Reporting reservation capacity to network functions in communication network

Abstract

Methods, systems, and computer-readable media for reporting reserved loads to network functions in a communication network are disclosed. A method includes determining, by an NF service producer, a current computational load metric value for an NF service producer operating in a communication network and detecting a number of active sessions supported at the NF service producer. The method further includes deriving a reserved calculated load metric value corresponding to a predicted number of subsequent service requests at the NF service producer based on the number of active sessions and the predictive reserved load percentage value, and calculating an adjusted reported calculated load metric value, the adjusted reported computational load metric value is equal to the sum of the current computational load metric value and the reserved computational load metric value.

Description

[0001] Priority claim

[0002] This application claims the benefit of priority to U.S. patent application serial No. 17 / 751,572, filed on May 23, 2022, the disclosure of which is incorporated herein by reference in its entirety. Technical Field

[0003] The subject matter described herein relates to a reserved load handling method for avoiding overload conditions and request rejections at a network function (NF) service producer in a fifth generation (5G) communication network. More particularly, the subject matter described herein relates to a method, system, and computer-readable medium for reporting reserved load to a network function in a communication network. Background Art

[0004] In telecommunications networks, a service endpoint is an address on a network node that uniquely identifies an entity that provides services to service consumers. A service endpoint may include an Internet Protocol (IP) address or a combination of an IP address and a transport layer port number, also known as an IP endpoint.

[0005] In the fifth generation (5G) telecommunication network, a network node that provides a service is called a producer network function (NF). A network node that consumes a service is called a consumer NF. A network function can be both a producer NF and a consumer NF, depending on whether it is consuming or providing a service.

[0006] A given producer NF can have many service endpoints. The producer NF registers with the Network Function Repository Function (NRF). The NRF maintains NF profiles of available NF instances and the services they support. Consumer NFs can subscribe to receive information about producer NF instances that have registered with the NRF. Once registered, NF instances in a 5G network can establish sessions with one or more Network Exposure Functions (NEFs). It is worth noting that NEF is a 3rd Generation Partnership Project (3GPP) network function that provides a means to securely expose services and capabilities provided by producer network functions that provide services to the network.

[0007] Currently, 3GPP allows the use of load metrics to specify the current computational load level of an NF instance. For example, the current computational load of an NF instance can be reported via a load control information (LCI) header as a percentage in the range of 0 to 100%, where 0 indicates that there is no computational load (or 0% load) at the reporting NF instance, and 100% indicates that there is a maximum load at the reporting NF instance (i.e., the NF instance has reached 100% load and / or no further load is expected). In many scenarios, the actual calculation and use of load metrics by NF instances are implementation specific. In many cases, the reported load parameters reported by the NF service producer (along with other parameters) can be used for NF service selection decisions and / or load balancing queries made by NF service consumers. For example, if the computational load level at a NF service producer is reported as "low level" at a given time, the NF service consumer may select the NF service producer for resource creation (e.g., SMF selects PCF for session creation) based primarily on the current load level information of the NF service producer. After being selected by an NF Service Consumer, the NF Service Producer is also typically configured to receive subsequent messages related to the accepted session and / or context. In some cases, the previously selected NF Service Producer may become overwhelmed in receiving and processing these aforementioned subsequent messages, thereby causing undesirable delays in the services provided to its subscribed NF Service Consumers. In view of these subsequent messages, simply reporting the current load level is typically not sufficient for the NF Service Consumer to protect itself from the possibility of future overload at the service NF Producer.

[0008] Thus, there is a need for improved methods and systems for reporting reservation load to a network function in a communication network. Summary of the invention

[0009] A method, system and computer-readable medium for reporting a reserved load to a network function in a communication network are disclosed. A method includes determining, by an NF service producer, a current computational load metric value of an NF service producer operating in a communication network and detecting the number of active sessions supported at the NF service producer. The method also includes deriving a reserved computational load metric value corresponding to a predicted number of subsequent service requests at the NF service producer based on the number of active sessions and a predicted reserved load percentage value, and calculating an adjusted reported computational load metric value, the adjusted reported computational load metric value being equal to the sum of the current computational load metric value and the reserved computational load metric value.

[0010] According to another aspect of the method described herein, the reservation computational load metric is adjusted by a weight modifier to produce a weighted reservation computational load metric value.

[0011] According to another aspect of the methods described herein, one or more report messages are generated that include adjusted reported computer load metric values.

