Bus network time-triggered traffic admission planning method considering rate-limited traffic

Abstract

The application relates to a bus network time-triggered traffic admission planning method considering rate-limited traffic, and belongs to the technical field of wired communication network resource dynamic scheduling and planning. The method comprises the following steps: obtaining an optional routing scheme based on an admission request of a new TT flow set; when a first routing scheme satisfying the condition that the end-to-end worst-case delay of each RC flow does not exceed the corresponding end-to-end delay deadline exists in the optional routing scheme, and a first scheduling scheme satisfying the scheduling constraint exists based on the first routing scheme, the first scheduling scheme is executed; otherwise, a second routing scheme in which the number of RC flows whose end-to-end worst-case delay exceeds the corresponding end-to-end delay deadline is as small as possible is selected from the optional routing scheme, and a second scheduling scheme satisfying the scheduling constraint exists based on the second routing scheme, and the second scheduling scheme is executed. The method greatly reduces the probability of causing the end-to-end delay of an RC flow to exceed the time limit and even causing communication interruption in the prior art when a new TT flow is admitted and planned.

Description

Technical Field

[0001] This invention belongs to the field of dynamic scheduling and planning technology of wired communication network resources, and particularly relates to a time-triggered traffic admission planning method for bus networks that takes into account rate-limited traffic. Background Technology

[0002] With the increasing trend of merging various integrated electronic system bus networks in aerospace, aviation, and automotive industries with Ethernet, software-defined networking is being introduced more and more into real-time control systems. This further enhances the network's explicit control over traffic and promotes research on dynamic planning and configuration of time-triggered network traffic, which previously required static verification and deployment.

[0003] Currently, research on dynamic programming for time-triggered networks (such as Time-Sensitive Networks, TSNs) that include mixed critical traffic with multiple priorities mainly focuses on time-triggered (TT) traffic, including responding to network component failures (such as links), switching network application migration decisions, end-to-end latency performance optimization, or incremental design of TT flows. These studies explore dynamic programming methods for TT flows from the perspective of many applications, which can improve the reusability, reliability, and performance of TSN networks.

[0004] However, even if scheduling verification is passed, reconfiguration schemes for TT flows (such as the addition of new traffic) may lead to changes in switch port load and network bandwidth resources, which in turn affect the end-to-end latency of rate-limited low-priority traffic (RC flows), and may even cause them to miss the worst deadline. This means that the quality of service of some relatively critical RC flows will be reduced or even communication continuity will be disrupted, resulting in resource waste or more serious consequences. Summary of the Invention

[0005] Based on the above analysis, the present invention aims to provide a bus network time-triggered traffic admission planning method that takes into account rate-limited traffic, minimizing the impact of dynamic planning of new TT flows to be admitted in the TSN network on RC flows, and avoiding the end-to-end delay problem of new TT flows affecting RC flows as much as possible.

[0006] This invention provides a time-triggered traffic admission planning method for bus networks that takes into account rate-limited traffic, specifically including the following steps:

[0007] Based on the admission requests received for the new TT stream set, obtain multiple optional routing schemes one by one;

[0008] If among the available routing schemes, there exists a first routing scheme that satisfies the worst-case end-to-end delay of each RC flow not exceeding the corresponding end-to-end delay period, and based on this first routing scheme, there exists a first scheduling scheme that satisfies the scheduling constraints, then the first scheduling scheme is executed; otherwise, the first scheduling scheme is executed.

[0009] Select a second routing scheme from the available routing schemes that minimizes the number of RC flows whose worst-case end-to-end delay exceeds the corresponding end-to-end delay period, and execute the second scheduling scheme if there exists a second scheduling scheme that satisfies the scheduling constraints based on the second routing scheme.

[0010] Furthermore, determining whether there exists a first routing scheme among the optional routing schemes that satisfies the worst-case end-to-end delay of each RC flow not exceeding the corresponding end-to-end delay period includes:

[0011] With the goal of optimizing the latency rate of each RC flow affected by the new TT flow, the optimal routing scheme is iteratively solved based on the available routing schemes;

[0012] In each iteration, based on the optimal routing scheme obtained in that iteration, it is determined whether the worst-case end-to-end delay of each RC flow does not exceed the corresponding end-to-end delay period:

[0013] If so, the preferred routing scheme obtained in this round of iteration is the first routing scheme;

[0014] If not, continue to the next iteration until the maximum number of iterations is reached.

