Traffic scheduling method, equipment, medium and product based on distributed network

Abstract

The present application provides a traffic scheduling method, device, medium and product based on a distributed network. According to an example of the present application, the method is applied to a traffic management device, and the method may include: receiving statistical data sent by each node device, the statistical data corresponding to each node device includes the upper limit of the sending bandwidth and the upper limit of the receiving bandwidth of each node device; based on the statistical data sent by each node device, logically abstracting each node device and converting it into a decision variable, constraint condition and objective function in a linear programming model; performing linear solution on the decision variable, objective function and constraint condition to generate a traffic scheduling strategy; sending the traffic scheduling strategy to each node device so that each node device performs traffic scheduling.

Description

Technical Field

[0001] The present application relates to the field of computer network technology, and in particular to a traffic scheduling method, device, medium and product based on a distributed network. Background Art

[0002] With the rapid development of the Internet, the management and optimization of network traffic has become increasingly important. A large amount of data transmission requires various network devices to process, but network resources are limited and cannot meet the needs of all users.

[0003] In the face of today's complex and ever-changing distributed network environment, the distributed network is composed of multiple nodes, which are connected together through the network to jointly complete network functions. The distributed network has good scalability and fault tolerance, can improve traffic distribution, and can adapt to the needs of large-scale networks.

[0004] Current network traffic scheduling methods based on distributed networks usually simply distribute network traffic to multiple nodes for processing and forwarding. Due to the lack of in-depth understanding and effective management of the complexity of distributed network nodes, network resources are often not fully utilized. For example, some nodes are overloaded while others are idle, or traffic is congested on some paths while idle on other paths. This not only reduces the overall performance of the network, but also affects the user's service quality experience. Summary of the invention

[0005] In order to overcome the problems existing in the related art, the present application provides a traffic scheduling method, device, medium and product based on a distributed network.

[0006] According to a first aspect of any embodiment of the present application, a traffic scheduling method based on a distributed network is provided, the method being applied to a traffic management device, the method comprising:

[0007] Receiving statistical data sent by each node device, wherein the statistical data corresponding to each node device includes a sending bandwidth upper limit and a receiving bandwidth upper limit of each node device;

[0008] Based on the statistical data sent by each node device, the each node device is logically abstracted and converted into decision variables, constraint conditions and objective functions in a linear programming model;

[0009] Performing linear solution on the decision variables, the objective function and the constraint conditions to generate a traffic scheduling strategy;

[0010] The traffic scheduling strategy is sent to each node device so that each node device performs traffic scheduling.

[0011] According to a second aspect of any embodiment of the present application, a traffic scheduling method based on a distributed network is provided, the method being applied to a node device, the method comprising:

[0012] Sending statistical data to a traffic management device so that the traffic management device performs logical abstraction conversion of the node device into decision variables, objective functions and constraints in a linear programming model, and performs linear solution on the decision variables, the objective function and the constraints to generate a traffic scheduling strategy, wherein the statistical data includes a sending bandwidth upper limit and a receiving bandwidth upper limit of the node device;

[0013] In response to receiving the traffic scheduling policy sent by the traffic management device, traffic scheduling is performed.

[0014] According to a third aspect of any embodiment of the present application, a traffic management device is provided, the traffic management device comprising:

[0015] A receiving module, used to receive statistical data sent by each node device, wherein the statistical data corresponding to each node device includes a sending bandwidth upper limit and a receiving bandwidth upper limit of each node device;

[0016] A conversion module, used to perform logical abstraction on each node device based on the statistical data sent by each node device, and convert the data into decision variables, constraint conditions and objective functions in a linear programming model;

[0017] A solution module, used for performing linear solution on the decision variables, the objective function and the constraint conditions to generate a traffic scheduling strategy;

[0018] The sending module is used to send the traffic scheduling strategy to each node device so that each node device performs traffic scheduling.

[0019] According to a fourth aspect of any embodiment of the present application, a node device is provided, the node device comprising:

[0020] A communication module is used to send statistical data to a traffic management device, so that the traffic management device performs a logical abstraction conversion of the node device into a decision variable, an objective function and a constraint condition in a linear programming model, and performs a linear solution on the decision variable, the objective function and the constraint condition to generate a traffic scheduling strategy, wherein the statistical data includes a sending bandwidth upper limit and a receiving bandwidth upper limit of the node device;

[0021] The scheduling module is used to perform traffic scheduling in response to receiving the traffic scheduling policy sent by the traffic management device.

[0022] According to a fifth aspect of any embodiment of the present application, there is provided an electronic device, including:

[0023] processor;

[0024] a memory for storing processor-executable instructions;

[0025] The processor executes the executable instructions to implement the method executed by the traffic management device or the method executed by the node device in any of the above-mentioned embodiments of the present application.

[0026] According to the sixth aspect of any embodiment of the present application, a computer-readable storage medium is provided, on which computer instructions are stored. When the instructions are executed by a processor, they implement the method executed by the traffic management device or the method executed by the node device in any embodiment of the present application as described above.

[0027] According to the seventh aspect of any embodiment of the present application, a computer program product is provided, on which a computer program / instruction is stored. When the computer program / instruction is executed by a processor, it implements the method executed by the traffic management device or the method executed by the node device in any embodiment of the present application as described above.

[0028] The technical solution provided by this application may have the following beneficial effects:

[0029] According to the above embodiments, by receiving statistical data sent by each node device, logical abstraction is performed on each node device based on the statistical data sent by each node device, and the data is converted into decision variables, constraints and objective functions in a linear programming model. The decision variables, objective functions and constraints are linearly solved to generate a traffic scheduling strategy, and the traffic scheduling strategy is sent to each node device so that each node device performs traffic scheduling. By simplifying the complex network nodes and connection relationships in the distributed network into a form that is easy to process linearly, the key features and constraints in the distributed network can be fully captured, so that the linear solution algorithm can be more widely applied to various complex network topologies, and real-time network traffic scheduling can be efficiently performed in a complex network environment, thereby maximizing the utilization of bandwidth resources in the distributed network.

