5G-TSN architecture, flow scheduling method thereof, medium and equipment

Abstract

The invention discloses a 5G-TSN architecture and a flow scheduling method, medium and equipment thereof, a TSN bridge and a 5GS bridge are deployed at each node of the 5G-TSN architecture, so that industrial equipment can be respectively connected to the TSN bridge and the 5GS bridge of the node to which the industrial equipment belongs through wired-wireless dual interfaces, thereby forming a dual-topology structure of a wired domain and a 5G domain, and improving the flow scheduling efficiency of the 5G-TSN architecture. A flexible networking mode is realized, and the time trigger flow generated by the node equipment is shunted through the TSN and the 5GS network bridge. Meanwhile, cross-domain cooperative scheduling is carried out based on a shunting technology, a target scheduling optimization problem is constructed by taking the maximum scheduling success rate of the time-triggered flow as a target, and a variety of cross-domain constraint conditions are combined, so that the problem definition better conforms to the architecture design and scheduling requirements, the scheduling details are enriched, and the scheduling efficiency is improved. The flow scheduling method for large-scale deterministic application is realized, and the robustness of a 5G-TSN architecture in a dynamic or large-scale scene is ensured.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a 5G-TSN architecture and its flow scheduling method, medium and device. Background Art

[0002] With the rapid development of industrial Internet of Things and fifth-generation (5G) wireless communication technology, many industrial devices are connected to industrial networks wirelessly, which makes the integration of wired and wireless necessary. Therefore, the Time-Sensitive Network (TSN) (5G-TSN) integrated with 5G has emerged and has received widespread attention from the industry and academia. Integrating 5G and TSN provides many advantages for industrial systems, including improving network flexibility, reducing debugging costs, and enhancing seamless interoperability between devices.

[0003] In the related technologies, the rigid network architecture design of deploying only one 5G system (5GS) bridge at the edge node severely limits the flexibility of the network and makes it difficult to take advantage of integrated 5G. In addition, most of the related technologies simplify the modeling and scheduling of the 5G domain. This simplification will lead to uncoordinated cross-domain flow scheduling, which cannot guarantee the robustness of the system in dynamic or large-scale scenarios. Summary of the invention

[0004] In view of this, the present application provides a 5G-TSN architecture and its flow scheduling method, medium and device, adopts a more flexible 5G-TSN architecture design, performs cross-domain collaborative scheduling based on diversion technology, significantly improves the carrying capacity and scheduling success rate of the 5G-TSN architecture, and ensures the robustness of the 5G-TSN architecture in dynamic or large-scale scenarios.

[0005] According to one aspect of the present application, a 5G-TSN architecture is provided, the architecture comprising:

[0006] A plurality of nodes, each of which is associated with at least one node device, and each of which is capable of generating a time-triggered flow, wherein the time-triggered flow is divided into a TSN flow and a 5GS flow;

[0007] TSN bridge, deployed at each node;

[0008] 5GS bridge, deployed at each node;

[0009] The node device is connected to the TSN bridge and the 5GS bridge of the node to which it belongs through a wired-wireless dual interface, so that the TSN diversion of the node device is transmitted and scheduled through the TSN bridge in the 5G-TSN architecture, and the 5GS diversion of the node device is transmitted and scheduled through the 5GS bridge in the 5G-TSN architecture.

[0010] According to another aspect of the present application, a flow scheduling method based on a 5G-TSN architecture is provided, including:

[0011] Taking the maximum scheduling success rate of the time-triggered flow generated by the node device in the 5G-TSN architecture as the goal, constructing the target scheduling optimization problem of the 5G-TSN architecture according to the scheduling state of the time-triggered flow and the number of the time-triggered flow, wherein the scheduling state includes scheduling success and scheduling failure;

[0012] Constructing target constraints of the target scheduling optimization problem according to the parameter data of the TSN bridge and the 5GS bridge and the division ratio of the time-triggered flow;

[0013] According to the target constraint conditions, solving the target scheduling optimization problem, and obtaining the target scheduling strategy of the 5G-TSN architecture;

[0014] The resource allocation operation of the 5G-TSN architecture is performed according to the target scheduling strategy.

[0015] According to another aspect of the present application, a flow scheduling device based on a 5G-TSN architecture is provided, including:

[0016] A construction module, for constructing a target scheduling optimization problem of the 5G-TSN architecture based on the scheduling status of the time-triggered flow and the number of the time-triggered flow, with the goal of maximizing the scheduling success rate of the time-triggered flow generated by the node device in the 5G-TSN architecture, wherein the scheduling status includes scheduling success and scheduling failure; and

[0017] Constructing target constraints of the target scheduling optimization problem according to the parameter data of the TSN bridge and the 5GS bridge and the division ratio of the time-triggered flow;

[0018] A solving module, used to solve the target scheduling optimization problem according to the target constraint condition, and obtain the target scheduling strategy of the 5G-TSN architecture;

[0019] An execution module is used to execute the resource allocation operation of the 5G-TSN architecture according to the target scheduling strategy.

[0020] According to another aspect of the present application, a readable storage medium is provided, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the above-mentioned flow scheduling method based on the 5G-TSN architecture are implemented.

[0021] According to another aspect of the present application, a computer device is provided, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, and when the processor executes the program, the steps of the above-mentioned flow scheduling method based on the 5G-TSN architecture are implemented.

[0022] By means of the above technical scheme, the present application provides a 5G-TSN architecture and its flow scheduling method, medium and device. In the 5G-TSN architecture described in the present application, each node deploys a TSN bridge and a 5GS bridge, so that industrial equipment can be connected to the TSN bridge and 5GS bridge of its affiliated node through the wired-wireless dual interface, respectively, thereby forming a dual topology structure of the wired domain and the 5G domain, realizing a more flexible networking mode, and making the time-triggered flow generated by the node device be diverted through the TSN bridge and 5GS bridge of its node. At the same time, the flow scheduling method based on the 5G-TSN architecture described in the present application performs cross-domain collaborative scheduling based on the diversion technology, takes the maximum scheduling success rate of the time-triggered flow as the goal, constructs a target scheduling optimization problem, and combines a variety of cross-domain constraints to make the problem definition more in line with the architecture design and scheduling requirements, enrich the scheduling details, improve the scheduling efficiency, and realize the flow scheduling method for large-scale deterministic applications, ensuring the robustness of the 5G-TSN architecture in dynamic or large-scale scenarios.

[0023] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0025] Figure 1 A schematic diagram of a flow chart of a flow scheduling method based on a 5G-TSN architecture provided in an embodiment of the present application is shown;

[0026] Figure 2 The 5G-TSN architecture provided by the embodiment of the present application is shown;

[0027] Figure 3 A scheduling success rate comparison diagram provided by an embodiment of the present application is shown;

[0028] Figure 4 A comparison diagram of wired domain throughput provided by an embodiment of the present application is shown;

[0029] Figure 5 A time cost comparison diagram provided by an embodiment of the present application is shown;

[0030] Figure 6 A convergence comparison diagram provided by an embodiment of the present application is shown;

[0031] Figure 7 Another scheduling success rate comparison diagram provided by an embodiment of the present application is shown;

[0032] Figure 8 A comparison diagram of average spectrum efficiency provided by an embodiment of the present application is shown;

[0033] Fig. 9 Another scheduling success rate comparison provided by an embodiment of the present application is shown;

[0034] Fig.10 A resource utilization comparison diagram provided by an embodiment of the present application is shown;

[0035] Fig.11 A structural block diagram of a flow scheduling device based on a 5G-TSN architecture provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0036] The present application will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0037] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be interpreted as limiting the present application.

[0038] Those skilled in the art will appreciate that, unless expressly stated, the singular forms "a", "an", "said" and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present application refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "connected" to another element, it may be directly connected or connected to the other element, or there may be intermediate elements. In addition, the "connection" or "connection" used herein may include wireless connection or wireless fusion. The term "and / or" used herein includes all or any unit and all combinations of one or more associated listed items.

[0039] Now, exemplary embodiments according to the present application will be described in more detail with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in a variety of different forms and should not be interpreted as being limited to the embodiments set forth herein. It should be understood that these embodiments are provided to make the disclosure of the present application thorough and complete, and to fully convey the concepts of these exemplary embodiments to those of ordinary skill in the art.