[0015] Furthermore, using the particle swarm optimization algorithm to optimize the latency rate of each RC flow affected by the new TT flow, an optimal routing scheme is iteratively solved based on the available routing schemes, including:

[0016] The optional routing schemes are numbered;

[0017] The combination of routing schemes for each new TT stream is used as a particle, and the particle position is described by the combination of the corresponding numbers of the routing schemes for each new TT stream in each particle. The particle swarm is randomly initialized based on the multiple optional routing schemes.

[0018] A fitness function is constructed based on the delay rate and maximum bandwidth constraints of the affected RC streams;

[0019] In each iteration, the optimal solution for the particle swarm is obtained based on the fitness function, thus yielding the preferred routing scheme for that iteration.

[0020] Furthermore, the fitness function constructed based on the delay rate and maximum bandwidth constraint of the affected RC stream includes:

[0021] Construct an objective function based on the delay rate of the affected RC stream;

[0022] Construct a penalty function based on the maximum bandwidth constraint;

[0023] The fitness function is constructed based on the objective function, the penalty function, and the weight coefficients of the penalty function.

[0024] Furthermore, the objective function is expressed as:

[0025]

[0026] Where Q(X) is the objective function used to calculate the time delay rate of the affected RC flow; X is a c-dimensional vector representing the particle position, X = [x1, x2, ..., x...]. m ]; c is the number of the new TT streams; Indicates the number of RC flows affected; Let vl represent the worst-case end-to-end delay of the i-th RC stream, where vl i Represents the route of the i-th RC flow, e αβ Indicates connection to network node v α v β The directed edge, This indicates that any frame in the RC stream originates from network node v. α Transmitted to network node v β Worst time interval; dl i D represents the end-to-end delay duration of the i-th RC stream; ave This represents the average delay rate of all RC streams. F RC | indicates the total number of RC flows in the network.

[0027] Furthermore, the penalty function is expressed as:

[0028]

[0029] Where E represents the set of directed communication links between nodes in the network topology; vl j Represents the route of the new TT flow or RC flow in the network mentioned in the j-th clause; e represents the directed communication link between nodes in the network topology; l j p represents the length of the data stream frame in the new TT stream or RC stream in the network described in clause j; j Indicates the data flow period of the new TT flow or RC flow in the network described in clause j; C j This indicates the maximum bandwidth service rate of a directed link; This indicates that the expected TT flow and the RC flow in the network should meet the maximum bandwidth constraint.

[0030] Furthermore, the worst-case end-to-end delay of each RC stream is calculated as follows:

[0031] Based on the preferred routing scheme, calculate the worst transmission interval for each RC flow between every two nodes in the network link;

[0032] The worst-case delay of each RC stream is calculated based on all worst-case transmission intervals of each RC stream.

[0033] Furthermore, the calculation of the worst-case transmission interval between every two nodes of the network link for each RC flow based on the routing scheme includes:

[0034]

[0035] in, This indicates that any frame in the RC stream originates from network node v. α Transmitted to network node v β The worst time interval; and These represent any frame of the RC stream transmitted to network node v. β and v α The moment; This indicates the technical latency introduced by each data frame; This represents the delay impact of the TT stream on the RC stream, where F TT f represents the set of other TT streams that are not the new TT stream. k This indicates that the network is not the new TT stream mentioned above, but other TT streams. This indicates that any frame of the RC stream originates from network node v. α Transmitted to network node v β The time offset between, l k p represents the data stream frame duration of the new TT stream described in the kth stream. k This represents the new TT data flow period described in the k-th entry; This represents the delay effect of other RC flows on this RC flow, where F RC Denotes the set of RC streams, f l Represents the new TT stream, F TTRec Represents the new TT stream set, l l p represents the frame duration of the lth other RC stream. k Indicates the flow cycle of the lth other RC flow; This indicates the additional latency of the TT flow to the RC due to the preemptive integration strategy; i This indicates the frame duration of the RC stream.

[0036] Furthermore, the process of obtaining multiple alternative routing schemes based on the admission request of the received new TT stream set includes:

[0037] Based on the admission requests of the received new TT flow set and the network topology, multiple alternative routing schemes are calculated using Dijkstra's algorithm.

[0038] Furthermore, selecting a second routing scheme from the available routing schemes that minimizes the number of RC flows with end-to-end latency exceeding the corresponding worst-case latency includes:

[0039] The second routing scheme is solved iteratively based on the aforementioned preferred routing schemes. In each iteration:

[0040] Select the preferred routing scheme with the fewest number of RC flows whose end-to-end latency exceeds the corresponding worst-case latency;

[0041] Based on the preferred routing scheme and scheduling constraints, the second scheduling scheme is solved:

[0042] If successful, the preferred routing scheme becomes the second routing scheme, and the iteration ends;

[0043] If it fails, continue to the next iteration.

