Deterministic network time-triggered flow scheduling optimization method, apparatus and device, and medium

Abstract

The invention discloses a deterministic network time-triggered flow scheduling optimization method and device, equipment and a medium. The method comprises the following steps: generating a network directed graph according to a topological structure of a deterministic network; searching the shortest path of the time-triggered flow in the network directed graph by using a breadth-first search algorithm; initializing a grey wolf population; iteratively executing the following steps until a preset termination condition is met, and obtaining an updated final grey wolf population: calculating the fitness of grey wolf individuals, and determining the first three grey wolf individuals sorted according to the fitness values from large to small as the first wolf; for each grey wolf individual except the head wolf, updating the position of the grey wolf individual according to the distance between the grey wolf individual and the head wolf position so as to obtain a corresponding updated time-triggered flow scheduling mode; and determining the time-triggered flow scheduling mode corresponding to the grey wolf individual with the maximum fitness value in the updated final grey wolf population as the optimal scheduling mode. The scheduling of the time-triggered stream is effectively realized, and the requirement of real-time application is met.

Description

Technical Field

[0001] The present application belongs to the field of communication technology, and in particular, relates to a deterministic network time-triggered flow scheduling optimization method, device, equipment and medium. Background Art

[0002] Deterministic networking is an emerging communication technology that works at the data link layer and aims to ensure deterministic low-latency communication by implementing a series of standards. IEEE 802.1 Qbv specifies a time-aware shaper (TAS) for scheduling time-triggered flows (TT) and other traffic. The standard allows up to 8 transmission queues on each egress port of the switch and uses a gated list (GCL) to control the traffic transmission time of each queue. The core of its deterministic communication lies in its scheduling capability for TT traffic. TT traffic is a hard real-time traffic that needs to be transmitted with deterministic low latency and jitter.

[0003] At present, the research and use of TT flow scheduling in deterministic networks focuses on Satisfiability Model Theory (SMT), Integer Linear Programming (ILP) or Constraint Programming (CP). SMT, CP and ILP generally have high computational complexity, rely on the performance of the solver, have difficulty in handling large-scale problems, and have low scalability. As the network scale increases, the efficiency of their solutions will drop sharply. In addition, there are problems such as high computational complexity, long scheduling time, and difficulty in adapting to dynamic network reconfiguration. Summary of the invention

[0004] Embodiments of the present application provide a deterministic network time-triggered flow scheduling optimization method, apparatus, device, and medium.

[0005] In a first aspect, an embodiment of the present application provides a deterministic network time-triggered flow scheduling optimization method, including: According to the topological structure of the deterministic network, a network directed graph corresponding to the deterministic network topology is generated; Using a breadth-first search algorithm to find the shortest path from a source node to a destination node corresponding to each time-triggered flow in the network directed graph; Initialize a gray wolf population based on the shortest path of the time-triggered flow and pre-set gray wolf algorithm parameters, wherein the gray wolf population includes a plurality of gray wolf individuals, and the plurality of gray wolf individuals respectively represent a plurality of different time-triggered flow scheduling modes, and the gray wolf algorithm parameters include at least a population size, a constraint condition, and an objective function; Iterate the following steps A to C until the preset termination condition is met to obtain the updated final gray wolf population: Step A: Calculate the fitness of each individual gray wolf in the gray wolf population using a pre-constructed fitness function, wherein the fitness function is constructed based on the average end-to-end delay of the time-triggered flow; Step B: according to the fitness values ​​of the individual gray wolves, the first three gray wolf individuals in the gray wolf population, which are ranked in descending order according to the fitness values, are determined as alpha wolves; Step C: For each individual gray wolf in the gray wolf population except the alpha wolf, update its own position according to the distance between itself and the alpha wolf to obtain the corresponding updated time-triggered flow scheduling mode; The time-triggered flow scheduling method corresponding to the gray wolf individual with the largest fitness value in the updated final gray wolf population is determined as the optimal scheduling method.