[0012] According to another aspect of the methods described herein, one or more report messages are sent to corresponding one or more consumer service network functions.

[0013] According to another aspect of the method described herein, each of the one or more report messages includes a load control information (LCI) header containing an adjusted reported calculated load metric value.

[0014] According to another aspect of the methods described herein, one or more report messages are sent to corresponding one or more Network Function Repository Functions (NRFs).

[0015] According to another aspect of the method described herein, each of the one or more report messages includes an NfProfile section that contains an adjusted reported computer load metric value.

[0016] According to another aspect of the disclosed subject matter described herein, a system for reporting a reserved load to a network function in a communication network includes an NF service producer supported by at least one processor and a memory and a load management engine implemented by at least one processor, the load management engine being configured to determine a current calculation load metric value of the NF service producer operating in the communication network and detect the number of active sessions supported at the NF service producer. The load management engine is also configured to derive a reserved calculation load metric value corresponding to a predicted number of subsequent service requests at the NF service producer based on the number of active sessions and a predicted reserved load percentage value, and calculate an adjusted reported calculation load metric value, the adjusted reported calculation load metric value being equal to the sum of the current calculation load metric value and the reserved calculation load metric value.

[0017] According to another aspect of the system described herein, the reservation computational load metric is adjusted by a weight correction factor to produce a weighted reservation computational load metric value.

[0018] According to another aspect of the systems described herein, the load management engine is configured to generate one or more report messages including an adjusted reported computer load metric value.

[0019] According to another aspect of the system described herein, the load management engine is configured to send one or more report messages to corresponding one or more consumer service network functions.

[0020] According to another aspect of the system described herein, each of the one or more report messages includes a load control information header containing an adjusted reported computer load metric value.

[0021] According to another aspect of the system described herein, the load management engine is configured to send one or more report messages to corresponding one or more network function repository functions.

[0022] According to another aspect of the system described herein, each of the one or more report messages includes an NfProfile section that contains an adjusted reported computer load metric value.

[0023] The subject matter described herein may be implemented in hardware, software, firmware, or any combination thereof. Therefore, the terms "function," "node," or "module" as used herein refer to hardware, which may also include software and / or firmware components, for implementing the described features. In an example embodiment, the subject matter described herein may be implemented using one or more computer-readable media having computer-executable instructions stored thereon, which control the computer to perform steps when executed by a processor of a computer. Example computer-readable media suitable for implementing the subject matter described herein include non-transitory computer-readable media, such as disk memory devices, chip memory devices, programmable logic devices, and application-specific integrated circuits. In addition, the computer-readable media implementing the subject matter described herein may be located on a single device or computing platform, or may be distributed across multiple devices or computing platforms. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The subject matter described herein will now be explained with reference to the accompanying drawings, in which:

[0025] Figure 1 is a network diagram illustrating an example fifth generation (5G) network architecture according to an embodiment of the subject matter described herein;

[0026] Figure 2 is a signaling diagram illustrating an exemplary network system configured to report a reservation load to a network function in a communication network according to an embodiment of the subject matter described herein;

[0027] Figure 3 is a block diagram illustrating a network function configured to report a reservation load to a network function in a communication network according to an embodiment of the subject matter described herein; and

[0028] Figure 4 is a flow chart illustrating an exemplary method for reporting a reservation load to a network function in a communication network according to an embodiment of the subject matter described herein. DETAILED DESCRIPTION

[0029] The subject matter described herein relates to methods, systems, and computer-readable media for reporting reserved loads to network functions in a communication network. In some embodiments, the disclosed subject matter provides a mechanism that allows NF service producers to report reserved computational load metrics together with current computational load metrics. It is noteworthy that subscriber NF service consumers can use reserved computational load metrics to better assess the possibility that associated NF service producers can provide services to NF service consumers without experiencing excessive computational load and / or processing. Reference will now be made in detail to various embodiments of the subject matter described herein, examples of which are shown in the accompanying drawings. Where possible, the same reference numerals will be used throughout the drawings to refer to the same or similar parts.

[0030] Figure 1 is a block diagram illustrating an example 5G system network architecture, e.g., a home 5G core (5GC) network. Figure 1 The architecture in includes NRF 100 and SCP 101, which can be located in the same home public land mobile network (PLMN). As described above, NRF 100 can maintain the profile of the available producer NF service instances and the services they support, and allow consumer NF or SCP to subscribe and be notified of the registration of new / updated NF service instances. SCP 101 can also support service discovery and selection of NF instances. SCP 101 can perform load balancing on the connection between consumer and producer NF. In addition, using the method described herein, SCP 101 can perform selection and routing based on preferred NF locations.