[0040] In this embodiment, a 5G-TSN architecture is provided. Figure 2 As shown, the architecture includes: multiple nodes, each node is associated with at least one node device, the node device can generate a time-triggered stream, and the time-triggered stream is divided into a TSN shunt and a 5GS shunt; a TSN bridge, deployed at each node; a 5GS bridge, deployed at each node; the node device is connected to the TSN bridge and the 5GS bridge of the node to which it belongs through a wired-wireless dual interface, so that the TSN shunt of the node device is transmitted and scheduled through the TSN bridge in the 5G-TSN architecture, and the 5GS shunt of the node device is transmitted and scheduled through the 5GS bridge in the 5G-TSN architecture.

[0041] In this embodiment, the 5G-TSN architecture is constructed in accordance with the integrated framework defined by the 3rd Generation Partnership Project (3GPP) standard. Unlike the traditional 5G-TSN architecture that only deploys 5GS bridges at edge nodes, this embodiment deploys a TSN bridge and a 5GS bridge at each node in the 5G-TSN architecture, which greatly enhances the access flexibility and realizes a more flexible networking method. At the same time, the node devices associated with the node are connected to the TSN and 5GS bridges of their affiliated nodes through wired-wireless dual interfaces, respectively, so that the node devices can access the network through the wired domain and the 5G domain at the same time, thereby forming a dual topology structure of the wired domain and the 5G domain. Among them, the wired domain is a network area constructed according to the TSN bridge, and the 5G domain is a network area constructed according to the 5GS bridge.

[0042] It is worth mentioning that the wired domain and the 5G domain are topologically independent and homogeneous, and can be coordinated and scheduled.

[0043] Exemplarily, a node may be associated with an industrial workshop, in which there are multiple industrial equipments for industrial manufacturing.

[0044] Furthermore, the time-triggered (TT) stream generated by the node device is shunted through the TSN bridge and 5GS bridge of the node device's attached node, and is divided into TSN shunting and 5GS shunting. TSN shunting and 5GS shunting are transmitted and scheduled through the wired domain transmission path composed of TSN bridges in the 5G-TSN architecture and the 5G domain transmission path composed of 5GS bridges according to the path pre-set by the time-triggered stream, thereby realizing "three-dimensional" transmission in the 5G-TSN architecture. Therefore, the 5G-TSN architecture described in this application can be called CUBE. It should be noted that the TSN shunting and 5GS shunting divided by the same time-triggered stream are synchronously transmitted through the TSN bridge and 5GS bridge of the same node respectively.

[0045] It can be understood that the time-triggered flow is a data flow that is transmitted end-to-end from the sender to the destination according to a pre-planned path. Flow scheduling is to reasonably arrange the time and allocate resources for the time-triggered flow in the 5G-TSN architecture to ensure that the time-triggered flow can be transmitted accurately within the specified time.

[0046] For example, Figure 2As shown, the time-triggered flow generated by the industrial equipment associated with node 1 is divided into TSN shunt and 5GS shunt by the TSN bridge and 5GS bridge of node 1. If the pre-set path of the time-triggered flow is node 1-node 4-node 3, the TSN shunt and 5GS shunt are synchronously transmitted to the industrial equipment associated with node 3 through the 5G domain transmission path ① (5GS bridge of node 1-5GS bridge of node 4-5GS bridge of node 3) and the wired domain transmission path ② (TSN bridge of node 1-TSN bridge of node 4-TSN bridge of node 3).

[0047] Furthermore, the 5G-TSN network in CUBE is uniformly scheduled through a centralized Software Defined Network (SDN) controller. During the entire network scheduling process, the Centralized User Configuration (CUC) collects the service requirements of all industrial devices and sends them to the Centralized Network Configuration (CNC). CNC has information about network topology and bridging functions, and performs traffic scheduling by combining the requirements collected by CUC.

[0048] Furthermore, the 5GS bridge acts as a logical TSN bridge for the external network, and its internal scheduling process is transparent to the TSN network. In order to implement this black box mode and realize the TSN bridge function, the 5GS bridge provides TSN ingress and egress ports through the device side TSN translator (DS-TT) and the network side TSN translator (NW-TT) to connect the TSN bridge or industrial equipment. The 5GS bridge receives TSN information from the CNC through the TSN application function (AF), based on which it derives TSN quality of service (QoS) information and related flow information. At the same time, the CNC provides forwarding rules to the TSNAF, and the TSNAF identifies the DS-TT MAC address of the corresponding protocol data unit (PDU) session based on the received information, the 5GS bridge delay, and the DS-TT residence time. In addition, the TSNAF builds a QoS mapping table based on the TSN QoS information and sends the information to the policy control function (PCF). The PCF finds the correct Session Management Function (SMF) based on the DS-TT MAC address of the PDU session. After mapping the QoS parameters received from the TSN offload to the 5GS QoS identifier (5QI), the PCF provides these rules to the corresponding SMF. After receiving the Policy and Charging Control (PCC) rules, the SMF triggers the PDU session modification process to establish or modify the QoS flow to transmit the TSN offload. At the same time, the SMF calculates the Time Sensitive Communication Assistance Information (TSCAI) of the QoS flow based on the QoS container and sends it to the Access Mobility Management Function (AMF). TSCAI includes the direction of the time-sensitive communication flow (uplink or downlink), the time interval between the start of two bursts, and the time when the burst data arrives at the base station entrance or terminal exit. The 5G base station (gNB) can then schedule the sending and receiving of TSN traffic based on the TSCAI information.

[0049] In this embodiment, a flow scheduling method based on the 5G-TSN architecture described in the above embodiment is provided. Figure 1 As shown, the method includes:

[0050] Step 101, with the goal of maximizing the scheduling success rate of the time-triggered flows generated by the node devices in the 5G-TSN architecture, construct a target scheduling optimization problem for the 5G-TSN architecture according to the scheduling status of the time-triggered flows and the number of time-triggered flows.

[0051] The scheduling status includes scheduling success and scheduling failure.

[0052] Step 102, construct the target constraint conditions of the target scheduling optimization problem according to the parameter data of the TSN bridge and the 5GS bridge and the division ratio of the time-triggered flow.

[0053] In this embodiment, the target scheduling optimization problem is constructed with the goal of maximizing the scheduling success rate of the time-triggered flow generated by the node device in the 5G-TSN architecture. The target constraints including the offset constraint of the time-triggered flow, the cyclic queue forwarding queue (CyclicQueueForwarding, CQF) resource constraint, the partition ratio constraint and the wireless resource configuration constraint are combined to make the problem definition more in line with the architecture design and scheduling requirements. In the subsequent steps, the target scheduling strategy obtained by solving the target optimization problem can improve the resource utilization and scheduling efficiency of the 5G-TSN architecture, making it more suitable for large-scale scenarios.

[0054] Specifically, the node devices in the 5G-TSN network topology are represented as The TSN bridge is represented as and the 5GS bridge is represented as in, is the number of nodes, is the number of TSN bridges, 5GS bridges and nodes.

[0055] Then, according to the node device, TSN bridge and 5GS bridge, we can get and in, Each element represents a transmission path in the 5G-TSN architecture. and Abstract the 5G-TSN network topology as a directed graph And according to the directed graph G, the time-triggered flow i (expressed as f i ) transmission path f i .path={f i TSN .path,f i 5GS .path}, where f i TSN .path is the time trigger stream f i TSN offloadi TSN The transmission path, f i 5GS .path is the time trigger stream f i 5GS diversion f i 5GS transmission path.

[0056] Then the time trigger flow f i Represented by the following 8-tuple:

[0057]

[0058] Among them, f i .src is f i The source device (sender), f i .dst is f i The target device (destination end), f i .period is f i The scheduling period of f i .size is f i The data frame size of f i .dd is f i The maximum allowed end-to-end delay time offset; f i .offset is f i The injection time offset is f i The time interval from the generation of the first frame of data to the sending to the 5G-TSN network is used to i To prevent all time-triggered flows from being sent to the 5G-TSN network at the time of generation, which would cause the 5G-TSN network to be paralyzed; i .sr∈[0,1] is f i The traffic diversion rule indicates that TSN diverts f i TSN For example, when f 1 .size = 100B and f 1 When sr = 0.4, f i TSN and f i 5GS The data volume is 40B and 60B respectively. represents a collection of time-triggered streams, Indicates the number of time-triggered flows.

[0059] It should be noted that the time-triggered flow is periodic data, each time-triggered flow corresponds to a scheduling period, and the time-triggered flow periodically generates data frames in its scheduling period.

[0060] It is worth mentioning that, in this embodiment, the scheduling super-period is determined according to the least common multiple of the scheduling periods of all time-triggered flows, which includes all possibilities of time-triggered flow generation. In addition, the target scheduling optimization problem and target constraint conditions are constructed according to the scheduling super-period to solve the target scheduling strategy in the scheduling super-period. Exemplarily, the scheduling super-period is represented as T hyper =LCM(period i ), the set of time slots in the scheduling supercycle is expressed as Among them, LCM (Least Common Multiple) is the least common multiple operator, period i represents f i .period, also f i It should be noted that the time slots involved in the following are all time slots within the scheduling super period.

