5G-TSN asynchronous traffic mapping and scheduling optimization method and device

Abstract

The invention discloses a 5G-TSN asynchronous traffic mapping and scheduling optimization method and device, and belongs to the technical field of cross-domain network traffic scheduling. Comprising the following steps: dividing 5G aperiodic time-sensitive services into different queues, and constructing a mapping rule of the 5G aperiodic time-sensitive services of the different queues and a priority queue of a TSN domain; based on a mapping rule, establishing different virtualization sub-channels between the 5G aperiodic time-sensitive services of different queues and the TSN domain by adopting flow virtualization, and mapping the 5G aperiodic time-sensitive services of different queues to a priority queue of the TSN domain; and performing optimization scheduling on the service flow of the TSN domain priority queue based on an asynchronous traffic shaper and a queuing model. According to the invention, the QoS requirement can be ensured at the same time, and the real-time performance and reliability of the network are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of cross-domain network traffic scheduling, and in particular to a 5G-TSN asynchronous traffic mapping and scheduling optimization method and device. Background Art

[0002] The International Telecommunication Union (ITU) has defined three major application scenarios for 5G, namely enhanced Mobile BroadBand (eMBB), Ultra-Reliable Low Latency Communications (uRLLC), and massive Machine Type of Communication (mMTC). There are burst services in the massive Machine Type of Communication (mMTC) scenario of the 5G network. For example, in the remote status monitoring of the distribution network in the smart grid, the status change triggers data upload, and the sensor data stream in environmental monitoring is bursty but has no strict latency requirement. How to reasonably allocate network resources in the coexistence scenario of burst services and services with ultra-reliable low-latency requirements is one of the current research hotspots in deterministic communication. Time Sensitive Network (TSN) is the key to future deterministic communication technology. Because TSN enables high-priority time-sensitive services to exclusively occupy timing resources within a specific time through a priority queue control method based on precise time, realizing the interleaved bearing of services with different priorities on timing resources, thereby ensuring the latency determinism of high-priority service transmission under the multi-service unity and enabling the coexistence of critical and best-effort services. In the TSN protocol, Asynchronous traffic shaper (ATS) in IEEE 802.1cr is a shaping scheme for handling mixed traffic and burst service traffic types. Its shaped queue and flow shaping based on the Urgency-based Scheduler (UBS) can improve link utilization. With the help of TSN ATS, it can better meet the needs of medium and large 5G transmission networks dominated by user-centric mixed service traffic.

[0003] Due to the essential differences between the dynamic scheduling mechanism of 5G and the static scheduling mechanism of TSN, the cross-domain mapping of 5G-TSN services is the key to ensuring the real-time and deterministic transmission of information in a time-sensitive network system. To enable the communication network to carry multiple services, Quality of Service (QoS) technology is introduced to provide network guarantees with different service qualities for services with various requirements. Network quality generally affects the throughput of the transmission link, packet forwarding delay and jitter, packet loss rate, etc. Therefore, bandwidth, delay, jitter, and packet loss rate are important QoS metrics for 5G-TSN networks. Mechanistically, the QoS guarantee model based on QoS flows in 5G networks requires different network elements in the network to jointly complete end-to-end QoS guarantee, which is a dynamic resource management strategy. The core QoS guarantee mechanism in TSN is its Shaper, which can isolate and control the rate of high-priority traffic and low-priority traffic through the Shaper, and can be regarded as a static guarantee mechanism. Currently, the solutions to the end-to-end QoS consistency guarantee problem of 5G-TSN are all based on the dynamic QoS mapping mechanism of K-means clustering and rough set theory, which divides the problem into two processes: traffic clustering and dynamic mapping. However, due to the characteristics of its algorithm itself, this mechanism has difficulty in processing non-periodic time-sensitive traffic, such nonlinear data, and is prone to falling into local optima.

[0004] The solution to the deterministic aperiodic flow allocation problem of 5G-TSN services in a converged network is mainly based on the Prioritized Resource Sharing Scheduling (PRSS) mechanism of semi-persistent scheduling. This mechanism mainly consists of three parts: pre-allocating resources for time-triggered time-sensitive flows to ensure their deterministic transmission; designing a prioritized resource sharing mechanism for event-triggered time-sensitive flows to share some reserved resources with time-triggered time-sensitive flows to improve system resource utilization and ensure their prioritized transmission; performing dynamic scheduling for non-time-sensitive flows based on the max-min fair sharing algorithm to ensure the fairness and high efficiency of resource allocation. However, the success rate of traffic scheduling in this solution is relatively low. Summary of the Invention

[0005] The purpose of the present invention is to provide a 5G-TSN asynchronous traffic mapping and scheduling optimization method and device, which establish a virtualization channel between 5G and TSN by combining traffic virtualization technology, enabling the efficient docking of the scheduling and management mechanisms of the two networks, achieving minimized end-to-end delay, and at the same time ensuring the QoS requirements.

