WO2023143258A1

WO2023143258A1 - Data processing method and apparatus

Info

Publication number: WO2023143258A1
Application number: PCT/CN2023/072645
Authority: WO
Inventors: 华郁秀; 李贤明; 于益俊
Original assignee: 华为技术有限公司
Priority date: 2022-01-26
Filing date: 2023-01-17
Publication date: 2023-08-03
Also published as: CN116546518A

Abstract

Disclosed in the present application are a data processing method and apparatus, by means of which invalid adjustment to network slice parameters can be reduced, thereby reducing the waste of resources. The data processing method comprises: acquiring performance data of a network slice; according to the performance data, predicting a first service level agreement (SLA) achievement degree of a first duration; when the first SLA achievement degree is less than a first threshold value, predicting a second SLA achievement degree of a second duration according to the performance data, wherein the second duration is less than the first duration; and when the second SLA achievement degree is less than a second threshold value, determining to adjust parameters of the network slice.

Description

Data processing method and device

This application claims the priority of a Chinese patent application filed with the State Intellectual Property Office of China on January 26, 2022, with application number 202210094817.4 and titled "Data Processing Method and Device", the entire contents of which are incorporated herein by reference.

technical field

The present application relates to the communication field, and more specifically, to a data processing method and device.

Background technique

The fifth generation (5th generation, 5G) mobile communication system needs to meet diverse business requirements, such as enhanced mobile broadband, large-scale machine-type communication, ultra-high reliability and low-latency communication, etc. Network slicing technology abstracts the network logically into one or more isolated network slices, where each network slice contains a series of logical network functions to meet the differentiated needs of different businesses.

The management data analysis function (MDAF) can analyze the performance data of the network slice to obtain the actual value and predicted value of the service level agreement (SLA) achievement of the network slice. In the case that any one of the actual value and the predicted value of the SLA achievement degree of the network slice is smaller than the threshold, the MDAF may determine to adjust the parameters of the network slice.

Due to network fluctuations, the SLA of network slicing may not reach the threshold in a short period of time. However, after adjusting the parameters of the network slice, it takes a period of time for the SLA achievement of the network slice to return to be greater than the threshold. Therefore, the adjustment of network slicing parameters based on the actual and predicted values of SLA attainment may be redundant, resulting in a waste of system resources.

Contents of the invention

The present application provides a data processing method and device, which can reduce invalid adjustments to network slice parameters and reduce waste of resources.

In a first aspect, a data processing method is provided, including: acquiring performance data of a network slice; predicting a first service level agreement achievement degree of a first duration according to the performance data; If it is less than the first threshold, according to the performance data, predict the second SLA achievement degree of the second duration, the second duration is less than the first duration; when the second service level agreement achievement degree is less than the second In the case of the threshold, it is determined to adjust the parameters of the network slice.

By predicting the second SLA achievement degree in a shorter period of time when the prediction result of the first SLA achievement degree in a longer period of time is less than the first threshold, it is possible to avoid frequent adjustments to network slicing parameters caused by fluctuations in SLA achievement degree .

Exemplarily, the first duration may be greater than or equal to the intended maintenance duration. The duration of intent maintenance is used to indicate the length of time required from determining to adjust the network slice to stabilizing the performance parameters of the network slice. The intention maintenance duration can be a statistical value, that is, the intention maintenance duration can be used to indicate the stability of the network slice performance parameters after each adjustment of the network slice is determined. The central tendency of the data for the desired length of time.

With reference to the first aspect, in some possible implementation manners, when the degree of achievement of the first service level agreement is less than a third threshold, the second duration is equal to the first length, and the third threshold is less than the third threshold A second threshold; in the case that the achievement degree of the first service level agreement is greater than or equal to the third threshold, the second duration is equal to a second step, and the first step is smaller than the second step .

The smaller the first SLA achievement degree is, the longer the time period during which the real-time SLA achievement degree is lower than the first threshold within the first duration may be longer, or the difference between the real-time SLA achievement degree and the first threshold within the first duration may be bigger. When the first SLA achievement degree is small and less than the third threshold, using a smaller step size to predict multiple second SLA achievement degrees in the first duration can make the adjustment of network slice parameters more timely .

With reference to the first aspect, in some possible implementations, the method further includes: determining the degree of achievement of a real-time service level agreement according to the performance data; predicting the first service level of the first duration according to the performance data agreement achievement degree, including: predicting the first service level agreement achievement degree according to the performance data when the real-time service level agreement achievement degree is less than a fourth threshold, and the fourth threshold value is greater than the second threshold value .

When the real-time SLA achievement degree is less than the fourth threshold, the first SLA achievement degree is predicted, which can reduce the number of predictions and reduce resource occupation.

With reference to the first aspect, in some possible implementation manners, the fourth threshold is associated with a service of the network slicing service.

When the real-time SLA achievement degree is less than the fourth threshold, the first SLA achievement degree is predicted, and the fourth threshold is associated with the business of the network slicing service, so that the conditions for predicting the first SLA achievement degree are more accurate. In line with business needs, the fourth threshold is more reasonable.

In a second aspect, a data processing method is provided, including: acquiring performance data of a network slice; determining a real-time service level agreement achievement degree according to the performance data; when the real-time service level agreement achievement degree is less than a fourth threshold , according to the performance data, predicting the achievement degree of the second service level agreement of the second duration; in the case that the achievement degree of the second service level agreement is less than a second threshold, determining to adjust the parameters of the network slice, the first The fourth threshold is greater than the second threshold.

With reference to the second aspect, in some possible implementations, the method includes: when the achievement degree of the real-time service level agreement is less than a fourth threshold, predicting the first service level of the first duration according to the performance data The degree of achievement of the agreement; the predicting the degree of achievement of the second service level agreement according to the performance data includes: in the case that the degree of achievement of the first service level agreement is less than the first threshold, according to the performance data, predicting The achievement degree of the second service level agreement, the second duration is shorter than the first duration.

With reference to the second aspect, in some possible implementation manners, when the degree of achievement of the first service level agreement is less than a third threshold, the second duration is equal to the first length, and the third threshold is less than the third threshold A second threshold; in the case that the achievement degree of the first service level agreement is greater than or equal to the third threshold, the second duration is equal to a second step, and the first step is smaller than the second step .

With reference to the second aspect, in some possible implementation manners, the fourth threshold is associated with a service of the network slicing service.

In a third aspect, a data processing device is provided, including: an acquisition module, configured to acquire performance data of a network slice; a processing module, configured to predict the achievement degree of a first service level agreement for a first duration according to the performance data; The processing module is further configured to, in the case that the achievement degree of the first service level agreement is less than a first threshold, according to the performance data, Predicting the degree of achievement of the second service level agreement for a second duration, the second duration being less than the first duration; the processing module is further configured to, when the degree of achievement of the second service level agreement is less than a second threshold , determining to adjust the parameters of the network slice.

With reference to the third aspect, in some possible implementation manners, when the degree of achievement of the first service level agreement is less than a third threshold, the second duration is equal to the first length, and the third threshold is less than the third threshold A second threshold; in the case that the achievement degree of the first service level agreement is greater than or equal to the third threshold, the second duration is equal to a second step, and the first step is smaller than the second step .

With reference to the third aspect, in some possible implementation manners, the processing module is further configured to, according to the performance data, determine a real-time service level agreement achievement degree; the processing module is further configured to, in the real-time service level agreement If the degree of achievement is less than a fourth threshold, predict the degree of achievement of the first service level agreement according to the performance data, and the fourth threshold is greater than the second threshold.

With reference to the third aspect, in some possible implementation manners, the fourth threshold is associated with a service of the network slicing service.

In a fourth aspect, a data processing device is provided, including: an acquisition module, configured to acquire performance data of network slices; a processing module, configured to determine the achievement degree of a real-time service level agreement according to the performance data; the processing module also uses In the case where the real-time service level agreement achievement degree is less than a fourth threshold, according to the performance data, predict the second service level agreement achievement degree of the second duration; the processing module is also used to, in the second In a case where the attainment degree of the second service level agreement is less than the second threshold, it is determined to adjust the parameters of the network slice, and the fourth threshold is greater than the second threshold.

With reference to the fourth aspect, in some possible implementation manners, the processing module is further configured to, if the achievement degree of the real-time service level agreement is less than a fourth threshold, according to the performance data, predict the first time period of the first duration. A service level agreement achievement degree; the processing module is further configured to predict the second service level agreement achievement degree according to the performance data when the first service level agreement achievement degree is less than a first threshold, The second duration is less than the first duration.

With reference to the fourth aspect, in some possible implementation manners, when the degree of achievement of the first service level agreement is less than a third threshold, the second duration is equal to the first length, and the third threshold is less than the third threshold A second threshold; in the case that the achievement degree of the first service level agreement is greater than or equal to the third threshold, the second duration is equal to a second step, and the first step is smaller than the second step .