[0006] In a second aspect, an embodiment of the present application provides a deterministic network time-triggered flow scheduling optimization device, the device comprising: A generation module, used for generating a network directed graph corresponding to the deterministic network topology according to the topological structure of the deterministic network; A search module, used for searching the shortest path from the source node to the destination node corresponding to each time-triggered flow in the network directed graph by using a breadth-first search algorithm; An initialization module is used to initialize a gray wolf population based on the shortest path of the time-triggered flow and pre-set gray wolf algorithm parameters, wherein the gray wolf population includes a plurality of gray wolf individuals, and the plurality of gray wolf individuals respectively represent a plurality of different time-triggered flow scheduling modes, and the gray wolf algorithm parameters include at least a population size, a constraint condition, and an objective function; The iteration module is used to iteratively execute the following steps A to C until the preset termination condition is met to obtain the updated final gray wolf population: Step A: Calculate the fitness of each individual gray wolf in the gray wolf population using a pre-constructed fitness function, wherein the fitness function is constructed based on the average end-to-end delay of the time-triggered flow; Step B: according to the fitness values ​​of the individual gray wolves, the first three gray wolf individuals in the gray wolf population, which are ranked in descending order according to the fitness values, are determined as alpha wolves; Step C: For each individual gray wolf in the gray wolf population except the alpha wolf, update its own position according to the distance between itself and the alpha wolf to obtain the corresponding updated time-triggered flow scheduling mode; The determination module is used to determine the time-triggered flow scheduling method corresponding to the gray wolf individual with the largest fitness value in the updated final gray wolf population as the optimal scheduling method.

[0007] In a third aspect, an embodiment of the present application provides an electronic device comprising: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, the steps of the deterministic network time-triggered flow scheduling optimization method as described in any one of the embodiments of the first aspect are implemented.

[0008] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the steps of the deterministic network time-triggered flow scheduling optimization method as described in any one of the embodiments of the first aspect are implemented.

[0009] In a fifth aspect, an embodiment of the present application provides a computer program product, which is stored in a storage medium and executed by at least one processor to implement the steps of the deterministic network time-triggered flow scheduling optimization method provided in the first aspect of the embodiment of the present application.

[0010] The deterministic network time-triggered flow scheduling optimization method, device, equipment and medium of the embodiment of the present application simulates the hunting behavior of gray wolves, has a strong global search capability, can effectively explore the solution space and find the optimal solution; can adapt to different types of optimization problems, including continuous and discrete problems, and has a wide range of application potential; has low computational complexity, can find a better solution within a reasonable time, and is suitable for large-scale problems. Therefore, the traffic scheduling mechanism of the deterministic network can ensure the priority and transmission time of the time-triggered flow in the network, thereby optimizing the utilization of network resources, reducing transmission delays, improving overall network performance, and meeting the needs of real-time applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solution of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0012] Figure 1 It is a flow chart of a deterministic network time-triggered flow scheduling optimization method provided by an embodiment of the present application; Figure 2 It is a flowchart of a method for searching all possible paths of a time-triggered flow provided in an embodiment of the present application; Figure 3 It is a flow chart of another deterministic network time-triggered flow scheduling optimization method provided by an embodiment of the present application; Figure 4 It is a structural diagram of a deterministic network time-triggered flow scheduling optimization device provided in an embodiment of the present application; Figure 5 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0013] Reference numerals: Deterministic network time-triggered flow scheduling optimization device 400, generation module 401, search module 402, initialization module 403, iteration module 404, determination module 405, Electronic device 500 , processor 501 , memory 502 , communication interface 503 , bus 510 . DETAILED DESCRIPTION

[0014] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by illustrating the examples of the present application.

[0015] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0016] Deterministic services have the characteristics of large bandwidth, high reliability, and low latency, and require the introduction of reliable communication methods to solve service access. Traditional network technologies (such as Ethernet and industrial fieldbus) have many shortcomings in providing deterministic communication services. Unpredictable delays may occur under high load conditions, which cannot meet the needs of real-time applications. The lack of interoperability between different industrial fieldbus standards limits the integration of devices and systems. Existing network technologies fail to provide a unified deterministic communication solution for deterministic services.

[0017] Deterministic networking is a network technology that focuses on the predictability and reliability of communications, ensuring that the delay and fluctuation of data transmission in the network are controllable, and can achieve deterministic latency, jitter, packet loss rate and bandwidth in information transmission. In order to promote the development of new automation systems, key deterministic network technologies are introduced into automation systems to achieve high reliability, low latency and intelligent development of automation systems.

[0018] Deterministic networking is an emerging communication technology that works at the data link layer and aims to ensure deterministic low-latency communication by implementing a series of standards. IEEE 802.1 Qbv specifies a time-aware shaper (TAS) for scheduling time-triggered flows (TT) and other traffic. The standard allows up to 8 transmission queues on each egress port of the switch and uses a gated list (GCL) to control the traffic transmission time of each queue. The core of its deterministic communication lies in its scheduling capability for TT traffic. TT traffic is a hard real-time traffic that needs to be transmitted with deterministic low latency and jitter.

[0019] At present, the research and use of TT flow scheduling in deterministic networks focuses on Satisfiability Model Theory (SMT), Integer Linear Programming (ILP) or Constraint Programming (CP). SMT, CP and ILP generally have high computational complexity, rely on the performance of the solver, have difficulty in handling large-scale problems, and have low scalability. As the network scale increases, the efficiency of their solutions will drop sharply. In addition, there are problems such as high computational complexity, long scheduling time, and difficulty in adapting to dynamic network reconfiguration.

