Route determination method, communication device, and storage medium

Abstract

A route determination method, a communication device, and a storage medium are provided, which include the steps of first acquiring a first network topology of an audio-video system, in which attribute information of links in the first network topology includes link transmission parameters, the link transmission parameters including a concave parameter, an additive parameter, and a multiplicative parameter, determining links from the first network topology that satisfy a concave parameter constraint, generating a second network topology using the links that satisfy the concave parameter constraint, then generating a third network topology based on key parameters of the links in the second network topology, and determining a plurality of candidate routes based on the third network topology, and determining a target route that satisfies a route selection constraint from the plurality of candidate routes.

Description

[Technical Field]

[0001] (Cross-reference of related applications) This application claims priority based on the Chinese patent application filed on May 29, 2023, with application number 202310622100.7, the entire contents of which are incorporated into this application by reference.

[0002] This disclosure relates to the field of audio and video communications, and more particularly to routing methods, communication devices, and storage media. [Background technology]

[0003] With the spread of mobile internet and the commercialization of 5th-generation mobile communication technology (5G), user demand for low-latency, highly reliable audio and video applications is increasing. Among several technologies, Real-Time Communication (RTC), thanks to its long-term technological accumulation in the video traffic field, can quickly provide the industry with highly parallel, low-latency, high-definition, smooth, secure, and reliable full-scene, fully interactive, and fully real-time audio and video services, applicable to various application scenarios such as online education, cloud conferencing, and social entertainment. Path planning is a core technology of Real-Time Networks (RTN), and path planning involves collecting and calculating edge data to plan the optimal transmission path between each edge node. [Overview of the project] [Means for solving the problem]

[0004] In one embodiment, an embodiment of the present disclosure provides a route determination method. The route determination method is: A step of obtaining a first network topology of an audio-video system, wherein the attribute information of the links in the first network topology includes the transmission parameters of the links, and the transmission parameters of the links include a concave parameter, an additivity parameter, and a multiplicative parameter. The steps include determining links that satisfy the concave parameter constraint from the first network topology, and generating a second network topology using the links that satisfy the concave parameter constraint, A step of generating a third network topology based on key parameters of links in a second network topology, wherein the key parameters are the parameters among the additive and multiplicative parameters that have the greatest impact on the target traffic type, and the attribute information of links in the third network topology includes a quantified score determined by the key parameters. The third step is to determine multiple candidate paths based on the network topology, The process includes the step of determining a target path that satisfies the path selection constraints from among multiple candidate paths.

[0005] In another embodiment, an embodiment of the present disclosure provides a route determination device. The route determination device is An acquisition module for acquiring a first network topology of an audio-video system, wherein the link attribute information in the first network topology includes link transmission parameters, and the link transmission parameters include concave parameters, additivity parameters, and multiplicative parameters. The system includes a processing module for determining links that satisfy the concave parameter constraint from the first network topology and for generating a second network topology using the links that satisfy the concave parameter constraint, The processing module is further used to generate a third network topology based on the key parameters of the links in the second network topology, where the key parameters are the additive and multiplicative parameters that have the greatest impact on the target traffic type, and the link attribute information in the third network topology includes a quantified score determined by the key parameters. The processing module is further used to determine multiple candidate paths based on the third network topology, and to determine a target path that satisfies the path selection constraints from among the multiple candidate paths.

[0006] In yet another embodiment, an embodiment of the present disclosure provides a communication device, the communication device including a processor and memory, the memory storing instructions that can be executed by the processor, and the processor being configured to cause the communication device to perform the method according to one embodiment described above when executing an instruction.

[0007] In yet another aspect, embodiments of the present disclosure provide a computer-readable storage medium that stores computer instructions, which are stored on the computer when executed by the computer. Note The method described in the description shall be carried out.

[0008] In yet another embodiment, embodiments of the present disclosure provide a computer program product that includes computer instructions. When these computer instructions are executed by a computer, they are performed on the computer. Note The method described in the description shall be carried out.