[0031] The NRF 100 is a repository for NF or service profiles of NF instances. In order to communicate with a NF instance, a consumer NF or SCP must obtain the NF service profile of the NF instance from the NRF 100. The NF or service profile is a JavaScript Object Notation (JSON) data structure defined in 3GPP Technical Specification (TS) 29.510. The NF or service profile definition includes at least one of a fully qualified domain name (FQDN), an Internet Protocol (IP) version 4 (IPv4) address, or an IP version 6 (IPv6) address. Figure 1 In the example shown, any node (except NRF 100) can be either a consumer NF or a producer NF, depending on whether they are requesting services or providing services. In the example shown, the nodes include a policy control function (PCF) 102 that performs policy-related operations in the network, a unified data management (UDM) function 104 that manages user data, and an application function (AF) 106 that provides application services. Figure 1The nodes shown in FIG. 1 also include a session management function (SMF) 108 that manages sessions between an access and mobility management function (AMF) 110 and the PCF 102. The AMF 110 performs mobility management operations similar to those performed by a mobility management entity (MME) in a 4G network. An authentication server function (AUSF) 112 performs authentication services for user devices such as user equipment (UE) 114 seeking access to the network.

[0032] The Network Slice Selection Function (NSSF) 116 provides network slicing services to devices seeking to access specific network capabilities and features associated with a network slice. The Network Exposure Function (NEF) 118 provides an Application Programming Interface (API) for application functions seeking to obtain information about Internet of Things (IoT) devices and other UEs attached to the network. The NEF 118 performs functions similar to the Service Capability Exposure Function (SCEF) in 4G networks.

[0033] Radio access network (RAN) 120 connects UE 114 to the network via a wireless link. A gNode B (gNB) ( Figure 1 The UE 114 may use a wireless access point (not shown) or other wireless access point to access the radio access network 120. The user plane function (UPF) 122 may support various proxy functionalities for user plane services. An example of such proxy functionality is multipath transmission control protocol (MPTCP) proxy functionality. The UPF 122 may also support performance measurement functionality that the UE 114 may use to obtain network performance measurements. Figure 1 Also illustrated in FIG. 1 is a data network (DN) 124 through which the UE accesses data network services, such as Internet services.

[0034] Security edge protection proxy (SEPP) 126 filters incoming traffic from another PLMN and performs topology hiding on traffic leaving the home PLMN. SEPP 126 can communicate with a SEPP in a foreign PLMN that manages the security of the foreign PLMN. Therefore, traffic between NFs in different PLMNs can traverse two SEPP functions, one for the home PLMN and the other for the foreign PLMN. In some embodiments, the SEPP is a gateway device located at the edge of the network.

[0035] The SEPP 126 may utilize an N32-c interface and an N32-f interface. The N32-c interface is a control plane interface between two SEPPs and may be used to perform an initial handshake (e.g., a TLS handshake) and negotiate various parameters for N32-f interface connection and related message forwarding. The N32-f interface is a forwarding interface between two SEPPs and may be used to forward various communications (e.g., 5GC requests) between a consumer NF and a producer NF after application-level security protection has been applied.

[0036] Figure 2 2 is a block diagram illustrating an example NF 200 configured to report a reserved load to a network function in a communication network according to an embodiment of the subject matter described herein. NF 200 may represent any suitable one or more entities (e.g., one or more nodes, devices, or computing platforms) for performing various aspects or functionalities described herein, such as determining a reserved capacity load and reporting it to other network functions in a communication network. In some embodiments, NF 200 may include one or more 3GPP defined functions and / or related functionality. For example, NF 200 may include an NF service producer, etc.

[0037] refer to Figure 2 , NF 200 may include one or more communication interfaces 202 for transmitting messages via a communication environment (e.g., one or more communication networks). For example, the (one or more) communication interface 202 may include one or more communication interfaces for communicating with various entities in a home network (e.g., a home public land mobile network (H-PLMN)), a visited network (e.g., a visited public land mobile network (V-PLMN)), and / or with other network functions operating in a 5G communication network.

