF server. The provisioning unit is configured to extract network slice usage data from the database based on the received request. The provisioning unit is configured to process the extracted network slice usage data to generate the plurality of network slice usage metrics. The provisioning unit is configured to categorize the generated plurality of network slice usage metrics slices based on one or more distinct access types.
[000148] The present disclosure provides a technical advancement related to telecommunications network management. This advancement addresses the limitations of existing solutions by introducing real-time network slice usage metrics segmented by regular, 3GPP, and Non-3GPP access types. The disclosure involves the novel aspects of periodically updating network slice usage data through the NSACF Service and efficiently retrieving this data via the NSACF Provisioning Service. These inventive aspects offer significant improvements in resource allocation and operational efficiency. By implementing this methodology, the disclosed invention enhances network slice utilization management, ensuring optimized resource allocation tailored to diverse access type requirements. This results in enhanced network performance, efficient service delivery, and improved quality of service (QoS) across telecommunications networks.
[000149] While the foregoing describes various embodiments of the invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof. The scope of the invention is determined by the claims that follow. The invention is not limited to the described embodiments, versions or examples, which are included to enable a person having ordinary skill in the art to make and use the invention when combined with information and knowledge available to the person having ordinary skill in the art.
TECHNICAL ADVANTAGES
[000150] The present disclosure described herein above has several technical advantages including, but not limited to, the realization of a provisioning unit that:
• provides real-time network slice utilization metrics.
• provides real-time network slice utilization metrics corresponding to the network slice access types;
• informs which segment of the network slices is overloaded; and
• uses the network slice utilization metrics to monitor and improve the utilization of the resources of each network slice in real-time.
,CLAIMS:CLAIMS
We Claim:
1. A provisioning unit (200) for generating a plurality of network slice usage metrics in real-time, the provisioning unit (200) comprising:
a receiving unit (202) configured to receive a request from a computing device (104) for providing the network slice usage metrics corresponding to a network slice; and
a processing unit (210) in communication with the receiving unit (202) configured to receive the request and is configured to:
establish a connection with at least one data source (150) associated with at least one network slice admission control function (NSACF) server (145);
extract network slice usage data corresponding to the network slice from the at least one source (150) based on the received request;
process the extracted network slice usage data to generate the plurality of network slice usage metrics corresponding to the network slice; and
categorize the plurality of generated network slice usage metrics slices based on one or more distinct access types.
2. The provisioning unit (200) as claimed in claim 1, further comprises a communication unit (212) configured to communicate the plurality of generated network slice usage metrics to the NSACF server.
3. The provisioning unit (200) as claimed in claim 1, wherein the at least one NSACF server (145) is configured to periodically update the at least one data source (150) by retrieving the network slice usage data from at least one network function.
4. The provisioning unit (200) as claimed in claim 1, wherein the plurality of network slice usage metrics includes latency, connection success rate, registered user equipment, and packet data unit (PDU) created sessions.
5. The provisioning unit (200) as claimed in claim 1, wherein the one or more distinct access types include a regular access type, a third-generation partnership project (3GPP) access type, and a non-3GPP access type.
6. The provisioning unit (200) as claimed in claim 3, wherein the at least one NSACF server (145) is configured to determine the one or more distinct access types by extracting a number of information associated with a session request received from the at least one network function.
7. The provisioning unit (200) as claimed in claim 1, wherein the processing unit (210) is configured to update an allocation of resources associated with the network slice based upon the categorized plurality of generated network slice usage metrics.
8. The provisioning unit (200) as claimed in claim 1, wherein the network slice usage data includes a number of registered user equipments (UEs), a number of active packet data unit (PDU) sessions, a number of UEs connected to a plurality of network slices, a ratio of the number of registered UEs on a specific network slice to the maximum number of registered UEs allowed, and a ratio of the number of established PDU sessions to the maximum allowed number of established PDU sessions.
9. A method (300) for generating a plurality of network slice usage metrics in real-time, the method (300) comprising:
receiving (302), by a receiving unit (202), a request from a computing device for providing the network slice usage metrics corresponding to a network slice;
establishing (304), by a processing unit (210), a connection with at least one data source (150) associated with at least one network slice admission control function (NSACF) server (145);
extracting (306), by the processing unit (210), network slice usage data corresponding to the network slice from the at least one data source (150)based on the received request;
processing (308), by the processing unit (210), the extracted network slice usage data to generate the plurality of network slice usage metrics corresponding to the network slice; and
categorizing (310), by the processing unit (210), the plurality of generated network slice usage metrics slices based on one or more distinct access types.
10. The method (300) as claimed in claim 9, further comprising updating, by the NSACF server (145), periodically the at least one data source (150) by retrieving the network slice usage data from at least one network function.
11. The method (300) as claimed in claim 9, wherein the one or more distinct access types include a regular access type, a third-generation partnership project (3GPP) access type, and a non-3GPP access type.
12. The method (300) as claimed in claim 9, further comprising determining, by the NSACF server (145), the one or more distinct access types by extracting a number of information associated with a session request received from the at least one network function.
13. The method (300) as claimed in claim 9, further includes updating, by the processing unit (210), an allocation of resources associated with the network slice based upon the categorized plurality of generated network slice usage metrics.
14. The method (300) as claimed in claim 9, wherein the plurality of network slice usage metrics includes latency, connection success rate, registered user equipment, and packet data unit (PDU) created sessions.
15. The method (300) as claimed in claim 9, wherein the network slice information includes a number of registered user equipments (UEs), a number of active packet data unit (PDU) sessions, a number of UEs connected to a plurality of network slices, a ratio of the number of registered UEs on a specific network slice to the maximum number of registered UEs allowed, and a ratio of the number of established PDU sessions to the maximum allowed number of established PDU sessions.
16. A user equipment (UE) (104) communicatively coupled with a provisioning unit (200), the coupling comprises steps of:
receiving a connection request;
sending an acknowledgment of the connection request to the provisioning unit (200); and
transmitting a plurality of signals in response to the connection request, wherein the provisioning unit (200) is configured to provide a plurality of network slice usage metrics in real-time as claimed in claim 1.