[0009] The accompanying drawings are used to provide a further understanding of this disclosure and, together with the embodiments of this disclosure, constitute part of the specification used for describing this disclosure and do not constitute limitations of this disclosure. [Brief explanation of the drawing]

[0010] [Figure 1]This is a schematic diagram of an audio-video system architecture relating to several embodiments. [Figure 2] This is a flowchart of a route determination method relating to several embodiments. [Figure 3] This is a schematic diagram of the network topology in several embodiments. [Figure 4] This is a schematic diagram of another network topology relating to several embodiments. [Figure 5] This is a schematic diagram of candidate pathways according to several embodiments. [Figure 6] This is a schematic diagram of a route determination device according to several embodiments. [Figure 7] This is a schematic diagram of a route determination device according to several embodiments. [Modes for carrying out the invention]

[0011] The inventions relating to embodiments of this disclosure will be described clearly and completely below with reference to the drawings of the embodiments of this disclosure. Clearly, the embodiments described are only a selection of embodiments of this disclosure, not all embodiments. All other embodiments that a person skilled in the art can obtain without creative work based on the embodiments of this disclosure are included within the scope that this disclosure seeks to protect.

[0012] In the descriptions of this disclosure, " / " means "or" unless otherwise specified, for example, A / B may mean A or B. In this specification, "and / or" is merely a relation to describe the related subjects, and it means that there may be three relationships, for example, A and / or B may mean A only, B only, and A and B. Also, "at least one" means one or more, and "multiple" means two or more. Expressions such as "first," "second," etc. are not limited to quantity or order of execution, and expressions such as "first," "second," etc. are not necessarily limited to being different.

[0013] It should be noted that in the present disclosure, expressions such as "by way of example" and "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described in the present disclosure as "by way of example" or "for example" should not be construed as being preferred or advantageous over other embodiments or designs. To be precise, the use of expressions such as "by way of example" and "for example" is intended to elaborate on related concepts.

[0014] In some technologies, the path planning technology in RTN usually uses one composite parameter to represent the characteristics of multiple parameters, constructs a function mapping model with the multiple parameters as function variables, and uses the function value as the criterion for path planning. However, this path planning method has significant limitations.

[0015] In practical applications, real-time audio and video communication traffic applications usually achieve high-speed and high-quality data transmission based on RTN systems, and may impose requirements on multiple mutually independent parameters such as network delay, packet loss rate, and bandwidth. Therefore, it is difficult to guarantee that each parameter of a path meets the traffic requirements based on the above path planning method.

[0016] In view of this, embodiments of the present disclosure provide a path determination method. The method includes the steps of: acquiring a first network topology of an audio-video system, wherein the attribute information of links in the first network topology includes transmission parameters of the links, and the transmission parameters of the links include a concavity parameter, an additive parameter, and a multiplicative parameter; determining links that satisfy a concavity parameter constraint condition from the first network topology, and generating a second network topology using the links that satisfy the concavity parameter constraint condition; generating a third network topology based on key parameters of the links in the second network topology, wherein the key parameter is the parameter with the highest degree of influence on a target traffic type among the additive parameter and the multiplicative parameter, and the attribute information of links in the third network topology includes a quantified score determined by the key parameter; determining a plurality of candidate paths based on the third network topology; and determining a target path that satisfies a path selection constraint condition from the plurality of candidate paths. In this way, the path planning can be applied to various traffic scenarios, and the practicability and accuracy of path planning can be improved.

[0017] Figure 1 illustrates an audio-video system architecture according to an embodiment of the present disclosure. As shown in Figure 1, the audio-video system may include a control node 110 and an edge node 120.

[0018] In some embodiments, the audio-video system may include an audio-video cloud network, which can be used to enable the distributed deployment of edge computing infrastructure resources and to manage these resources in an integrated manner. Resource-rich, concentrated deployment points are called central clouds, while resource-rich, widely distributed deployment points are called edge clouds (also called edge cloud nodes). Due to the relatively wide distribution of edge clouds, they may be understood as cloud computing platforms closer to the terminal devices used by users, i.e., edge clouds are cloud computing platforms closer to terminal devices, while central clouds may be understood as cloud platforms that unify the management of multiple edge clouds.

[0019] The control node 110 is located in the central cloud described above and is responsible for controlling and managing the media nodes, and can receive and process node information and link quality information reported from the media nodes. In some embodiments, the control node 110 may also be responsible for controlling and scheduling the edge nodes 120 in the audio-video system, as well as managing system data. In some embodiments, the control node 110 may include a network awareness module, a simplification module, a quantification module, a computing module, and a matching module.

[0020] The edge node 120 is located in the edge cloud described above, and the data transmission path may consist of multiple edge nodes 120 and links between nodes. The edge node 120 is composed of multiple small nodes and can be widely distributed. The edge node 120 can transfer traffic data in the RTN network at high speed and with high quality.