[0030] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The accompanying drawings, which are incorporated in the specification and constitute a part of this application, illustrate embodiments consistent with the application and, together with the description, serve to explain the principles of the application.

[0032] Figure 1is a flow chart of a traffic scheduling method based on a distributed network according to an exemplary embodiment of the present application;

[0033] Figure 2 is a flow chart of another distributed network-based traffic scheduling method according to an exemplary embodiment of the present application;

[0034] Figure 3 is a schematic diagram of a network topology structure of a distributed network according to an exemplary embodiment of the present application;

[0035] Figure 4 is an interactive diagram of a traffic scheduling method based on a distributed network according to an exemplary embodiment of the present application;

[0036] Figure 5 is a structural schematic diagram of an electronic device according to an exemplary embodiment of the present application;

[0037] Figure 6 is a block diagram of a traffic management device according to an exemplary embodiment of the present application;

[0038] Figure 7 It is a block diagram of a node device shown in the present application according to an exemplary embodiment. DETAILED DESCRIPTION

[0039] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0040] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0041] It should be understood that although the terms first, second, third, etc. may be used in the present application to describe various information, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0042] Current network traffic scheduling methods lack an in-depth understanding and effective management of the complexity of distributed network nodes, and are unable to fully utilize network resources, affecting the overall performance of the network and the user's service quality experience.

[0043] In order to solve the above problems, the present application proposes a traffic scheduling method based on a distributed network. Before introducing the traffic scheduling method, in order to make the description of the method easier to understand, some concepts involved in the subsequent description of the embodiments of the present application are first explained:

[0044] 1) Network traffic scheduling

[0045] Network traffic scheduling refers to the management and optimization of network traffic to ensure the effective use of network resources and the stability of services. Network traffic scheduling is crucial to ensure smooth network operation and service quality.

[0046] 2) Distributed Network

[0047] A distributed network is a network composed of multiple nodes, which are connected together through the network. There is no central node in the network, and multiple nodes work together to complete network functions. Distributed networks have good scalability and fault tolerance, can improve traffic distribution, and can adapt to the needs of large-scale networks.

[0048] 3) Linear Programming (LP)

[0049] Linear programming is a mathematical theory and method for studying the extreme value problem of linear objective functions under linear constraints. It is a mathematical method to assist scientific management. Linear programming can rationally use limited resources to make optimal decisions and provide a scientific basis.

[0050] To further illustrate the present application, the following examples are provided:

[0051] See also Figure 1 , Figure 1This is a flow chart of a traffic scheduling method based on a distributed network according to an exemplary embodiment of the present application. The traffic scheduling method can be applied to traffic management devices such as servers and gateways, and the traffic management device can communicate with each node device in the distributed network. The traffic scheduling method may include the following steps:

[0052] Step 102: Receive statistical data sent by each node device, where the statistical data corresponding to each node device includes a sending bandwidth upper limit and a receiving bandwidth upper limit of each node device.

[0053] In this step, each node device in the distributed network can collect statistics related to the traffic of the node device in real time and send the statistics to the traffic management device. The traffic management device receives the statistics sent by each node device and caches the statistics.

[0054] Among them, statistical data is data collected and reported by each node device in the distributed network, which is used to reflect the node device's ability information in terms of traffic transmission. The statistical data corresponding to each node device at least includes the upper limit of the sending bandwidth and the upper limit of the receiving bandwidth of each node device. The upper limit of the sending bandwidth and the upper limit of the receiving bandwidth are the maximum rates that the node device can achieve when sending and receiving data.

[0055] The upper limit of the sending bandwidth is the maximum amount of data that a node device can send per unit time, and the upper limit of the receiving bandwidth is the maximum amount of data that a node device can receive per unit time.

[0056] Step 104: Based on the statistical data sent by each node device, each node device is logically abstracted and converted into decision variables, constraints and objective functions in a linear programming model.

[0057] In this step, the traffic management device performs logical abstraction on each node device based on the statistical data sent by each node device, converts it into decision variables, constraints and objective functions in the linear programming model, and simplifies the complex network structure into a mathematical form that is easy to understand and calculate for subsequent solutions.

[0058] The traffic management device builds decision variables based on the upper and lower limits of the sending and receiving bandwidths of each node device and the traffic distribution that needs to be determined in traffic scheduling. According to the actual conditions and requirements of the distributed network, constraints such as bandwidth restrictions and traffic conservation are formulated. The objective function is constructed with the goals of maximizing network throughput and minimizing latency.

[0059] Among them, the decision variable is the unknown quantity that needs to be determined in the traffic scheduling problem. It is used to indicate the plans and measures expressed in quantities in the planning problem. It can be the planned scheduling traffic, etc., which can be determined and controlled by the decision maker.

[0060] Constraints are the constraints and restrictions on the values ​​of decision variables, which are used to reflect the actual conditions and requirements of traffic scheduling problems and can be expressed by equations or inequalities containing decision variables. Objective functions are functions constructed based on decision variables, which are used to reflect the goals pursued by traffic scheduling problems.

[0061] Step 106: Perform linear solution on the decision variables, objective function and constraint conditions to generate a traffic scheduling strategy.

[0062] In this step, the traffic management device inputs the decision variables, objective function and constraints into the linear programming model, uses the linear programming model to solve the decision variables, objective function and constraints, and finds a traffic scheduling strategy that meets all constraints and optimizes the objective function.

[0063] Among them, the traffic scheduling strategy is a traffic allocation plan formulated according to the network status and node device capabilities, which is used to guide node devices to perform traffic scheduling.

[0064] Step 108: Send the traffic scheduling strategy to each node device so that each node device performs traffic scheduling.

[0065] In this step, the traffic management device sends a traffic scheduling policy to each node device, and each node device applies the traffic scheduling policy to the actual distributed network based on the received traffic scheduling policy and performs traffic scheduling according to the policy.