[0044] The present invention can achieve at least one of the following beneficial effects:

[0045] By considering the latency rate and maximum bandwidth constraints of the affected RC flows, the admission rules for new TT flows are determined, including routing and scheduling schemes. The dynamic planning of new TT flows to be admitted in the TSN network minimizes the impact on RC flows, greatly reducing the probability of new TT flows affecting the end-to-end latency of RC traffic, and avoiding secondary reconstruction of subsequent RC flows as much as possible from the source.

[0046] By modeling with particle swarm optimization algorithm, and considering the combination of different routing schemes for the new TT flow to be admitted, and whether the worst-case end-to-end delay of each RC flow does not exceed the corresponding end-to-end delay period, the optimal solution of the routing scheme and scheduling scheme of the new TT flow that meets the requirements can be quickly obtained.

[0047] Other features and advantages of the invention will be set forth in the following description, and some advantages may become apparent from the description or be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained from what is particularly pointed out in the description, claims, and drawings. Attached Figure Description

[0048] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0049] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0050] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0051] A specific embodiment of the present invention discloses a bus network time-triggered traffic admission planning method that takes into account rate-limited traffic, specifically including steps S01 to S03:

[0052] Step S01: Based on the admission requests of the received new TT stream set, obtain multiple optional routing schemes one by one;

[0053] Step S02: If there is a first routing scheme among the optional routing schemes that satisfies the worst-case end-to-end delay of each RC flow not exceeding the corresponding end-to-end delay period, and there is a first scheduling scheme that satisfies the scheduling constraints based on the first routing scheme, then execute the first scheduling scheme; otherwise, proceed to step S03.

[0054] Step S03: Select a second routing scheme from the available routing schemes that minimizes the number of RC flows whose worst-case end-to-end delay exceeds the corresponding end-to-end delay period, and execute the second scheduling scheme if there is a second scheduling scheme that satisfies the scheduling constraints based on the second routing scheme.

[0055] This embodiment discloses a bus network time-triggered traffic admission planning method that takes into account rate-limited traffic. It determines the admission rules for new TT flows by considering the impact of new TT flows to be admitted on the end-to-end delay of RC flows in the network, including routing schemes and scheduling schemes. This method minimizes the impact of dynamic planning of new TT flows to be admitted in the TSN network on RC flows and reduces the probability of new TT flows affecting the end-to-end delay of RC traffic as much as possible.

[0056] It should be noted that the bus network time-triggered traffic admission planning method that takes into account rate-limited traffic described in this invention adopts the IEEE 802.1Qbv-based TAS scheduling model, which includes time-triggered (TT) traffic, rate-limited (RC) traffic (such as audio / video AVB traffic), and traditional Ethernet best-effort (BE) traffic. Furthermore, this invention only considers TT and RC traffic. Without loss of generality, RC traffic in this invention is only periodically transmitted at the source system; no additional scheduling algorithms are executed at the ports of the passing switches, and only the basic first-in-first-out (FIFO) principle is followed. This assumption is reasonable and backward compatible with more complex scheduling algorithms, thus having the widest applicability.

[0057] Specifically, the TSN network topology is represented as a directed graph G(V,E), where V represents the set of network nodes of the terminal systems (ES) and switches (SW), and E represents the set of directed communication links between the network nodes. Let v1 and v2 represent two adjacent nodes, satisfying v1,v2∈V. Then [v1,v2] and [v2,v1] can be used to represent bidirectional data flow links. Edge e st It can be described as e st =[v s ,v t ]∈E,v s ,v t ∈V. Multi-priority traffic in Time-Sensitive Networking (TSN) primarily includes TT, Rate-Limited (RC), and Best-effort (BE) traffic. TT traffic is forwarded according to an offline scheduled timetable and has the highest priority. RC traffic is sent only during the source system's execution cycle and is rate-limited by the Bandwidth Allocation Gaps (BAG), thus its priority is lower than TT traffic. BE traffic is traditional Ethernet traffic with no QoS guarantees and has the lowest priority; it will not be discussed in this invention.