[0038] In some embodiments, NF 200 may include a load management engine (LME) 204. The load management engine 204 may be any suitable entity (e.g., software executing on at least one processor) for performing various operations associated with determining capacity load and reporting it to other network functions. For example, the load management engine 204 may be configured to i) determine a current calculated load metric value for a NF service producer operating in a communication network, ii) detect the number of active sessions supported at the NF service producer, iii) derive a reserved calculated load metric value corresponding to a predicted number of subsequent service requests at the NF service producer based on the number of active sessions and a predictive reserved load percentage value, and iv) calculate an adjusted reported calculated load metric value that is equivalent to the sum of the current calculated load metric value and the reserved calculated load metric value. The manner in which the load management engine performs these functions is described in more detail below (e.g., see Figure 3and Figure 4 description).

[0039] In some embodiments, the load management engine 204 may include implementation logic configured to determine the current computational load level of NF 200 (e.g., this may be expressed as a current computational load metric), which may be a NF service producer. It is noteworthy that the load management engine 204 is able to consider different parameters to calculate the current computational load level of the NF. For example, the load management engine 204 may be configured to calculate the computational load level by considering various resources (such as CPU utilization, memory usage, disk utilization, etc.). In addition, the load management engine 204 may determine how these parameters are utilized when reporting. Based on these evaluations, the load management engine 204 may determine the current computational load metric by evaluating the percentage (A%) of the underlying computing resources used to process the current traffic rate.

[0040] In addition, the load management engine is further configured to determine the number of active sessions currently supported by NF 200 (e.g., NF service producer). It is worth noting that the number of active sessions supported by the NF service producer can be used to calculate the reserved capacity load of the NF service producer (e.g., reserved calculation load metric value).

[0041] In some embodiments, the load management engine 204 is suitable for determining the reserved load capacity of the NF service producer. For example, the load management engine can determine a predefined percentage (R%) of active sessions (and / or contexts and / or resources) representing the predictive reserved capacity load of the NF service producer. In some embodiments, this percentage is referred to as a reserved calculation load metric. For example, if the NF service producer (i.e., NF 200) is supporting 50,000 active sessions and the predefined operator parameter indicates that the subsequent request message accounts for 10% of the currently supported active sessions (i.e., the predictive reserved load percentage value), then the load management engine 204 can be configured to calculate a reserved calculation load metric, which represents the rate at which subsequent messages associated with the active session are to be received within a predefined time period (e.g., within 24 hours) (e.g., the number of sessions per second or the number of sessions per minute). In some embodiments, a parameter value of 10% is defined based on the traffic mix typically experienced in the operator network. In this scenario, the reserved calculation load metric will be equal to a rate of 5000 sessions per second (i.e., 10% of 50,000 active sessions).

[0042] In some embodiments, the load management engine 204 is further configured to determine a weight modifier parameter (W%) associated with the reserved capacity load. Note that the weight modifier is used to set an appropriate weight for the reserved capacity load (ie, the reserved computation load metric).

[0043] After the load management engine 204 determines each of the current computational load metric value, the reserved computational load metric value, and the weight correction factor parameter, an adjusted reported computational load metric value may be determined. In some embodiments, the lower value of either [A%+(R%*W%)] or 100% may be used to determine the adjusted reported computational load metric value. In particular, the load management engine 204 may determine the adjusted reported computational load metric value represented by the following operation:

[0044] min(A%+(R%*W%),'100%')

[0045] For example, if the sum of the actual load percentage and the weighted reserved load is less than 100%, the load management engine 204 will report the calculated value as the reported load (i.e., the adjusted reported calculated load metric value). If the calculated value is greater than 100%, the load management engine will instead report 100% as the adjusted reported calculated load metric value.

[0046] exist Figure 2 In the example, NF 200 and / or load management engine 204 can access (e.g., read information from and / or write information to) data storage device 206. Data storage device 206 can be any suitable entity (e.g., computer-readable medium or memory) for storing predetermined weight correction factors, computational load determination logic, and / or any other predefined network operator values.

[0047] It will be recognized that Figure 2 , and related descriptions thereof, are for illustrative purposes, and NF 200 may include additional and / or different modules, components, or functionalities.

[0048] Figure 3 An exemplary process for determining an adjusted reported load level is illustrated. In particular, Figure 3 Depicted are a NF service consumer 322, a NF service producer 324, and an NRF 326 operating in a communication network. Notably, the NF service producer 324 includes a load management engine (LME) 330, which may be configured to perform one or more of the operations described herein.