[0021] In some embodiments, the audio-video system may further include a client 130 that accesses the edge node 120. The client 130 may be a variety of physical devices or software applications that support the generation, transmission, and display of data such as audio and video (e.g., a mobile phone application (Application, App), a software development kit (SDK), or a terminal device (e.g., a mobile phone, tablet, desktop, laptop, handheld computer, note computer, ultra-mobile personal computer (UMPC), netbook, and terminal devices such as a personal digital assistant (PDA), augmented reality (AR) / virtual reality (VR) device)).

[0022] The system architecture shown in Figure 1 is not limited to the embodiments of this disclosure, and it should be understood that the system architecture shown in Figure 1 may include more or fewer components than those shown, may be a combination of certain components, or may have different configurations.

[0023] The methods related to this disclosure will be described in detail below with reference to the attached drawings.

[0024] As shown in Figure 2, an embodiment of the present disclosure provides a route determination method comprising the following steps.

[0025] S101 acquires the first network topology of the audio-video system. The attribute information of a link in the first network topology includes the transmission parameters of the link, which include concave parameters, additivity parameters, and multiplicative parameters.

[0026] In some embodiments, the concave parameter includes bandwidth, the additive parameter includes cost and / or delay, and the multiplicative parameter includes packet loss rate. In some embodiments, the additive parameter may also include parameters such as jitter and node hop count.

[0027] In some embodiments, for any two nodes in the first network topology, the value of the concavity parameter for that path is equal to the minimum value of the concavity parameters of all the links that make up the path.

[0028] For example, consider bandwidth. G=(V,E) represents the first network topology, where V represents a set of nodes and E represents a set of links between connected nodes. P(s,d) is the transmission path set in the first network topology, where s∈V represents a source node and d∈V represents a destination node. The bandwidth of path p can be determined based on the following equation (1).

number

[0029] In some embodiments, for any two nodes in a first network topology, the value of the additivity parameter of that path is obtained by performing an addition operation based on the values ​​of the additivity parameters of all links that make up that path.

[0030] For example, the cost of path p can be determined based on the following equation (2).

number

[0031] For example, the delay of path p can be determined based on the following equation (3).

number

[0032] In some embodiments, for any two nodes in the first network topology, the value of the multiplicity parameter of the path is obtained by multiplying the values ​​of the multiplicity parameters of all links that make up the path.

[0033] For example, the packet loss rate of route p can be determined based on the following equation (4).

number

[0034] In some embodiments, the control node of the audio-video system can recognize data in real time at a preset frequency in order to obtain the real-time network topology of the audio-video system.

[0035] In some embodiments, the network awareness described above can be achieved through periodic tasks. The control node can use a network awareness module to analyze the relationships between edge nodes in order to obtain probe data such as packet loss rate, latency, jitter, and bandwidth usage of the links between each edge node, and send network probe requests to each node in the edge cloud so that the edge nodes periodically perform network probes on the connected nodes based on the preset frequencies described above.

[0036] In some embodiments, the control node may also retrieve and record unique data for individual nodes, such as node IP addresses, node identifiers (identity documents, IDs), and node hardware parameters (e.g., CPU or memory).

[0037] In some embodiments, the control node can perform network recognition and acquire data, then perform abnormal processing of the received data, such as detecting whether the data was returned according to a standard format or whether the data exceeds a predetermined range, to determine if the data is abnormal and process the data in which abnormalities are detected.

[0038] In some embodiments, if the network topology of an audio-video system changes (e.g., by adding and removing nodes, or adding and removing connection links), the control node can perform network awareness again based on the real-time network topology. For example, the control node can send new network probe requests and data collection requests to each edge node to acquire new data and update the first network topology based on the newly acquired data.

[0039] In S102, links that satisfy the concave parameter constraint are determined from the first network topology, and a second network topology is generated using the links that satisfy the concave parameter constraint.

[0040] In some embodiments, when the concavity parameter includes bandwidth, the pre-setting conditions for the concavity parameter (i.e., the concave parameter constraints mentioned above) This includes the bandwidth being below a preset threshold. In other words, the control node can obtain the second network topology by simplifying the network topology by removing links in the first network topology that do not satisfy the preset conditions for the concavity parameter. Taking control node 110 in Figure 1 as an example, control node 110 can obtain the second network topology by simplifying the first network topology of the audio-video system using a simplification module.