[0020] In order to solve the problems of related technologies, the embodiments of the present application provide a deterministic network time-triggered flow scheduling optimization method, device, equipment and medium.

[0021] The deterministic network time-triggered flow scheduling optimization method provided by the embodiment of the present application is described in detail below with reference to the accompanying drawings through specific embodiments and their application scenarios.

[0022] Figure 1 FIG. 1 is a flow chart showing a method for optimizing the scheduling of a deterministic network time-triggered flow according to an embodiment of the present application. Figure 1 As shown, the deterministic network time-triggered flow scheduling optimization method may specifically include the following steps: S101, generating a network directed graph corresponding to the deterministic network topology according to the topological structure of the deterministic network; S102, using a breadth-first search algorithm to find the shortest path from the source node to the destination node corresponding to each time-triggered flow in the network directed graph; S103, based on the shortest path of the time-triggered flow and pre-set gray wolf algorithm parameters, initialize a gray wolf population, the gray wolf population includes a plurality of gray wolf individuals, the plurality of gray wolf individuals respectively represent a plurality of different time-triggered flow scheduling modes, the gray wolf algorithm parameters include at least a population size, constraints and an objective function; S104, iteratively perform the following steps A to C until a preset termination condition is met to obtain an updated final gray wolf population: Step A: Calculate the fitness of each individual gray wolf in the gray wolf population using a pre-constructed fitness function, wherein the fitness function is constructed based on the average end-to-end delay of the time-triggered flow; Step B: according to the fitness values ​​of the individual gray wolves, the first three gray wolf individuals in the gray wolf population, which are ranked in descending order according to the fitness values, are determined as alpha wolves; Step C: For each individual gray wolf in the gray wolf population except the alpha wolf, update its own position according to the distance between itself and the alpha wolf to obtain the corresponding updated time-triggered flow scheduling mode; S105. Determine the time-triggered flow scheduling method corresponding to the gray wolf individual with the largest fitness value in the updated final gray wolf population as the optimal scheduling method.

[0023] Therefore, according to the transmission path and start transmission time of each flow in the network, a queue-free delay calculation method is adopted to ensure that the transmission time slots of each flow do not conflict with each other on each link and in each super cycle. This mechanism effectively realizes the scheduling of flows, optimizes the utilization of network resources, reduces transmission delays, and improves the overall network performance. In addition, the hunting behavior of gray wolves is simulated, with strong global search capabilities, which can effectively explore the solution space and find the optimal solution; it can adapt to different types of optimization problems, including continuous and discrete problems, and has a wide range of application potential; the computational complexity is low, and it can find a better solution within a reasonable time, which is suitable for large-scale problems and suitable for rapid application of various optimization problems.

[0024] Table 1 below shows the meaning of each parameter in the formula below.

[0025] Table 1

[0026] It should be noted that, in this embodiment, each time trigger flow is defined is a nine-tuple, including its period , length, or window size required to transmit a frame , Deadline , frame priority , source node , destination node , frame fitness , the number of links in the route and a collection containing the route and the scheduling information of each link in the route Nine elements. That is: .

[0027] It should be noted that for each scheduling , including three parameters, described as a triple . Among them, sz represents the routing links allocated in reverse order, that is, For the destination link, is the source link; It is expressed as the number of instances of frames in the link; Expressed as an offset for each instance from the start of the instance's period.

[0028] The specific implementation methods of the above steps are introduced below.

[0029] In some embodiments, in S101, the topological structure information of the deterministic network and the basic information of all time-triggered flows and the set of all links in the deterministic network are obtained, so as to abstract the deterministic network topology into a network directed graph using the graph representation method in graph theory according to the obtained information. Specifically, the network directed graph Include Vertex Set and edge set : The vertex set Characterizing a Deterministic Network End Device Set With deterministic network switch collection The union of can be expressed as , here The symbol represents the union of sets, which means V Included ES and SW All elements in , excluding duplicates; the edge set Characterizing several links in a deterministic network .

[0030] Each link It is a unidirectional link, and the link corresponds to a predefined identifier one by one, that is, it is defined by its unique identifier. It should also be understood that since the transmission link in the deterministic network is full-duplex, for the communication between any two nodes in the deterministic network, there are two independent links in opposite directions, which are used for data transmission in opposite directions respectively.

[0031] In some embodiments, in S102, a breadth-first search algorithm (BFS) is used to find all transmission paths from the source node to the destination node corresponding to each time-triggered flow in the network directed graph; the cost function values ​​of all transmission paths of the time-triggered flow are calculated, and the cost function is minimized to obtain the shortest path of the time-triggered flow.