[0061] Furthermore, since the scheduling process within each 5GS bridge is independent and hidden from the external TSN system, this embodiment abstracts the 5GS bridge into a multi-user uplink communication model. Specifically, for any 5GS bridge, the 5GS bridge faces the uplink orthogonal frequency division multiple access (OFDMA) scenario, and determines the device or bridge that is connected to the 5GS bridge and sends a signal to the 5GS bridge as the communication user of the 5GS bridge. The 5GS bridge divides the uplink communication channel between the 5GS bridge and the communication user into multiple sub-channels so that each communication user independently occupies a sub-channel to avoid interference, so that the communication user of the 5GS bridge uses its corresponding sub-channel to send 5GS diversion to the 5GS bridge. Each 5GS diversion arriving at the 5GS bridge will be cached in the transmission queue corresponding to the corresponding communication user in the 5GS bridge inlet port and transmitted on the UE (user)-gNB link. The 5GS bridge outlet port executes a circular queuing forwarding queue mechanism to forward the 5GS diversion to the next node. In each time slot, the transmission capacity of the communication user is affected by the wireless resource allocation and channel state information (CSI). The 5GS diversion exceeding the capacity will be rejected to ensure the successful execution of the circular queuing forwarding queue mechanism in the 5GS bridge egress port.

[0062] Among them, each communication user is connected to the TSN converter on the device side to form the input port of the 5GS bridge; the gNB in ​​the 5GS bridge is connected to the TSN converter on the network side through the 5GC (5G core network) in the 5GS bridge to form the output port of the 5GS bridge.

[0063] It should be noted that uplink refers to the process of transmitting information from a lower-layer network node to an upper-layer node in communication, that is, the transmission of information from a lower-layer network node to an upper-layer node.

[0064] For example, for any 5GS bridge in a time slot t 5GS Bridge The total bandwidth of the uplink communication channel is expressed as B total , 5GS Bridge The bandwidth of the subchannel corresponding to the communication user m is expressed as (Unit: MHz). Communication user m on 5GS bridge The real-time transmission queue length of the corresponding transmission queue in the ingress port is expressed as The channel gain of the subchannel corresponding to communication user m is It follows Rayleigh fading and can be modeled as in, represents the distance-dependent path loss of the UE-gNB link, represents small-scale fading, is a complex Gaussian distribution; 5GS bridge is the 5GS bridge corresponding to node k, 5GS bridge The communication user set is

[0065] Furthermore, 5GS Bridge 5GS traffic of communication user m received by gNB in ​​time slot t It can be expressed as:

[0066]

[0067] in, is the transmission power of communication user m, Divide the 5GS traffic transmitted by communication user m, is the additive white Gaussian noise in the subchannel corresponding to communication user m, and σ is the noise variance in the subchannel corresponding to communication user m.

[0068] 5GS Bridge The uplink signal-to-noise ratio of communication user m in time slot t It can be expressed as:

[0069]

[0070] According to Shannon's theorem, 5GS bridge The real-time channel capacity of the subchannel corresponding to the communication user m in time slot t It can be expressed as:

[0071]

[0072] It is worth mentioning that based on the characteristics of the same topological structure of the wired domain and the 5G domain in the 5G-TSN architecture of this embodiment, the TSN bridge is also provided with an input port and an output port. Specifically, the input port of the TSN bridge is used to buffer the TSN diversion, and the output port of the TSN bridge also implements a circular queuing forwarding queue mechanism to send the TSN diversion to the next node. In addition, the number of input ports and the number of output ports of the TSN bridge and the 5GS bridge are the same, and the circular queuing forwarding queue resource restrictions of the output ports of the TSN bridge and the 5GS bridge are also the same.

[0073] Furthermore, with the goal of maximizing the scheduling success rate of the time-triggered flow, a target scheduling optimization problem of the joint wired domain and 5G domain based on offloading is constructed, which is expressed as:

[0074]

[0075] At the same time, the target constraint condition of the target scheduling optimization problem is constructed, which is expressed as:

[0076] st0≤f i .sr≤1 (6)

[0077] S i ∈{0,1} (7)

[0078]

[0079] in, Indicates 5GS bridge The sub-channel bandwidth set corresponding to the communication users; represents the set of scheduling states of time-triggered flows, S i Represents the time-triggered flow f i The scheduling state, if f i If the scheduling is successful, then S i =1, otherwise S i =0; is the time-triggered flow f i Arrives at TSN bridge b corresponding to node k at time slot t k The state of the e-th output port, if the time trigger flow f i Arrives at TSN bridge b corresponding to node k at time slot t k The e-th output port of otherwise is the time-triggered flow f i Arrives at the 5GS bridge corresponding to node k at time slot t The state of the e-th output port, if the time trigger flow f iArrives at the 5GS bridge corresponding to node k at time slot t The e-th output port of otherwise is the time-triggered flow f i The upper bound of the injection time offset, hop i Represents f i Pathf i .Number of hops on the path; represents the wireless transmission delay within the 5GS bridge; Q CQF The queue resource upper limit of each circular queuing forwarding queue, that is, the maximum amount of data that the circular queuing forwarding queue can accommodate; Represents the injection time offset set of the time-triggered stream; Represents a set of partition ratios for a time-triggered flow.

[0080] In this embodiment, formula (6) is a constraint on the time-triggered flow division ratio, formula (7) is a constraint on the time-triggered flow scheduling state, formula (8) is a constraint on the time-triggered flow injection time offset, formula (9) is a constraint on the wired domain round-robin queuing forwarding queue resources, formula (10) is a constraint on the 5G domain round-robin queuing forwarding queue resources, and formulas (11) and (12) are constraints on the 5GS bridge's allocation of sub-channel bandwidth to its communication users.

[0081] In this embodiment, a circular queuing forwarding queue mechanism is used to ensure bounded delay and jitter of time-triggered flows, and the number of schedulable flows is further increased by defining the target scheduling optimization problem as maximizing the scheduling success rate.

[0082] Step 103, solving the target scheduling optimization problem according to the target constraints, and obtaining the target scheduling strategy of the 5G-TSN architecture.

[0083] In this embodiment, the target scheduling optimization problem is solved according to target constraints including injection time offset constraints, round-robin forwarding queue resource constraints, partition ratio constraints, and wireless resource configuration constraints, so that the target scheduling strategy obtained is more in line with the architecture design and scheduling requirements.

[0084] Further, as a refinement and extension of the specific implementation methods of the above-mentioned embodiment, in order to fully illustrate the specific implementation process of this embodiment, step 103, that is, according to the target constraint conditions, solving the target scheduling optimization problem to obtain the target scheduling strategy of the 5G-TSN architecture, specifically includes: decoupling the target scheduling optimization problem, determining the wired scheduling problem corresponding to the TSN bridge in the 5G-TSN architecture and the wireless scheduling problem corresponding to the 5GS bridge in the 5G-TSN architecture; solving the wired scheduling problem according to the target constraint conditions, and determining the wired scheduling strategy for the wired scheduling problem; solving the wireless scheduling problem according to the target constraint conditions and the wired scheduling strategy, and determining the wireless scheduling strategy for the wireless scheduling problem; determining the target scheduling strategy according to the wired scheduling strategy and the wireless scheduling strategy.

[0085] In this embodiment, according to the time-varying characteristics of the wireless communication environment and the black-box characteristics of the internal resource scheduling of the 5GS bridge, the target scheduling optimization problem is decoupled into two problems: wired scheduling in the wired domain and wireless scheduling in the 5G domain. The throughput of the wired domain is maximized through the wired scheduling problem, and based on the obtained wired scheduling strategy, the allocation of communication user sub-channel bandwidth in each time slot of each 5GS bridge is optimized to further maximize the scheduling success rate.

[0086] Furthermore, as a refinement and extension of the specific implementation methods of the above-mentioned embodiments, in order to fully illustrate the specific implementation process of this embodiment, the steps of determining the wired scheduling problem corresponding to the TSN bridge specifically include: taking the maximum throughput corresponding to the TSN bridge in the 5G-TSN architecture as the goal, constructing a wired scheduling problem according to the scheduling period of the time-triggered flow, the data frame size of the time-triggered flow, and the division ratio of the time-triggered flow.

[0087] In this embodiment, the wired scheduling problem is a global optimization problem, which optimizes the injection time offset and division ratio of all time-triggered flows under the resource constraints of the circular queuing forwarding queue, so as to maximize the throughput of the wired domain and minimize the traffic load on the 5G domain, thereby improving the scheduling success rate.