[0049] In box 302, the NF service producer 324 and / or the load management engine 330 are configured to determine a current computational load metric representing the computational load being experienced at the NF service producer 324. In some embodiments, the current computational load metric may be represented as an activity load parameter (A%), which may represent a percentage representation of the current computational load being experienced at the NF service producer 324. For example, the load management engine 330 may determine that the current ingress traffic rate being processed by the NF service producer is 20,000 session requests per second (i.e., 20K / sec) and represents 40% of the maximum computational load handling capacity of the NF service producer 324. Notably, this numerical rate represents the total ingress message load received by the NF service producer, i.e., represents the initial create session / resource request message and the subsequent session / resource request messages (associated with the initial create request message). In some embodiments, the current ingress traffic rate is determined via a predefined moving average (e.g., a rate determined over the past 24 hour period). Thus, the load management engine 330 may designate the determined 40% value as the current load parameter "A%" (ie, a parameter representing the current calculated load metric value).

[0050] As indicated above, the current load level (i.e., the current computational load metric value) of the NF service provider instance may be evaluated using implementation logic provided and / or included in the load management engine 330. Thus, the load management engine 330 may be configured to take into account different operational metrics and parameters to reflect the current computational load level of the NF service producer 324. In some instances, the load management engine 330 may calculate the current computational load level by evaluating various usage levels of the underlying resources of the NF (such as CPU, memory, network bandwidth and processing latency, disk utilization, etc.) that the NF service producer 324 is using at the reporting moment.

[0051] In block 303, the NF service producer 324 and / or the load management engine 330 are configured to determine or detect the number of active sessions currently being supported by the NF service producer 324. In some embodiments, the load management engine 330 may utilize the implementation logic mentioned above to determine the number of active sessions supported at the NF service producer. For example, the load management engine 330 may determine that the NF service producer 324 is currently supporting 50,000 active sessions, which may include the sum of create session request message transfers and / or create resource request message transfers accepted by the NF service producer and not yet terminated (e.g., the SM association accepted by the PCF from the SMF for the Npcf_SMPolicyControl_Create service operation).

[0052] In block 304, the NF service producer 324 and / or the load management engine 330 are configured to derive a reserved calculated load metric value (e.g., a reserved load parameter). Notably, this reserved calculated load metric value may represent predicted active sessions, contexts, and / or resources (i.e., predicted message traffic for existing sessions) associated with active sessions and subsequently received by the NF service producer 324. In some embodiments, the load management engine 330 may be configured to derive this value by applying a predefined reserved load parameter (R%) (or a predictive reserved load percentage value) to the active session load. For example, the load management engine 330 may determine, based on the operator network / traffic mix data, that for currently accepted active sessions (i.e., currently 50,000 active sessions) supported by the NF service producer, there is a 10% chance of receiving a subsequent (or subsequent) service request. Applying the predetermined 10% reserved load parameter (R%) to the 50,000 active sessions by the load management engine then results in determining a predicted traffic rate of 5000 sessions per second. In some embodiments, 5K sessions / second is referred to as "reserved capacity" or "predictive capacity". If 40% of the maximum computational load handling capability of the NF service producer is consumed when handling 20K ingress sessions per second, then in order to handle the "reserved capacity" of 5K sessions, an additional 10% capacity is required (i.e., (40% / 20K)*5K=10%). Here, the 10% parameter is referred to as the "reserved capacity load" or "predictive load" (i.e., the reserved load parameter, R%).

[0053] In box 306, the NF service producer 324 and / or the load management engine 330 are configured to determine a weight correction factor parameter (W%) for the reserved load. In some embodiments, the weight correction factor parameter is a parameter value predefined by a network administrator. The weight correction factor parameter can represent a mechanism by which an operator can assign different weights (or importance) to a reserved calculated load metric value. It is worth noting that the operator can specify or set the weight correction factor parameter to be equal to any value between 0% and 100%. For example, if the load management engine has been configured to assign a weight correction factor of 50% to the reserved calculated load metric value, which is equivalent to 10%, then the resulting weighted reserved calculated load metric value will be 5% (i.e., 50%*10%). In some embodiments, the calculation and / or utilization of the weight correction factor parameter is an optional functionality and / or feature.