With reference to the fourth aspect, in some possible implementation manners, the fourth threshold is associated with a service of the network slicing service.

In a fifth aspect, there is provided a data processing device, including a memory and a processor, the memory is used to store program instructions; when the program instructions are executed in the processor, the processor is used to execute the first aspect or The method described in the second aspect.

In a sixth aspect, a computer-readable medium is provided, the computer-readable medium stores program code for execution by a device, and the program code includes a method for executing the method in any one of the implementation manners of the first aspect or the second aspect .

In a seventh aspect, a computer program product including instructions is provided, and when the computer program product is run on a computer, it causes the computer to execute the method in any one of the above-mentioned first aspect or the second aspect.

In an eighth aspect, a chip is provided, the chip includes a processor and a data interface, and the processor reads instructions stored on the memory through the data interface, and executes any one of the above-mentioned first aspect or second aspect method in the implementation.

Optionally, as an implementation manner, the chip may further include a memory, the memory stores instructions, the processor is configured to execute the instructions stored in the memory, and when the instructions are executed, the The processor is configured to execute the method in any one of the implementation manners of the first aspect or the second aspect.

The aforementioned chip may specifically be a field-programmable gate array (field-programmable gate array, FPGA) or an application-specific integrated circuit (application-specific integrated circuit, ASIC).

It should be understood that in this application, the method in the first aspect may specifically refer to the first aspect and the method in any of the various implementation manners in the first aspect.

The technical effects that can be achieved by any possible implementation of any one of the above-mentioned second to eighth aspects can be described with reference to the technical effects that can be achieved by any possible implementation of any of the above-mentioned first aspects, Duplication will not be discussed.

Description of drawings

FIG. 1 is a schematic diagram of a possible network architecture of an embodiment of the present application.

Figure 2 is a schematic diagram of network slicing.

Fig. 3 is a schematic flowchart of a communication method.

FIG. 4 and FIG. 5 are schematic structural diagrams of a communication system provided by an embodiment of the present application.

Fig. 6 is a schematic flowchart of a communication method.

Fig. 7 is a schematic diagram of SLA achievement degree.

Fig. 8 is a schematic flowchart of a data processing method provided by an embodiment of the present application.

Fig. 9 is a schematic flowchart of another data processing method provided by the embodiment of the present application.

Fig. 10 is a schematic flowchart of another data processing method provided by the embodiment of the present application.

Fig. 11 is a schematic diagram of the forecast period, the number of forecast steps and the second duration provided by the embodiment of the present application.

Fig. 12 is a schematic flowchart of another data processing method provided by the embodiment of the present application.

Fig. 13 is a schematic structural diagram of a data processing device provided by an embodiment of the present application.

Fig. 14 is a schematic structural diagram of another data processing device provided by an embodiment of the present application.

Detailed ways

The technical solution in this application will be described below with reference to the accompanying drawings.

The technical solutions of the embodiments of the present application can be applied to various communication systems, such as: global system for mobile communications (GSM) system, code division multiple access (code division multiple access, CDMA) system, wideband code division multiple access ( wideband code division multiple access (WCDMA) system, general packet radio service (general packet radio service, GPRS), long term evolution (long term evolution, LTE) system, LTE frequency division duplex (frequency division duplex, FDD) system, LTE time division Duplex (time division duplex, TDD), universal mobile telecommunications system (UMTS), worldwide interoperability for microwave access (WiMAX) communication system, fifth generation (5th generation, 5G) system Or new wireless (new radio, NR), etc.

The terminal in the embodiment of the present application may refer to user equipment (user equipment, UE), access terminal, subscriber unit, user station, mobile station, mobile station, remote station, remote terminal, mobile device, user terminal, terminal, wireless communication device, user agent, or user device. The terminal can also be a cellular phone, a cordless phone, a session initiation protocol (session initiation protocol (SIP) telephone, wireless local loop (wireless local loop, WLL) station, personal digital assistant (personal digital assistant, PDA), handheld device with wireless communication capabilities, computing device, or other processing device connected to a wireless modem , vehicle-mounted devices, wearable devices, terminals in a 5G network or terminals in a future evolving public land mobile network (PLMN), etc., are not limited in this embodiment of the present application.

The access network device in the embodiment of the present application may be a device for communicating with a terminal, and the access network device may be a global system for mobile communications (GSM) system or a code division multiple access (code division multiple access, The base transceiver station (BTS) in CDMA) can also be the base station (NodeB, NB) in the wideband code division multiple access (wideband code division multiple access, WCDMA) system, and it can also be the evolved base station in the LTE system (evoled NodeB, eNB or eNodeB), or a wireless controller in a cloud radio access network (cloud radio access network, CRAN) scenario, or the access network device can be a relay station, access point, vehicle-mounted device, or Wearable devices and access network equipment in the 5G network or access network equipment in the future evolved PLMN network, one or a group (including multiple antenna panels) antenna panels of the base station in the 5G system, or, can also be composed of A network node of a gNB or a transmission point, such as a baseband unit (baseband unit, BBU), or a distributed unit (distributed unit, DU), etc., is not limited in this embodiment of the present application.

In some deployments, a gNB may include a centralized unit (CU) and a DU. The gNB may also include an active antenna unit (AAU). The CU implements some functions of the gNB, and the DU implements some functions of the gNB. For example, the CU is responsible for processing non-real-time protocols and services, and realizing the functions of radio resource control (radio resource control, RRC) and packet data convergence protocol (packet data convergence protocol, PDCP) layer. The DU is responsible for processing physical layer protocols and real-time services, realizing the functions of the radio link control (radio link control, RLC) layer, media access control (media access control, MAC) layer and physical (physical, PHY) layer. The AAU implements some physical layer processing functions, radio frequency processing and related functions of active antennas. Since the information of the RRC layer will eventually become the information of the PHY layer, or be transformed from the information of the PHY layer, under this architecture, high-level signaling, such as RRC layer signaling, can also be considered to be sent by the DU , or, sent by DU+AAU. It can be understood that the access network device may be a device including one or more of a CU node, a DU node, and an AAU node. In addition, the CU can be divided into access network devices in the access network (radio access network, RAN), and the CU can also be divided into access network devices in the core network (core network, CN). Do limited.

In the embodiment of the present application, the terminal or the access network device includes a hardware layer, an operating system layer running on the hardware layer, and an application layer running on the operating system layer. The hardware layer includes hardware such as a central processing unit (CPU), a memory management unit (MMU), and memory (also called main memory). The operating system may be any one or more computer operating systems that implement business processing through processes, for example, Linux operating system, Unix operating system, Android operating system, iOS operating system, or windows operating system. The application layer includes applications such as browsers, address books, word processing software, and instant messaging software. Moreover, the embodiment of the present application does not specifically limit the specific structure of the execution subject of the method provided by the embodiment of the present application, as long as the program that records the code of the method provided by the embodiment of the present application can be run to provide the method according to the embodiment of the present application. For example, the execution body of the method provided by the embodiment of the present application may be a terminal or an access network device, or a functional module in a terminal or an access network device that can call a program and execute the program.

Additionally, various aspects or features of the present application may be implemented as a method, apparatus, or article of manufacture using standard programming and/or engineering techniques. For example, computer-readable media may include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, or magnetic tape, etc.), optical discs (e.g., compact disc (CD), digital versatile disc (digital versatile disc, DVD), etc.), smart cards, and flash memory devices (e.g., erasable programmable read-only memory (erasable programmable read-only memory) -only memory, EPROM), card, stick or key drive, etc.). Additionally, various storage media described herein can represent one or more devices and/or other machine-readable media for storing information. The term "machine-readable medium" may include, but is not limited to, wireless channels and various other media capable of storing, containing and/or carrying instructions and/or data.

FIG. 1 is a schematic diagram of a possible network architecture of an embodiment of the present application. Taking the 5G network architecture as an example, the network architecture includes: terminal 101, (wireless) access network equipment (radio access network, (R)AN) 102, user plane function (user plane function, UPF) network element 103, data network (data network, DN) network element 104, authentication server function (authentication server function, AUSF) network element 105, AMF network element 106, session management function (session management function, SMF) network element 107, network exposure function (network exposure function , NEF) network element 108, network repository function (network repository function, NRF) network element 109, policy control function module (policy control function, PCF) network element 110, unified data management (udified data management, UDM) network element 111 and NSSF network element 112 . The following UPF network element 103, DN network element 104, AUSF network element 105, AMF network element 106, SMF network element 107, NEF network element 108, NRF network element 109, policy control function (policy control function, PCF) network element 110. The UDM network element 111 and the NSSF network element 112 are referred to as UPF103, DN104, AUSF105, AMF106, SMF107, NEF108, NRF109, PCF110, UDM111, and NSSF112 for short.