[0088] Exemplarily, the wireline scheduling problem is expressed as:

[0089]

[0090] In this embodiment, the wired scheduling problem is constrained by the partition ratio constraint (Formula (6)), the injection time offset constraint (Formula (8)) and the wired domain round-robin queuing forwarding queue resource constraint (Formula (9)) in the target constraint condition.

[0091] Furthermore, as a refinement and extension of the specific implementation methods of the above-mentioned embodiments, in order to fully illustrate the specific implementation process of this embodiment, the steps for determining the wireless scheduling problem corresponding to the 5GS bridge specifically include: taking the maximization of the scheduling success rate of the time-triggered flow as the goal, determining the wireless scheduling problem according to the scheduling status of the time-triggered flow, the number of time-triggered flows, and the sub-channel bandwidth of the communication users of the 5GS bridge.

[0092] Among them, the communication user is a device that is connected to the 5GS bridge for communication and sends signals to the 5GS bridge; the 5GS bridge divides the uplink communication channel between the 5GS bridge and the communication user into sub-channels so that the communication users correspond to the sub-channels one by one.

[0093] In this embodiment, based on the obtained wired scheduling strategy, the real-time wireless scheduling strategy of each 5GS bridge is further optimized to maximize the scheduling success rate.

[0094] Exemplarily, the wireless scheduling problem is expressed as:

[0095]

[0096] In this embodiment, the wireless scheduling problem is constrained by the scheduling state constraint in the target constraint conditions (Formula (7)), the 5G domain round-robin forwarding queue resource constraint (Formula (10)), and the constraint on the sub-channel bandwidth allocation of the 5GS bridge to its communication users (Formulas (11) and (12)).

[0097] Furthermore, as a refinement and expansion of the specific implementation methods of the above-mentioned embodiments, in order to fully illustrate the specific implementation process of this embodiment, the wired scheduling problem is solved according to the target constraint conditions, and the steps of determining the wired scheduling strategy of the wired scheduling problem specifically include: initializing a feasible solution to the wired scheduling problem, the feasible solution is determined according to the injection time offset of the time trigger flow and the division ratio of the time trigger flow, and the injection time offset is the time interval from the generation of the time trigger flow to the injection of the 5G-TSN architecture; performing norm constraints on the feasible solution; obtaining the target matrix of the circular queuing forwarding queue in the TSN bridge and the 5GS bridge output port; according to the target matrix and the constrained feasible solution, respectively reconstructing the wired scheduling problem and the target constraint conditions, and determining the first reconstruction problem and the first reconstruction condition; calculating the first reconstruction condition in the first reconstruction condition The target convex upper bound and the target linear lower bound of the objective function; based on the target convex upper bound and the target linear lower bound, according to the first reconstruction condition, solve the target flow state of the circular queuing forwarding queue in the first reconstruction problem, and obtain the target feasible solution; determine the flow state difference according to the current target flow state and the previous target flow state; if the flow state difference is greater than or equal to the preset convergence threshold, and the current number of iterations is less than or equal to the preset maximum number of iterations, perform norm constraints on the target feasible solution until the preset stop condition is reached, and the preset stop condition is that the flow state difference is less than the preset convergence threshold or the current number of iterations is greater than the preset maximum number; according to the target feasible solution when the preset stop condition is reached, determine the target time offset and the target division ratio of the time-triggered flow, and determine the target time offset and the target division ratio as the wired scheduling strategy.

[0098] It should be noted that the constraints of the wireline scheduling problem include nonlinear constraints and integer constraints, where the search space of the injection time offset of the time-triggered flow is Therefore, the cable scheduling problem is a mixed integer non-linear programming (MINLP) problem, which is a non-deterministic polynomial time (NP-hard) problem. The methods used in related technologies to solve NP-hard problems are variable decoupling alternating optimization and heuristics, but it is difficult to find high-quality solutions to large-scale problems within an acceptable time.

[0099] In this embodiment, the wired scheduling problem and the target constraint condition are reconstructed based on the l-0 norm constraint to obtain a first reconstructed problem and a first reconstructed condition, so as to transform the wired scheduling problem into an equivalent nonlinear programming (NLP) problem, and further adopt the continuous convex approximation (SCA) method to obtain a wired scheduling strategy for the wired scheduling problem, thereby reducing the complexity of the solution and obtaining an approximate optimal solution, thereby ensuring the reliability of the wired scheduling.

[0100] Further, as a refinement and expansion of the specific implementation method of the above embodiment, in order to fully illustrate the specific implementation process of this embodiment, according to the target matrix and the feasible solution after the constraints, the wired scheduling problem and the target constraint conditions are reconstructed respectively, and the steps of determining the first reconstruction problem and the first reconstruction condition include: determining the flow state of the cyclic queuing forwarding queue in each time slot within the scheduling super period according to the target matrix and the feasible solution after the constraints, and the scheduling super period is determined according to the scheduling period of the time-triggered flow; according to the flow state and the feasible solution after the constraints, the wired scheduling problem and the target constraint conditions are reconstructed respectively. Reconstruct the first reconstruction problem and the initial reconstruction condition; use the continuous smooth function to approximate the feasible solution after the constraint, determine the first objective function, and determine the first inequality and the second inequality of the first objective function; perform the first-order Taylor expansion on the first objective function, determine the convex upper bound of the first objective function, and reconstruct the second inequality according to the convex upper bound; perform linear approximation on the first inequality, determine the linear lower bound of the first objective function, and reconstruct the first inequality according to the linear lower bound; determine the first reconstruction condition according to the reconstructed first inequality, second inequality and the initial reconstruction condition.

[0101] In this embodiment, the injection time offset and the partition ratio are effectively combined using the l-0 norm, thereby reducing scheduling complexity and maximizing the wired domain throughput, thereby ensuring good performance and lower time overhead in large-scale scenarios. At the same time, the wired scheduling problem is solved using convex optimization theory, which improves the quality and interpretability of the final scheduling result.

[0102] Specifically, based on the mutual influence between the injection time offset and the division ratio of the time-triggered flow in the wired scheduling problem, a unified variable is used to represent the joint scheduling strategy of the two (wired scheduling strategy), thereby re-expressing the wired scheduling problem in a combination rather than decoupling manner.

[0103] For example, by real variable represents a feasible solution to the wireline scheduling problem. The dimension of x represents all feasible injection time offsets. The element value x in x nrepresents the division ratio of the corresponding injection time offset setting, that is, the real variable x contains the injection time offset and division ratio of all time trigger flows.

[0104] Next, for the time-triggered flow f i , we can get and Among them, lower i with upper i Represents the time-triggered flow f i All possible injection time offsets are set in the subscript of the corresponding dimension in x, offset j max is the time-triggered flow f j The injection time offset upper bound. Time-triggered flow f i The injection time offset f i .offset and division ratio f i .sr can be determined from a subset of x, that is, by

[0105] x i ={x n |x n ∈x,n∈[lower i ,upper i ]} is given. Among them, x i represents f i The corresponding feasible solution is f i .offset and f i .sr.

[0106] Furthermore, for x i The elements of are subjected to the l-0 norm constraint as shown in equation (15) below to ensure the uniqueness of the feasible solution:

[0107]

[0108] According to formula (15), at x i There is only one non-zero element in Further f i .offset and f i .sr means:

[0109]

[0110] Among them, n * for The subtitle of .

[0111] It can be understood that in equation (16), the time-triggered flow f i The injection time offset fi .offset is determined by The time trigger flow f i The division ratio f i .sr by The value of is determined.

[0112] Furthermore, according to the parameter data of the 5G-TSN architecture, the real-time traffic conditions of all cyclic queue forwarding queues are obtained, and the static coefficient matrix (target matrix). E k The TSN bridge b corresponding to node k k or 5GS Bridge The number of output ports.

[0113] Specifically, the column vector c of C n Indicates c n The resource occupancy of the circular queue forwarding queue under the corresponding injection time offset. That is, the column vector c of C n Indicates that when f i .offset=n-lower i (n∈[lower i ,upper i ]) is the occupancy status of the circular queue forwarding queue resources. Among them, c n Each element c in u,n The state can be reached by the corresponding flow or Determine, u∈[1,U], n∈[1,N]. For example, if the injection time offset of the time-triggered flow is set to 0, the time-triggered flow occupies which node's corresponding 5GS bridge or TSN bridge outbound port's circular queuing forwarding queue in which time slot, which can correspond to c 0 This column.