[0006] To achieve the above object, the present invention is implemented by the following technical solutions:

[0007] In a first aspect, the present invention provides a 5G-TSN asynchronous traffic mapping and scheduling optimization method, including:

[0008] Analyze 5G aperiodic time-sensitive service data based on TSN-based traffic virtualization, and calculate the burst arrival time of the service entry port and the traffic transmission path;

[0009] Divide the 5G aperiodic time-sensitive service into different queues based on the burst arrival time of the service entry port, bandwidth requirements, and end-to-end delay requirements, and construct a mapping rule between the 5G aperiodic time-sensitive service of different queues and the priority queue of the TSN domain;

[0010] Based on the mapping rule, use traffic virtualization to establish different virtual sub-channels between the 5G aperiodic time-sensitive service of different queues and the TSN domain, and map the 5G aperiodic time-sensitive service of different queues to the priority queue of the TSN domain;

[0011] Optimize and schedule the traffic flow of the TSN domain priority queue based on an asynchronous traffic shaper and a queuing model.

[0012] As a further limitation of the first aspect of the present invention, the mapping rule is expressed as:

[0013] ,

[0014] Wherein, represents the mapping rule, , , respectively represent the high, medium, and low priorities of the TSN domain, represents the end-to-end delay requirement, is the critical delay of the time-sensitive flow, is the maximum delay allowed by the TSN.

[0015] As a further limitation of the first aspect of the present invention, the virtual sub-channel is expressed as:

[0016] ,

[0017] Wherein, represents the virtual sub-channel, represents the 5G aperiodic time-sensitive service queue, represents the priority queue of the TSN domain, represents the maximum tolerable delay of the time-sensitive flow.

[0018] As a further limitation of the first aspect of the present invention, the optimizing and scheduling of the traffic flow of the TSN domain priority queue based on an asynchronous traffic shaper and a queuing model includes:

[0019] Based on the Kendall queuing model, the process of 5G aperiodic time-sensitive services being processed by an asynchronous traffic shaper is abstracted into an M / G / m / / FCFS queuing system, where M indicates that the arrival time interval distribution of the service flow follows an exponential distribution, G indicates that the service time distribution follows a general distribution, m indicates the number of asynchronous traffic shapers providing services currently, represents the maximum service volume that a single asynchronous traffic shaper can carry per unit time, and FCFS indicates that the service policy is first-come, first-served;

[0020] With the goal of minimizing the traffic loss rate of the queuing system, the traffic flow allocation problem of the asynchronous traffic shaper is established, expressed as:

[0021] ,

[0022] where, is the minimization function, is the deterministic aperiodic traffic arrival rate, represents the average service rate of each traffic flow;

[0023] Solving the traffic flow allocation problem obtains the optimal configuration of each asynchronous traffic shaper, completing the optimal scheduling of the traffic flow.

[0024] As a further limitation of the first aspect of the present invention, solving the traffic flow allocation problem needs to satisfy the following constraints:

[0025]

[0026] ,

[0027] ,

[0028] ,

[0029] ,

[0030] : Each shaping queue is only associated with one ingress port;

[0031] : Each shaping queue is only associated with one internal priority of the asynchronous traffic shaper;

[0032] : Each shaping queue is only associated with one priority in the previous hop;

[0033] where, represents the maximum delay of the traffic flow in the hop, 、 , respectively represent the latency, latency jitter, and reliability budget of the service flow . , , respectively represent the end-to-end latency, jitter, and reliability budget of the network represents the link set represents the maximum rate of the service flow in the th hop All service flows with a lower priority than the service flow in the th hop represent the maximum frame length represents the minimum frame length of the service flow in the th hop The set of service flows with the same priority as the service flow in the th hop All service flows with a higher priority than the service flow in the th hop represent the channel capacity of the link represents the set of service flows . represents the rate of the service flow in the

[0034] As a further limitation of the first aspect of the present invention, the constraint is optimized to:

[0035] ,

[0036] ,

[0037] ,

[0038] ,

[0039] ,

[0040] ,

[0041] ,

[0042] ,

[0043] Among them, Represents the upper limit of the number of service flows, Represents the committed rate of the service flow, Represents the asynchronous traffic shaper at a priority The average number of service flows of the shaped queues served simultaneously on, Represents the asynchronous traffic shaper at a priority The average number of service flows of the shaped queues served simultaneously on, Represents the priority The effective link capacity perceived, Represents a new decision variable, Represents the maximum burst size of the service flow, Represents the maximum frame length, Represents the delay budget of the link, Represents the global upper limit parameter of delay or jitter, Is a binary variable, when the shaped queue Is assigned to the priority Takes 1, otherwise takes 0, Represents when the shaped queue Is assigned to the priority The variable value at that time, Represents a set of shaped queues, Represents a set of priority queues, Represents the total bandwidth capacity of the link.

[0044] In a second aspect, the present invention provides a 5G-TSN asynchronous traffic mapping and scheduling optimization device for implementing the above-mentioned 5G-TSN asynchronous traffic mapping and scheduling optimization method. The device includes:

[0045] An analysis and calculation module, configured to: analyze 5G aperiodic time-sensitive service data based on the traffic virtualization of TSN, and calculate the burst arrival time of the service entry port and the traffic transmission path;

[0046] A mapping rule construction module, configured to: divide the 5G aperiodic time-sensitive service into different queues based on the burst arrival time of the service entry port, the bandwidth requirement, and the end-to-end delay requirement, and construct a mapping rule between the 5G aperiodic time-sensitive service of different queues and the priority queues in the TSN domain;

[0047] A mapping module, configured to: based on the mapping rule, establish different virtualized sub-channels between the 5G aperiodic time-sensitive service of different queues and the TSN domain by using traffic virtualization, and map the 5G aperiodic time-sensitive service of different queues to the priority queues in the TSN domain;

[0048] The optimization scheduling module is configured to optimize and schedule the traffic flow of the TSN domain priority queue based on an asynchronous traffic shaper and a queuing model.

[0049] In a third aspect, the present invention provides a computer-readable storage medium storing a computer program, which is adapted to be loaded and executed by a processor to perform the 5G-TSN asynchronous traffic mapping and scheduling optimization method as described above.

[0050] In a fourth aspect, the present invention provides a computer device, including: a processor and a computer-readable storage medium;

[0051] The processor is adapted to execute a computer program;

[0052] The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the 5G-TSN asynchronous traffic mapping and scheduling optimization method as described above is implemented.

[0053] In a fifth aspect, the present invention provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the 5G-TSN asynchronous traffic mapping and scheduling optimization method as described above is implemented.

[0054] The beneficial effects achieved by the present invention are as follows:

[0055] Aiming at the 5G-TSN cross-domain service matching mapping and flow allocation problems, the present invention proposes a 5G-TSN asynchronous traffic mapping scheme based on traffic virtualization and an ATS scheduling optimization method based on queuing theory. By combining the traffic virtualization technology, a virtualization channel is established between 5G and TSN, enabling the efficient docking of the scheduling and management mechanisms of the two networks. At the same time, based on ATS and queuing theory, the long-term configuration of asynchronous TSN is designed to minimize the end-to-end delay while ensuring the QoS requirements, improving the network real-time and reliability guarantee capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a novel 5G-TSN cooperative transmission architecture provided by an embodiment of the present invention based on 3GPP R16;

[0057] Figure 2 It is a schematic diagram of the 5G-TSN asynchronous traffic virtual mapping and scheduling optimization process provided by an embodiment of the present invention;

[0058] Figure 3 It is a schematic diagram of the 5G-TSN asynchronous traffic virtual mapping process provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0059] To make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below in conjunction with the embodiments and the drawings. Herein, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but do not limit the present invention.

[0060] Herein, it should also be noted that in order to avoid obscuring the present invention due to unnecessary details, only the structures and / or processing steps closely related to the solution according to the present invention are shown in the drawings, while other details less related to the present invention are omitted.

[0061] It should be emphasized that the term "comprising / including" when used herein refers to the presence of features, elements, steps or components, but does not exclude the presence or addition of one or more other features, elements, steps or components.

[0062] It should be emphasized here that the step marks mentioned hereinafter are not intended to limit the order of the steps. Instead, it should be understood that the steps can be executed in the order mentioned in the embodiments, or different from the order in the embodiments, or several steps can be executed simultaneously.