Among them, the terminal 101 mainly accesses the 5G network through the wireless air interface and obtains services. The terminal interacts with the RAN through the air interface, and interacts with the AMF of the core network through non-access stratum signaling (non-access stratum, NAS). RAN102 is responsible for air interface resource scheduling and air interface connection management for terminal access network. UPF103 is responsible for forwarding and receiving user data in the terminal. For example, UPF can receive user data from the data network and transmit it to the terminal through the access network device, and can also receive user data from the terminal through the access network device and forward it to the data network. In UPF103, the transmission resources and scheduling functions that provide services for terminals are managed and controlled by SMF network elements. AUSF105 belongs to the control plane network element of the core network, and is mainly responsible for authenticating and authorizing users to ensure that users are legal users. AMF106 belongs to the core network element and is mainly responsible for signaling processing, such as: access control, mobility management, attachment and detachment, and gateway selection functions, and AMF106 can also provide services for sessions in terminals, A storage resource on the control plane is provided for the session to store the session ID, the SMF network element ID associated with the session ID, and the like. SMF107 is responsible for user plane network element selection, user plane network element redirection, Internet protocol (internet protocol, IP) address allocation, bearer establishment, modification and release, and quality of service (quality of service, QoS) control. The NEF108 belongs to the network element of the control plane of the core network, and is responsible for the opening of mobile network capabilities to the outside world. The NRF109 belongs to the control plane network element of the core network, and is used for dynamic registration of service capabilities of network functions and discovery of network functions. PCF110 mainly supports the provision of a unified policy framework to control network behavior, provides policy rules to the network functions of the control layer, and is responsible for obtaining user subscription information related to policy decisions. UDM111 belongs to the control plane network element of the core network and belongs to the user server. It can be used for unified data management and supports functions such as 3GPP authentication, user identity operation, authority grant, registration, and mobility management. The NSSF112 is used to complete the network slice selection function for the terminal. The NSSF 112 belongs to the core network control plane entity, and is responsible for selecting the target NSI.

In this network architecture, Nausf is the service-based interface presented by AUSF105, Namf is the service-based interface presented by AMF106, Nsmf is the service-based interface presented by SMF107, Nnef is the service-based interface presented by NEF108, and Nnrf is the service-based interface presented by NRF109 Npcf is the service-based interface presented by PCF110, Nudm is the service-based interface presented by UDM111, and Nnssf is the service-based interface presented by NSSF112. N1 is the reference point between UE101 and AMF106, N2 is the reference point between (R)AN102 and AMF106, use Used for sending non-access stratum (NAS) messages, etc.; N3 is a reference point between (R)AN102 and UPF103, used to transmit user plane data, etc.; N4 is a reference between SMF107 and UPF103 The point is used to transmit information such as the tunnel identification information of the N3 connection, the data cache indication information, and the downlink data notification message; the N6 interface is a reference point between the UPF103 and the DN104, and is used to transmit data on the user plane.

The terms involved in this application are introduced below.

Network slice (network slice): refers to the customization of different logical networks according to different service requirements on the physical or virtual network infrastructure. Network slicing can be a complete end-to-end network including terminals, access network, transmission network, core network and application server, which can provide telecommunication services and have certain network capabilities; network slicing can also be the above-mentioned terminal, access network , transmission network, core network and application server in any combination, for example, network slicing only includes access network and core network.

Network slice subnet (NSS): A network system consisting of connectivity, computing, and storage resources, possibly including network functions and network management entities, forming a complete instantiated logical/physical network to support certain network and business characteristics. A network slice subnet cannot be activated independently as an overall (end-to-end) network slice, and must be interconnected with other network slice subnets to form a network slice.

The function of network slicing can be realized by one or more network slicing instances. A network slice can contain one or more network slice instances.

Network slice instance (NSI): It is a logical network that is actually running and can meet certain network characteristics or service requirements. A network slice instance may provide one or more services. A network slice instance can be created by a network management system. A network management system may create multiple network slice instances and manage them at the same time, including performance monitoring and fault management during the running of the network slice instance. When multiple network slice instances coexist, some network resources and network functions may be shared between network slice instances. A network slice instance may or may not be created from a network slice template. A complete network slice instance can provide complete end-to-end network services, and a network slice instance can be composed of a network slice subnet instance (network slice subnet instance) and/or network functions. Network functions may include physical network functions and/or virtual network functions.

The function of a network slicing subnet can be implemented by one or more network slicing subnet instances. A network slice subnet can include one or more network slice subnet instances. A network slice subnet can be hosted on one or more network slice subnet instances on resources.

Network slice subnet instance (NSSI): The network slice subnet instance does not need to provide end-to-end complete network services, and the network slice subnet instance can be composed of network functions of the same equipment vendor in the network slice instance A collection may also be a collection of network functions divided by domain, such as a core network network slice subnet instance, an access network network slice subnet instance, or a collection formed in other ways. A network slice subnet instance may be shared by multiple network slice instances. An example of network slicing subnet is proposed, which can facilitate the management of the network management system. Each network slice instance consists of several network functions and/or several network slice subnet instances, that is, a network slice instance may consist of several network slice subnet instances and network functions that are not divided into network slice subnet instances; a network A slice instance may also consist of only a few network functions.

Network function (Network function, NF): It is a kind of processing function in the network, which defines the functional behavior and interface. It is implemented in the form of virtual functions on the hardware platform. Therefore, from the perspective of implementation, network functions can be divided into physical network functions and virtual network functions. From the perspective of use, network functions can be divided into dedicated network functions and shared Network functions, specifically, for multiple (sub) network slice instances, different network functions can be used independently, this network function is called a dedicated network function, and the same network function can also be shared, this network function is called For shared network functions.

Network slice subnet management function module (network slice subnet management function, NSSMF): used for slice management and design of network slice subnet. Receive requirements for network slicing subnet instances from NSMF to manage the life cycle, performance, and faults of network slicing subnet instances (hereinafter referred to as management for life cycle, performance, and fault management), and arrange the composition of network slicing instances. NSSMF reports the capabilities of network slicing subnet instances to NSMF, and after receiving the deployment requirements decomposed by NSMF, realizes autonomous deployment and enabling within network slicing subnet instances. During operation, NSSMF is used to manage and monitor network slices of network slice subnet instances.

Service Level Agreement (SLA): A contract (or part of a contract) between a service provider and a customer to establish a shared understanding of services, priorities, responsibilities, etc. SLA indicators mainly include user rate, end-to-end delay, packet reliability, positioning accuracy, and clock synchronization. Cut out multiple "slices" from a physical network through network slicing technology, and provide customized, differentiated and deterministic network service capabilities for enterprise services (to business) with business SLA as the center.

Fig. 2 shows a schematic diagram of network slicing. For example, network slice A includes NSI1, network slice B includes NSI1 and NSI2, and network slice C includes NSI3. Different network slices provide different services, such as Internet access services, voice services, ultra-low latency services, or Internet of Things services.

Fig. 3 is a schematic flowchart of a communication method.

The communication method 300 includes S310 and S320, configured to realize SLA guarantee of the network slice subnet.

In S310, intent management.

Translate the intent management information received from the operator/user to determine the analysis object and the key performance indicator (key performance indicator, KPI) and other indicators that need to be monitored.

At S320, the value of the KPI and other indicators of the analysis object is perceived.

At S330, the analysis is performed according to the KPI of the analysis object and the values of other performance data.

The SLA achievement degree of the analysis object can be determined according to the value of the KPI and other performance data of the analysis object.

At S340, a decision is made.

In the case that the SLA attainment degree is greater than or equal to 100%, return to S320.

When the SLA achievement degree is less than 100%, conduct root cause analysis to determine the SLA guarantee plan and issue the SLA guarantee plan.

Through the method 300, the achievement degree of the SLA can be satisfied to 100%.

FIG. 4 and FIG. 5 are schematic structural diagrams of a communication system provided by an embodiment of the present application. Each step in the method 300 can be performed in the communication system 400 or the communication system 500 .

The communication system 400 includes a network management system (network management system, NMS) and an network element management system (element management system, EMS). The NMS is responsible for the operation, management and maintenance of the network. The EMS is responsible for managing certain types of network elements. The EMS manages the functions and capacities of each network element. The number of managed network elements can be one or more.

As shown in FIG. 4 , the NMS includes an intent driven management service (intent driven management service, IDMS) consumer (consumer) and the like. EMS includes IDMF, management data analysis function (management data analysis function, MDAF), NSSMF, etc.

MDAF is used to provide management data analysis services for NF, NSSMF, NSSI, etc.

Alternatively, in the communication system 500 shown in FIG. 5 , the IDMF may also be located in the NMS.

Fig. 6 is a schematic flowchart of a data processing method.

The method 600 is applied to the communication system 400 or the communication system 500, and is used to realize the SLA guarantee of the network slice subnet. The method 600 includes S601 to S609.

In S601, an IDMS user (consumer) sends SLA guarantee intent information to an intent driven management function (intent driven management, IDMF).