[0114] Therefore, according to the target matrix and the constrained feasible solution, the traffic state z=C*x of all cyclic queuing forwarding queues in each time slot is obtained. Then, according to the traffic state and the constrained feasible solution, the wired scheduling problem is reconstructed to obtain the first reconstruction problem. The first reconstruction problem is expressed as:

[0115]

[0116] in,

[0117] According to the traffic state and the feasible solution after constraints, the partition ratio constraint (Equation (6)) in the target constraint problem and the wired domain round-robin forwarding queue resource constraint (Equation (9)) are reconstructed to obtain the initial reconstruction condition. The initial reconstruction condition is expressed as:

[0118] st0≤x n ≤1 (18)

[0119] 0≤z u ≤Q CQF (19)

[0120]

[0121] Among them, equation (18) is equivalent to equation (6), and equation (19) is equivalent to equation (9). The injection of time offset constraint into the target constraint (equation (8)) is guaranteed by the dimension of the feasible solution x. Therefore, the first reconstruction problem is equivalent to the wireline scheduling problem.

[0122] Furthermore, based on the non-smooth and non-convex characteristics of formula (15), a continuous smooth function is used to approximate the 1-0 norm to obtain the first objective function. The first objective function g δ (x n ) is expressed as:

[0123]

[0124] Among them, δ>0, δ is a parameter that controls the smoothness of the approximation.

[0125] Then equation (15) can be relaxed into the first inequality shown in equation (21) and the second inequality shown in equation (22):

[0126]

[0127] in, and are constants slightly less than 1 and slightly greater than 1 respectively.

[0128] Based on the fact that the first objective function is a concave function and the non-convexity of the second inequality, the convex upper bound of the first objective function is obtained by using the continuous convex approximation algorithm through the first-order Taylor expansion. The convex upper bound of the first objective function at the lth iteration is The specific formula is as follows (23):

[0129]

[0130] in, is x at the l-1th iteration n .

[0131] Then, according to the convex upper bound of the first objective function, the second inequality is reconstructed as:

[0132]

[0133] Furthermore, the first inequality is linearly approximated to obtain the linear lower bound of the first objective function, so as to reduce the complexity and improve the performance of the solution in large-scale scenarios and improve the solution efficiency. The linear lower bound of the first objective function at the lth iteration is It is expressed as:

[0134]

[0135] in,

[0136] Then, according to the linear lower bound of the first objective function, the first inequality is reconstructed as:

[0137]

[0138] Thus, according to the reconstructed first inequality, the second inequality and equations (18) and (19) in the initial reconstruction condition, the first reconstruction condition is determined to constrain the first reconstruction problem using the first reconstruction condition. When solving the first iterative solution, the first reconstruction problem and the first reconstruction condition are expressed as:

[0139]

[0140] st0≤x n ≤1 (18)

[0141] 0≤z u ≤Q CQF (19)

[0142]

[0143] In actual application scenarios, based on the characteristic that the first reconstruction problem is a convex optimization (Convex Optimization, Convex, CVX) problem, a convex optimization software package can be used to solve it and performed in the CUC in the SDN controller to achieve global scheduling.

[0144] In the process of solving the actual application scenario, first initialize the feasible solution x (0) , and generate the target matrix C of the circular queue forwarding queue according to the parameter data of the 5G-TSN architecture in the actual application scenario, such as network and traffic parameters. At the same time, δ and η are pre-set according to the needs of the actual application scenario. 1 , η 2 , convergence threshold th and maximum number of iterations T max , for example T max =50. Then, according to the target matrix C and the feasible solution x (0), based on equations (20) and (23), the target convex upper bound and target linear lower bound of the first objective function are calculated. Then, based on the target convex upper bound and target linear lower bound, according to the first reconstruction condition, the first reconstruction problem is solved to obtain the target flow state of the circular queuing forwarding queue, and the target feasible solution is obtained at the same time. Among them, the target flow state at the lth iteration is expressed as The target flow state at the l-1th iteration is expressed as The feasible solution at the lth iteration is represented as x (l) Furthermore, the flow state difference between the current target flow state and the previous target flow state is calculated, which is specifically expressed as: If the flow state difference is greater than or equal to the preset convergence threshold, and the current iteration number is less than or equal to the maximum iteration number, the next iteration is performed according to the target feasible solution solved by the current iteration, and the target convex upper bound and the target linear lower bound are recalculated until the flow state difference is less than the preset convergence threshold or the current iteration number is greater than the preset maximum number, that is, until the preset stop condition is reached. Thus, according to the target feasible solution when the preset stop condition is reached, the target time offset and target division ratio of the time-triggered flow are determined, and the target time offset and target division ratio are determined as the wired scheduling strategy.

[0145] Further, as a refinement and expansion of the specific implementation methods of the above-mentioned embodiment, in order to fully illustrate the specific implementation process of this embodiment, the wireless scheduling problem is solved according to the target constraint conditions and the wired scheduling strategy, and the steps of determining the wireless scheduling strategy for the wireless scheduling problem specifically include: obtaining the uplink transmittable flow ratio of the target transmission queue corresponding to the communication user in the ingress port of the 5GS bridge, and the uplink signal-to-noise ratio of the 5GS diversion sent by the communication user; determining the transmission queue length of the target transmission queue according to the wired scheduling strategy and the scheduling status of the previous time slot time-triggered flow in the scheduling supercycle, and the scheduling supercycle is determined according to the scheduling period of the time-triggered flow; according to the uplink signal-to-noise ratio, The target constraint condition is reconstructed based on the transmission queue length, the sub-channel bandwidth of the communication user and the uplink transmittable flow ratio, and the second reconstruction condition is determined; the wireless scheduling problem is reconstructed based on the second reconstruction condition to determine the second reconstruction problem; the second objective function of the second reconstruction problem is determined; according to the second objective function, the nonlinear programming condition of the second reconstruction problem is determined; the nonlinear programming condition is solved to determine the target sub-channel bandwidth of the communication user and the target uplink transmittable flow ratio; based on the target constraint condition, the scheduling state of the time-triggered flow is updated according to the target transmission queue length and the target uplink transmittable flow ratio; the target sub-channel bandwidth and the updated scheduling state are determined as the wireless scheduling strategy.

[0146] It can be understood that the wireless scheduling problem is also a mixed integer nonlinear programming problem.

[0147] In this embodiment, the mixed integer programming problem is converted into a linear programming problem, and the wireless scheduling closed-form solution is obtained by solving the Karush-Kuhn-Tucker optimization condition (Karush-Kuhn-Tucker, KKT) condition, thereby reducing the solution complexity and more effectively solving the wireless scheduling problem.

[0148] It should be noted that the KKT condition is a set of necessary conditions in optimization theory, which is applicable to solving nonlinear programming problems with equality and inequality constraints.

[0149] Specifically, for a 5GS bridge in a certain time slot, after obtaining the wired scheduling strategy and the scheduling status of the time-triggered flow updated in the previous time slot, the real-time transmission queue length of the transmission queue corresponding to the communication user of the 5GS bridge in the 5GS bridge inlet port is observable. Further, the uplink transmittable flow ratio of the transmission queue corresponding to the communication user of the 5GS bridge in the 5GS bridge inlet port is obtained. Specifically, each transmission queue in the 5GS bridge inlet port has a real-time uplink transmittable flow ratio. For example, transmission queue 1 can transmit 80% of the data in transmission queue 1. Then, according to the uplink signal-to-noise ratio, uplink transmittable flow ratio and sub-channel bandwidth corresponding to the communication user of the 5GS bridge, the sub-channel bandwidth allocation constraint (Formula (12)) in the target constraint condition is reconstructed to determine the second reconstruction condition.

[0150] Exemplarily, the second reconstruction condition is expressed as:

[0151]

[0152] in, 5GS bridge for time slot t Communication users m in 5GS bridge Ratio of the uplink transmittable flow rate of the corresponding transmit queue on the ingress port.

[0153] Then, the wireless scheduling problem is reconstructed according to the second reconstruction condition to determine the second reconstruction problem. The second reconstruction problem is expressed as:

[0154]

[0155] in, Indicates 5GS bridge The uplink transmittable flow ratio set corresponding to the communication users.

[0156] Here, the constraints of the second reconstruction problem are updated as follows:

[0157]

[0158]

[0159] In this embodiment, the scheduling success rate of the time-triggered flow is equal to the weighted uplink transmittable flow ratio. It can be understood that the larger the uplink transmittable flow ratio is, the higher the scheduling success rate of the time-triggered flow is. At the same time, the integer constraint of the wired domain circular queuing forwarding queue constraint (Formula (9)) in the target constraint condition is eliminated, which greatly reduces the complexity of the solution.