[0032] refer to Figure 2 , is a flowchart of the specific steps for finding all possible paths of the time-triggered flow. Figure 2 As shown, for each source node in the set F, a queue Q and a set visited are constructed; the source node is inserted into the queue and marked as visited; when the queue Q is not empty, a node is taken out of the queue and processed (the path is recorded); all neighbor nodes of the current node are traversed: if the neighbor node has not been visited, it is added to the queue and marked as visited; when the queue is empty, the path is stored; thus, all possible paths to the nodes in the set F are returned.

[0033] Optionally, as shown in Table 2 below, the specific algorithm logic for the above-mentioned steps of searching for all possible paths of the time trigger flow may include: Table 2

[0034] In this case, we traverse the graph starting from the source node using the breadth-first search algorithm. , use a queue to store the nodes to be visited, use the set visited to mark the visited nodes to avoid repeated visits, and add the traversed nodes to the paths list in order, and finally return the list. That is, for each source node in the set F, calling the bfs function can find all possible paths starting from the source node, add the path of each source node to the all_paths list, and finally return the list.

[0035] Further, as an optional embodiment, the cost function values ​​of all transmission paths of the time-triggered flow can be calculated according to the following formulas (1) and (2), and the cost function can be minimized to obtain the shortest path of the time-triggered flow: ; (1) ; (2) in, Indicates A time-triggered flow; Represents the set of all time-triggered flows; Indicates i Time-triggered stream The source node of Indicates i Time-triggered stream The destination node; Indicates that the link exists, 1 if it exists, and 0 if it does not exist; Represents the cost function of each flow path.

[0036] In this way, based on the cost function of the shortest path of each TT flow in formulas (1) and (2), the paths that the flow passes through are summed up and expressed as a cost function. The cost function value is calculated for all possible paths of the flow and minimized to find the shortest path that the flow passes through.

[0037] Furthermore, in some embodiments, corresponding objective functions and constraints are set according to network topology, TT flow parameters, and scheduling requirements.

[0038] Optionally, the objective function is: ; (3) in, Indicates i Time-triggered stream end-to-end delay; n represents the number of time-triggered flows. That is, the objective function is to minimize the end-to-end delay of the TT flow.

[0039] Optionally, the constraint conditions include a delay constraint, a time slot occupancy constraint, and a maximum transmission offset constraint; The delay constraint is: ; (4) in, Indicates i Time-triggered stream cycle; The time slot occupancy constraint is: ; (5) in, Indicates i Time-triggered stream The number of links traversed; Indicates i Time-triggered stream The length of the transmission window or the required transmission window size; The maximum transmission offset constraint is: ; (6) in, Represents the collection of all time-triggered flows F Trigger flow at any time in , the offset of each time-triggered stream.

[0040] That is, the delay constraint is that the TT flow must complete the transmission within its cycle; the time slot occupancy constraint is to determine whether the TT flow can be optimized; the maximum transmission offset constraint is that there must be a maximum offset for each flow In this embodiment, the maximum transmission offset allowed for each flow is defined by using the flow period. The set F of flows, the offset of the flow Less than or equal to the period of the stream .

[0041] Therefore, the gray wolf population and population parameters can be initialized according to the shortest path of all TT flows by the objective function and the constraint conditions, that is, S103 is executed.

[0042] It should be noted that, in this embodiment, multiple gray wolf individuals in the gray wolf population respectively represent multiple different time-triggered flow scheduling modes.

[0043] Furthermore, in some embodiments, the fitness function is designed based on the average end-to-end delay and link utilization of the TT flow. , used to evaluate the scheduling priority of TT flows and thus define the position of each TT flow scheduling scheme in the scheduling scheme, that is, to initialize the head wolf (α, β, δ wolf) in the gray wolf population, which is used to guide the movement of other wolves (i.e., position update) in the subsequent iteration process. Therefore, the TT flow scheduling with low fitness is subject to the TT flow scheduling with high fitness, and is optimized and improved.

[0044] In specific implementation, the fitness function can be constructed according to the following formula (7): ; (7) in, Indicates i Time-triggered stream Adaptability; represents the average end-to-end delay; represents the number of cycles in which time slots exist; represents the cumulative sum of all unused time slots; Indicates i Time-triggered stream cycle; 、 is the adjustment factor.

[0045] Understandably, the adjustment factor 、 The role of is to balance the impact of time delay and time slot in the fitness evaluation process. Specifically, the coefficient is selected based on the numerical range of time delay and time slot, in order to ensure that these two factors can be fairly compared in the fitness evaluation.