[0041] The pre-configured threshold is a value in the form of a pre-configured bandwidth value or a real-time value determined based on the actual network topology, or a real-time bandwidth ratio to the total available bandwidth of the link. If the bandwidth of a link exceeds the pre-configured threshold, it indicates that the link is about to become saturated and can no longer be allocated traffic, so the control node may remove the link from the first network topology.

[0042] In some embodiments, a control node can generate a second network topology based on a simplified operation targeting a first network topology using a simplified model.

[0043] For example, this simplified model is pre-configured with the concave parameter constraints described above (e.g., bandwidth is less than a pre-set threshold). A control node may input the first network topology and the bandwidth of all links between nodes in that first network topology into the simplified model. This simplified model then takes the first network topology and the concave parameter constraints Does not satisfy The second network topology described above can be obtained by dynamically removing links.

[0044] As an example, as shown in Figure 3, the nodes in the first network topology obtained by the control node performing network recognition include Node A, Node B, Node C, Node D, Node E, and Node F. The pre-configured conditions for the concavity parameter include the bandwidth being less than 90% of the total available bandwidth of the link. If the real-time bandwidth of the link from Node B to Node E is 2.7 Gbps, and this reaches 90% of the total available bandwidth of the link, which is 3 Gbps, then, as shown in Figure 3, the control node will 1 Network Topolo Jika By temporarily deleting the link in question, a second network topology can be obtained.

[0045] In some embodiments, the control node can update the second network topology in real time based on data obtained by network recognition. If subsequent network recognition data indicates that the bandwidth value of the deleted link will satisfy the concave parameter constraint, the control node can update the second network topology based on new network recognition data and dynamically restore the link in the updated second network topology.

[0046] S103. Based on the key link parameters in the second network topology, a third network topology is generated. The key parameter is the parameter among the additive and multiplicative parameters that has the greatest impact on the target traffic type. Link attribute information in the third network topology includes a quantifiable score determined by the key parameter.

[0047] In some embodiments, the control node can determine the key parameters of a target traffic type based on the traffic requests corresponding to the target traffic type to which the target traffic belongs.

[0048] For example, target traffic may include cloud PCs, interactive whiteboards, video conferencing, video IoT, interactive live broadcasting, extended reality (XR), and other possible traffic.

[0049] In one example, if the target traffic is such as cloud PC or interactive whiteboard traffic, the tolerance for latency is extremely low, and only a small amount of packet loss is tolerated, the control node can determine that the target traffic type is latency-priority traffic (i.e., latency is the key parameter of the target traffic type). In another example, if the target traffic is the transmission of compressed data files, or other traffic where the requirement for packet loss rate takes precedence over the requirement for latency or other requirements, the control node can determine that the target traffic type is packet loss rate-priority traffic, i.e., the key parameter of the target traffic type is packet loss rate. Furthermore, the target traffic types in the embodiments of this disclosure may include other possible traffic types, but they are not listed here.

[0050] In some embodiments, a control node can obtain a link quantification score based on the link's key parameters, use the structure of the second network topology as the structure of the third network topology, and generate the third network topology using the link quantification score as the link attribute information in the third network topology.

[0051] The link's quantifiable score is used to characterize the reliability of the link for transmitting traffic data of that type.

[0052] For example, the quantification score may have a negative correlation with confidence. That is, the lower the quantification score of a link, the more reliable that link is for transmitting traffic data of that traffic type. Conversely, the higher the quantification score of a link, the less reliable that link is for transmitting traffic data of that traffic type.

[0053] As another example, the quantification score may have a positive correlation with the confidence level. The relationship between the quantification score and the confidence level primarily depends on the algorithm used to determine the quantification score. The following will focus on the negative correlation between the quantification score and the confidence level, and will explain this in detail.

[0054] In some embodiments, a control node can obtain a third network topology based on key parameters and a quantification model for the target traffic type. The quantification model can map the key parameters of a link to a value representing the quantification score of that link, according to a predefined mathematical transformation relationship. Taking control node 110 in Figure 1 as an example, the quantification of control node 110 module The quantification model can be pre-configured, and the control node 110 can determine the third network topology using the quantification module.

[0055] For example, a control node can input the second network topology and the key parameters of the links between all nodes in that second network topology into a quantitative model to obtain the third network topology described above.