[0066] The traffic scheduling method based on a distributed network of the present embodiment receives statistical data sent by each node device, performs logical abstraction on each node device based on the statistical data sent by each node device, converts the statistical data into decision variables, constraints and objective functions in a linear programming model, performs linear solution on the decision variables, objective functions and constraints, generates a traffic scheduling strategy, and sends the traffic scheduling strategy to each node device so that each node device performs traffic scheduling. By simplifying the complex network nodes and connection relationships in the distributed network into a form that is easy to process linearly, the key features and constraints in the distributed network can be fully captured, so that the linear solution algorithm can be more widely applied to various complex network topologies, and real-time network traffic scheduling can be efficiently performed in a complex network environment, thereby maximizing the utilization of bandwidth resources in the distributed network.

[0067] Moreover, the constraints and objective functions constructed through logical abstraction are flexible and modifiable, allowing traffic scheduling strategies to be easily adapted to various complex network scenarios. By dynamically adjusting the constraints and objective functions, it can be ensured that the traffic scheduling strategy always conforms to changes in network conditions and business needs, thereby achieving efficient and reliable traffic management.

[0068] See also Figure 2 , Figure 2 This is a flow chart of another traffic scheduling method based on a distributed network according to an exemplary embodiment of the present application. The traffic scheduling method can be applied to a node device, which can be a computer device, an Internet of Things device, etc. in a distributed network, and the node device can communicate with a traffic management device. The traffic scheduling method may include the following steps:

[0069] Step 202: Send statistical data to the traffic management device so that the traffic management device can perform logical abstraction of the node devices into decision variables, objective functions and constraints in the linear programming model, and perform linear solutions to the decision variables, objective functions and constraints to generate a traffic scheduling strategy. The statistical data includes the upper limit of the sending bandwidth and the upper limit of the receiving bandwidth of the node devices.

[0070] In this step, a traffic statistics tool is pre-deployed on the node device, and the traffic statistics tool is used to collect statistical data of the node device in real time and send the statistical data to the traffic management device.

[0071] Based on the received statistical data, the traffic management device performs logical abstraction on the node devices, simplifies the complex network structure and traffic relationship into an easy-to-handle linear programming model, constructs the decision variables, objective functions and constraints in the linear programming model, performs linear solutions to the decision variables, objective functions and constraints, and generates the optimal traffic scheduling strategy.

[0072] Step 204: In response to receiving the traffic scheduling policy sent by the traffic management device, traffic scheduling is performed.

[0073] In this step, a network scheduling response tool is pre-deployed on the node device, and the network scheduling response tool is used to receive the traffic scheduling strategy sent by the traffic management device. In response to receiving the traffic scheduling strategy, traffic scheduling is performed to ensure that the traffic in the distributed network is transmitted according to the traffic allocation plan in the traffic scheduling strategy.

[0074] The traffic scheduling method based on a distributed network of this embodiment performs traffic scheduling by sending statistical data to a traffic management device and responding to the traffic scheduling strategy sent by the traffic management device. The traffic management device is used to simplify the complex network nodes and connection relationships in the distributed network into a form that is easy to process linearly. The key features and constraints in the distributed network can be fully captured, so that the linear solution algorithm can be more widely applied to various complex network topologies, and real-time network traffic scheduling can be efficiently performed in a complex network environment, thereby maximizing the utilization of bandwidth resources in the distributed network.

[0075] In the above-mentioned embodiment, it is introduced that the optimal traffic scheduling strategy is generated based on the statistical data of each node device through the steps of logical abstraction and linear solution, so as to achieve the optimized scheduling of distributed network traffic. In the following embodiment, the generation process of the traffic scheduling strategy will be introduced in more detail, and it can be applied to any of the above embodiments.

[0076] In one embodiment, the traffic management device divides each node device into a receiving device and a sending device according to the upper limit of the sending bandwidth and the upper limit of the receiving bandwidth of each node device.

[0077] If the sending bandwidth upper limit of the node device is not 0, it can be determined that the node device is a sending device. If the receiving bandwidth upper limit of the node device is not 0, it can be determined that the node device is a receiving device. If both the sending bandwidth upper limit and the receiving bandwidth upper limit of the node device are not 0, it can be determined that the node device is both a sending device and a receiving device.

[0078] The traffic management device constructs a two-dimensional linear matrix based on the receiving device and the sending device. The row dimension of the two-dimensional linear matrix is ​​constructed with the receiving device, and the column dimension of the two-dimensional linear matrix is ​​constructed with the sending device. The planned scheduling traffic of each node device is used as an element in the two-dimensional linear matrix. Each element in the two-dimensional linear matrix corresponds to a specific traffic transmission relationship between the sending device and the receiving device, and a two-dimensional linear matrix is ​​constructed.

[0079] Among them, the receiving device is a node device responsible for receiving traffic from other devices in the distributed network, and the sending device is a node device responsible for sending traffic to other devices in the distributed network. The same node device can logically be both a receiving device and a sending device.

[0080] A two-dimensional linear matrix is ​​a rectangular array consisting of rows and columns. It is a visual representation of a linear programming model and is used to represent the traffic transmission relationship between each node device in a distributed network. The row dimension of the two-dimensional linear matrix represents the receiving device, the column dimension of the two-dimensional linear matrix represents the sending device, and the elements in the two-dimensional linear matrix represent the planned scheduling traffic of each node device. The planned scheduling traffic is used to reflect the traffic sent by each sending device to each receiving device.

[0081] See also Figure 3 , Figure 3 A schematic diagram of a network topology structure of a distributed network is shown. Exemplarily, the distributed network may include: node device A, node device B, node device C, node device D, node device E and node device F.

[0082] Among them, the upper limit of the sending bandwidth of node device A is 0, the upper limit of the receiving bandwidth of node device A is 10Gbps, and node device A only receives traffic but does not send traffic. The upper limit of the sending bandwidth of node device B is 0, the upper limit of the receiving bandwidth of node device B is 20Gbps, and node device B only receives traffic but does not send traffic.

[0083] The upper limit of node device C's sending bandwidth is 10 Gbps, and the upper limit of node device C's receiving bandwidth is 30 Gbps. Node device C can send traffic and receive traffic. The yellow arrows indicate the destination nodes to which node device C can send traffic, including node device A, node device B, and node device D.