[0046] Further, in some embodiments, in S104, after initializing α, β, and δ wolves according to the fitness function, the fitness value is calculated to evaluate whether each population individual can be optimized, that is, to traverse and determine whether each flow scheduling scheme can be optimized, and if it can be optimized, the optimizable scheduling scheme is updated; the scheduling scheme is iteratively adjusted, that is, after traversing each TT flow, the fitness value is returned to be evaluated, and α, β, and δ wolves are selected, and the steps of traversing and optimizing each flow scheduling scheme are repeated until the preset scheduling end condition is met, and the iterative adjustment of the scheduling scheme is terminated. Finally, all solutions in the solution space are traversed, and the scheduling solution that minimizes the cost value of the scheduling objective function is output, and the scheduling result is output.

[0047] Optionally, according to the following formula (8), for each individual gray wolf in the gray wolf population except the alpha wolf, the position of each individual gray wolf can be updated according to the distance between the individual individual and the alpha wolf to obtain the corresponding updated time-triggered flow scheduling mode, so as to realize the updated optimizable scheduling scheme: ; (8) in, Indicates i The position of the time-triggered stream in the next generation after optimization; , , They represent the positions of the three time-triggered flow scheduling time slots corresponding to the alpha, beta, and delta wolves in the iteration process respectively; Represents the coefficient that changes with the number of iterations and is used to control the search range; , , Respectively represent i The distance between the scheduling time slot of a time-triggered flow and the three time-triggered flows corresponding to α, β, and δ.

[0048] Optionally, as shown in Table 3 below, the specific algorithm logic for determining the optimal scheduling method may include: Table 3

[0049] It can be seen that the network topology information, TT flow information and the possible path of each TT flow are obtained; each TT flow is traversed, and the cost of each flow is calculated, and the TT flows are sorted according to the cost to determine the shortest path of each TT flow; the population is initialized and optimized; all TT flows are traversed, the delay of each flow is accumulated, and the total end-to-end delay of all TT flows is obtained; the constraints are defined, including delay constraints, time slot occupancy constraints and maximum offset constraints; the objective function is defined as minimizing the end-to-end delay; when the pre-set termination conditions are not met, the scheduling scheme is iteratively adjusted: each individual in the population is traversed, its fitness is calculated, and the best three wolves (α, β, δ) are selected to determine whether the individual can be optimized. If it can be optimized, its scheduling scheme is updated, that is, after selecting the best three wolves, the positions of all individuals in the population are updated, specifically by calculating the position of the next generation of the TT flow and updating the position of the flow, so that the scheduling scheme that can be optimized is updated; when the termination conditions are met, the optimal scheduling scheme is output. This method has strong global search capabilities and fast convergence speed.

[0050] In this way, by initializing the population, selecting the leader, evaluating and updating the fitness, and updating the position (scheduling plan), after judging that the termination conditions are met, the optimal traffic scheduling plan can be finally determined, and the scheduling plan for the TT flow can be optimized, thereby reducing end-to-end delays, ensuring the real-time and reliability of communication between industrial equipment, and thus improving the production efficiency and stability of the entire industrial automation system.

[0051] also, Figure 3 Another deterministic network time-triggered flow scheduling optimization method provided by an embodiment of the present application is shown. Figure 3 As shown in the figure, another method for optimizing the scheduling of time-triggered flow in a deterministic network specifically includes the following steps: first, initialize the network topology, abstract the deterministic network topology into a network directed graph, and find the shortest path from the source node to the destination node for each flow; initialize the population and parameters, determine the parameters of the gray wolf algorithm, including the population size, constraints, maximum number of iterations and exploration factor, and randomly generate a group of gray wolf individuals to represent the scheduling of different flows; then, according to the defined fitness function, evaluate the fitness of each gray wolf individual, the fitness value reflects whether the scheduling scheme can be optimized, and save the three values ​​with the best fitness as α, β, δ (leader); according to the position of the leader and the positions of other individuals, adjust the position of each individual to explore a better scheduling scheme; repeat the fitness evaluation and position update, gradually optimize the flow scheduling scheme, and stop the algorithm when the scheduling end condition is met (for example, the maximum number of iterations is reached or the fitness change is less than the set threshold); select the individual with the highest fitness from the final gray wolf population as the optimal flow scheduling scheme, and output the scheduling result.

[0052] Therefore, the algorithm adopts a bionic optimization strategy to analyze the entire network topology from the perspective of the overall network and identify the key links that have the greatest impact on network performance. These links may be bandwidth-limited, delay-sensitive, or high-data-traffic links. The set of scheduling frames is analyzed, which usually contains multiple time-triggered (TT) frames that need to be transmitted within strict time constraints. For these TT frames and key links, the algorithm formulates a globally optimized scheduling strategy. The algorithm ensures the time sensitivity requirements of the frames and the scheduling decisions between different links to avoid network congestion and conflicts, and finally achieves the global optimal scheduling result.