[0054] In block 308, the NF service producer 324 and / or the load management engine 330 are configured to determine an adjusted reported calculated load metric value. In some embodiments, the load management engine 330 may utilize the current load (e.g., the current calculated load metric value), the reserved load (e.g., the reserved calculated load metric value), and / or the weighted percentage value (e.g., the weight correction factor) as input parameters for calculating the adjusted reported calculated load metric value. For example, the load management engine 330 may utilize the min(A%+R%*W%, '100') formula indicated above to determine the adjusted reported calculated load metric value. Notably, the adjusted reported calculated load metric value may be determined to be equal to the smaller of 40%+10%*50% (as previously calculated in step 306), which is equivalent to 40%+5%=45%. Notably, 45% is less than 100%, so the load management engine 330 will be configured to use 45% in this example. If no weight modification factor is applied (i.e., no reserved computational load metric values ​​are used), then the adjusted reported computational load metric value will be 40% (i.e., 40% + 0%). If the entire weight modification factor is to be applied (i.e., all reserved computational load metric values ​​should be used / considered), then the adjusted reported computational load metric value will be 50% (i.e., 40% + 10%).

[0055] After determining the adjusted reported computation load metric value, the NF service producer 324 may then receive a service request message 310 from the NF service consumer 322. Notably, the service request message 310 may be any message from a NF service consumer that requests a NF service from the receiving NF service producer 324.

[0056] In response to receiving a service request message 310 from a NF service consumer 322, the NF service producer 324 and / or the load management engine 330 may be configured to proactively report current and predicted load capacity information (i.e., the adjusted reported calculated load metric value) to the requesting NF service consumer (in addition to the available NF service information). For example, the NF service producer 324 and / or the load management engine 330 may be configured to send a service response message 312 to the NF service consumer 322, the message including the adjusted reported calculated load metric value as a load metric parameter (e.g., via an LCI header).

[0057] Although not entirely within the scope of the disclosed subject matter, the NF service consumer 322 can utilize the adjusted reported calculated load metric value to evaluate whether the sending NF service producer 324 is a suitable service provider and / or in any other manner. For example, the NF service consumer 322 can utilize the adjusted reported calculated load metric value to perform load balancing management operations in the communication network and to perform NF service producer selection.

[0058] In some embodiments, the NF service producer 324 and / or the load management engine 330 are configured to report the adjusted reported calculated load metric value to other NF instances (such as NRFs and NF service consumers) without being triggered by a NF service request message. For example, the NF service producer 324 and / or the load management engine 330 can be configured to send a report load message 314 (e.g., load information included in the NfProfile) to the NRF 326 without any prompt and / or trigger.

[0059] Figure 4 A flow chart of a method 400 for reporting a reserved load to a network function in a communication network is illustrated. In some embodiments, the example method 400 described herein or portions thereof (e.g., operations or steps) may be performed at or by a network function and / or a module or engine (e.g., a load management engine) supported by the network function.

[0060] In step 402, the current computational load metric of the NF service producer operating in the communication network is determined. In some embodiments, the load management engine is configured to use implementation logic to determine the current computational load level at the NF service producer. It is worth noting that the NF service producer can consider different parameters to calculate the computational load level at the NF service producer. For example, the load management engine can be configured to calculate the computational load level by considering various resources (such as CPU utilization, memory usage, disk utilization, etc.). In addition, the load management engine can determine how to use these parameters when reporting. Based on these evaluations, the load management engine can determine the current computational load metric.

[0061] In step 404, the number of active sessions supported at the NF service producer is detected. In some embodiments, the load management engine may include implementation logic to determine the number of active sessions supported at the NF service producer. For example, the load management engine may determine that the NF service producer currently supports 50,000 active sessions, which may include the sum of create session requests, create resource requests, and any subsequent related messaging.

[0062] In step 406, a reserved calculated load metric value corresponding to the predicted number of subsequent service requests at the NF service producer is derived based on the number of active sessions and the predictive reserved load percentage value. In some embodiments, the load management engine can be configured to determine that the NF service producer will receive approximately 10% of the subsequent service request messages per second for the supported current active sessions within a predefined period (e.g., the next 24 hours) based on the traffic mix of the operator network. For example, if it is determined that 50,000 active sessions are currently supported (e.g., in step 404), then the reserved rate representing the predicted subsequent traffic for existing sessions will be equal to 5000 sessions per second (i.e., 10% of the 50,000 active sessions). In some embodiments, this value is referred to as a reserved calculated load metric value, which can be based on the predicted number of subsequent service requests at the NF service producer based on the number of active sessions and the predictive reserved load percentage value.