[0058] Furthermore, all communication tasks in the network are transmitted via data streams, including data frame instances. Let F = F TT ∪F RC Represents the set of all data streams. Any TT stream f m ∈F TT and arbitrary RC flow f n ∈F RC This can be represented by the following 7-tuples and 6-tuples respectively:

[0059] f m ={s m ,d m ,l m ,p m ,dl m ,vl m O m} and f n ={s n ,d n ,l n ,p n ,dl n ,vl n};

[0060] Where s m d m l m p m dl m and vl mThese represent the source ES, destination ES, frame length (frame length divided by link bandwidth) in the time dimension, traffic period, end-to-end (ETE) latency, and routing information, respectively. m Unique to TT traffic, this refers to the set of offsets for that TT flow between the first data frame instance and the start time of the period on each directed link. These offset sets for all TT flows collectively constitute the network's time scheduling table. Simply put, admission control and dynamic planning for TT flows mean reallocating routing information and corresponding offset times for TT flows.

[0061] Specifically, in one embodiment of the present invention, in step S01, when an admission request for a new TT flow set is received, multiple optional routing schemes are obtained based on the current TSN network topology.

[0062] Specifically, based on the admission requests of the received new TT flow set and the network topology, Dijkstra's algorithm is used to calculate k possible routing schemes (such as...). Figure 1 (As shown in reference numeral 1). Since using Dijkstra's algorithm to solve for alternative routing schemes is an existing technique, it will not be discussed further in this invention.

[0063] Furthermore, step S02 includes steps S021 to S022:

[0064] S021. Determine whether there is a first routing scheme among the optional routing schemes that satisfies the worst-case end-to-end delay of each RC flow not exceeding the corresponding end-to-end delay period. If yes, proceed to step S022; otherwise, proceed to step S03.

[0065] S022, Solve for the first scheduling scheme that satisfies the scheduling constraints based on the first routing scheme (e.g., ... Figure 1 As shown in number 4), if the solution is successful (e.g. Figure 1 If (as shown in number 5), then the first scheduling scheme (such as...) will be executed. Figure 1 (As shown in number 6), if the solution fails, proceed to step S03.

[0066] Furthermore, step S021 includes:

[0067] With the objective of optimizing the latency rate of each RC flow affected by the new TT flow, the optimal routing scheme (e.g., ...) is iteratively solved based on the available routing schemes. Figure 1 (As shown in reference number 2);

[0068] In each iteration, based on the optimal routing scheme obtained in that iteration, it is determined whether the worst-case end-to-end delay of each RC flow does not exceed the corresponding end-to-end delay period (e.g., ...). Figure 1 (As shown in reference number 3):

[0069] If so, the preferred routing scheme obtained in this round of iteration is the first routing scheme;

[0070] If not, continue to the next iteration (e.g.) Figure 1 (As shown in numbers 2 and 3), until the maximum number of iterations is reached (as shown in the figure). Figure 1 (As shown in reference number 8).

[0071] Furthermore, in step S021, the particle swarm optimization algorithm is used to iteratively solve for the optimal route scheme based on the available route schemes. The process of solving for the optimal route scheme using the particle swarm optimization algorithm includes:

[0072] Step-a: Number the k optional routing schemes;

[0073] Step-b: Using the combination of routing schemes for each new TT stream as particles, the particle position is described by the combination of the corresponding numbers of the routing schemes for each new TT stream in each particle, and the particle swarm is randomly initialized based on the multiple optional routing schemes.

[0074] Step-c: Construct a fitness function based on the delay rate and maximum bandwidth constraints of the affected RC streams;

[0075] Step-d: In each iteration, the optimal solution for the particle swarm is obtained based on the fitness function, and the preferred routing scheme for that iteration is recorded.

[0076] Furthermore, in step-a, the range of optional routing scheme numbers is from 1 to k.

[0077] Furthermore, in step-b, the position of each particle is represented as an M-dimensional vector X = [x1, x2, ..., x...]. M ], where x m (m∈[1,M]) represents the new TT flow f m The routing scheme number is an integer ranging from 1 to k. The particle velocity is represented as V = [v1, v2, ..., vk]. M ], where v m (m∈[1,M]) represents the distance the particle moves in the m-th dimension, with a value ranging from -(k-1) to (k-1).

[0078] Furthermore, in step-c, the fitness function is constructed based on the delay rate and maximum bandwidth constraint of the affected RC flow, including:

[0079] Construct an objective function based on the delay rate of the affected RC stream;

[0080] Construct a penalty function based on the maximum bandwidth constraint;

[0081] The fitness function is constructed based on the objective function, the penalty function, and the weight coefficients of the penalty function.