[0053] It should be noted that the above describes some embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0054] Based on the same technical concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a deterministic network time-triggered flow scheduling optimization device 400.

[0055] like Figure 4 As shown, the deterministic network time-triggered flow scheduling optimization device 400 may include: A generating module 401 is used to generate a network directed graph corresponding to a deterministic network topology according to the topological structure of the deterministic network; A search module 402 is used to use a breadth-first search algorithm to search for the shortest path from the source node to the destination node corresponding to each time-triggered flow in the network directed graph; Initialization module 403, used for initializing a gray wolf population based on the shortest path of the time-triggered flow and pre-set gray wolf algorithm parameters, wherein the gray wolf population includes a plurality of gray wolf individuals, and the plurality of gray wolf individuals respectively represent a plurality of different time-triggered flow scheduling modes, and the gray wolf algorithm parameters include at least a population size, a constraint condition, and an objective function; Iteration module 404 is used to iteratively execute the following steps A to C until a preset termination condition is met to obtain an updated final gray wolf population: Step A: Calculate the fitness of each individual gray wolf in the gray wolf population using a pre-constructed fitness function, wherein the fitness function is constructed based on the average end-to-end delay of the time-triggered flow; Step B: according to the fitness values ​​of the individual gray wolves, the first three gray wolf individuals in the gray wolf population, which are ranked in descending order according to the fitness values, are determined as alpha wolves; Step C: For each individual gray wolf in the gray wolf population except the alpha wolf, update its own position according to the distance between itself and the alpha wolf to obtain the corresponding updated time-triggered flow scheduling mode; The determination module 405 is used to determine the time-triggered flow scheduling mode corresponding to the gray wolf individual with the largest fitness value in the updated final gray wolf population as the optimal scheduling mode.

[0056] Optionally, the network directed graph includes a vertex set and an edge set, the vertex set represents a union of a deterministic network terminal device set and a deterministic network switch set, and the edge set represents a number of links in the deterministic network; Each of the links is a unidirectional link, and the links correspond one-to-one to a predefined identifier; for communication between any two nodes in a deterministic network, there are two independent links in opposite directions, which are used for data transmission in opposite directions respectively.

[0057] In some embodiments, the search module 402 is specifically used to use a breadth-first search algorithm to search for all transmission paths from the source node to the destination node corresponding to each time-triggered flow in the network directed graph; calculate the cost function value of all transmission paths of the time-triggered flow according to the following formula, and minimize the cost function to obtain the shortest path of the time-triggered flow:

[0058] ; in, Indicates A time-triggered flow; Represents the set of all time-triggered flows; Indicates i Time-triggered stream The source node of Indicates i Time-triggered stream The destination node; Indicates that the link exists (1 if it exists, 0 if it does not exist); Represents the cost function of each flow path.

[0059] Optionally, the objective function is: ; in, Indicates i Time-triggered stream end-to-end delay;n Indicates the number of time-triggered flows; The constraints include delay constraints, time slot occupancy constraints and maximum transmission offset constraints; The delay constraint is: ; in, Indicates i Time-triggered stream cycle; The time slot occupancy constraint is: ; in, Indicates i Time-triggered stream The number of links traversed; Indicates i Time-triggered stream The length of the transmission window or the required transmission window size; The maximum transmission offset constraint is: ; in, Represents the collection of all time-triggered flows F Trigger flow at any time in , the offset of each time-triggered stream.

[0060] In some embodiments, the deterministic network time-triggered flow scheduling optimization device 400 further includes a construction module ( Figure 4 ), used to construct the fitness function according to the following formula: ; in, Indicates i Time-triggered stream Adaptability; represents the average end-to-end delay; represents the number of cycles in which time slots exist; represents the cumulative sum of all unused time slots; Indicates i Time-triggered stream cycle; , is the adjustment factor.

[0061] As an optional embodiment, the iteration module 404 is specifically configured to perform updating according to the following formula: ; in, Indicates i The position of the time-triggered stream in the next generation after optimization; , , They represent the positions of the three time-triggered flow scheduling time slots corresponding to the alpha, beta, and delta wolves in the iteration process respectively; Represents the coefficient that changes with the number of iterations and is used to control the search range; , , Respectively represent i The distance between the scheduling time slot of a time-triggered flow and the three time-triggered flows corresponding to α, β, and δ.

[0062] It should be noted that, for the convenience of description, the above device is described in various modules according to their functions. Of course, when implementing this application, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0063] The device of the above embodiment is used to implement the corresponding deterministic network time-triggered flow scheduling optimization method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0064] Based on the same technical concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides an electronic device.