[0056] Figure 4 shows a third network topology with latency, packet loss rate, and cost as key parameters. As shown in Figure 4, the third network topology includes nodes A, B, C, ..., and F. Taking the third network topology with latency as a key parameter as an example, the quantification score for link AB between node A and node B is 6, the quantification score for link AC is 3, the quantification score for link BD is 5, the quantification score for link BC is 2, the quantification score for link CE is 4, the quantification score for link CD is 3, the quantification score for link DE is 2, the quantification score for link DF is 3, and the quantification score for link EF is 6.

[0057] In one implementation, the control node can pre-configure and determine a quantified network topology corresponding to multiple possible key parameters, and further, based on the traffic type to which the target traffic belongs, it can select a quantified network topology corresponding to that target traffic type (i.e., the third network topology described above).

[0058] In another implementation, the control node can first determine the target traffic type, and then determine the third network topology based on the traffic requests corresponding to that target traffic type and the second network topology of the audio-video system.

[0059] Furthermore, the above invention classifies parameters in an audio-video system using a routing method based on key parameters, first simplifies the first network topology using concave parameters, and obtains the second network topology. Then, based on the differences between different traffics and the key parameters with the greatest impact on the target traffic, a third network topology is determined, and candidate routes are determined from the third network topology. In this way, third network topologies corresponding to different traffic types can be calculated based on different key parameters, eliminating the need for parameter synthesis, i.e., the need to determine a function mapping model for synthesized parameters, thereby reducing computational complexity and improving the feasibility of routing planning.

[0060] S104, multiple candidate paths are determined based on the third network topology.

[0061] In some embodiments, a control node can determine multiple paths between a first node and a second node based on a third network topology, and determine the path score for each of these paths. The path score for a path is obtained based on the quantified scores of all links that make up the path. The first node and the second node are any two nodes in the third network topology.

[0062] Similar to the quantification score of a link, the route score of a route characterizes the reliability of that route for transmitting traffic data of a given traffic type. The route score may also have a negative correlation with reliability, or it may have a positive correlation. It should be understood that the relationship between the route score and reliability primarily depends on the algorithm used to determine the route score. The following will explain in detail the negative correlation between the route score and reliability.

[0063] Furthermore, the control node can determine the top N paths with the minimum path score from among multiple paths as multiple candidate paths between the second node and the first node, where N is a positive integer greater than 1. For example, taking control node 110 in Figure 1, the above multiple candidate paths can be determined by the calculation module.

[0064] In some embodiments, the multiple paths between the first and second nodes described above include the first path, which is the shortest path from the first node to the second node, and the path from the first node to the third node... (Also called a divergence node) fart The The path from the third node to the second node shortest It includes a second path composed of the first and second paths. The third node is any node on the first path other than the first and second nodes.

[0065] In the third network topology, the shortest path from the first node to the second node can be understood as the path from the first node to the second node with the smallest path score in that third network topology.

[0066] For example, the process by which a control node determines multiple candidate paths can be implemented in the following steps: S1, the control node determines the first node (source node) and the second node (destination node) in the third network topology. As an example, a control node can be defined as G=(V,E) where G represents the first network topology, V represents the set of nodes, and E represents the set of links between connected nodes. The control node is denoted as s (s∈V) for the first node and d (d∈V) for the second node. The i-th candidate path from node s to node d is p. i It can be written as (s,d), and a set of multiple paths can be represented as P n (s,d)={p1(s,d),p2(s,d),…,p n It can be written as (s,d)}.

[0067] S2. The control node calculates the shortest path from the first node s to the second node d, that is, the path with the minimum path score in the third network topology. In addition, the control node may denote this shortest path as p1(s,d).

[0068] S3. The control node determines all nodes other than the second node on p1(s,d) as deviation nodes, and defines the deviation node set D={d1,d2,…,d x}. The deviation node set has a total of x deviation nodes.

[0069] S4. The control node, for each deviation node d i (i=1,2,...,x), may determine the shortest path from the deviation node d i to the second node d, that is, the path with the minimum quantification score, and take it as a candidate path.

[0070] S5. The control node determines a path between the first node and the second node based on the path from the first node to the deviation node d i in p1(s,d) and the shortest path from the deviation node d i to the second node determined in S4, and records the determined path in the second path set C(s,d).