[0160] It can be understood that the second reconstruction problem is an uplink transmission optimization problem, and the wireless domain round-robin forwarding queue constraint (Formula (10)) in the target constraint is considered at the egress port of the 5GS bridge, and is therefore not included in the constraints of the second reconstruction problem.

[0161] Furthermore, based on the fact that the second reconstruction problem is convex and satisfies the Strongly Convex Relaxation Condition (Slater), it can be concluded that the KKT condition is a necessary and sufficient condition for obtaining the optimal solution to the second reconstruction problem, and thus the Lagrangian function (second objective function) of the second reconstruction problem is obtained. It is expressed as:

[0162]

[0163] Among them, β m , m , m and ρ are Lagrange multipliers.

[0164] Further, according to the second objective function, the KKT condition (nonlinear programming condition) of the second reconstruction problem is determined. The KKT condition of the second reconstruction problem is expressed as:

[0165]

[0166] Among them, β m ≥0,λ m ≥0,ξ m ≥0,ρ≥0, Evaluate the sign for partial derivatives.

[0167] By solving the KKT condition, the optimal solution to the second reconstruction problem is obtained, that is, the ratio of the target subchannel bandwidth of the communication user of the 5GS bridge to the target uplink transmittable flow. Compared with the target uplink transmittable traffic Respectively expressed as:

[0168]

[0169] Furthermore, under the constraint of the 5G domain circular queuing forwarding queue resources in the target constraint condition (Formula (10)), the scheduling state of the time-triggered flow of the current time slot is updated according to the target sub-channel bandwidth and the target uplink transmittable flow ratio. And the wireless scheduling strategy is determined according to the target sub-channel bandwidth and the updated scheduling state.

[0170] In actual application scenarios, the solution to the wireless scheduling problem can be performed in 5GC to achieve real-time scheduling.

[0171] Step 104, performing resource allocation operations of the 5G-TSN architecture according to the target scheduling strategy.

[0172] In this embodiment, the resource allocation operation of the 5G-TSN architecture is performed according to the target scheduling strategy, which significantly improves the network capacity and network carrying capacity, while reducing the time cost and realizing large-scale deterministic flow scheduling.

[0173] In one embodiment, if Figure 3 As shown, the scheduling success rate of the wired scheduling problem described in this application is compared with other methods in the related art. Figure 3 It can be seen that the scheduling success rate is negatively correlated with the number of data-triggered flows. This is because limited network resources cause the growth rate of successfully scheduled time-triggered flows to be lower than the growth rate of time-triggered flows. In addition, the wired scheduling strategy obtained according to the wired scheduling problem described in this application can guarantee a success rate of more than 97% when the scheduling traffic (the total number of time-triggered flows) is less than 2200, and can still guarantee a success rate of 82.84% in a large-scale scenario of 3000 flows, which is 22.18%, 26.49% and 76.98% higher than the Differential Evolution algorithm (DE), the Greedy algorithm (Greedy) and the Naive algorithm (NV), respectively.

[0174] The performance comparison of the wired domain throughput of the wired scheduling problem described in this application is as follows: Figure 4 As shown. Figure 4 It can be seen that Figure 4 The trend and Figure 3 When the total number of time-triggered flows is different, the average wired domain throughput percentage of the wired scheduling strategy obtained according to the wired scheduling problem described in this application is 75.95%, which is 10.90%, 44.86% and 65.40% higher than the DE algorithm, Greedy algorithm and NV algorithm, respectively, thereby being able to reduce the traffic load of the 5G domain by improving the throughput of the wired domain and improving the success rate.

[0175] The time cost comparison between the wired scheduling problem described in this application and different algorithms in related technologies is as follows: Figure 5As shown. It can be seen that the time cost is proportional to the number of time-triggered flows, among which the NV algorithm has the lowest complexity, and the time cost is between 0.07min and 0.11min. Due to the large search space, the DE algorithm and Greedy algorithm have the largest time overhead, reaching 79.92min and 70.12min respectively when scheduling 3000 flows. On the contrary, the time cost is significantly reduced when solving the wired scheduling problem described in this application. When scheduling 3000 flows, it takes 34.09min to solve the wired scheduling problem described in this application, saving nearly 50% of the time.

[0176] The convergence of the wired scheduling problem described in this application is compared with that of different algorithms in related technologies. Figure 6 As shown. It can be observed that when solving the wired scheduling problem described in this application, convergence is achieved within 7 iterations, and the throughput is increased by about 200% at different traffic scales, further proving its good convergence. In summary, the flow scheduling method based on the 5G-TSN architecture described in this application saves time consumption while ensuring the success rate, and has obvious advantages in large-scale scheduling scenarios.

[0177] In one embodiment, the wireless scheduling problem and the scheduling success rate of the wireless scheduling strategy described in this application are compared with other solutions in the related art. Figure 7 As shown. Figure 7 It can be seen that the success rates of the Maximize Sum Rate (MSR) scheme and the Average (Avg) scheme are relatively low, because these two schemes do not consider the real-time traffic characteristics, resulting in a mismatch between resource allocation and actual conditions. At the same time, the average scheduling success rate of the wireless scheduling strategy obtained according to the wireless scheduling problem described in this application is 93.90%, which is 10% to 16% higher than other benchmark schemes.

[0178] The spectral efficiency (SE) of the wireless scheduling problem described in this application is compared with other solutions in the related art. Figure 8 As shown. Figure 8It can be seen that among the three benchmark schemes, the Proportional Fairness (PF) scheme has the highest average SE, which is about 3.34 bits / s / Hz. At the same time, the unbalanced resource allocation of the wireless scheduling strategy obtained according to the wireless scheduling problem described in this application and the fixed allocation scheme of the Avg scheme will lead to a waste of spectrum resources, thereby obtaining a lower average SE. The average SE of the wireless scheduling strategy obtained according to the wireless scheduling problem described in this application is about 4.7 bits / s / Hz, which is 1.4 times that of the PF scheme and about 10 times that of the MSR scheme and the Avg scheme. In summary, the stream scheduling method based on the 5G-TSN architecture described in this application can more effectively utilize spectrum resources while improving the scheduling success rate.

[0179] In one embodiment, the 5G-TSN architecture described in this application and the traditional 5G-TSN architecture are respectively applied to the ring topology, linear topology, and hybrid topology in the related technology, and different period-deadline (PD) are configured in different topologies, including the first configuration, the second configuration, and the third configuration. Thus, the scheduling success rate of the 5G-TSN architecture described in this application and the traditional 5G-TSN architecture under different topologies and different PD configurations is obtained, and the scalability and robustness of the 5G-TSN architecture described in this application are evaluated. The scheduling success rate of the 5G-TSN architecture described in this application and the traditional 5G-TSN architecture is compared. Fig. 9 As shown. Fig. 9 It can be seen that under different topologies and PD settings, the scheduling success rate of the 5G-TSN architecture described in this application is above 70%, and under the hybrid topology, due to the large number of queue resources, the scheduling success rate based on the 5G-TSN architecture described in this application exceeds 88%. At the same time, under the third configuration, the success rate is relatively high. This is because the offset solution space of the third configuration is larger, making the scheduling strategy more flexible. The average success rate of the 5G-TSN architecture described in this application is 86.41%, which is 44% higher than the traditional 5G-TSN architecture. The 5G-TSN architecture described in this application is more flexible and efficient.

[0180] The resource utilization ratio of the 5G-TSN architecture described in this application and the circular queuing forwarding queue of the traditional 5G-TSN architecture is compared as follows: Fig.10 Resource utilization refers to the ratio of the allocated round-robin forwarding queue to the total number of queues on all ports (excluding ports connected to the host). Fig.10It can be seen that under different topologies and PD settings, the flow scheduling method based on the 5G-TSN architecture described in this application can guarantee more than 95% resource utilization, which is 10% higher than the traditional 5G-TSN. This is because traffic segmentation supports more flexible offset scheduling, allowing for more efficient use of queue resources.

[0181] The present application provides a 5G-TSN architecture and its flow scheduling method, medium and device, which realizes a more flexible networking architecture and a more efficient scheduling algorithm, and can effectively cope with large-scale deterministic flow scheduling scenarios. In the 5G-TSN architecture described in the present application, the wired domain and the 5G domain are topologically independent and coordinated in scheduling, so that industrial equipment can access the network in dual mode. The flow scheduling method based on the 5G-TSN architecture described in the present application decomposes the target scheduling optimization problem into a wired scheduling problem and a wireless scheduling problem, and solves these two problems separately in different ways to obtain the target scheduling strategy. In the process of solving the wired scheduling problem, the wired domain throughput is maximized with low complexity. In the process of solving the wireless scheduling problem, the closed-form solution of wireless scheduling is obtained by solving the KKT condition. Experimental results show that in a large-scale scenario where 3000 flows are scheduled in each super cycle (about 5ms), compared with other benchmark schemes, the scheduling success rate of the flow scheduling method based on the 5G-TSN architecture described in the present application is increased by 20% to 70%, and the time cost is reduced by 50%.