The SLA guarantee intent information can be carried in the intent creation message. IDMS consumer can send an intent to create message to IDMF.

Intents are used to express expectations about the system, including requirements, goals, constraints, etc. The SLA guarantee intention information may include information such as analysis objects, analysis indicators, and limit values of analysis indicators.

The SLA guarantee intention information may be, for example, network slice subnet configuration information (for example, SliceProfile information).

Analysis objects can be used to indicate network slices, for example, can be represented by network slice identifiers. The network slice identifier may be, for example, network slice selection assistance information (single network slice selection assistance information, S-NSSAI) and the like.

Analysis indicators can be understood as SLA performance parameters of network slicing subnets. The limit value of the analysis index is used to indicate the range of the analysis index. For example, the analysis index may be delay, and the limit value of the analysis index may be 40 milliseconds (ms), indicating that the maximum delay does not exceed 40 ms.

At S602, the IDMF performs intent translation on the SLA guarantee intent information.

Intent translation can convert intents into executable commands, rules, sub-intent processes, etc.

IDMF can determine the analysis object, analysis index and the limit value of the analysis index through the intention translation of the SLA guarantee intention information, and determine the calculation formula according to the analysis index and the limit value of the analysis index. The calculation formula is used to express the detection of the analysis index The relationship between value and SLA achievement.

At S603, the IDMF sends task creation information to the MDAF.

The task creation information may include all or part of the translated SLA guarantee intent information. The task creation information may include, for example, analysis objects and calculation formulas, and may also include information such as analysis periods.

IDMF can determine the analysis cycle according to business or MDAF, etc.

At S604, the MDAF acquires the performance data of the network slice subnet from the NSSMF according to the analysis object and according to the analysis cycle. Performance data is used to represent detection values of analytics metrics.

Each network slice can be used to provide services for a certain business. In other words, network slices can correspond to services one-to-one. A network slice can include network slice subnets for multiple domains. For each domain, the network slice subnet belonging to the network slice in the domain may be determined according to the network slice indicated by the analysis object.

MDAF can obtain the performance data of the network slice subnet corresponding to the NSSMF from each NSSMF. MDAF can obtain the performance data of one or more network slice subnets in the network slice and perform subsequent processing. That is to say, the method 600 may be performed for the SLA of the network slice, or may be performed for the SLA of the network slice subnet. Exemplarily, the MDAF with a cross-domain management function can acquire performance data of multiple network slice subnets in the network slice.

In S605, the MDAF determines the real-time SLA achievement degree according to the performance data and using a calculation formula.

MDAF can bring the detection value of the analysis index into the calculation formula to calculate the real-time SLA achievement degree.

If the real-time SLA attainment degree is less than 100%, go to S606; if the real-time SLA attainment degree is greater than or equal to 100%, go to S607.

At S606, the MDAF determines the achievement degree of the predicted SLA according to the performance data.

Determining the degree of achievement of the predicted SLA can also be understood as performing degradation prediction. The predicted SLA achievement degree is used to indicate the SLA achievement degree within a preset time period in the future.

If the predicted SLA achievement degree is greater than or equal to 100%, return to S605; if the real-time SLA achievement degree is less than 100%, perform S607.

In S607, the MDAF determines an adjustment strategy according to the performance data, the configuration information of the network slice subnet, the environment information, and the like.

At S608, the MDAF sends the adjustment policy to the IDMF.

In S609, the IDMF adjusts the parameters of the network slice subnet according to the adjustment strategy.

The adjustment strategy may be, for example, adjustment of a resource allocation algorithm of a network slice subnet, a cell coverage parameter, and the like.

Specifically, the IDMF can evaluate the adjustment strategy to determine whether the adjustment strategy can make the SLA reach 100%. When it is determined that the adjustment strategy can make the SLA reach 100%, the IDMF can adjust the network slice subnet according to the adjustment strategy.

The number of adjustment policies sent by the MDAF to the IDMF may be multiple. The IDMF can determine a target adjustment strategy that has less impact on other performance indicators (such as energy saving, network capacity, etc.) among the multiple adjustment strategies, and adjust the network slice subnet according to the target adjustment strategy.

The method 600 may also include S610.

At S610, the IDMF sends the report information to the IDMS consumer.

The reporting message may be used to represent the real-time SLA achievement degree and/or the predicted SLA achievement degree. Exemplarily, the IDMF may periodically or aperiodically send reporting information to the IDMS consumer. Alternatively, the report information can be sent to the IDMS consumer when the real-time SLA achievement degree and the predicted SLA achievement degree of the network slice recover to more than 100%.

Due to network fluctuations (such as channel deterioration due to temporary occlusion, resources temporarily preempted by high-priority services, etc.), the SLA may not reach 100% in a short period of time. As shown in FIG. 7 , it is the change over time of the actual value of the SLA achievement degree of the network slice subnet under the condition that no adjustment is made according to the SLA achievement degree. After time 2, the SLA achievement degree of the network slicing subnet drops to less than 100%; after time 6, the SLA achievement degree of the network slicing subnet recovers to be higher than 100%.

According to method 600, at time 2, the predicted SLA achievement degree determined in S606 is less than 100%, and the predicted SLA achievement degree (that is, the predicted value in the figure) can be used to represent the prediction result of the average value of the SLA achievement degree from time 2 to time 3 . After S606, S607 and S608 are performed to determine the adjustment strategy, and adjust the network elements in the network slicing subnet, so that the achievement degree of the SLA reaches 100%.

After adjusting the network elements in the network slicing subnet, it will take some time for the SLA achievement of the network slicing subnet to return to greater than 100%. In method 600, from the predicted SLA attainment degree determined in S606 being less than 100% to the SLA attainment degree recovering to 100% after performing S607-S609, the length of time elapsed may be referred to as the intention maintenance duration. If the intended maintenance duration is longer than the duration between time 2 and time 6, the execution of S607 and S608 is redundant, resulting in waste of system resources.

In order to solve the above problem, an embodiment of the present application provides a data processing method.

Fig. 8 is a schematic flowchart of a data processing method provided by an embodiment of the present application. Method 800 includes S810 to S840.

At S810, the performance data of the network slice is acquired.

At S820, predict a first SLA achievement degree for a first duration according to the performance data.

At S830, if the first SLA achievement degree is less than a first threshold, predict a second SLA achievement degree for a second duration according to the performance data, where the second duration is shorter than the first duration.

At S840, in a case where the second SLA achievement degree is less than a second preset value, it is determined to adjust the parameter of the network slice.

A network slice can include multiple network slice subnets. At S810, the performance data of all or part of the network slice subnets in the network slice may be acquired, and at S840 parameters of the whole or part of the network slice subnets may be adjusted.

Method 800 predicts the second SLA achievement degree in a shorter period of time when the prediction result of the first SLA achievement degree in a longer period of time is less than the first threshold, so as to avoid network slicing parameters caused by fluctuations in the SLA achievement degree. Adjust frequently.

The parameters of network slicing can include the maximum number of radio resource control (RRC) connected users in the cell, the available resource block (RB) in the cell, RB resource control strategy, network element isolation, computing and storage resources, etc. one or more of the . Among them, the maximum number of users in the RRC connection state of the cell, the available amount of RB in the cell, and the RB resource control policy are the parameters of the access network network slice subnet, and the network element isolation degree and computing and storage resources are the parameters of the core network network slice subnet.

The performance data of the network slice may be acquired periodically or aperiodically at S810. Therefore, at S820, the first SLA achievement degree may be predicted according to the latest acquired performance data; and at S830, the second SLA achievement degree may be predicted according to the latest acquired performance data.

Because S830 and S820 are not performed at the same time, S830 is performed after S820 and when the first SLA achievement degree is less than the first threshold, so the performance data used to calculate the second SLA achievement degree in S830 is the same as that used in S820. The performance data of the first SLA attainment degree may be the same or different.

That is to say, in some embodiments, after S820, the latest performance data of the network slice may be reacquired. At S830, the second SLA attainment degree is predicted according to the latest performance data.

At S830, the second duration may be determined according to the degree of achievement of the first SLA, so as to predict the degree of achievement of the second SLA.

Specifically, when the first SLA achievement degree is less than the third threshold, the first step can be used as the second duration, and the third threshold is less than the second threshold; when the first SLA achievement degree is greater than or equal to the third threshold In the case of , the second step length can be used as the second duration, and the first step length is smaller than the second step length.

At S830, if the first SLA achievement degree is less than the third threshold, predict the second SLA achievement degree according to the first step; if the first SLA achievement degree is greater than or equal to the third threshold , according to the second step length, predict the second SLA attainment degree.