[0082] Furthermore, the objective function is expressed as:

[0083]

[0084] Where Q(X) is the objective function used to calculate the time delay rate of the affected RC flow; X is a c-dimensional vector representing the particle position, X = [x1, x2, ..., x...]. m ]; c is the number of the new TT streams; Indicates the number of RC flows affected; Let vl represent the worst-case end-to-end delay of the i-th RC stream, where vl i The route e represents the i-th RC flow. αβ Indicates connection to network node v α v β The directed edge, This indicates that any frame in the RC stream originates from network node v. α Transmitted to network node v β Worst time interval; dl i D represents the end-to-end delay duration of the i-th RC stream; ave This represents the average delay rate of all RC streams. F RC | indicates the total number of RC flows in the network.

[0085] Furthermore, the penalty function is expressed as:

[0086]

[0087] Where E represents the set of directed communication links between nodes in the network topology; vl j Represents the route of the new TT flow or RC flow in the network mentioned in the j-th clause; e represents the directed communication link between nodes in the network topology; l j p represents the length of the data stream frame in the new TT stream or RC stream in the network described in clause j; j Indicates the data flow period of the new TT flow or RC flow in the network described in clause j; C j This indicates the maximum bandwidth service rate of a directed link; This indicates that the expected TT flow and the RC flow in the network should meet the maximum bandwidth constraint.

[0088] Furthermore, a fitness function is constructed based on the objective function, penalty function, and weight coefficients of the penalty function to evaluate the performance of the routing scheme represented by particle positions. The lower the fitness function value, the better the performance of the selected route.

[0089] The fitness function is expressed as: F(X)=Q(X)+γE(X); where γ is the penalty weight, which varies depending on the topology and flow rate, and preferably is 300.

[0090] Furthermore, in step-d, the optimal solution for the particle swarm is sought based on the fitness function. Specifically, during the search for the optimal solution, the position and velocity of the particles are updated according to two values: the current optimal solution pBest for a single particle and the optimal solution gBest for all particles in the swarm. The search process is represented as:

[0091] V t+1 =ωV t +c1r1(pBest-X t )+c2r2(gBest-X t );

[0092] X t+1 =X t +V t ;

[0093] Where ω is the inertia weight, used to adjust the degree to which the particle maintains its original velocity, with a value ranging from 0 to 1, preferably set to 0.9; c1 and c2 represent self-learning and social learning factors, respectively, used to balance local and global search capabilities. c1 controls the speed and direction of the particle's self-updating within the search space; a larger value indicates a greater tendency to search along the direction of its historical best solution, while a smaller value indicates a greater tendency to explore randomly. c2 controls the speed at which the particle updates its search based on the best solutions of its neighboring particles; a larger value indicates a greater tendency to search under the influence of neighboring particles, while a smaller value indicates a greater tendency to search independently. 1 The sum of c1 and c2 is no greater than 4. Preferably, c1 and c2 are set to 2 and 0.9 respectively; r1 and r2 are random numbers between 0 and 1; t represents the number of searches in the solution process.

[0094] Furthermore, in step S021, in each iteration, based on the preferred routing scheme obtained in that iteration, it is determined whether the worst-case end-to-end delay of each RC flow does not exceed the corresponding end-to-end delay period.

[0095] The method for calculating the worst-case end-to-end delay of each RC stream is as follows:

[0096] Based on the preferred routing scheme, calculate the worst transmission interval for each RC flow between every two nodes in the network link;

[0097] The worst-case delay of each RC stream is calculated based on all worst-case transmission intervals of each RC stream.

[0098] Furthermore, the calculation of the worst transmission interval between every two nodes of the network link for each RC flow based on the preferred routing scheme includes:

[0099]

[0100] in, This indicates that any frame in the RC stream originates from network node v. α Transmitted to network node v β The worst time interval; and These represent any frame of the RC stream transmitted to network node v. β and v α The moment; This indicates the technical latency introduced by each data frame; This represents the delay impact of the TT stream on the RC stream, where F TT f represents the set of other TT streams that are not the new TT stream. k This refers to other TT streams that are not the new TT stream. This indicates that any frame of the RC stream originates from network node v. α Transmitted to network node v β The time offset between, l k p represents the data stream frame duration of the new TT stream described in the kth stream. k This represents the new TT data flow period described in the k-th entry; This represents the delay effect of other RC flows on this RC flow, where F RC Denotes the set of RC streams, f l Represents the new TT stream, F TTRec Represents the new TT stream set, l l p represents the frame duration of the lth other RC stream. k Indicates the flow cycle of the lth other RC flow; This indicates the additional latency of the TT flow to the RC due to the preemptive integration strategy; i This indicates the frame duration of the RC stream.