[0063] Based on the 3rd Generation Partnership Project Release 16 (3GPP R16), a 5G-TSN collaborative transmission architecture is proposed, and a TSN Application Function (TSN-AF) is newly added to the control plane of the 5G core network, and a protocol conversion gateway DS-TT and a network side TSN Translator (NW-TT) are newly added to the user plane. As Figure 1 shown, in this embodiment, an Abstraction Layer (AL) is introduced in the TSN-AF based on this architecture. The purpose is to decouple the 5G dynamic scheduling traffic from the TSN static scheduling mechanism based on Traffic Virtualization (TV), configure the corresponding QoS template in combination with the TSN service priority, and implement the QoS guarantee of TSN through the 5G internal signaling. Figure 1Among them, the User Plane Function (UPF), Access and Mobility Management Function (AMF), Session Management Function (SMF), Policy Control Function (PCF), Unified Data Management (UDM), Radio Access Network (RAN), Network Exposure Function (NEF), and User Equipment (UE) are inherent network elements in the 5G network. The Terminal Side TSN Converter (DS-TT), TSN Application Function (TSN-AF), and Network Side TSN Converter (NW-TT) are new network elements proposed in 3GPP R16 for the 5G system to act as a TSN bridge collaborative architecture. N1, N2, N3, N7, N11, N52, N53, Ns, etc. are transmission protocols for connecting network elements.

[0064] On the user plane, the UPF (User Plane Function) implements a scheduling and forwarding mechanism based on precise time and provides bridged layer-2 services. The forwarding plane includes a group of TSN bridges supporting Asynchronous Traffic Shaper (ATS). There is an ATS at the exit end of all TSN bridges, which connects the exit end of one TSN bridge and the entrance end of another bridge. Each ATS is responsible for scheduling the frames of a physical link. For ease of research, in this embodiment, the TSN bridges are divided into two types: edge nodes and transit nodes. Edge nodes are directly connected to a 5G entity instance, and transit nodes are connected to another TSN bridge in the network.

[0065] The ATS standard defines an asynchronous method for handling frames at the exit ports of TSN bridges. The ATS queuing model includes a set of shaping queues for interleaved shaping and a set of priority queues, both following the first-come, first-served rule. The ATS enables each flow to satisfy a leaky bucket shaping constraint, and the leaky bucket constraint limits the amount of transmitted data accumulated within the duration interval to , where and and are the committed information rate and committed burst value in the TSN standard, respectively. A service flow is assigned to the priority queue according to strict priority after passing through the shaping queue. Whether a flow is assigned to the current ATS is subject to the following rules: Each shaped queue is associated with only one input port, one priority in the previous hop, and one internal priority.

[0066] Based on the above architecture, the first embodiment of the present invention provides a 5G-TSN asynchronous traffic mapping and scheduling optimization method, as Figure 2 shown, including:

[0067] S1: The TV function in TSN-AF analyzes the key information of 5G aperiodic time-sensitive services and TSN QoS parameters. 5G aperiodic time-sensitive services belong to the QoS flow of Non-Guaranteed Bit Rate (Non-GBR), and the main parameters include 5QI, allocation and reservation priorities, etc. TV calculates information such as the burst arrival time at the service entry port and the traffic transmission path according to the per-stream filtering and policing (PSFP) information and transmission gating scheduling parameters received from the Centralized network configuration (CNC).

[0068] S2. TV establishes a time-sensitive virtualized sub-channel between the 5G and TSN domains, as Figure 3 shown, to isolate and associate 5G aperiodic time-sensitive services. Through time-aware priority mapping and dynamic traffic shaping algorithms, it ensures the delay consistency of cross-domain traffic and reduces delay jitter.

[0069] Specifically, it can include the following sub-steps:

[0070] S21. On the 5G side and the TSN side, classify the 5G aperiodic time-sensitive service data through the following characteristics:

[0071] Burst arrival time distribution : Represents the randomness of traffic arrival;

[0072] Bandwidth requirement ;

[0073] End-to-end delay requirement .

[0074] Based on these characteristics, the aperiodic time-sensitive service data can be divided into different virtualized sub-channels.

[0075] S22. The 5G-TSN virtualized sub-channel is used for the end-to-end transmission of time-sensitive traffic, expressed as:

[0076] (1)

[0077] Among them, represents the 5G aperiodic time-sensitive service queue, is the priority queue in the TSN domain, is the maximum tolerable delay of the time-sensitive flow, and these parameters can be obtained through step S1. is the correspondence between the priorities on the 5G side and the TSN domain, that is, the mapping rule.