[0065] Figure 5 A more specific schematic diagram of the hardware structure of an electronic device provided by this embodiment is shown.

[0066] The electronic device 500 may include a processor 501 and a memory 502 storing computer program instructions.

[0067] Specifically, the processor 501 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0068] The memory 502 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 502 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, the memory 502 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 502 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 502 is a non-volatile solid-state memory.

[0069] In certain embodiments, the memory may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage media device, an optical storage media device, a flash memory device, an electrical, optical or other physical / tangible memory storage device. Thus, typically, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to an aspect of the present application.

[0070] The processor 501 reads and executes the computer program instructions stored in the memory 502 to implement any one of the deterministic network time-triggered flow scheduling optimization methods in the above embodiments.

[0071] In some examples, the electronic device 500 may further include a communication interface 503 and a bus 510. Figure 5 As shown, the processor 501, the memory 502, and the communication interface 503 are connected via a bus 510 and communicate with each other.

[0072] The communication interface 503 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0073] Bus 510 includes hardware, software or both, and couples the components of online data traffic billing equipment to each other. For example, but not limitation, bus 510 may include accelerated graphics port (AGP) or other graphics bus, enhanced industrial standard architecture (EISA) bus, front-side bus (FSB), hypertransport (HT) interconnection, industrial standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnect (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. Where appropriate, bus 510 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the present application considers any suitable bus or interconnection.

[0074] Exemplarily, the electronic device 500 may be a mobile phone, a tablet computer, a laptop computer, a PDA, an in-vehicle electronic device, an ultra-mobile personal computer (UMPC), a netbook, or a personal digital assistant (PDA).

[0075] Based on the same technical concept, corresponding to any of the above-mentioned embodiments and methods, the present application also provides a non-transitory computer-readable storage medium. Computer program instructions are stored on the computer-readable storage medium; when the computer program instructions are executed by the processor, any of the deterministic network time-triggered flow scheduling optimization methods in the above-mentioned embodiments is implemented. Examples of computer-readable storage media include non-transitory computer-readable storage media, such as portable disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, etc.

[0076] Based on the same technical concept, corresponding to any of the above-mentioned embodiments, the present application also provides a computer program product, which includes computer program instructions. In some embodiments, the computer program instructions can be executed by one or more processors of a computer so that the computer and / or the processor execute the deterministic network time-triggered flow scheduling optimization method. Corresponding to the execution subject corresponding to each step in each embodiment of the deterministic network time-triggered flow scheduling optimization method, the processor that executes the corresponding step can belong to the corresponding execution subject.

[0077] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0078] The functional blocks shown in the structural block diagram described above can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application-specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier. "Machine-readable medium" may include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROMs, flash memories, erasable ROMs (EROMs), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0079] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0080] The above describes various aspects of the present application with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the functions / actions specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It can also be understood that each box in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs a specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0081] The above is only a specific implementation of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present application is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the protection scope of this application.

Claims

1. A deterministic network time-triggered flow scheduling optimization method, characterized in that: include: According to the topological structure of the deterministic network, a network directed graph corresponding to the deterministic network topology is generated; Using a breadth-first search algorithm to find the shortest path from a source node to a destination node corresponding to each time-triggered flow in the network directed graph; Initialize a gray wolf population based on the shortest path of the time-triggered flow and pre-set gray wolf algorithm parameters, wherein the gray wolf population includes a plurality of gray wolf individuals, and the plurality of gray wolf individuals respectively represent a plurality of different time-triggered flow scheduling modes, and the gray wolf algorithm parameters include at least a population size, a constraint condition, and an objective function; Iterate the following steps A to C until the preset termination condition is met to obtain the updated final gray wolf population: Step A: Calculate the fitness of each individual gray wolf in the gray wolf population using a pre-constructed fitness function, wherein the fitness function is constructed based on the average end-to-end delay of the time-triggered flow; Step B: according to the fitness values ​​of the individual gray wolves, the first three gray wolf individuals in the gray wolf population, which are ranked in descending order according to the fitness values, are determined as alpha wolves; Step C: For each individual gray wolf in the gray wolf population except the alpha wolf, update its own position according to the distance between itself and the alpha wolf to obtain the corresponding updated time-triggered flow scheduling mode; The time-triggered flow scheduling method corresponding to the gray wolf individual with the largest fitness value in the updated final gray wolf population is determined as the optimal scheduling method.