[0101] The following explains the principle of calculating the worst-case transmission interval between every two nodes in a network link for each RC flow:

[0102] In the method of this invention, it is reasonably assumed that all RC flows have passed the worst-case end-to-end delay verification during the design phase, i.e. (wcd nThis represents the worst-case end-to-end delay of the nth RC flow. When determining whether the worst-case end-to-end delay of each RC flow does not exceed its corresponding end-to-end delay period, it is only necessary to judge the RC flows whose routes intersect with the new TT flow. This is because, in delay analysis, the end-to-end delay of disjoint RC flows may change, but their worst-case end-to-end delay wcd remains unchanged (regardless of network calculus or delay calculation methods such as trajectory methods).

[0103] Furthermore, the end-to-end delay of RC streams, besides transmission delay, is mainly affected by four factors: ① related technology delays, such as delays caused by data validity and integrity checks between nodes, and delays caused by bandwidth allocation interval errors; ② delays caused by competition for bandwidth resources between RC streams; ③ delays caused by high-priority TT traffic to RC streams; ④ delays caused by the integration of TT and RC streams, which are mainly divided into three types according to different integration strategies: shuffling, preemption, and timely blocking. The method of this invention adopts the most common preemption strategy (this strategy is the most general, representative, and practical).

[0104] Furthermore, the end-to-end delay of an RC stream mainly consists of two parts: ① the queuing delay of the source ES; ② the delay of each intermediate node (link).

[0105] First, consider the queuing delay at the source ES. Specifically, the set of RC flows that intersect with the routes of the new TT flow is labeled F. RC Since the initial phase (offset) of the route corresponding to the RC flow can be allocated using a traditional offset allocation algorithm, therefore for Its vl i The initial offset is denoted as Furthermore, the transmission time when it leaves the end node and enters the network (denoted as...) The following formula can be used for calculation:

[0106]

[0107] Where FIFO(j,i) represents the TT flow f j The data frame precedes the RC stream f i The data frame enters the buffer; when FIFO(j,i) is empty. Therefore, the RC flow f entering the network i The offset can be represented by several possible discrete values.

[0108] Next, the delay calculation of each intermediate node (link) is considered. Specifically, this invention adopts an iterative strategy of overall timing analysis and uses the concept of busy period to analyze the delay of affected RC flows in the network. For any data frame of an RC flow, its busy period on a certain link can be defined as a time interval, that is, from the time the frame arrives at node v. α The frame is transmitted to the next node v. β The time interval is expressed as Define the frame arriving at node v α The frame is transmitted to the next node v. β time interval The end-to-end delay of the RC stream is then expressed as: To ensure that the worst-case end-to-end delay of each RC stream does not exceed the corresponding end-to-end delay period dl i Worst time interval Described as (for any known) ):

[0109]

[0110] in, This indicates the technical latency introduced by each data frame; This indicates the delay impact of the TT stream on the RC stream; This indicates the delay impact of other RC flows on this RC flow; This indicates the additional latency of the TT stream to the RC due to the preemptive integration strategy.

[0111] Since the above formula includes both sides The function is recursive, therefore a recursion tree method can be used to solve it. This yields... Indicates from From the moment it begins, link e αβ RC flow f i The worst-case end-to-end delay, which guarantees the transmission of all TT streams and other backlogged RC streams arriving earlier within that time period. Can be used as a vl i The start time of the next link is used to resolve the busy period until the result is obtained. until.

[0112] The above explains the principle of calculating the worst transmission interval between every two nodes in a network link for each RC flow.

[0113] Furthermore, in step S021, determining whether the worst-case end-to-end delay of each RC flow does not exceed the corresponding end-to-end delay period can be expressed as:

[0114]

[0115] Furthermore, in step S022, an integer linear programming solver is used to solve for a first scheduling scheme that satisfies the scheduling constraints based on the first routing scheme. The solver can be, for example, gurobi or cplex. Since both the solver and the solution method are existing technologies, they will not be discussed further here.