[0182] Furthermore, if Fig.11 As shown, as a specific implementation of the above-mentioned flow scheduling method based on the 5G-TSN architecture, an embodiment of the present application provides a flow scheduling device 1100 based on the 5G-TSN architecture, and the flow scheduling device 1100 based on the 5G-TSN architecture includes: a construction module 1101, a solution module 1102 and an execution module 1103.

[0183] The construction module 1101 is used to construct a target scheduling optimization problem of the 5G-TSN architecture based on the scheduling status of the time-triggered flow and the number of the time-triggered flow, with the goal of maximizing the scheduling success rate of the time-triggered flow generated by the node device in the 5G-TSN architecture, and the scheduling status includes scheduling success and scheduling failure; and

[0184] According to the parameter data of TSN bridge and 5GS bridge and the division ratio of time-triggered flow, the target constraint conditions of the target scheduling optimization problem are constructed;

[0185] A solving module 1102 is used to solve the target scheduling optimization problem according to the target constraint conditions and obtain the target scheduling strategy of the 5G-TSN architecture;

[0186] The execution module 1103 is used to execute the resource allocation operation of the 5G-TSN architecture according to the target scheduling strategy.

[0187] In one embodiment, the solving module 1102 is specifically used to decouple the target scheduling optimization problem, determine the wired scheduling problem corresponding to the TSN bridge in the 5G-TSN architecture and the wireless scheduling problem corresponding to the 5GS bridge in the 5G-TSN architecture; solve the wired scheduling problem according to the target constraint conditions, and determine the wired scheduling strategy of the wired scheduling problem; solve the wireless scheduling problem according to the target constraint conditions and the wired scheduling strategy, and determine the wireless scheduling strategy of the wireless scheduling problem; determine the target scheduling strategy according to the wired scheduling strategy and the wireless scheduling strategy.

[0188] In one embodiment, the solving module 1102 is specifically used to construct a wired scheduling problem based on the scheduling period of the time-triggered flow, the data frame size of the time-triggered flow, and the division ratio of the time-triggered flow, with the goal of maximizing the throughput corresponding to the TSN bridge in the 5G-TSN architecture.

[0189] In one embodiment, the solution module 1102 is specifically used to initialize a feasible solution to the wired scheduling problem, where the feasible solution is determined based on an injection time offset of a time-triggered stream and a division ratio of the time-triggered stream, where the injection time offset is the time interval from the generation of the time-triggered stream to the injection into the 5G-TSN architecture; perform norm constraints on the feasible solution; obtain a target matrix of a circular queuing forwarding queue in an egress port of a TSN bridge and a 5GS bridge; reconstruct the wired scheduling problem and the target constraint conditions according to the target matrix and the constrained feasible solution, and determine a first reconstruction problem and a first reconstruction condition; calculate a target convex upper bound and a target linear lower bound of a first objective function in the first reconstruction condition; based on the target convex upper bound and the target linear lower bound, According to the first reconstruction condition, the target flow state of the circular queuing forwarding queue in the first reconstruction problem is solved, and a target feasible solution is obtained; according to the current target flow state and the previous target flow state, the flow state difference is determined; if the flow state difference is greater than or equal to a preset convergence threshold, and the current number of iterations is less than or equal to a preset maximum number of iterations, a norm constraint is applied to the target feasible solution until a preset stop condition is reached, and the preset stop condition is that the flow state difference is less than a preset convergence threshold or the current number of iterations is greater than a preset maximum number; according to the target feasible solution when the preset stop condition is reached, a target time offset and a target division ratio of the time-triggered flow are determined, and the target time offset and the target division ratio are determined as a wired scheduling strategy.

[0190] In one embodiment, the solution module 1102 is specifically used to determine the traffic state of the cyclic queuing forwarding queue in each time slot within the scheduling super period according to the target matrix and the feasible solution after constraints, and the scheduling super period is determined according to the scheduling period of the time-triggered flow; according to the traffic state and the feasible solution after constraints, the wired scheduling problem and the target constraint conditions are reconstructed respectively to determine the first reconstruction problem and the initial reconstruction conditions; the feasible solution after approximation of the constraints using a continuous smooth function is used to determine the first objective function, and the first inequality and the second inequality of the first objective function are determined; the first objective function is expanded by a first-order Taylor expansion to determine the convex upper bound of the first objective function, and the second inequality is reconstructed according to the convex upper bound; the first inequality is linearly approximated to determine the linear lower bound of the first objective function, and the first inequality is reconstructed according to the linear lower bound; the first reconstruction condition is determined according to the reconstructed first inequality, the second inequality and the initial reconstruction condition.

[0191] In one embodiment, the solution module 1102 is specifically used to determine the wireless scheduling problem with the goal of maximizing the scheduling success rate of the time-triggered flow, according to the scheduling status of the time-triggered flow, the number of time-triggered flows, and the sub-channel bandwidth of the communication users of the 5GS bridge; wherein the communication user is a device connected to the 5GS bridge for communication; the 5GS bridge divides the uplink communication channel between the 5GS bridge and the communication user into sub-channels so that the communication users correspond one-to-one to the sub-channels.

[0192] In one embodiment, the solution module 1102 is specifically used to obtain the uplink transmittable flow ratio of the target transmission queue corresponding to the communication user in the ingress port of the 5GS bridge, and the uplink signal-to-noise ratio of the 5GS diversion sent by the communication user; determine the transmission queue length of the target transmission queue according to the wired scheduling strategy and the scheduling state of the time-triggered flow in the previous time slot in the scheduling supercycle, and the scheduling supercycle is determined according to the scheduling period of the time-triggered flow; reconstruct the target constraint condition according to the uplink signal-to-noise ratio, the transmission queue length, the sub-channel bandwidth of the communication user and the uplink transmittable flow ratio, and determine the second reconstruction condition; reconstruct the wireless scheduling problem according to the second reconstruction condition to determine the second reconstruction problem; determine the second objective function of the second reconstruction problem; determine the nonlinear programming condition of the second reconstruction problem according to the second objective function; solve the nonlinear programming condition to determine the target sub-channel bandwidth of the communication user and the target uplink transmittable flow ratio; based on the target constraint condition, update the scheduling state of the time-triggered flow according to the target transmission queue length and the target uplink transmittable flow ratio; determine the target sub-channel bandwidth and the updated scheduling state as the wireless scheduling strategy.

[0193] For the specific definition of the flow scheduling device based on the 5G-TSN architecture, please refer to the definition of the flow scheduling method based on the 5G-TSN architecture above, which will not be repeated here. Each module in the above-mentioned flow scheduling device based on the 5G-TSN architecture can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0194] Based on the above Figure 1 The method shown in the embodiment of the present application accordingly provides a readable storage medium on which a computer program is stored, and when the program is executed by a processor, the above-mentioned Figure 1 The flow scheduling method based on 5G-TSN architecture is shown.

[0195] Based on this understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each implementation scenario of the present application.

[0196] Based on the above Figure 1 The method shown, and Fig.11 In order to achieve the above-mentioned purpose, the embodiment of the present application further provides a computer device, which can be a personal computer, a server, a network device, etc. The computer device includes a storage medium and a processor; the storage medium is used to store a computer program; the processor is used to execute the computer program to achieve the above-mentioned Figure 1 The flow scheduling method based on 5G-TSN architecture is shown.

[0197] Optionally, the computer device may further include a user interface, a network interface, a camera, a radio frequency (RF) circuit, a sensor, an audio circuit, a WI-FI module, etc. The user interface may include a display, an input unit such as a keyboard, etc., and the optional user interface may also include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (such as a Bluetooth interface, a WI-FI interface), etc.

[0198] Those skilled in the art will appreciate that the computer device structure provided in this embodiment does not limit the computer device, and may include more or fewer components, or a combination of certain components, or different component arrangements.

[0199] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages and saves the hardware and software resources of the computer device, and supports the operation of information processing programs and other software and / or programs. The network communication module is used to realize communication between the components inside the storage medium, and communication with other hardware and software in the physical device.

[0200] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform, or the embodiments of the present application can be implemented by hardware.