[0078] S23. The mapping rule is executed by the 5G Policy Control Function (PCF). The Application Function (AF) session initiated by the TSN-AF to the PCF contains the TSN QoS requirement information assigned to the 5G bridge. After receiving the information, the PCF sets the 5G QoS profile according to the virtual mapping result and triggers the Packet Data Unit (PDU) session modification process to establish a new QoS flow. Among them, the mapping rule refers to mapping the QoS flow (dynamic scheduling) of 5G to the priority queue (static scheduling) of TSN to achieve end-to-end consistency. If the TSN flows are of the same service category, use the same egress port, and the burst arrival time distributions are compatible, the priority correspondence maps these TSN flows to the same virtualized sub-channel , so that they have the same QoS parameters. The mapping priority is based on the time sensitivity and bandwidth requirements of the flow:

[0079] (2)

[0080] Among them, , , respectively represent the high, medium, and low priorities of TSN, is the critical delay of the time-sensitive flow, is the maximum delay allowed by TSN.

[0081] S3. For time-sensitive services, the base station caches all the arriving traffic flows into different queues waiting for downlink scheduling, and performs user scheduling and resource allocation every Transmission Time Interval (TTI). This embodiment designs a resource configuration scheme by using the ATS bridging characteristics and queuing model. In this embodiment, the resource configuration scheme aims to maximize the system traffic acceptance rate and needs to meet certain constraints.

[0082] Combined with Figure 1 the architecture diagram described in, this embodiment abstracts the traffic scheduling and shaping process of the TSN-ATS pair in the TSN and 5G backhaul network into an M / G / m / / FCFS queuing system based on the Kendall queuing model, where M represents that the distribution of the service arrival time interval follows an exponential distribution, G represents that the service time distribution follows a general distribution, m represents the number of ATSs providing services currently, represents the capacity of the queuing system, that is, the maximum service volume that a single ATS can carry per unit time, and FCFS represents that the service policy is first come, first served.

[0083] In this embodiment, the network is first modeled as a directed graph , where and represent the vertices (TSN bridges) and edges (links) of the graph respectively, and it is assumed that there is a fixed path selection algorithm for traffic allocation paths. , respectively represent a set of shape queues for interleaved shaping and a set of traffic priority sorting queues for the edge .

[0084] Specifically, the resource configuration process may include the following sub-steps:

[0085] S31. Analysis of the 5G-TSN backhaul network state based on the queuing model:

[0086] Since in the 802.1Qcr ATS standard, the path from the source host to the destination host within the TSN domain is divided into hops. The first hop refers to the source host to the first node, and the last hop refers to the last node to the destination host. Therefore, in order to find the optimal scheduling for each ATS, the resource configuration goal of this embodiment is to maximize the effective arrival rate of the queuing system . This embodiment defines the deterministic aperiodic traffic arrival rate as , that is, the average number of aperiodic time-sensitive services arriving at the queuing system per unit time.

[0087] Since is usually a constant greater than 1, the goal can be transformed into the form of minimizing the traffic loss rate:

[0088] (5)

[0089] Wherein, is the effective arrival rate of the queuing system, is the blocking probability of the flow.

[0090] Satisfy:

[0091] (6)

[0092] Wherein, is the service rate per unit time of the system.

[0093] Because the service rate per unit time of the TSN network is reciprocal to the traffic sojourn time, therefore, the blocking probability experienced by the flow in the BN (bandwidth manager) can be expressed as follows:

[0094] (7)

[0095] Wherein, Represents the average service rate of each flow.

[0096] To minimize the traffic suppression probability of service data and maximize the traffic rejection rate, the traffic flow allocation problem of ATS in Equation (5) can be formulated as:

[0097] (8)

[0098] where is the minimization function.

[0099] In addition to the network transmission QoS requirements, to avoid the propagation problem of overloading of non-compliant traffic, the following constraints need to be satisfied:

[0100] : To prevent different flows from entering the same ATS, each shape queue is only associated with one incoming port associated with representing the number of ports;

[0101] : Each shape queue is only associated with one internal ATS priority;

[0102] : Each shape queue is only associated with one priority in the previous hop.

[0103] From this, the relationship between the number of shape queues required to implement the internal priority during the ATS shaping process can be obtained as follows:

[0104] (9)

[0105] where is the current input port receiving the traffic from the previous hop with the ATS priority of for.