[0116] Furthermore, if the first scheduling scheme is successfully solved based on the first routing scheme, then the first scheduling scheme is dispatched (e.g., Figure 1 As shown in reference number 6, this is the new configuration scheme A (e.g. Figure 1 As shown in section 7, the configuration is assigned to the corresponding node in the network for switching to execute the first scheduling scheme. If this fails, it is determined whether the maximum number of iterations of the particle swarm optimization algorithm has been reached. If the maximum number of iterations has not been reached, the algorithm re-enters step S021 to solve for the first routing scheme in the next iteration; if the maximum number of iterations has been reached, it indicates that solving the first scheduling scheme has failed, and the algorithm proceeds to step S03. It should be noted that, generally, solving the first scheduling scheme based on the first routing scheme is related to network load, and the probability of failure in practical applications is extremely small.

[0117] Furthermore, in step S03, based on all the preferred routing schemes obtained by the particle swarm optimization algorithm in step S02, the second routing scheme is iteratively solved. Specifically, in each iteration:

[0118] The preferred routing scheme with the fewest RC flows whose end-to-end latency exceeds the corresponding worst-case latency is selected (e.g., ...). Figure 1 (As shown in the reference number 9);

[0119] Based on the preferred routing scheme and scheduling constraints, the second scheduling scheme is solved (e.g., ...). Figure 1 (As shown in the reference number 10):

[0120] If successful (e.g.) Figure 1 If the preferred routing scheme is the second routing scheme (as shown in reference numeral 11), then the iteration ends, and the second scheduling scheme is dispatched (e.g., ...). Figure 1 As shown in reference number 13, this is the new configuration scheme B (such as...). Figure 1 (As shown in reference number 14) is assigned to the corresponding node in the network for configuration switching to execute the second scheduling scheme;

[0121] If it fails, then determine whether all preferred routing schemes have been traversed (e.g., Figure 1 (As shown in number 12), if not, continue to the next iteration (as shown in number 12). Figure 1 (As shown in number 9); if so, it means that solving the second scheduling scheme failed and the new TT flow admission request was not approved (e.g. Figure 1 (As shown in reference number 15).

[0122] It should be noted that, since the solution of the second scheduling scheme based on the selected preferred routing scheme and scheduling constraints in each iteration is related to the network load, the probability of failure in solving the second scheduling scheme in practical applications is extremely small.

[0123] This embodiment discloses a bus network time-triggered traffic admission planning method that takes into account rate-limited traffic. By considering the latency rate and maximum bandwidth constraints of the affected RC flow, the method determines the admission rules for new TT flow, including routing and scheduling schemes. This minimizes the impact of dynamic planning of new TT flow to be admitted in the TSN network on RC flow, thus solving the problem of end-to-end latency of new TT flow affecting RC traffic.

[0124] By modeling with particle swarm optimization algorithm, and considering the combination of different routing schemes for the new TT flow to be admitted, and whether the worst-case end-to-end delay of each RC flow does not exceed the corresponding end-to-end delay period, the optimal solution of the routing scheme and scheduling scheme of the new TT flow that meets the requirements can be quickly obtained.

[0125] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A time-triggered traffic admission planning method for bus networks that takes into account rate-limited traffic, characterized in that, Includes the following steps: Based on the admission request received for the new TT stream set, obtain multiple routing schemes; Determining whether a first routing scheme exists among the multiple routing schemes that satisfies the condition that the end-to-end delay of each RC flow does not exceed the corresponding end-to-end delay period includes: iteratively solving for a target routing scheme based on the multiple routing schemes, with the objective of optimizing the delay rate of each RC flow affected by the new TT flow; in each iteration, judging whether the end-to-end delay of each RC flow does not exceed the corresponding end-to-end delay period based on the target routing scheme obtained in that iteration: if yes, then the target routing scheme obtained in that iteration is the first routing scheme; if not, then continue to the next iteration until the maximum number of iterations is reached; wherein, particle swarm optimization is used. The algorithm aims to optimize the latency rate of each RC flow affected by the new TT flow. It iteratively solves for the target routing scheme based on multiple routing schemes, including: numbering the multiple routing schemes; using combinations of routing schemes for each new TT flow as particles, describing the particle position using combinations of the corresponding numbers of the routing schemes for each new TT flow within each particle, and randomly initializing a particle swarm based on the multiple routing schemes; constructing a fitness function based on the latency rate of the affected RC flows and the maximum bandwidth constraint; and in each iteration, finding the optimal solution for the particle swarm based on the fitness function to obtain the target routing scheme for that iteration. If among the multiple routing schemes, there exists a first routing scheme that satisfies the condition that the end-to-end delay of each RC flow does not exceed the corresponding end-to-end delay period, and based on this first routing scheme, there exists a first scheduling scheme that satisfies the scheduling constraints, then the first scheduling scheme is executed; otherwise, the first scheduling scheme is executed. Select the second routing scheme from the plurality of routing schemes, which has the fewest RC flows with end-to-end delay exceeding the corresponding end-to-end delay period, and execute the second scheduling scheme if there is a second scheduling scheme that satisfies the scheduling constraints based on the second routing scheme.