[0201] Those skilled in the art will appreciate that the accompanying drawings are only schematic diagrams of a preferred implementation scenario, and the modules or processes in the accompanying drawings are not necessarily necessary for implementing the present application. Those skilled in the art will appreciate that the modules in the devices in the implementation scenario can be distributed in the devices of the implementation scenario according to the description of the implementation scenario, or can be changed accordingly and located in one or more devices different from the present implementation scenario. The modules of the above-mentioned implementation scenario can be combined into one module, or can be further split into multiple submodules.

[0202] The above serial numbers of this application are only for description and do not represent the advantages and disadvantages of the implementation scenarios. The above disclosure is only a few specific implementation scenarios of this application, but this application is not limited to them, and any changes that can be thought of by technicians in this field should fall within the scope of protection of this application.

Claims

1. A 5G-TSN architecture, characterized in that: The architecture includes: A plurality of nodes, each of which is associated with at least one node device, and each of which is capable of generating a time-triggered flow, wherein the time-triggered flow is divided into a TSN flow and a 5GS flow; TSN bridge, deployed at each node; 5GS bridge, deployed at each node; The node device is connected to the TSN bridge and the 5GS bridge of the node to which it belongs through a wired-wireless dual interface, so that the TSN diversion of the node device is transmitted and scheduled through the TSN bridge in the 5G-TSN architecture, and the 5GS diversion of the node device is transmitted and scheduled through the 5GS bridge in the 5G-TSN architecture.

2. A flow scheduling method based on the 5G-TSN architecture of claim 1, characterized in that: The method comprises: Taking the maximum scheduling success rate of the time-triggered flow generated by the node device in the 5G-TSN architecture as the goal, constructing the target scheduling optimization problem of the 5G-TSN architecture according to the scheduling state of the time-triggered flow and the number of the time-triggered flow, wherein the scheduling state includes scheduling success and scheduling failure; Constructing target constraints of the target scheduling optimization problem according to the parameter data of the TSN bridge and the 5GS bridge and the division ratio of the time-triggered flow; According to the target constraint conditions, solving the target scheduling optimization problem, and obtaining the target scheduling strategy of the 5G-TSN architecture; The resource allocation operation of the 5G-TSN architecture is performed according to the target scheduling strategy.

3. The flow scheduling method based on 5G-TSN architecture according to claim 2 is characterized in that: Solving the target scheduling optimization problem according to the target constraint condition to obtain the target scheduling strategy of the 5G-TSN architecture includes: Decoupling the target scheduling optimization problem, determining the wired scheduling problem corresponding to the TSN bridge in the 5G-TSN architecture and the wireless scheduling problem corresponding to the 5GS bridge in the 5G-TSN architecture; Solving the wired scheduling problem according to the target constraint condition and determining a wired scheduling strategy for the wired scheduling problem; Solving the wireless scheduling problem according to the target constraint condition and the wired scheduling strategy, and determining the wireless scheduling strategy of the wireless scheduling problem; The target scheduling strategy is determined according to the wired scheduling strategy and the wireless scheduling strategy.

4. The flow scheduling method based on 5G-TSN architecture according to claim 3 is characterized in that: The determining of the wired scheduling problem corresponding to the TSN bridge includes: With the goal of maximizing the throughput corresponding to the TSN bridge in the 5G-TSN architecture, the wired scheduling problem is constructed according to the scheduling period of the time-triggered flow, the data frame size of the time-triggered flow, and the division ratio of the time-triggered flow.

5. The flow scheduling method based on 5G-TSN architecture according to claim 4 is characterized in that: The step of solving the wired scheduling problem according to the target constraint condition and determining a wired scheduling strategy for the wired scheduling problem includes: Initialize a feasible solution to the wired scheduling problem, where the feasible solution is determined according to an injection time offset of the time trigger flow and a division ratio of the time trigger flow, where the injection time offset is a time interval from the generation of the time trigger flow to the injection into the 5G-TSN architecture; Performing norm constraints on the feasible solution; Obtain a target matrix of a round-robin forwarding queue in an egress port of the TSN bridge and the 5GS bridge; Reconstructing the wired scheduling problem and the target constraint conditions according to the target matrix and the constrained feasible solution, respectively, to determine a first reconstructed problem and a first reconstructed condition; Calculate a target convex upper bound and a target linear lower bound of a first objective function in the first reconstruction condition; Based on the target convex upper bound and the target linear lower bound, according to the first reconstruction condition, solving the target traffic state of the circular queuing forwarding queue in the first reconstruction problem, and obtaining a target feasible solution; Determine the flow state difference according to the current target flow state and the previous target flow state; If the flow state difference is greater than or equal to a preset convergence threshold, and the current number of iterations is less than or equal to a preset maximum number of iterations, a norm constraint is applied to the target feasible solution until a preset stop condition is reached, where the preset stop condition is that the flow state difference is less than the preset convergence threshold or the current number of iterations is greater than the preset maximum number; According to the target feasible solution when the preset stop condition is reached, a target time offset and a target division ratio of the time-triggered flow are determined, and the target time offset and the target division ratio are determined as the wired scheduling strategy.

6. The flow scheduling method based on 5G-TSN architecture according to claim 5, characterized in that: The step of reconstructing the wired scheduling problem and the target constraint condition according to the target matrix and the constrained feasible solution to determine a first reconstructed problem and a first reconstructed condition includes: Determine the flow state of the circular queuing forwarding queue in each time slot within a scheduling super period according to the target matrix and the constrained feasible solution, wherein the scheduling super period is determined according to the scheduling period of the time-triggered flow; Reconstructing the wired scheduling problem and the target constraint conditions according to the traffic state and the constrained feasible solution, and determining a first reconstructed problem and an initial reconstructed condition; Determine the first objective function by using the feasible solution constrained by a continuous smooth function approximation, and determine a first inequality and a second inequality of the first objective function; Performing a first-order Taylor expansion on the first objective function to determine a convex upper bound of the first objective function, and reconstructing the second inequality according to the convex upper bound; Performing linear approximation on the first inequality, determining a linear lower bound of the first objective function, and reconstructing the first inequality according to the linear lower bound; The first reconstruction condition is determined according to the reconstructed first inequality, the second inequality and the initial reconstruction condition.

7. The flow scheduling method based on 5G-TSN architecture according to claim 3 is characterized in that: The determining of the wireless scheduling problem corresponding to the 5GS bridge includes: Taking the maximum scheduling success rate of the time-triggered flow as the goal, determining the wireless scheduling problem according to the scheduling state of the time-triggered flow, the number of the time-triggered flows, and the sub-channel bandwidth of the communication user of the 5GS bridge; Among them, the communication user is a device that is connected to the 5GS bridge for communication and sends signals to the 5GS bridge; the 5GS bridge divides the uplink communication channel between the 5GS bridge and the communication user into the sub-channels so that the communication users correspond one to one with the sub-channels.

8. The flow scheduling method based on 5G-TSN architecture according to claim 7, characterized in that: The step of solving the wireless scheduling problem according to the target constraint condition and the wired scheduling strategy to determine the wireless scheduling strategy for the wireless scheduling problem includes: Obtaining the uplink transmittable flow ratio of the target transmission queue corresponding to the communication user in the ingress port of the 5GS bridge, and the uplink signal-to-noise ratio of the 5GS split sent by the communication user; Determining the transmission queue length of the target transmission queue according to the wired scheduling strategy and the scheduling state of the time-triggered flow in the previous time slot in the scheduling supercycle, wherein the scheduling supercycle is determined according to the scheduling period of the time-triggered flow; Reconstructing the target constraint condition according to the uplink signal-to-noise ratio, the transmission queue length, the subchannel bandwidth of the communication user and the uplink transmittable flow ratio to determine a second reconstruction condition; Reconstruct the wireless scheduling problem according to the second reconstruction condition to determine a second reconstruction problem; determining a second objective function of the second reconstruction problem; Determining a nonlinear programming condition for the second reconstruction problem according to the second objective function; Solving the nonlinear programming condition to determine the target subchannel bandwidth and target uplink transmittable flow ratio of the communication user; Based on the target constraint, and according to the ratio of the transmission queue length to the target uplink transmittable flow, updating the scheduling state of the time-triggered flow; The target sub-channel bandwidth and the updated scheduling state are determined as the wireless scheduling strategy.

9. A readable storage medium having a program or instruction stored thereon, characterized in that: When the program or instruction is executed by the processor, the steps of the flow scheduling method based on the 5G-TSN architecture as described in any one of claims 2 to 8 are implemented.

10. A computer device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, characterized in that: When the processor executes the program, the flow scheduling method based on the 5G-TSN architecture as described in any one of claims 2 to 8 is implemented.

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