The step size is used to indicate the predicted time length corresponding to the second SLA achievement degree. In the case that the first SLA achievement degree is less than the first threshold, the smaller the first SLA achievement degree, it means that the time length during which the real-time SLA achievement degree is lower than the first threshold within the first duration may be longer, or, in the first duration The greater the difference between the inner real-time SLA attainment degree and the first threshold may be (that is, the worse the real-time SLA attainment degree is). When the first SLA achievement degree is less than the third threshold, a smaller step size is used to predict multiple second SLA achievement degrees in the first duration, which can make the parameter adjustment of the network slice more timely.

Exemplarily, at S830, the prediction may be performed according to the prediction period, the number of prediction steps, and the second duration. The time interval between forecast time points is equal to the forecast period. At each prediction time point, a prediction can be made, and the number of second SLA achievement degrees obtained is the same as the number of prediction steps. Each second SLA achievement degree is used to represent multiple consecutive second-duration SLAs after the prediction time point degree of achievement. The forecast period can be greater than, less than or equal to the product of the forecast steps and the second duration.

The prediction cycle and the number of prediction steps may be preset, or received, or determined according to the second duration. For example, different step sizes may correspond to different prediction periods, and different step sizes may correspond to different numbers of prediction steps.

In some embodiments, multiple second SLA attainment degrees can be predicted according to the performance data acquired when S830 is started.

In some other embodiments, the performance data may be acquired according to the forecast period, and the second SLA attainment degree may be predicted according to the number of forecast steps and the second duration according to the performance data acquired in each forecast period.

As shown in Figure 11, at the prediction time point 1, the performance data can be obtained, and the prediction can be made according to the performance data according to the number of prediction steps and the second duration. If the number of prediction steps is 5, then five second SLA achievement degrees can be obtained. The SLAs are respectively used to indicate the SLA achievement degree of the network slice for five consecutive second time periods after the prediction time point 1.

The time interval between the prediction time point 2 and the prediction time point 1 may be a prediction period. At the prediction time point 2, the performance data can be acquired again, and the prediction can be made according to the number of prediction steps and the second duration according to the new performance data.

The forecast period can be greater than, less than or equal to the product of the forecast steps and the second duration. Exemplarily, the prediction period may be equal to a positive integer multiple of the second duration.

In yet other embodiments, performance data may be acquired periodically or aperiodically. According to the latest acquired performance data, the second SLA achievement degree can be predicted according to the forecast cycle, the number of forecast steps, and the second duration.

In order to reduce the number of predictions, before S820, the real-time SLA attainment degree can be determined according to the performance data. At S820, if the real-time SLA achievement degree is less than a fourth threshold, predict the first SLA achievement degree according to the performance data, where the fourth threshold is greater than the second threshold.

The fourth threshold is associated with services of the network slicing service.

At S820, the fourth threshold sent by the IDMF may be received. In some embodiments, the IDMF can acquire the SLA guarantee intent creation information sent by the IDMS consumer. The SLA assurance intent creation information may include a fourth threshold. The business corresponding to the IDMS consumer is the business of network slicing service, and the IDMS consumer can determine the fourth threshold according to the business requirements. Therefore, the fourth threshold has an associated relationship with services of the network slicing service.

In some other embodiments, the IDMF may determine the fourth threshold according to the received SLA guarantee intention creation information or other information related to the service of the network slicing service.

FIG. 9 is a schematic flowchart of a data processing method provided by an embodiment of the present application. The method 900 includes S901 to S912.

In S901, the IDMS consumer sends SLA guarantee intent information and a first predicted trigger threshold to an intent driven management function (intent driven management, IDMF).

IDMS consumer can determine the SLA guarantee intention information and the first predictive trigger threshold according to the business. The SLA guarantee intent information can be used to indicate information such as analysis objects and calculation formulas.

The analysis object can include all or part of the network slice subnets in the network slice that provides services for the IDMS consumer.

The calculation formula is used to express the relationship between the performance parameters of the analysis object and the achievement of the SLA. Analyzing the performance of objects The parameters may include key performance indicators (key performance indicator, KPI), such as packet delay (packet delay), throughput (throughput), and so on. For example, the SLA assurance intent information may include analysis indicators and limit values of the analysis indicators. According to the analysis index and the limit value of the analysis index, IDMF can determine the calculation formula. The number of analysis indicators in the SLA guarantee intent information may be one or more. Each analysis index can also be understood as a performance parameter.

Exemplarily, according to the calculation formula, the SLA attainment degree may be the mean, median, maximum value, minimum value, and mode of the achievement degrees of multiple analysis indicators. That is to say, the achievement degree of SLA can be used to represent the central trend of the achievement degree of multiple analysis indicators.

The attainment degree of each analysis index can be expressed as the difference between the detection value of the analysis index and the limit value of the analysis index. For example, the index attainment degree of the average delay can be expressed as 1+(τ0-τ)/τ0, τ0 is used to represent the average delay threshold, and τ is used to represent the average delay calculated by using the collected performance data (that is, the average delay time detection value).

The first predicted trigger threshold may be greater than 100%, for example, may be 120%, 130%, and so on.

At S902, the IDMF performs intent translation on the SLA guarantee intent information and the first predicted trigger threshold.

At S903, the IDMF sends the translated SLA guarantee intention information and the first predicted trigger threshold to the MDAF.

At S904, the MDAF acquires the performance data of the network slice subnet corresponding to the NSSMF from the NSSMF according to the analysis object.

It should be understood that different NSSMFs may correspond to different network slice subnets. MDAF can obtain performance data from one or more NSSMFs. That is to say, the performance data acquired by the MDAF may include values of performance parameters of one or more network slice subnets.

In S905, the MDAF determines the real-time SLA attainment degree by using a calculation formula according to the performance data.

The MDAF may acquire the performance data periodically or aperiodically, and perform S905 after acquiring the performance data each time. That is to say, MDAF can periodically or non-periodically calculate the real-time SLA attainment degree.

The cycle in which MDAF acquires performance data may also be referred to as an analysis cycle. The analysis period can be preset in MDAF, or can be determined by MDAF according to parameters such as computing power, or can be sent to MDAF by IDMF. Exemplarily, IDMF receives the analysis period sent by IDMS consumer, and forwards it to MDAF.

At S906, the MDAF judges whether the real-time SLA attainment degree is greater than or equal to the first predictive trigger threshold.

In the case that the real-time SLA attainment degree is greater than or equal to the first predicted trigger threshold, return to execute S904, S905 and subsequent steps.

When the real-time SLA attainment degree is less than the first predicted trigger threshold, go to S907.

At S907 , the MDAF predicts the achievement degree of the first SLA of the first duration according to the performance data.

The first duration may be greater than or equal to the intention maintenance duration. The duration of intent maintenance can be understood as the length of time required from the MDAF determining that the network slice adjustment condition is met to the adjusted network slice performance parameters being stable. The network slice adjustment condition may be that the first SLA achievement degree or the second SLA achievement degree is less than the second prediction trigger threshold.

The first duration may be equal to a positive integer multiple of the analysis period. Exemplarily, the analysis period may be 1 minute (min), and the first duration may be 30 minutes (min).

The first SLA achievement degree of the first duration may be determined by the MDAF according to the predicted value type and the SLA achievement degrees of multiple time points in the first duration. Predicted value types can be used to indicate maximum, minimum, or average values, etc.

The predicted value type can be preset in MDAF, or preset in IDMF and sent to MDAF, or sent to IDMF by IDMS consumer, and then sent to MDAF through IDMF translation.

The first SLA attainment degree may be predicted according to the first number of prediction steps. The first prediction step number can be a positive integer. That is to say, when the first number of prediction steps is greater than 1, multiple second SLA achievement degrees can be obtained each time the prediction in S907 is performed, and the number of obtained second SLA achievement degrees is the same as the first number of prediction steps. The multiple first SLA achievement degrees are respectively used to represent the SLA achievement degrees of a plurality of continuous first durations after the time point when S907 is performed.

In S908, the MDAF judges whether the first SLA attainment degree is greater than or equal to the second predictive trigger threshold.

The second predictive trigger threshold can be preset in MDAF or IDMF, can also be sent to IDMF by IDMS consumer and sent to MDAF through IDMF translation, and can also be determined by MDAF or IDMF.

The second predictive trigger threshold may be less than or equal to the first predictive trigger threshold. Exemplarily, the adjustment trigger threshold may be 100%.

In the case that the first SLA achievement degree is greater than or equal to the second prediction trigger threshold, return to S907 and S908 until the number of times predicted by the first SLA achievement degree reaches the first predicted number.

That is to say, after S906, the number of times that the first SLA attainment degree can be predicted, that is, the number of execution times of S907, can be at most the first number of times of prediction. When the number of executions of S907 reaches the first predicted number, if the first SLA attainment degree is still greater than or equal to the second predicted trigger threshold, S904 and S905 and subsequent steps may be executed back.

The execution of S907 may be periodic or aperiodic. The repetition period of S907 may be greater than, equal to, or less than the product of the first number of predicted steps and the first duration.