[0084] The upper limit of node device D's sending bandwidth is 30Gbps, and the upper limit of node device D's receiving bandwidth is 10Gbps. Node device D can send traffic and receive traffic. The red arrows indicate the destination nodes to which node device D can send traffic, including node device A, node device B, and node device C.

[0085] The upper limit of the sending bandwidth of node device E is 20 Gbps, and the upper limit of the receiving bandwidth of node device E is 0. Node device E only sends traffic and does not receive traffic. The blue arrows indicate the destination nodes to which node device E can send traffic, including node device A, node device B, node device C, and node device D.

[0086] The upper limit of the sending bandwidth of node device F is 10Gbps, and the upper limit of the receiving bandwidth of node device F is 0. Node device F only sends traffic and does not receive traffic. The green arrows indicate the destination nodes to which node device F can send traffic, including node device A, node device B, node device C, and node device D.

[0087] The traffic management device divides each node device in the distributed network. The divided receiving devices include node device A, node device B, node device C and node device D, and the sending devices include node device C, node device D, node device E and node device F.

[0088] The traffic management device converts each node device into a two-dimensional coordinate dimension of a two-dimensional linear matrix, constructs the row dimension of the two-dimensional linear matrix with the receiving device, and constructs the column dimension of the two-dimensional linear matrix with the sending device, and can construct the two-dimensional linear matrix shown in Table 1:

[0089] Table 1 Two-dimensional linear matrix

[0090]

[0091] Among them, receiving device a is converted by node device A, and the upper limit of receiving bandwidth is 10Gbps, receiving device b is converted by node device B, and the upper limit of receiving bandwidth is 20Gbps, receiving device c is converted by node device C, and the upper limit of receiving bandwidth is 30Gbps, and receiving device d is converted by node device D, and the upper limit of receiving bandwidth is 10Gbps.

[0092] Sending node 1 is converted by node device F, and the upper limit of sending bandwidth is 10Gbps. Sending node 2 is converted by node device E, and the upper limit of sending bandwidth is 20Gbps. Sending node 3 is converted by node device D, and the upper limit of sending bandwidth is 30Gbps. Sending node 4 is converted by node device C, and the upper limit of sending bandwidth is 10Gbps.

[0093] Xa1, Xa2, Xa3 and Xa4 represent the planned scheduled traffic sent by sending device 1, sending device 2, sending device 3 and sending device 4 to receiving device a respectively. Xb1, Xb2, Xb3 and Xb4 represent the planned scheduled traffic sent by sending device 1, sending device 2, sending device 3 and sending device 4 to receiving device b respectively.

[0094] Xc1, Xc2, Xc3 and Xc4 represent the planned scheduled traffic sent by the sending device 1, the sending device 2, the sending device 3 and the sending device 4 to the receiving device c. Xd1, Xd2, Xd3 and Xd4 represent the planned scheduled traffic sent by the sending device 1, the sending device 2, the sending device 3 and the sending device 4 to the receiving device d.

[0095] As described above, by dividing each node device into a receiving device and a sending device according to the upper limit of the sending bandwidth and the upper limit of the receiving bandwidth of each node device, the row dimension of the two-dimensional linear matrix is ​​constructed with the receiving device, and the column dimension of the two-dimensional linear matrix is ​​constructed with the sending device. The planned scheduling traffic of each node device is used as an element in the two-dimensional linear matrix to construct a two-dimensional linear matrix. The complex network traffic scheduling problem can be intuitively represented, and the simple visual representation of the linear programming model helps to better understand the network traffic scheduling problem and the solution process.

[0096] In one embodiment, the traffic management device uses the planned scheduled traffic of each node device in the distributed network as a decision variable, which can reflect a feasible traffic allocation scheme. The objective function is constructed by maximizing the sum of the decision variables so that the total traffic received by all receiving nodes in the distributed network reaches the maximum sum.

[0097] The constraints may also include a sending upper limit constraint and a receiving upper limit constraint. The traffic management device sets a sending upper limit constraint based on the sending bandwidth upper limit of each node device, and sets a receiving upper limit constraint based on the receiving bandwidth upper limit of each node device to ensure the feasibility of the traffic distribution plan.

[0098] The upper limit of transmission constraint is a constraint condition for limiting the planned scheduling traffic according to the upper limit of the transmission bandwidth of each node device, and is used to ensure that the transmission device does not exceed its physical or logical transmission capacity.

[0099] The receiving upper limit constraint is a constraint that limits the planned scheduling traffic according to the receiving bandwidth upper limit of each node device. It is used to ensure that the receiving device can process the traffic from the sending device without crashing or performance degradation due to overload.

[0100] Taking the two-dimensional linear matrix shown in Table 1 as an example, the traffic management device takes the planned scheduling traffic Xa1, Xb1, Xc1, Xd1, Xa2, Xb2, Xc2, Xd2, Xa3, Xb3, Xc3, Xd3, Xa4, Xb4, Xc4 and Xd4 of each node device as decision variables.

[0101] The traffic management device sets the sending upper limit constraint condition 1 based on the sending bandwidth upper limit of the sending device 1:

[0102] Send upper limit constraint 1

[0103] The traffic management device sets the sending upper limit constraint condition 2 based on the sending bandwidth upper limit of the sending device 2:

[0104] Send upper limit constraint 2

[0105] The traffic management device sets the sending upper limit constraint condition 3 based on the sending bandwidth upper limit of the sending device 3:

[0106] Send upper limit constraint 3

[0107] The traffic management device sets the sending upper limit constraint condition 4 based on the sending bandwidth upper limit of the sending device 4:

[0108] Send upper limit constraint 4

[0109] The traffic management device sets the receiving upper limit constraint condition 1 based on the receiving bandwidth upper limit of the receiving device a:

[0110] Receive upper limit constraint 1

[0111] The traffic management device sets the receiving upper limit constraint condition 2 based on the receiving bandwidth upper limit of the receiving device b:

[0112] Receive upper limit constraint 2

[0113] The traffic management device sets the receiving upper limit constraint condition 3 based on the receiving bandwidth upper limit of the receiving device c:

[0114] Accept upper limit constraint 3

[0115] The traffic management device sets the receiving upper limit constraint condition 4 based on the receiving bandwidth upper limit of the receiving device d:

[0116] Accept upper limit constraint 4

[0117] The traffic management device maximizes the sum of decision variables and constructs objective function 1:

[0118] Objective function 1

[0119] in, Represents the maximum value of the sum of decision variables.