[0071] S6. After traversing all deviation nodes in D={d1,d2,…,d x}, the control node determines the path with the minimum path score in C(s,d), which may be denoted as p2(s,d). Further, the control node may p 2 (s,d) may be moved from the set C(s,d) to the set P n (s,d). p2(s,d) is the second shortest path between the first node and the second node, and may be taken as a candidate path.

[0072] S7. The control node updates the deviation node set based on the determination method shown in S3 to S6, and Candidate routes the set P n (s,d) (That is, the set of multiple paths mentioned above) New candidate paths can be determined until the number of paths within the system reaches N.

[0073] As an example, in a third network topology where delay is the key parameter, as shown in Figure 5, candidate paths from source node A to destination node F are determined, and N is set to 4. Furthermore, based on this third network topology, the control node can determine that the path score for candidate path A→C→D→F is 9, the path score for candidate path A→C→E→D→F is 12, the path score for candidate path A→C→E→F is 13, and the path score for candidate path A→C→B→D→F is 13.

[0074] In some embodiments, the control node can calculate candidate paths between any two nodes in the third network topology, and further determine multiple candidate paths between the source node and the destination node from all candidate paths in the third network topology, based on the source and destination nodes of the target traffic.

[0075] S105. From multiple candidate paths, a target path that satisfies the path selection constraints is determined.

[0076] In some embodiments, a control node can obtain the transmission parameters of all links constituting a candidate path and determine the transmission parameters of the candidate path based on the transmission parameters of all links constituting the candidate path.

[0077] For example, regarding the concavity parameter, the value of the concavity parameter for a candidate path is equal to the minimum value of the concavity parameters of all the links that make up the candidate path. For instance, the bandwidth value of a candidate path can be determined based on equation (1) above.

[0078] In another example, regarding the additivity parameter, the additivity parameter of a candidate path is obtained by performing an addition operation based on the values ​​of the additivity parameters of all the links that make up the candidate path. For example, the cost of a candidate path can be determined based on equation (2) above, and the delay of a candidate path can be determined based on equation (3) above.

[0079] In yet another example, regarding the multiplicity parameter, the value of the multiplicity parameter for a candidate route is obtained by performing a multiplication operation based on the values ​​of the multiplicity parameters of all links that make up the route. For example, the packet loss rate of a candidate route can be determined based on equation (4) above.

[0080] In some embodiments, the control node can also represent the transmission parameters of the candidate path using a parametric expression.

[0081] As an example, let's consider a third network topology where latency is the key parameter, and the route information is "A→C→D→F". The latency for link A→C is 30ms, the packet loss rate is 1%, the jitter is 10ms, and the cost is 1. The latency for link C→D is 40ms, the packet loss rate is 1%, the jitter is 10ms, and the cost is 2. The latency for link D→F is 30ms, the packet loss rate is 1%, the jitter is 10ms, and the cost is 3. Then, the latency for route A→C→D→F is 100ms, the packet loss rate is 3%, and the jitter is 30 The delay is ms and the cost is 6. Therefore, the parameter expression for the candidate route can be expressed as "type=1,path='ACD-F',score=9,delay=100,jitter=30,lost=3,hop=4,cost=6". type=1 indicates the traffic type, i.e., the traffic type where delay is prioritized.

[0082] This allows the control node to determine the target path from among multiple candidate paths, based on the transmission parameters of each of the multiple candidate paths, and which path satisfies the path selection constraints.

[0083] Route selection constraints are used to restrict the transmission parameters of a target route. These constraints are determined based on the parameter requirements of the target traffic. If the target traffic imposes constraints on multiple transmission parameters, the route selection constraints are used to restrict those multiple transmission parameters of the target route. Naturally, if the target traffic imposes constraints on only one transmission parameter, the route selection constraints are used to restrict that single transmission parameter of the target route.

[0084] In some embodiments, the route selection constraints may include multiplicative and / or additive parameters of the route's transmission parameters, such as delay, packet loss rate, jitter, and node hop count. The route selection constraints may also include concave parameters of the route's transmission parameters. In some embodiments, the route selection constraints may include traffic information of the target traffic (e.g., information such as the traffic type, source node, and destination node of the target traffic).