The first number of predictions may be preset in the MDAF, or may be sent from the IDMF to the MDAF, which is not limited in this embodiment of the present application. The first number of prediction steps preset in MDAF may be 1, for example.

In the case that the first SLA attainment degree is smaller than the second predictive trigger threshold, go to S909.

In S909, the MDAF determines the second duration according to the relationship between the achievement degree of the first SLA and the parameter threshold.

In the case that the first SLA achievement degree is less than the parameter threshold, the second duration is the first duration.

In the case that the first SLA attainment degree is greater than or equal to the parameter threshold, the second duration is the second step. The length of the first step is smaller than the length of the second step.

The first step size and the second step size may be preset in the MDAF, or may be sent from the IDMF to the MDAF.

In S910, predict the second SLA achievement degree according to the second duration.

The second duration is less than the first duration. That is to say, both the first step length and the second step length are shorter than the first duration. The second duration may be, for example, 1 min, 2 min, or 5 min.

When predicting the second SLA attainment degree according to the length of the first step, the second number of prediction steps may be the number of the first step corresponding to the length of the first step. When predicting the second SLA attainment degree according to the second step size, the second prediction step number may be the second step number corresponding to the second step size. That is to say, each time the prediction at S910 can obtain multiple second SLA achievement degrees, at least one second SLA achievement degree can be obtained, and the number of obtained second SLA achievement degrees is the same as the number of second prediction steps. The multiple second SLA achievement degrees are respectively used to represent the SLA achievement degrees of a plurality of continuous second durations after the time point when S910 is performed.

The first step number corresponding to the first step length and the second step number corresponding to the second step length may be equal or unequal.

The first step length, the second step length, the first step number corresponding to the first step length, and the second step number corresponding to the second step length can be preset in MDAF, or can be sent to IDMF by IDMS consumer and passed IDMF translation sent to MDAF.

In S911, determine the magnitude relationship between the second SLA attainment degree and the adjusted trigger threshold.

Adjusting the trigger threshold can be preset in MDAF, or can be sent to IDMF by IDMS consumer, and then sent to MDAF through IDMF translation.

The adjusted trigger threshold may be greater than, less than, or equal to the second predicted trigger threshold. Exemplarily, the adjustment trigger threshold may be 100%.

In the case that the second SLA attainment degree is greater than or equal to the adjustment trigger threshold, return to S910 and S911.

S910 may be performed according to the second prediction period, and the second prediction period may be greater than, less than or equal to the product of the second prediction step number and the second duration.

For example, in the case that the achievement degree of the second SLA is less than 80%, the second duration is equal to the length of the first step being 1 minute, the second forecasting steps can be 5, and the second forecasting period can be 5 minutes; when the second SLA is achieved When the degree is between 80% and 100%, the second duration is equal to the second step length being 5 minutes, the second forecasting step number may be 1, and the second forecasting period may be 5 minutes.

After S909, the second SLA attainment degree prediction may be performed for the second prediction times at most. That is to say, after each execution of S909, the number of executions of S910 is at most the second predicted number of times. When the number of executions of S910 reaches the second predicted number of times, if the second SLA attainment degree is still greater than or equal to the adjustment trigger threshold, S904 and S905 and subsequent steps may be executed back.

The second number of predictions may be preset in the MDAF, may also be sent by the IDMF, or may be determined according to the first duration, the second prediction period, and the like. For example, the second number of forecasts may be obtained by rounding the quotient of dividing the first duration by the second forecast period.

In the case that the second SLA attainment degree is smaller than the adjustment trigger threshold, go to S912.

It should be understood that, when the second prediction step number is greater than 1, that is, when the number of second SLA achievement degrees obtained by performing S910 is multiple, any second SLA achievement degree among the plurality of second SLA achievement degrees may be less than In the case of adjusting the trigger threshold, go to S912.

In S912, parameters of the network slice are adjusted.

Specifically, when the second SLA achievement degree is less than the adjustment trigger threshold, the MDAF may send indication information to the IDMF to instruct the IDMF to determine the adjustment policy. After the IDMF determines the adjustment strategy, it can adjust the parameters of each network element in the network slice subnet according to the adjustment strategy.

Or, in the case that the second SLA achievement degree is less than the adjustment trigger threshold, the MDAF may determine the adjustment policy and send the adjustment policy to the IDMF.

In some embodiments, if it is determined in S908 that the first SLA attainment degree is less than the second predictive trigger threshold, S912 may be performed.

FIG. 10 is a schematic flowchart of a data processing method provided by an embodiment of the present application. The method 1000 includes S901 to S912 and S1001 to S1003 in the method 900 .

At S903, the IDMF may also send the first prediction parameter to the MDAF.

The first forecast parameter may include one or more of a first forecast step number, a first forecast period, and a first duration.

After S903, the MDAF may establish an analysis instance according to the SLA assurance intention information, and proceed to S1001.

At S1001, the MDAF sends response information to the IDMF. The response information may include an instance identifier, which is used to indicate the analysis instance.

After S903, MDAF can use the analysis instance to perform S904 to S908.

If it is determined in S908 that the first SLA attainment degree is greater than or equal to the second predictive trigger threshold, return to S904 and S905.

If it is determined in S908 that the first SLA attainment degree is less than the second predictive trigger threshold, go to S1002.

At S1002, the MDAF sends the first SLA achievement degree to the IDMF. The reported information may include the achievement degree of the first SLA and the instance identifier.

After receiving the reported information, the IDMF may perform S909.

In S909, the IDMF determines the forecast step size according to the first SLA achievement degree.

In the case that the first SLA achievement degree is less than the parameter threshold, the IDMF may determine that the prediction step size is the first step size.

In the case that the first SLA attainment degree is greater than or equal to the parameter threshold, the IDMF may determine that the prediction step size is the second step size. The length of the first step is smaller than the length of the second step.

After S909, S1003 can be performed.

At S1003, the IDMF sends the second prediction parameter and the instance identifier to the MDAF.

The second prediction parameter may include one or more of a second prediction step size, a second prediction period, a second duration, and the like.

After S1003, MDAF may perform S910 and S911 according to the second prediction parameter. In the case that the second SLA attainment degree is smaller than the adjustment trigger threshold, go to S912.

In S912, parameters of the network slice subnet are adjusted.

It should be understood that, compared with method 1000, in method 900, the MDAF may determine the first prediction parameter and the second prediction parameter, thereby reducing signaling overhead between the MDAF and the IDMF.

Fig. 12 is a schematic flowchart of a data processing method provided by an embodiment of the present application. The method 1200 includes S1210 to S1240.

At S1210, the performance data of the network slice is acquired.

At S1220, according to the performance data, determine the achievement degree of the real-time service level agreement.

At S1230, if the real-time service level agreement achievement degree is less than a fourth threshold, predict a second service level agreement achievement degree for a second duration according to the performance data.

In S1240, in a case that the achievement degree of the second service level agreement is less than a second threshold, it is determined to adjust the parameters of the network slice, and the fourth threshold is greater than the second threshold.

Through the method 1200, when the real-time SLA achievement degree is less than the fourth threshold, the second SLA achievement degree is predicted, which can reduce the number of predictions and reduce resource occupation.

Optionally, when the real-time service level agreement achievement degree is less than a fourth threshold, the first service level agreement achievement degree for the first duration may be predicted according to the performance data.

In S1230, in the case that the achievement degree of the first service level agreement is less than a first threshold, predict the achievement degree of the second service level agreement according to the performance data, and the second duration is less than the first duration .

Exemplarily, the first duration may be greater than or equal to the intended maintenance duration. The intent maintenance duration is used to indicate the length of time required from determining to adjust the network slice to stabilizing the performance parameters of the network slice. The intention maintenance duration may be a statistical value, that is, the intention maintenance duration may be used to represent the data central trend of the length of time required for network slice performance parameters to be stable after each adjustment of the network slice is determined.

Optionally, when the degree of achievement of the first service level agreement is less than a third threshold, the second duration is equal to the first step, and the third threshold is smaller than the second threshold; In a case where the achievement degree of the service level agreement is greater than or equal to the third threshold, the second duration is equal to a second step, and the first step is smaller than the second step.

Optionally, the fourth threshold is associated with services of the network slicing service.

The method embodiment of the embodiment of the present application is described above with reference to FIG. 1 to FIG. 12 , and the device embodiment of the embodiment of the present application is described below with reference to FIG. 13 to FIG. 14 . It should be understood that the descriptions of the method embodiments correspond to the descriptions of the device embodiments, therefore, for parts not described in detail, reference may be made to the foregoing method embodiments.

The data processing device 2000 includes an acquisition module 2010 and a processing module 2020 .

In some embodiments, the acquiring module 2010 is configured to acquire performance data of network slices.

The processing module 2020 is configured to, according to the performance data, predict the fulfillment degree of the first service level agreement for the first duration.