[0120] The traffic management device uses a linear programming model to calculate the constructed decision variables, constraints and objective functions, obtains the solution results of 16 decision variables, and generates a traffic scheduling strategy with the highest traffic efficiency.

[0121] As mentioned above, by taking the planned scheduling traffic of each node device as the decision variable, each possible traffic transmission relationship can be directly optimized to ensure the accuracy of traffic distribution; based on the sending bandwidth upper limit of each node device, the sending upper limit constraint is set, and based on the receiving bandwidth upper limit of each node device, the receiving upper limit constraint is set, which can ensure that the use of traffic resources will not exceed the physical limitations of the node device, avoiding network congestion and performance degradation; by maximizing the sum of decision variables and constructing the objective function, the total scheduling traffic in the distributed network can be maximized, which helps to improve the overall throughput and efficiency of the distributed network.

[0122] In one embodiment, the traffic management device may combine the scheduling priority of each node device when constructing the objective function. Based on the scheduling priority of each node device, the weight of the decision variable corresponding to each node device is set. While considering the maximum transmission of traffic, the scheduling priority requirements of different node devices also need to be considered to maximize the weighted sum of the decision variables and construct the objective function.

[0123] Among them, the scheduling priority is the priority order set for each node device based on network requirements such as business needs, network conditions or policy requirements. During the traffic scheduling process, high-priority node devices will get the required traffic resources first, while low-priority node devices will get less traffic when resources are limited.

[0124] The weight of the decision variable is used to reflect the relative importance of each node device relative to other node devices in the traffic scheduling process.

[0125] Continuing with the two-dimensional linear matrix shown in Table 1 as an example, each receiving node has a different scheduling priority. When the total sending traffic sent by all sending nodes is insufficient to meet the total receiving traffic of all receiving nodes, the traffic receiving demand of the receiving device a with the highest scheduling priority is first met, followed by the traffic receiving demand of the receiving devices b and c with the secondary scheduling priority, and finally the traffic receiving demand of the receiving device d with the lowest scheduling priority.

[0126] The traffic management device can set the weight of the decision variable corresponding to the receiving device a to r, the weight of the decision variable corresponding to the receiving devices b and c to s, and the weight of the decision variable corresponding to the receiving device d to t. The magnitude relationship of the weights r, s, and t is that the weight r is greater than the weight s and the weight s is greater than the weight t. The specific values ​​of the weights r, s, and t can be set and adjusted according to actual network requirements.

[0127] Taking the weight r as 1, the weight s as 0.8, and the weight t as 0.5 as an example, the traffic management device constructs objective function 2 by maximizing the weighted sum of decision variables:

[0128] Objective Function 2

[0129] in, Represents the maximum value of the weighted sum of the decision variables.

[0130] As described above, by setting the weights of the decision variables corresponding to each node device based on the scheduling priority of each node device to maximize the weighted sum of the decision variables, constructing the objective function, and introducing the scheduling priority as the basis for the weight of the decision variable, network resources can be preferentially allocated to node devices with higher priorities while maximizing resource utilization, thereby improving the reliability and quality of network services.

[0131] In one embodiment, the traffic management device can determine the scheduling path requirements for traffic transmission in the distributed network according to the network requirements of the distributed network. Based on the scheduling path requirements, the scheduling path constraints of each node device are constructed to ensure that the traffic is transmitted along the predetermined path in the distributed network.

[0132] The traffic management device can also determine the scheduling upper limit requirements for traffic transmission in the distributed network according to the network requirements of the distributed network. Based on the scheduling upper limit requirements, the scheduling upper limit constraints of each node device are constructed to ensure that the traffic transmission does not exceed the carrying capacity of the distributed network.

[0133] Among them, the scheduling path constraint condition is a condition that limits the traffic transmission path in the distributed network. The scheduling upper limit constraint condition is a condition that limits the maximum flow of traffic transmission in the distributed network.

[0134] Continuing to take the two-dimensional linear matrix shown in Table 1 as an example, if the scheduling path requirement is that sending node 1 cannot send traffic to receiving device a, the traffic management device constructs scheduling path constraint 1 based on the scheduling path requirement:

[0135] Scheduling path constraints 1

[0136] If the scheduling upper limit requirement is that the traffic sent by sending node 2 to receiving device b cannot exceed the scheduling upper limit of 3 Gbps, the traffic management device constructs scheduling upper limit constraint condition 1 based on the scheduling upper limit requirement:

[0137] Scheduling upper limit constraint 1

[0138] As described above, by constructing the scheduling path constraints of each node device, it is ensured that the traffic is transmitted along the predetermined path, avoiding unnecessary traffic detours or erroneous transmissions, thereby accurately controlling the flow and distribution of traffic; by constructing the scheduling upper limit constraints of each node device, it helps to protect the normal operation of the node device and avoid equipment performance degradation or failure due to overload.

[0139] It can be understood that the constraints and objective functions shown in the embodiments of the present application are only examples. Constraints and objective functions in different network scenarios can also be constructed based on the network requirements of the distributed network. The embodiments of the present application do not limit this.

[0140] In one embodiment, each node device may use a traffic statistics tool to obtain the current scheduled traffic of each node device, and send statistical data including the current scheduled traffic, the upper limit of the sending bandwidth, and the upper limit of the receiving bandwidth to the traffic management device.

[0141] The traffic management device can perform logical abstraction and linear solution on each node device according to the upper limit of the sending bandwidth and the upper limit of the receiving bandwidth of each node device to generate a traffic scheduling strategy. Based on the target scheduling traffic of each node device in the traffic scheduling strategy and the current scheduling traffic received, the target scheduling traffic is compared with the current scheduling traffic to determine the traffic to be adjusted for each node device.