[0106] The delay / jitter budget and transmission capacity of the traffic flow allocation problem in Equation (5) should be subject to the following rules:

[0107] (10)

[0108] (11)

[0109] (12)

[0110] (13)

[0111] (14)

[0112] Among them represents the maximum latency of the service flow in the th hop, , , respectively represent the latency, latency jitter, and reliability budget of the service flow . , , respectively represent the end-to-end network latency, jitter, and reliability budget, which are defined by the 802.1Qcr standard; represents the link set, represents the maximum rate of the service flow in the th hop, represents the maximum frame length of all service flows with a lower priority than the service flow in the th hop, represents the minimum frame length of the service flow represents the maximum number of burst data packets of the service flow in the th hop, represents the set of service flows with the same priority as the service flow in the th hop, represents the set of service flows with a higher priority than the service flow in the th hop, represents the channel capacity of the link , represents the rate of the service flow in the

[0113] It can be known from mathematical analysis that the optimization algorithm formula (8) and its constraints are a convex function with respect to . By solving this function, the optimal configuration of each ATS can be deduced.

[0114] S4. Recalculate the network delay of each hop according to the optimal configuration of each ATS, and design a network optimization configuration algorithm to obtain the budget delay boundary in the network.

[0115] Specifically, it may include the following sub-steps:

[0116] S41. Using the flow average sojourn time , the traffic arrival rate between each source-destination pair in the BN (bandwidth manager), and the link average failure time and the promised information rate as the input process

[0117] S42. Estimate the number of data packet copies required for each 5QI :

[0118] Using the TSN 802.1CB frame elimination mechanism (frame replication and elimination for reliability, FRER), the source node sends different copies of the data packet through disjoint paths to ensure the minimum level of network transmission reliability so as to estimate the number of data packet copies required for each 5QI . Assume that each path consists of independent links, and their failure times follow an exponential distribution with a mean of . Then the following conditions need to be satisfied:

[0119] (15)

[0120] S43. Path selection and delay / jitter budget allocation:

[0121] Taking as the input, select a path selection algorithm with the goal of balancing the workload, and select the 5G-TSN service transmission path between each source-destination pair. Assume that the link capacity is known, represents the total bandwidth capacity of the link, represents the end-to-end delay, then the delay budget under the given link :

[0122] (16)

[0123] S44. Through the above solution process, the optimal configuration of each ATS can be obtained. Its core idea is to find the traffic allocation of the traffic shaping buffer and the priority buffer, so as to find the maximum capacity of the 5G-TSN service corresponding to the ATS allocation

[0124] Furthermore, to ensure that the average number of traffic flows provided by the current ATS does not exceed its corresponding link capacity, and to ensure that the number of data flow shape queues does not exceed the sum of the traffic flows provided by all its priorities simultaneously, etc. QoS requirements, the following corrections need to be made to the constraint conditions C1~C5:

[0125] (17)

[0126] (18)

[0127] (19)

[0128] (20)

[0129] (21)

[0130] (22)

[0131] (23)

[0132] (24)

[0133] These constraints are all corrections to C1 - C5. Among them represents the upper limit of the number of traffic flows, which is determined by the link capacity or buffer, represents the committed rate of the traffic flow, represents the average number of shaped queues that the asynchronous traffic shaper serves simultaneously at priority , and represents the priority perceived effective link capacity, represents a new decision variable, represents the priority perceived effective link capacity, represents the maximum burst size of the traffic flow, represents the maximum frame length, represents the global upper limit parameter of delay or jitter, represents the queuing system capacity, which must be equal to the sum of the flow numbers allocated at all priorities and be non - negative, is a binary variable that takes 1 when the shaped queue is allocated to priority and 0 otherwise, represents the variable value when the shaped queue is allocated to priority

[0134] ​S5. The PCF binds the PDU session through the MAC address of the DS-TT port, derives the 5QI based on the new QoS information, and generates a 5G network data traffic management control policy (Policy Control and Charging, PCC) rule according to the TV-related information, 5QI, and the information describing the traffic flow provided by the Allocation and Retention Priority (ARP) exported from the TSN-AF. The two network elements, the Session Management Function (SMF) and the Access and Mobility Management Function (AMF), obtain the PCC rule through control plane interaction. As Figure 3 shown, the AMF carries the rule to the Radio Access Network (RAN) through the N2 interface, and the SMF carries it to the UPF through the N4 interface. Finally, the UPF and the UE (User Equipment) map the time-sensitive traffic flows with different QoS requirements to the appropriate PDU sessions and QoS flows, realizing the differentiated QoS scheduling of different traffic flows in the 5G-TSN system.