The processing module 2020 is further configured to predict, according to the performance data, the achievement degree of the second service level agreement for a second duration when the achievement degree of the first service level agreement is less than the first threshold, and the second duration is less than said first duration;

The processing module 2020 is further configured to determine to adjust the parameters of the network slice when the achievement degree of the second service level agreement is less than a second threshold.

Optionally, in a case where the achievement degree of the first service level agreement is smaller than a third threshold, the second duration is equal to the first step, and the third threshold is smaller than the second threshold.

In a case where the achievement degree of the first service level agreement is greater than or equal to the third threshold, the second duration is equal to a second step, and the first step is smaller than the second step.

Optionally, the processing module 2020 is further configured to, according to the performance data, determine the achievement degree of the real-time service level agreement.

The processing module 2020 is specifically configured to predict the first service level agreement achievement degree according to the performance data when the real-time service level agreement achievement degree is less than a fourth threshold, and the fourth threshold value is greater than the second threshold.

In some other embodiments, the acquiring module 2010 is configured to acquire performance data of network slices.

The processing module 2020 is configured to, according to the performance data, determine the achievement degree of the real-time service level agreement.

The processing module 2020 is further configured to, in the case that the real-time SLA achievement degree is less than a fourth threshold, predict the second service level agreement achievement degree of the second duration according to the performance data.

The processing module 2020 is further configured to determine to adjust the parameters of the network slice when the achievement degree of the second service level agreement is less than a second threshold, and the fourth threshold is greater than the second threshold.

Optionally, the processing module 2020 is further configured to, if the real-time service level agreement achievement degree is less than a fourth threshold, predict the first service level agreement achievement degree of the first duration according to the performance data.

The processing module 2020 is specifically configured to predict the achievement degree of the second service level agreement according to the performance data in the case that the achievement degree of the first service level agreement is less than the first threshold, and the second duration is less than the First time.

Optionally, when the degree of achievement of the first service level agreement is less than a third threshold, the second duration is equal to The first step is long, the third threshold is smaller than the second threshold.

Fig. 14 is a schematic structural diagram of a data processing device provided by an embodiment of the present application.

The data processing device 3000 includes a memory 3010 and a processor 3020 .

The memory 3010 is used to store program instructions.

When the program instructions are executed in the processor 3020, the processor 3020 is configured to execute the method described above.

Specifically, in some embodiments, the processor 3020 is configured to acquire performance data of network slices.

The processor 3020 is further configured to, according to the performance data, predict the fulfillment degree of the first service level agreement for the first duration.

The processor 3020 is further configured to predict, according to the performance data, a second service level agreement achievement degree for a second duration when the first service level agreement achievement degree is less than a first threshold, and the second duration is less than said first duration.

The processor 3020 is further configured to determine to adjust the parameter of the network slice when the achievement degree of the second service level agreement is less than a second threshold.

Optionally, the processor 3020 is further configured to, according to the performance data, determine the attainment degree of the real-time service level agreement.

The processor 3020 is further configured to predict the first service level agreement achievement degree according to the performance data when the real-time service level agreement achievement degree is less than a fourth threshold, and the fourth threshold value is greater than the second threshold.

Optionally, the fourth threshold is associated with services of the network slicing service. In other embodiments, the processor 3020 is configured to acquire performance data of network slices.

The processor 3020 is further configured to, according to the performance data, determine the attainment degree of the real-time service level agreement.

The processor 3020 is further configured to, if the real-time service level agreement achievement degree is less than a fourth threshold, predict a second service level agreement achievement degree for a second duration according to the performance data.

The processor 3020 is further configured to determine to adjust the parameters of the network slice when the achievement degree of the second service level agreement is less than a second threshold, and the fourth threshold is greater than the second threshold.

Optionally, the processor 3020 is further configured to, if the real-time service level agreement achievement degree is less than a fourth threshold, predict the first service level agreement achievement degree of the first duration according to the performance data.

The processor 3020 is further configured to predict, according to the performance data, the achievement degree of the second service level agreement in the case that the achievement degree of the first service level agreement is less than the first threshold, and the second duration is less than the First time.

In addition, all or part of the units in the above devices can be integrated together, or can be implemented independently. exist In one implementation, these units are integrated together and implemented in the form of a system-on-a-chip (SOC). The SOC may include at least one processor for implementing any of the above methods or realizing the functions of each unit of the device. The at least one processor may be of different types, such as including CPU and FPGA, CPU and artificial intelligence processor, CPU and graphics processing unit (graphics processing unit, GPU), etc.

An embodiment of the present application further provides a computer program storage medium, wherein the computer program storage medium has program instructions, and when the program instructions are executed, the foregoing method is executed.

An embodiment of the present application further provides a system-on-a-chip, wherein the system-on-a-chip includes at least one processor, and when program instructions are executed on the at least one processor, the foregoing method is executed.

An embodiment of the present application further provides a program product, where the computer program product includes program instructions, and when the program instructions are executed in a computer device, the foregoing data processing method is executed.

It should be understood that the processor in the embodiment of the present application may be a central processing unit (central processing unit, CPU), and the processor may also be other general-purpose processors, digital signal processors (digital signal processor, DSP), application specific integrated circuits (application specific integrated circuit, ASIC), off-the-shelf programmable gate array (field programmable gate array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, or the like.

It should also be understood that the memory in the embodiments of the present application may be a volatile memory or a nonvolatile memory, or may include both volatile and nonvolatile memories. Among them, the non-volatile memory can be read-only memory (read-only memory, ROM), programmable read-only memory (programmable ROM, PROM), erasable programmable read-only memory (erasable PROM, EPROM), electrically programmable Erases programmable read-only memory (electrically EPROM, EEPROM) or flash memory. Volatile memory can be random access memory (RAM), which acts as external cache memory. By way of illustration and not limitation, many forms of random access memory (RAM) are available, such as static random access memory (static RAM, SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory Access memory (synchronous DRAM, SDRAM), double data rate synchronous dynamic random access memory (double data rate SDRAM, DDR SDRAM), enhanced synchronous dynamic random access memory (enhanced SDRAM, ESDRAM), synchronous connection dynamic random access memory Access memory (synchlink DRAM, SLDRAM) and direct memory bus random access memory (direct rambus RAM, DR RAM).

The above-mentioned embodiments may be implemented in whole or in part by software, hardware, firmware or other arbitrary combinations. When implemented using software, the above-described embodiments may be implemented in whole or in part in the form of computer program products. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on the computer, the processes or functions according to the embodiments of the present application will be generated in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website, computer, server or data center Transmission to another website site, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium that can be accessed by a computer, or a data storage device such as a server or a data center that includes one or more sets of available media. The available media may be magnetic media (eg, floppy disk, hard disk, magnetic tape), optical media (eg, DVD), or semiconductor media. The semiconductor medium may be a solid state drive.

It should be understood that the term "and/or" in this article is only an association relationship describing associated objects, indicating that there may be three relationships, for example, A and/or B may mean: A exists alone, and A and B exist at the same time , there are three cases of B alone, where A and B can be singular or plural. In addition, the character "/" in this article generally indicates that the related objects are an "or" relationship, but it may also indicate an "and/or" relationship, which can be understood by referring to the context.

In this application, "at least one" means one or more, and "multiple" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one item (piece) of a, b, or c can represent: a, b, c, a-b, a-c, b-c, or a-b-c, where a, b, c can be single or multiple .

Prefixes such as "first" and "second" are used in the embodiments of this application only to distinguish different description objects, and have no limiting effect on the position, order, priority, quantity or content of the described objects. For example, if the described object is "time length", the ordinal number before "time length" in "first time length" and "second time length" does not limit the position or order or priority of "time length"; The description object is "step size", then the ordinal number before "step size" in "first step size" and "second step size" does not limit the position or order or priority of "step size".

It should be understood that, in various embodiments of the present application, the sequence numbers of the above-mentioned processes do not mean the order of execution, and the execution order of the processes should be determined by their functions and internal logic, and should not be used in the embodiments of the present application. The implementation process constitutes any limitation.

Those skilled in the art can appreciate that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may use different methods to implement the described functions for each specific application, but such implementation should not be regarded as exceeding the scope of the present application.

Those skilled in the art can clearly understand that for the convenience and brevity of the description, the specific working process of the above-described system, device and unit can refer to the corresponding process in the foregoing method embodiment, which will not be repeated here.

In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods may be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or May be integrated into another system, or some features may be ignored, or not implemented. In another point, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or units may be in electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, each unit may exist separately physically, or two or more units may be integrated into one unit.

If the functions described above are realized in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including Several instructions are used to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in the various embodiments of the present application. and The aforementioned storage medium includes: various media capable of storing program codes such as USB flash drive, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disk or optical disk.

The above is only a specific implementation of the application, but the scope of protection of the application is not limited thereto. Anyone familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the application. Should be covered within the protection scope of this application. Therefore, the protection scope of the present application should be determined by the protection scope of the claims.