[0142] The traffic management device generates an updated traffic scheduling strategy based on the traffic to be adjusted of each node device, and sends the updated traffic scheduling strategy to each node device. Each node device receives the updated traffic scheduling strategy sent by the traffic management device, and performs traffic scheduling based on the received updated traffic scheduling strategy and the traffic to be adjusted as needed.

[0143] Among them, the current scheduling traffic is the traffic actually sent and received by each node device, and the current scheduling traffic can include the current sending traffic and the current receiving traffic. The target scheduling traffic is the ideal traffic value set for each node device, which is the solution result of the decision variable and is used to reflect the ideal traffic state that the distributed network should achieve.

[0144] The traffic to be adjusted is the traffic value that needs to be adjusted calculated for each node device based on the difference between the current scheduling traffic and the target scheduling traffic, which is used to reflect the direction and degree of network traffic that needs to be scheduled. The updated traffic scheduling strategy is generated based on the target scheduling traffic and the current scheduling traffic of the node device to determine the traffic to be adjusted.

[0145] As described above, by determining the flow to be adjusted of each node device based on the target scheduling flow and the current scheduling flow of each node device, and using the flow to be adjusted of each node device as the updated traffic scheduling strategy, the traffic management device centrally calculates the flow to be adjusted, which can significantly reduce the computing pressure of the node device and enable the node device to focus more on scheduling traffic.

[0146] In order to further introduce the traffic scheduling process based on distributed networks, Figure 4 An interactive diagram of a traffic scheduling method based on a distributed network is shown. Figure 4 As shown, the traffic scheduling method may include the following steps:

[0147] Step 402: The node device sends statistical data to the traffic management device.

[0148] In this step, the node device uses a traffic statistics tool to obtain real-time statistical data such as the current scheduling traffic, the upper limit of the sending bandwidth and the upper limit of the receiving bandwidth of the node device, and sends the statistical data to the traffic management device.

[0149] Step 404: The traffic management device root divides each node device into a receiving device and a sending device.

[0150] In this step, the traffic management device divides each node device into a receiving device and a sending device according to the upper limit of the sending bandwidth and the upper limit of the receiving bandwidth of each node device in the statistical data.

[0151] Step 406: The traffic management device constructs the row dimension of the two-dimensional linear matrix with the receiving device and constructs the column dimension of the two-dimensional linear matrix with the sending device.

[0152] Step 408: The traffic management device uses the planned scheduling traffic of each node device as an element in a two-dimensional linear matrix to construct a two-dimensional linear matrix.

[0153] Step 410: The planned scheduling traffic of each node device is used as a decision variable.

[0154] In this step, the traffic management device performs logical abstraction on each node device and uses the planned scheduling traffic of each node device as the decision variable in the linear programming model.

[0155] Step 412: The traffic management device sets a sending upper limit constraint condition and a receiving upper limit constraint condition based on the sending bandwidth upper limit and the receiving bandwidth upper limit of each node device.

[0156] In this step, the traffic management device performs logical abstraction on each node device, constructs constraints in the linear programming model, sets a sending upper limit constraint based on the sending bandwidth upper limit of each node device, and sets a receiving upper limit constraint based on the receiving bandwidth upper limit of each node device.

[0157] Step 414: The traffic management device sets the weight of the decision variable corresponding to each node device based on the scheduling priority of each node device.

[0158] In this step, the traffic management device sets a weight corresponding to the scheduling priority for the decision variable corresponding to each node device based on the scheduling priority of each node device.

[0159] Step 416: The traffic management device constructs an objective function to maximize the weighted sum of the decision variables.

[0160] Step 418: The traffic management device performs linear solutions on the decision variables, objective functions and constraints to generate a traffic scheduling strategy.

[0161] In this step, the traffic management device substitutes the converted decision variables, objective functions and constraints into the linear programming model, performs linear programming solution, and generates a traffic scheduling strategy.

[0162] Step 420: The traffic management device determines the to-be-adjusted traffic of each node device based on the target scheduling traffic and the current scheduling traffic of each node device.

[0163] In this step, the traffic management device compares the target scheduling traffic and the current scheduling traffic of each node device based on the target scheduling traffic and the current scheduling traffic, and determines the traffic to be adjusted of each node device.

[0164] Step 422: The traffic management device generates an updated traffic scheduling strategy according to the traffic to be adjusted of each node device.

[0165] Step 424: The traffic management device sends the updated traffic scheduling policy to each node device.

[0166] Step 426: The node device performs traffic scheduling in response to receiving the updated traffic scheduling policy.

[0167] In this step, the node device receives the updated traffic scheduling strategy, and in response to receiving the updated traffic scheduling strategy, performs traffic scheduling based on the traffic to be adjusted of the node device. This can not only simplify complex network traffic scheduling problems, but also improve the efficiency and accuracy of problem solving, thereby effectively improving the utilization of network resources, reducing business traffic costs, and improving customer experience in using distributed networks.

[0168] Figure 5 1 is a schematic diagram of the structure of an electronic device according to an exemplary embodiment of the present application. The electronic device may be, for example, a mobile phone, a computer, a digital broadcast terminal, a message transceiver, a game console, a tablet device, a personal digital assistant, a server, a smart home appliance, a car computer, etc. Figure 5 At the hardware level, the electronic device includes a processor 502, an internal bus 504, a network interface 506, a memory 508, and a non-volatile memory 510, and may also include hardware required for other services. The processor 502 reads the corresponding computer program from the non-volatile memory 510 into the memory 508 and then runs it, forming a traffic management device or a node device at the logical level. Of course, in addition to the software implementation, this application does not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc., that is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0169] Figure 6 is a block diagram of a traffic management device according to an exemplary embodiment of the present application. Figure 6 , the traffic management device may include: a receiving module 602, a conversion module 604, a solution module 606 and a sending module 608, wherein:

[0170] The receiving module 602 is used to receive statistical data sent by each node device, and the statistical data corresponding to each node device includes the upper limit of the sending bandwidth and the upper limit of the receiving bandwidth of each node device;

[0171] The conversion module 604 is used to perform logical abstraction on each node device based on the statistical data sent by each node device, and convert the data into decision variables, constraint conditions and objective functions in a linear programming model;

[0172] The solution module 606 is used to perform linear solution on the decision variables, the objective function and the constraint conditions to generate a traffic scheduling strategy;

[0173] The sending module 608 is used to send the traffic scheduling strategy to each node device so that each node device performs traffic scheduling.