[0135] Based on the same inventive concept, another embodiment of the present invention provides a 5G-TSN asynchronous traffic mapping and scheduling optimization device, including:

[0136] An analysis and calculation module, configured to: analyze the 5G aperiodic time-sensitive service data based on the traffic virtualization of the TSN, and calculate the burst arrival time of the service entry port and the traffic transmission path;

[0137] A mapping rule construction module, configured to: divide the 5G aperiodic time-sensitive service into different queues based on the burst arrival time of the service entry port, the bandwidth requirement, and the end-to-end delay requirement, and construct a mapping rule between the 5G aperiodic time-sensitive service of different queues and the priority queue in the TSN domain;

[0138] A mapping module, configured to: based on the mapping rule, establish different virtualized sub-channels between the 5G aperiodic time-sensitive service of different queues and the TSN domain by using traffic virtualization, and map the 5G aperiodic time-sensitive service of different queues to the priority queue in the TSN domain;

[0139] An optimization scheduling module, configured to: optimize the scheduling of the traffic flow in the TSN domain priority queue based on the asynchronous traffic shaper and the queuing model.

[0140] It should be noted that the device embodiments correspond to the above method embodiments, and the implementation manners of the above method embodiments are all applicable to the device embodiments and can achieve the same or similar technical effects, so they will not be elaborated here.

[0141] The third embodiment of the present invention provides a computer-readable storage medium storing a computer program, and the computer program is adapted to be loaded and executed by a processor to perform the 5G-TSN asynchronous traffic mapping and scheduling optimization method of the first embodiment above.

[0142] The fourth embodiment of the present invention provides a computer device, including: a processor and a computer-readable storage medium;

[0143] The processor is adapted to execute a computer program;

[0144] The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, it implements the 5G-TSN asynchronous traffic mapping and scheduling optimization method as in the first embodiment above.

[0145] The fifth embodiment of the present invention provides a computer program product, and the computer program product includes a computer program, and when the computer program is executed by a processor, it implements the 5G-TSN asynchronous traffic mapping and scheduling optimization method as in the first embodiment above.

[0146] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0147] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0148] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction device that implements the functions specified in one or more processes and / or blocks Figure 1 in one or more processes and / or blocks Figure 1 of the function specified.

[0149] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, such that a series of operation steps are performed on the computer or other programmable apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one or more processes and / or blocks Figure 1 in one or more processes and / or blocks Figure 1 of the function specified.

[0150] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent replacements can still be made to the specific embodiments of the present invention. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. A 5G-TSN asynchronous traffic mapping and scheduling optimization method, characterized in that: include: Analyze 5G non-periodic time-sensitive service data based on TSN traffic virtualization, and calculate the service ingress port burst arrival time and traffic transmission path; The 5G non-periodic time-sensitive service is divided into different queues based on the burst arrival time, bandwidth requirement and end-to-end delay requirement of the service ingress port, and mapping rules between the 5G non-periodic time-sensitive service of different queues and the priority queue of the TSN domain are constructed; Based on the mapping rules, traffic virtualization is used to establish different virtualized sub-channels between 5G non-periodic time-sensitive services in different queues and the TSN domain, and 5G non-periodic time-sensitive services in different queues are mapped to the priority queues in the TSN domain; The service flows of the priority queues in the TSN domain are optimized and scheduled based on the asynchronous traffic shaper and queuing model.

2. A 5G-TSN asynchronous traffic mapping and scheduling optimization method according to claim 1, characterized in that: The mapping rule is expressed as: ， in, Indicates the mapping rule, , , Respectively represent the high, medium and low priorities of the TSN domain. represents the end-to-end delay requirement, is the critical delay of the time-sensitive flow, is the maximum latency allowed by TSN.

3. A 5G-TSN asynchronous traffic mapping and scheduling optimization method according to claim 2, characterized in that: The virtualized subchannel is represented as: ， in, Represents a virtualized subchannel, Indicates the 5G non-periodic time-sensitive service queue. Represents the priority queue of the TSN domain, Indicates the maximum tolerable delay of a time-sensitive flow.