2. The method according to claim 1, characterized in that The method of using a breadth-first search algorithm to find the shortest path from a source node to a destination node corresponding to each time-triggered flow in the network directed graph includes: Using a breadth-first search algorithm to find all transmission paths from a source node to a destination node corresponding to each time-triggered flow in the network directed graph; The cost function values ​​of all transmission paths of the time-triggered flow are calculated according to the following formula, and the cost function is minimized to obtain the shortest path of the time-triggered flow: ； ； in, Indicates A time-triggered flow; Represents the set of all time-triggered flows; Indicates i Time-triggered stream The source node of Indicates i Time-triggered stream The destination node; Indicates that the link exists, 1 if it exists, and 0 if it does not exist; Represents the cost function of each flow path.

3. The method according to claim 1, characterized in that The objective function is: ; in, Indicates i Time-triggered stream end-to-end delay; n Indicates the number of time-triggered flows; The constraints include delay constraints, time slot occupancy constraints and maximum transmission offset constraints; The delay constraint is: ; in, Indicates i Time-triggered stream cycle; The time slot occupancy constraint is: ; in, Indicates i Time-triggered stream The number of links traversed; Indicates i Time-triggered stream The length of the transmission window or the required transmission window size; The maximum transmission offset constraint is: ; in, Represents the collection of all time-triggered flows F Trigger flow at any time in , the offset of each time-triggered stream.

4. The method according to claim 1, characterized in that: Before calculating the fitness of each individual gray wolf in the gray wolf population using a pre-constructed fitness function, the method further includes: The fitness function is constructed according to the following formula: ； in, Indicates i Time-triggered stream Adaptability; represents the average end-to-end delay; represents the number of cycles in which time slots exist; represents the cumulative sum of all unused time slots; Indicates i Time-triggered stream cycle; 、 is the adjustment factor.

5. The method according to claim 1, characterized in that: The method of updating the position of each individual gray wolf except the alpha wolf in the gray wolf population according to the distance between the individual gray wolf and the alpha wolf to obtain the corresponding updated time-triggered flow scheduling method includes: Update according to the following formula: ； in, Indicates i The position of the time-triggered stream in the next generation after optimization; , , They represent the positions of the three time-triggered flow scheduling time slots corresponding to the alpha, beta, and delta wolves in the iteration process respectively; Represents the coefficient that changes with the number of iterations and is used to control the search range; , , Respectively represent i The distance between the scheduling time slot of a time-triggered flow and the three time-triggered flows corresponding to α, β, and δ.

6. The method according to claim 1, characterized in that The network directed graph includes a vertex set and an edge set, the vertex set represents a union of a deterministic network terminal device set and a deterministic network switch set, and the edge set represents a plurality of links in the deterministic network; Each of the links is a unidirectional link, and the links correspond one-to-one to a predefined identifier; for communication between any two nodes in a deterministic network, there are two independent links in opposite directions, which are used for data transmission in opposite directions respectively.

7. A deterministic network time-triggered flow scheduling optimization device, characterized in that: The device comprises: A generation module, used for generating a network directed graph corresponding to the deterministic network topology according to the topological structure of the deterministic network; A search module, used for searching the shortest path from the source node to the destination node corresponding to each time-triggered flow in the network directed graph by using a breadth-first search algorithm; An initialization module is used to initialize a gray wolf population based on the shortest path of the time-triggered flow and pre-set gray wolf algorithm parameters, wherein the gray wolf population includes a plurality of gray wolf individuals, and the plurality of gray wolf individuals respectively represent a plurality of different time-triggered flow scheduling modes, and the gray wolf algorithm parameters include at least a population size, a constraint condition, and an objective function; The iteration module is used to iteratively execute the following steps A to C until the preset termination condition is met to obtain the updated final gray wolf population: Step A: Calculate the fitness of each individual gray wolf in the gray wolf population using a pre-constructed fitness function, wherein the fitness function is constructed based on the average end-to-end delay of the time-triggered flow; Step B: according to the fitness values ​​of the individual gray wolves, the first three gray wolf individuals in the gray wolf population, which are ranked in descending order according to the fitness values, are determined as alpha wolves; Step C: For each individual gray wolf in the gray wolf population except the alpha wolf, update its own position according to the distance between itself and the alpha wolf to obtain the corresponding updated time-triggered flow scheduling mode; The determination module is used to determine the time-triggered flow scheduling method corresponding to the gray wolf individual with the largest fitness value in the updated final gray wolf population as the optimal scheduling method.

8. An electronic device, characterized in that: The device comprises: a processor and a memory storing computer program instructions; when the processor calls the computer program instructions, the method for optimizing the deterministic network time-triggered flow scheduling is implemented as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer program instructions, and when the computer program instructions are called by the processor, the deterministic network time-triggered flow scheduling optimization method according to any one of claims 1 to 6 is implemented.

10. A computer program product, characterized in that When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the deterministic network time-triggered flow scheduling optimization method as described in any one of claims 1-6.

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