[0174] In one example, the conversion module 604, when used to perform logical abstraction on each node device based on the statistical data sent by each node device and convert it into decision variables, constraints and objective functions in a linear programming model, includes: dividing each node device into a receiving device and a sending device according to the upper limit of the sending bandwidth and the upper limit of the receiving bandwidth of each node device; constructing a two-dimensional linear matrix based on the receiving devices and the sending devices; wherein the two-dimensional linear matrix is ​​a visual representation of the linear programming model, the row dimension of the two-dimensional linear matrix represents the receiving device, the column dimension of the two-dimensional linear matrix represents the sending device, and the elements in the two-dimensional linear matrix represent the planned scheduling traffic of each node device, and the planned scheduling traffic is used to reflect the traffic sent by each sending device to each receiving device.

[0175] In one example, the constraints include a sending upper limit constraint and a receiving upper limit constraint; the conversion module 604, when used to perform logical abstraction on the respective node devices based on the statistical data sent by the respective node devices and convert them into decision variables, constraints and objective functions in a linear programming model, includes: taking the planned scheduling traffic of the respective node devices as the decision variable; setting the sending upper limit constraint based on the sending bandwidth upper limit of the respective node devices; setting the receiving upper limit constraint based on the receiving bandwidth upper limit of the respective node devices; and constructing the objective function by maximizing the sum of the decision variables.

[0176] In one example, the conversion module 604, when used to construct the objective function by maximizing the sum of the decision variables, includes: setting the weights of the decision variables corresponding to each node device based on the scheduling priority of each node device; and constructing the objective function by maximizing the weighted sum of the decision variables.

[0177] In one example, the constraint condition further includes at least one of the following: a scheduling path constraint condition of each node device and a scheduling upper limit constraint condition of each node device.

[0178] In one example, the statistical data also includes the current scheduling traffic of each node device; the traffic scheduling strategy includes the target scheduling traffic of each node device; the sending module 608, when used to send the traffic scheduling strategy to each node device, includes: determining the traffic to be adjusted of each node device based on the target scheduling traffic and current scheduling traffic of each node device; generating an updated traffic scheduling strategy based on the traffic to be adjusted of each node device; and sending the updated traffic scheduling strategy to each node device.

[0179] Figure 7 is a block diagram of a node device according to an exemplary embodiment of the present application. Figure 7 , the node device may include: a communication module 702 and a scheduling module 704, wherein:

[0180] The communication module 702 is used to send statistical data to the traffic management device, so that the traffic management device performs logical abstraction conversion of the node device into decision variables, objective functions and constraints in a linear programming model, and performs linear solution on the decision variables, the objective functions and the constraints to generate a traffic scheduling strategy, wherein the statistical data includes the upper limit of the sending bandwidth and the upper limit of the receiving bandwidth of the node device;

[0181] The scheduling module 704 is used to perform traffic scheduling in response to receiving the traffic scheduling policy sent by the traffic management device.

[0182] In one example, the statistical data also includes the current scheduled traffic of the node device; the communication module 702, before being used to send the statistical data to the traffic management device, also includes: before sending the statistical data to the traffic management device, the method also includes: the scheduling module 704, when used to perform traffic scheduling in response to receiving the traffic scheduling policy sent by the traffic management device, includes: receiving the updated traffic scheduling policy sent by the traffic management device; the updated traffic scheduling policy is generated according to the target scheduling traffic of the node device and the current scheduling traffic to determine the traffic to be adjusted; based on the traffic to be adjusted, traffic scheduling is performed.

[0183] The implementation process of the functions and effects of each unit in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, and will not be repeated here.

[0184] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiment described above is only schematic, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present application scheme. A person of ordinary skill in the art can understand and implement it without paying creative labor.

[0185] In an exemplary embodiment, a non-temporary computer-readable storage medium including instructions is also provided, such as a memory including instructions, and the above instructions can be executed by a processor to implement a method performed by a traffic management device or a method performed by a node device as in any of the above embodiments.

[0186] The non-temporary computer-readable storage medium may be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc., and the present application does not limit this.

[0187] In an exemplary embodiment, a computer program product including a computer program / instruction is also provided. The computer program / instruction can be executed by a processor to implement the method executed by the traffic management device or the method executed by the node device in any of the above embodiments.

[0188] The above describes specific 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 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.

[0189] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the inventions claimed herein. The present application is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

[0190] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A traffic scheduling method based on a distributed network, characterized in that: The method is applied to a traffic management device, and the method comprises: Receiving statistical data sent by each node device, wherein the statistical data corresponding to each node device includes a sending bandwidth upper limit and a receiving bandwidth upper limit of each node device; Based on the statistical data sent by each node device, the each node device is logically abstracted and converted into decision variables, constraint conditions and objective functions in a linear programming model; Performing linear solutions on the decision variables, the objective function and the constraint conditions to generate a traffic scheduling strategy that maximizes the total scheduling traffic in the distributed network; Sending the traffic scheduling strategy to each of the node devices so that each of the node devices performs traffic scheduling; The statistical data sent by each node device is logically abstracted and converted into decision variables, constraint conditions and objective functions in a linear programming model, including: According to the upper limit of the transmission bandwidth and the upper limit of the reception bandwidth of each node device, the node devices are divided into receiving devices and sending devices; Based on the receiving device and the sending device, a two-dimensional linear matrix is ​​constructed; wherein the two-dimensional linear matrix is ​​a visual representation of the linear programming model, the row dimension of the two-dimensional linear matrix represents the receiving device, the column dimension of the two-dimensional linear matrix represents the sending device, and the elements within the two-dimensional linear matrix represent the planned scheduling traffic of each node device, and the planned scheduling traffic is used to reflect the traffic sent by each sending device to each receiving device.