4. A 5G-TSN asynchronous traffic mapping and scheduling optimization method according to claim 3, characterized in that: The method of optimizing and scheduling the service flow of the TSN domain priority queue based on the asynchronous traffic shaper and the queuing model includes: Based on the Kendall queuing model, the process of 5G non-periodic time-sensitive services being processed by the asynchronous traffic shaper is abstracted into an M / G / m / / FCFS queuing system, where M indicates that the time distribution of service flow arrivals follows an exponential distribution, G indicates that the service time distribution follows a general distribution, and m indicates the number of asynchronous traffic shapers currently providing services. Indicates the maximum service volume that a single asynchronous traffic shaper can carry per unit time. FCFS means the service policy is first-come, first-served. With the goal of minimizing the traffic loss rate of the queuing system, the service flow allocation problem of the asynchronous traffic shaper is established, which can be expressed as: ， in, To minimize the function, is the deterministic non-periodic traffic arrival rate, Indicates the average service rate of each business flow; The service flow allocation problem is solved to obtain the optimal configuration of each asynchronous traffic shaper, thereby completing the optimized scheduling of the service flow.

5. A 5G-TSN asynchronous traffic mapping and scheduling optimization method according to claim 4, characterized in that: The solution to the traffic flow allocation problem must meet the following constraints: ， ， ， ， : Each shape queue is associated with only one ingress port; : Each shape queue is associated with only one asynchronous traffic shaper internal priority; : Each shape queue is associated with only one priority in the previous hop; in, Indicates Business flow in the jump The maximum delay, , , Respectively represent business flows latency, jitter, and reliability budgets, , , They represent the network end-to-end delay, jitter and reliability budget respectively, represents a set of links, Indicates Business flow in the jump The maximum rate, Indicates All service flows in the jump The maximum frame length of the low priority service flow, Indicates business flow The minimum frame length, Indicates Business flow in the jump The maximum value of burst packets, Indicates Hopping and business flow A collection of service flows with the same priority. Indicates Hop ratio service flow A collection of high-priority business flows. Indicates link The channel capacity, Indicates business flow gather, Indicates Business flow in the jump rate.

6. A 5G-TSN asynchronous traffic mapping and scheduling optimization method according to claim 5, characterized in that: The constraints are optimized to: ， ， ， ， ， ， ， ， in, Indicates the upper limit of the number of service flows. Indicates the committed rate of the service flow. Indicates that the asynchronous traffic shaper is at priority The shape queues that are served simultaneously The average number of business flows, Indicates that the asynchronous traffic shaper is at priority The shape queues that are served simultaneously The average number of business flows, Indicates priority The perceived effective link capacity, represents the new decision variable, Indicates the maximum burst size of the service flow. Indicates the maximum frame length. represents the delay budget of the link, Indicates the global upper limit parameter for delay or jitter, is a binary variable, when the shape queue Assigned to priority 1 if yes, 0 otherwise. Indicates when the shape queue Assigned to priority The variable value at represents a set of shape queues, represents a set of priority queues, Indicates the total bandwidth capacity of the link.

7. A 5G-TSN asynchronous traffic mapping and scheduling optimization device, characterized in that: Used to implement the 5G-TSN asynchronous traffic mapping and scheduling optimization method according to any one of claims 1 to 6, the device includes: The analysis and calculation module is configured to: analyze 5G non-periodic time-sensitive service data based on TSN traffic virtualization, and calculate the service inlet port burst arrival time and traffic transmission path; A mapping rule construction module is configured to: divide the 5G non-periodic time-sensitive services into different queues based on the burst arrival time, bandwidth requirement and end-to-end delay requirement of the service ingress port, and construct mapping rules between the 5G non-periodic time-sensitive services of different queues and the priority queues of the TSN domain; A mapping module is configured to: based on the mapping rule, establish different virtualized sub-channels between 5G non-periodic time-sensitive services of different queues and the TSN domain by using traffic virtualization, and map the 5G non-periodic time-sensitive services of different queues to the priority queue of the TSN domain; The optimization scheduling module is configured to optimize the scheduling of the service flow of the TSN domain priority queue based on the asynchronous traffic shaper and the queuing model.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which is suitable for being loaded by a processor and executing the 5G-TSN asynchronous traffic mapping and scheduling optimization method as described in any one of claims 1 to 6.

9. A computer device, characterized in that: include: a processor and a computer readable storage medium; a processor adapted to execute a computer program; A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by the processor, the 5G-TSN asynchronous traffic mapping and scheduling optimization method as described in any one of claims 1 to 6 is implemented.

10. A computer program product, characterized in that The computer program product includes a computer program, and when the computer program is executed by a processor, it implements the 5G-TSN asynchronous traffic mapping and scheduling optimization method as described in any one of claims 1 to 6.

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