[0063] In step 408, an adjusted reported computational load metric value is calculated, which is equal to the sum of the current computational load metric value and the reserved computational load metric value. In some embodiments, the load management engine may use the determined current computational load metric value, the reserved computational load metric value, and / or the (optional) weight correction factor as input parameters for calculating the adjusted reported computational load metric value. For example, the load management engine 330 may use the min(A%+R%*W%,'100') formula indicated above to determine the adjusted reported computational load metric value.

[0064] In some embodiments, each NF service producer can be configured to calculate the adjusted reported computational load metric (or reserved capacity) in a unique manner (i.e., each reserved load can be calculated differently for each NF service instance). Some example configurations include "PercentageReqForActiveSession", which requires the load management engine to determine the predictive number of requests for active sessions currently accepted by the NF service producer instance. It is worth noting that this process will provide reserved capacity to calculate the reserved load. In addition, the network operator can derive this value from its traffic mix or other analytical data available through internal or external sources. To disable this feature, the operator can set it to 0 (e.g., via the load management engine). The second configuration for calculating reserved capacity includes a "WeightageReservedCapacity" configuration, which represents a weighted value of the reserved computational load relative to the actual computational load. To disable this weight correction factor feature, the operator can set it to 0.

[0065] Similarly, the load management engine can be configured to publish reserved capacity towards the NRF or LCI using the "IncludeReservedCapacityToLoad" configuration. When set to "true", the reported load (i.e., the adjusted reported calculated load metric value) includes the reserved load (at the NRF and LCI). However, when this configuration is set to "false", the actual reported calculated load (at the NRF or LCI) is the actual calculated load, and the current calculated load metric value (actual load) together with the reserved calculated load metric value (reserved capacity) are published in a custom header with the LCI (i.e., using the construction and structure of the LCI, but as a custom header, for example, together with 3gpp-Sbi-Lci (publishing load values)). The load management engine can also publish a custom header "vendor-reserved-Lci" (for actual calculated load and reserved capacity).

[0066] For simplicity, the network operator may choose one of the following options when configuring the load management engine to calculate the reserved calculated load metric for the NF service producer. First, the load management engine may set "WeightedReservedLoad" to 100% while adjusting "PercentageReqForActiveSession" to obtain the adjusted reserved load (i.e., the adjusted reported calculated load value). Alternatively, the load management engine may set "PercentageReqForActiveSession" to 100% while adjusting "WeightedReservedLoad" to obtain the adjusted reserved load. In addition, the load management engine may appropriately set each of "PercentageReqForActiveSession" and "WeightedReservedLoad" to obtain the adjusted reserved load (as described above).

[0067] It should be noted that the NF instance(s), load management engine and / or functionality described herein may constitute or be facilitated by a dedicated computing device. Additionally, the load management engine and / or functionality described herein may improve the field of network function communication technology by providing a mechanism by which NF service consumers may more reliably select NF service producers based on a combination of current computational load and predicted computational load. Thus, load balancing between NF service producers and corresponding network communications may be more efficiently utilized.

[0068] Additionally, utilizing the reserved load information as described above has several other benefits. For example, the probability of an NF service producer reaching an overload condition is significantly reduced. As recommended by the 3GPP standard, an NF service consumer can select an NF service producer with a lower calculated load value. By taking into account the predicted / expected traffic of existing sessions when calculating the reported load, the NF service producer will avoid frequently experiencing overload conditions.

[0069] Similarly, the disclosed subject matter can reduce traffic rejections and improve performance. Since the NF service producer considers the potential messages for accepted sessions, the load management engine can help reserve bandwidth and processing power. Therefore, using the disclosed subject matter will result in a reduction in the number of message rejections (e.g., rejections caused by overload conditions), resulting in improved throughput and performance.

[0070] The disclosure of each of the following references is incorporated herein by reference in its entirety.