2. The bus network time-triggered traffic admission planning method according to claim 1, characterized in that, The fitness function constructed based on the delay rate and maximum bandwidth constraints of the affected RC stream includes: Construct an objective function based on the delay rate of the affected RC stream; Construct a penalty function based on the maximum bandwidth constraint; The fitness function is constructed based on the objective function, the penalty function, and the weight coefficients of the penalty function.

3. The bus network time-triggered traffic admission planning method according to claim 2, characterized in that, The objective function is expressed as: ； in, Let X be the objective function used to calculate the time delay rate of the affected RC flow; X is a c-dimensional vector representing the particle position. c represents the number of new TT streams; Indicates the number of RC flows affected; Let represent the end-to-end delay of the i-th RC stream, where This represents the route for the i-th RC flow. Indicates connection to network nodes , The directed edge, This indicates that any frame in the RC stream originates from a network node. Transmitted to network node The time interval; This represents the end-to-end delay duration of the i-th RC stream; This represents the average delay rate of all RC streams. , This represents the total number of RC flows in the network.

4. The bus network time-triggered traffic admission planning method according to claim 3, characterized in that, The penalty function is expressed as: ； in, This represents the set of directed communication links between nodes in a network topology. This indicates the route of the new TT flow or RC flow in the network mentioned in the j-th clause; This represents a directed communication link between nodes in a network topology. This represents the length of the data stream frame of the new TT stream or RC stream in the network mentioned in clause j; This represents the data flow period of the new TT flow or RC flow in the network described in clause j; This indicates the maximum bandwidth service rate of a directed link; This indicates that the expected TT flow and the RC flow in the network should meet the maximum bandwidth constraint.

5. The bus network time-triggered traffic admission planning method according to claim 1, characterized in that, The method for calculating the end-to-end delay of each RC stream is as follows: Calculate the transmission interval of each RC flow between every two nodes in the network link based on the target routing scheme; The delay of the corresponding RC stream is calculated based on all transmission intervals of each RC stream.

6. The bus network time-triggered traffic admission planning method according to claim 5, characterized in that, Based on the target routing scheme, the transmission interval of each RC flow between every two nodes in the network link is calculated as follows: ； in, This indicates that any frame in the RC stream originates from a network node. Transmitted to network node The time interval; and These represent any frame of the RC stream transmitted to the network node. and The moment; This indicates the technical latency introduced by each data frame; This indicates the delay impact of the TT stream on the RC stream, where This represents the set of other TT streams that are not the new TT stream. This refers to other TT streams that are not the new TT stream. This indicates that any frame of the RC stream originates from a network node. Transmitted to network node The time offset between them This represents the data stream frame duration of the new TT stream described in the k-th stream. This represents the period of the new TT stream data stream described in the kth entry; This indicates the delay impact of other RC flows on this RC flow, where Represents a set of RC streams. This refers to the new TT stream. This represents the new TT stream set. Indicates the frame duration of the lth other RC stream. Indicates the period of the lth other RC stream data stream; This indicates the additional latency of the TT stream to the RC stream due to the preemptive integration strategy. This indicates the frame duration of the RC stream.

7. The bus network time-triggered traffic admission planning method according to any one of claims 1-6, characterized in that, The admission request based on the received new TT stream set, obtaining multiple routing schemes includes: Based on the admission requests of the received new TT flow set and the network topology, multiple routing schemes are calculated using Dijkstra's algorithm.

8. The bus network time-triggered traffic admission planning method according to claim 1, characterized in that, The second routing scheme, which selects the fewest RC flows whose end-to-end latency exceeds the corresponding end-to-end latency period from the plurality of routing schemes, includes: The second routing scheme is solved iteratively based on the aforementioned target routing schemes. In each iteration: Select the target routing scheme with the fewest number of RC flows whose end-to-end latency exceeds the corresponding delay; Based on the target routing scheme and scheduling constraints, the second scheduling scheme is solved: If successful, the target routing scheme becomes the second routing scheme, and the iteration ends; If it fails, continue to the next iteration.

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