2. The method according to claim 1, characterized in that The constraint conditions include a sending upper limit constraint condition and a receiving upper limit constraint condition; The statistical data sent by each node device is logically abstracted and converted into decision variables, constraint conditions and objective functions in a linear programming model, including: Taking the planned scheduling traffic of each node device as the decision variable; Based on the upper limit of the transmission bandwidth of each node device, setting the transmission upper limit constraint condition; Based on the upper limit of the receiving bandwidth of each node device, setting the receiving upper limit constraint condition; The objective function is constructed to maximize the sum of the decision variables.

3. The method according to claim 2, characterized in that The objective function is constructed to maximize the sum of the decision variables, including: Based on the scheduling priority of each node device, setting the weight of the decision variable corresponding to each node device; The objective function is constructed to maximize the weighted sum of the decision variables.

4. The method according to claim 2, characterized in that: The constraint conditions also include at least one of the following: a scheduling path constraint condition of each node device and a scheduling upper limit constraint condition of each node device.

5. The method according to claim 1, characterized in that The statistical data also includes the current scheduling traffic of each node device; The traffic scheduling strategy includes the target scheduling traffic of each node device; The sending the traffic scheduling strategy to each node device includes: Determine the flow to be adjusted of each node device based on the target scheduling flow and the current scheduling flow of each node device; Generate an updated traffic scheduling strategy according to the traffic to be adjusted of each node device; Send the updated traffic scheduling strategy to each node device.

6. A traffic scheduling method based on a distributed network, characterized in that: The method is applied to a node device, and the method comprises: Sending statistical data to a traffic management device so that the traffic management device performs a logical abstraction conversion of the node device into decision variables, objective functions and constraints in a linear programming model, and performs a linear solution to the decision variables, the objective functions and the constraints to generate a traffic scheduling strategy that maximizes the total scheduling traffic in the distributed network, wherein the statistical data includes a sending bandwidth upper limit and a receiving bandwidth upper limit of the node device; the sending bandwidth upper limit and the receiving bandwidth upper limit are used to divide the receiving device and the sending device, and a two-dimensional linear matrix is ​​constructed based on the receiving device and the sending device; wherein the two-dimensional linear matrix is ​​a visual representation of the linear programming model, the row dimension of the two-dimensional linear matrix represents the receiving device, the column dimension of the two-dimensional linear matrix represents the sending device, and the elements in the two-dimensional linear matrix represent the planned scheduling traffic of each node device, and the planned scheduling traffic is used to reflect the traffic sent by each sending device to each receiving device; In response to receiving the traffic scheduling policy sent by the traffic management device, traffic scheduling is performed.

7. The method according to claim 6, characterized in that The statistical data also includes the current scheduling traffic of the node device; Before sending the statistical data to the traffic management device, the method further includes: Obtaining the current scheduled traffic of the node device; The performing traffic scheduling in response to receiving the traffic scheduling policy sent by the traffic management device includes: Receiving an updated traffic scheduling strategy sent by the traffic management device; the updated traffic scheduling strategy is generated based on the target scheduling traffic of the node device and the current scheduling traffic to be adjusted; Based on the flow to be adjusted, flow scheduling is performed.

8. A traffic management device, characterized in that: The traffic management device comprises: A receiving module, used to receive statistical data sent by each node device, wherein the statistical data corresponding to each node device includes a sending bandwidth upper limit and a receiving bandwidth upper limit of each node device; A conversion module, used to perform logical abstraction on each node device based on the statistical data sent by each node device, and convert the data into decision variables, constraint conditions and objective functions in a linear programming model; A solution module, used for performing linear solution on the decision variables, the objective function and the constraint conditions to generate a flow scheduling strategy for maximizing the total scheduling flow in the distributed network; A sending module, used for sending the traffic scheduling strategy to each node device, so that each node device performs traffic scheduling; The conversion module, when used to perform logical abstraction on each node device based on the statistical data sent by each node device and convert it into decision variables, constraints and objective functions in a linear programming model, includes: dividing each node device into a receiving device and a sending device according to the upper limit of the sending bandwidth and the upper limit of the receiving bandwidth of each node device; constructing a two-dimensional linear matrix based on the receiving devices and the sending devices; wherein the two-dimensional linear matrix is ​​a visual representation of the linear programming model, the row dimension of the two-dimensional linear matrix represents the receiving device, the column dimension of the two-dimensional linear matrix represents the sending device, and the elements in the two-dimensional linear matrix represent the planned scheduling traffic of each node device, and the planned scheduling traffic is used to reflect the traffic sent by each sending device to each receiving device.

9. A node device, characterized in that: The node device comprises: A communication module is used to send statistical data to a traffic management device so that the traffic management device performs a logical abstraction conversion of the node device into decision variables, objective functions and constraints in a linear programming model, and performs a linear solution to the decision variables, the objective functions and the constraints to generate a traffic scheduling strategy that maximizes the total scheduling traffic in the distributed network, wherein the statistical data includes a sending bandwidth upper limit and a receiving bandwidth upper limit of the node device; the sending bandwidth upper limit and the receiving bandwidth upper limit are used to divide the receiving device and the sending device, and a two-dimensional linear matrix is ​​constructed based on the receiving device and the sending device; wherein the two-dimensional linear matrix is ​​a visual representation of the linear programming model, the row dimension of the two-dimensional linear matrix represents the receiving device, the column dimension of the two-dimensional linear matrix represents the sending device, and the elements in the two-dimensional linear matrix represent the planned scheduling traffic of each node device, and the planned scheduling traffic is used to reflect the traffic sent by each sending device to each receiving device; The scheduling module is used to perform traffic scheduling in response to receiving the traffic scheduling policy sent by the traffic management device.

10. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor implements the method according to any one of claims 1 to 5 or the method according to any one of claims 6 to 7 by running the executable instructions.

11. A computer-readable storage medium having computer instructions stored thereon, characterized in that: When the instruction is executed by the processor, the method described in any one of claims 1-5 or the method described in any one of claims 6-7 is implemented.

12. A computer program product having a computer program / instructions stored thereon, characterized in that: When the computer program / instruction is executed by a processor, the method according to any one of claims 1 to 5 or the method according to any one of claims 6 to 7 is implemented.

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