[0071] References

[0072] 1.3rd Generation Partnership Project; Technical Specification 5G; 5G System;

[0073] Technical Realization of Service Based Architecture; Phase 3 (Release 16) 3GPP TS29.500V16.7.0 (2021-04)

[0074] 2.3rd Generation Partnership Project; Technical Specification 5G; 5G System;

[0075] Network function repository services; Phase 3 (Release 16)

[0076] 3GPP TS29.510V16.8.0(2021-08)

[0077] It will be understood that various details of the presently disclosed subject matter may be changed without departing from the scope of the presently disclosed subject matter.In addition, the foregoing description is for the purpose of illustration only, and not for the purpose of limitation.

Claims

1. A method for reporting a reserved load to a network function (NF) in a communication network, the method comprising: determining, by the NF service producer, a current computational load metric value of the NF service producer operating in the communication network; Detect the number of active sessions supported at the NF service producer; deriving a reserved calculated load metric value corresponding to a predicted number of subsequent service requests at the NF service producer based on the number of active sessions and the predictive reserved load percentage value; as well as An adjusted reported computation load metric value is calculated, the adjusted reported computation load metric value being equal to a sum of the current computation load metric value and the reserved computation load metric value.

2. The method of claim 1, wherein the reservation computational load metric is adjusted by a weight correction factor to produce a weighted reservation computational load metric value.

3. A method as claimed in claim 1 or claim 2, comprising generating one or more report messages including the adjusted reported computer load metric value.

4. The method of claim 3, comprising sending the one or more report messages to corresponding one or more consumer service network functions.

5. The method of claim 3 or claim 4, wherein each of the one or more report messages includes a load control information (LCI) header containing an adjusted reported computer load metric value.

6. The method of claim 3, comprising sending the one or more report messages to corresponding one or more Network Function Repository Functions (NRFs).

7. The method of claim 6, wherein each of the one or more report messages includes an NfProfile section containing an adjusted reported computer load metric value.

8. A system for reporting a reserved load to a network function (NF) in a communication network, the system comprising: A NF service producer supported by at least one processor and a memory; as well as A load management engine implemented by the at least one processor, the load management engine being configured to: determine a current computational load metric value of a NF service producer operating in a communication network, detect a number of active sessions supported at the NF service producer, derive a reserved computational load metric value corresponding to a predicted number of subsequent service requests at the NF service producer based on the number of active sessions and a predictive reserved load percentage value, and calculate an adjusted reported computational load metric value, the adjusted reported computational load metric value being equal to a sum of the current computational load metric value and the reserved computational load metric value.

9. The system of claim 8, wherein the reservation computational load metric is adjusted by a weight modification factor to produce a weighted reservation computational load metric value.

10. The system of claim 8 or claim 9, wherein the load management engine is configured to generate one or more report messages including the adjusted reported computer load metric value.

11. The system of claim 10, wherein the load management engine is configured to send the one or more report messages to corresponding one or more consumer service network functions.

12. The system of claim 10 or claim 11, wherein each of the one or more report messages includes a load control information (LCI) header containing an adjusted reported computer load metric value.

13. The system of claim 10, the load management engine configured to send the one or more report messages to corresponding one or more network function repository functions (NRFs).

14. The system of claim 13, wherein each of the one or more report messages includes an NfProfile section containing an adjusted reported computer load metric value.

15. One or more non-transitory computer-readable media having executable instructions stored thereon, which when executed by at least one processor of a computer cause the computer to perform the steps comprising: determining, by a network function (NF) service producer, a current computational load metric value of the NF service producer operating in a communication network; Detect the number of active sessions supported at the NF service producer; deriving a reserved calculated load metric value corresponding to a predicted number of subsequent service requests at the NF service producer based on the number of active sessions and the predictive reserved load percentage value; as well as An adjusted reported computation load metric value is calculated, the adjusted reported computation load metric value being equal to a sum of the current computation load metric value and the reserved computation load metric value.

16. The one or more non-transitory computer-readable media of claim 15, wherein the reservation computational load metric is adjusted by a weight modification factor to produce a weighted reservation computational load metric value.

17. The one or more non-transitory computer-readable media of claim 15 or claim 16, comprising generating one or more report messages including the adjusted reported computer load metric value.

18. The one or more non-transitory computer-readable media of claim 17, comprising sending the one or more report messages to corresponding one or more consumer service network functions.

19. One or more non-transitory computer-readable media as described in claim 17 or claim 18, wherein each of the one or more report messages includes a load control information (LCI) header, which contains an adjusted reported computer load metric value.

20. The one or more non-transitory computer-readable media of claim 17, comprising sending the one or more report messages to corresponding one or more network function repository functions (NRFs).

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