Claims

A data processing method, characterized in that, comprising:

Obtain performance data of network slices;

According to the performance data, predict the achievement degree of the first service level agreement of the first duration;

In a case where the first service level agreement achievement degree is less than a first threshold, predicting a second SLA achievement degree for a second duration according to the performance data, the second duration being less than the first duration;

In a case where the achievement degree of the second service level agreement is less than a second threshold, it is determined to adjust the parameter of the network slice.
The method according to claim 1, characterized in that,

In the case where the achievement degree of the first service level agreement is less than a third threshold, the second duration is equal to the first step, and the third threshold is smaller than the second threshold;

In a case where the achievement degree of the first service level agreement is greater than or equal to the third threshold, the second duration is equal to a second step, and the first step is smaller than the second step.
The method according to claim 1 or 2, characterized in that the method further comprises:

According to the performance data, determine the achievement degree of the real-time service level agreement;

The predicting the achievement degree of the first service level agreement for the first time according to the performance data includes: predicting the achievement degree of the first service level agreement according to the performance data when the achievement degree of the real-time service level agreement is less than a fourth threshold. In terms of achievement of the service level agreement, the fourth threshold is greater than the second threshold.
The method according to claim 3, wherein the fourth threshold is associated with services of the network slicing service.
A data processing method, characterized in that, comprising:

Obtain performance data of network slices;

According to the performance data, determine the achievement degree of the real-time service level agreement;

In a case where the real-time service level agreement achievement degree is less than a fourth threshold, predicting a second service level agreement achievement degree for a second duration according to the performance data;

In a case where the achievement degree of the second service level agreement is less than a second threshold, it is determined to adjust the parameters of the network slice, and the fourth threshold is greater than the second threshold.
The method according to claim 5, characterized in that,

The method includes: when the real-time service level agreement achievement degree is less than a fourth threshold, predicting the first service level agreement achievement degree of the first duration according to the performance data;

The predicting the achievement degree of the second-duration second service level agreement according to the performance data includes: predicting the achievement degree of the second service level agreement according to the performance data when the achievement degree of the first service level agreement is less than a first threshold. SLA achievement degree, the second duration is shorter than the first duration.
The method according to claim 6, characterized in that,

In the case where the achievement degree of the first service level agreement is less than a third threshold, the second duration is equal to the first step, and the third threshold is smaller than the second threshold;

In the case that the achievement degree of the first service level agreement is greater than or equal to the third threshold, the second duration, etc. In a second step size, the first step size is smaller than the second step size.
The method according to any one of claims 5-7, wherein the fourth threshold is associated with services of the network slicing service.
A data processing method, characterized in that, comprising:

The network slice subnet management functional entity sends the performance data of the network slice to the management data analysis functional entity;

The management data analysis function entity acquires the performance data of the network slice from the network slice subnet management function entity;

The management data analysis function entity predicts the achievement degree of the first service level agreement of the first duration according to the performance data;

When the achievement degree of the first service level agreement is less than the first threshold, the management data analysis functional entity predicts the second SLA achievement degree of the second duration according to the performance data, and the second duration is less than the first duration;

In a case where the achievement degree of the second service level agreement is less than a second threshold, the management data analysis function entity determines to adjust the parameters of the network slice.
The method according to claim 9, characterized in that,

In the case where the achievement degree of the first service level agreement is less than a third threshold, the second duration is equal to the first step, and the third threshold is smaller than the second threshold;

In a case where the achievement degree of the first service level agreement is greater than or equal to the third threshold, the second duration is equal to a second step, and the first step is smaller than the second step.
The method according to claim 9 or 10, characterized in that the method further comprises:

The management data analysis function entity determines the achievement degree of the real-time service level agreement according to the performance data;

According to the performance data, the management data analysis function entity predicts the achievement degree of the first service level agreement of the first duration, including:

When the real-time service level agreement achievement degree is less than a fourth threshold, the management data analysis function entity predicts the first service level agreement achievement degree according to the performance data, and the fourth threshold value is greater than the second threshold.
The method according to claim 11, wherein the fourth threshold is associated with services of the network slicing service.
A data processing method, characterized in that, comprising:

The network slice subnet management functional entity sends the performance data of the network slice to the management data analysis functional entity;

The management data analysis function entity acquires the performance data of the network slice from the network slice subnet management function entity;

The management data analysis function entity determines the achievement degree of the real-time service level agreement according to the performance data;

In a case where the real-time service level agreement achievement degree is less than a fourth threshold, the management data analysis function entity predicts a second service level agreement achievement degree of a second duration according to the performance data;

In a case where the achievement degree of the second service level agreement is less than a second threshold, the management data analysis function entity determines to adjust the parameters of the network slice, and the fourth threshold is greater than the second threshold.
The method according to claim 13, characterized in that the method comprises:

In the case that the real-time service level agreement achievement degree is less than the fourth threshold, the management data analysis function entity predicts the first service level agreement achievement degree of the first duration according to the performance data;

The management data analysis function entity predicts the conclusion of a second service level agreement for a second duration according to the performance data degree, including: if the achievement degree of the first service level agreement is less than the first threshold, the management data analysis functional entity predicts the achievement degree of the second service level agreement according to the performance data, and the second The duration is less than the first duration.
The method according to claim 14, characterized in that,

In the case where the achievement degree of the first service level agreement is less than a third threshold, the second duration is equal to the first step, and the third threshold is smaller than the second threshold;

In a case where the achievement degree of the first service level agreement is greater than or equal to the third threshold, the second duration is equal to a second step, and the first step is smaller than the second step.
The method according to any one of claims 13-15, wherein the fourth threshold is associated with services of the network slicing service.
A data processing device, characterized in that it comprises:

An acquisition module, configured to acquire performance data of network slices;

A processing module, configured to predict the achievement degree of the first service level agreement of the first duration according to the performance data;

The processing module is further configured to predict, according to the performance data, a second service level agreement achievement degree of a second duration when the first service level agreement achievement degree is less than a first threshold, and the second duration is less than the first duration;

The processing module is further configured to determine to adjust the parameters of the network slice when the achievement degree of the second service level agreement is less than a second threshold.
The device according to claim 17, characterized in that,

In the case where the achievement degree of the first service level agreement is less than a third threshold, the second duration is equal to the first step, and the third threshold is smaller than the second threshold;

In a case where the achievement degree of the first service level agreement is greater than or equal to the third threshold, the second duration is equal to a second step, and the first step is smaller than the second step.
Apparatus according to claim 17 or 18, characterized in that

The processing module is further configured to, according to the performance data, determine the achievement degree of the real-time service level agreement;

The processing module is further configured to predict the achievement of the first service level agreement according to the performance data when the achievement degree of the real-time service level agreement is less than a fourth threshold, and the fourth threshold is greater than the fourth threshold Two thresholds.
The apparatus according to claim 19, wherein the fourth threshold is associated with a service of the network slicing service.
A data processing device, characterized in that it comprises:

An acquisition module, configured to acquire performance data of network slices;

A processing module, configured to determine the achievement degree of the real-time service level agreement according to the performance data;

The processing module is further configured to predict, according to the performance data, a second service level agreement achievement degree for a second duration when the real-time service level agreement achievement degree is less than a fourth threshold;

The processing module is further configured to determine to adjust the parameters of the network slice when the degree of achievement of the second service level agreement is less than a second threshold, and the fourth threshold is greater than the second threshold.
The device according to claim 21, characterized in that,

The processing module is further configured to predict the achievement degree of the first service level agreement for the first duration according to the performance data when the achievement degree of the real-time service level agreement is less than the fourth threshold;

The processing module is further configured to predict the achievement degree of the second service level agreement according to the performance data in the case that the achievement degree of the first service level agreement is less than the first threshold, and the second duration is less than the specified threshold. State the first duration.
The device according to claim 22, characterized in that,

In the case where the achievement degree of the first service level agreement is less than a third threshold, the second duration is equal to the first step, and the third threshold is smaller than the second threshold;

In a case where the achievement degree of the first service level agreement is greater than or equal to the third threshold, the second duration is equal to a second step, and the first step is smaller than the second step.
The apparatus according to any one of claims 21-23, wherein the fourth threshold is associated with a service of the network slicing service.
A data processing device, characterized by comprising a memory and a processor, the memory is used to store a program, and the processor is used to execute the program to perform the method according to any one of claims 1-16.
A computer program product, characterized by comprising program instructions, when the program instructions are executed, the method according to any one of claims 1 to 16 is executed.
A computer-readable storage medium, characterized in that the computer-readable medium stores program codes for device execution, and when the program instructions are executed, the method according to any one of claims 1 to 16 be executed.
A chip, characterized in that the chip includes at least one processor, and when program instructions are executed by the at least one processor, the method according to any one of claims 1 to 16 is executed.