[0085] In some embodiments, the control node can represent the routing constraints for target traffic using a multiple constraint logic expression. The method of representing the transmission parameters in the routing constraints expressed in the multiple constraint logic expression must be consistent with the parameter expressions described above (e.g., including source node (src), destination node (dest), delay (delay), packet loss rate (lost), jitter (jitter), node hop count (hop), etc.). The control node can also construct the multiple constraint logic expression by combining the detailed constraints of each parameter in the routing constraints with operators.

[0086] If the route selection constraints include that the source node is A, the destination node is F, the traffic type is cloud computing, and the total transmission delay of the link is less than 350ms or the jitter is less than 50ms, the packet loss rate is less than 5%, and the number of node hops is less than 5, then the control node can determine "src='A' and dest='F' and type='1' and delay<350 or jitter<50 and lost<5 and hop<5" as the multiple constraint logic expression.

[0087] Furthermore, the control node can determine the target route by matching the multiple constraint logic expression representing the route selection constraints with the parameter expression of each candidate route mentioned above. Taking control node 110 in Figure 1 as an example, the control node 110 can match the traffic route selection constraints with the transmission parameters of the candidate routes using a matching module.

[0088] For example, the parameter expressions for multiple candidate paths are "type=1,path='ACD-F',delay=100,jitter=30,lost=3,hop=4,cost=6", "type=1,path='ACED-F',delay=200,jitter=30,lost=6,hop=5,cost=10", "type=1,path='ACE-F',delay=300,jitter=40,lost=4,hop=4,cost=15", and "type=1,path='ACBD-F',delay=400,jitter=40 If the condition is ",lost=4,hop=5,cost=13", then based on the above multiple constraint logic expression, it is determined that the parameter expressions that match the said multiple constraint logic expression include "type=1,path='ACD-F',delay=100,jitter=30,lost=3,hop=4,cost=6" and "type=1,path='ACE-F',delay=300,jitter=40,lost=4,hop=4,cost=15", and the path corresponding to the matching parameter expression can be determined as the target path.

[0089] Based on the above embodiment, this method first simplifies the network topology based on concavity parameters, and then determines a third network topology based on different traffic key parameters to facilitate the determination of candidate routes. Furthermore, it more accurately reflects multiple types of parameters for each candidate route, and based on the parameters of the candidate routes, it can determine a route that satisfies the requirements of multiple types of traffic parameters from the candidate routes. This enables more accurate route planning. In addition, the route planning can be applied to various traffic scenarios, increasing the practicality of route planning.

[0090] The inventions described above have been presented primarily from the perspective of the interactions between each node. It should be understood that each node, for example, a device or apparatus, includes hardware configurations and / or software modules corresponding to each function in order to implement the functions described above. Referring to the steps of the algorithms in each example disclosed herein, as will be readily apparent to those skilled in the art, the disclosures can be implemented in hardware or in combination of hardware and computer software. Whether a function is performed in hardware or by driving hardware with computer software depends on the specific application and design constraints of the invention. Those skilled in the art may use different methods for each specific application to implement the described functions, but such implementations should not be construed as departing from the scope of the invention.

[0091] Figure 6 is a schematic diagram of a route determination device applied to the first node according to several embodiments. As shown in Figure 6, the route determination device 60 includes an acquisition module 601 and a processing module 602.

[0092] In some embodiments, the acquisition module 601 is used to acquire a first network topology of an audio-video system, where the link attribute information in the first network topology includes the link transmission parameters, which include concave parameters, additivity parameters, and multiplicative parameters. The processing module 602 determines the links that satisfy the concave parameter constraint from the first network topology and generates a second network topology using the links that satisfy the concave parameter constraint. The processing module 602 is further used to generate a third network topology based on the key parameters of the links in the second network topology. The key parameters are the additivity parameters and multiplicative parameters that have the greatest influence on the target traffic type. The link attribute information in the third network topology includes a quantification score determined by the key parameters. The processing module 602 is further used to determine multiple candidate paths based on the third network topology and to determine a target path that satisfies the path selection constraint from the multiple candidate paths.

[0093] In some embodiments, the concave parameter includes bandwidth, the additive parameter includes cost and / or delay, and the multiplicative parameter includes packet loss rate.

[0094] In some embodiments, if the concavity parameter includes bandwidth, the concavity parameter constraints The conditions include the bandwidth being below a pre-configured threshold.

[0095] In some embodiments, the processing module 602 is used to obtain a quantification score for a link based on the link's key parameters, to use the structure of the second network topology as the structure of the third network topology, and to generate the third network topology using the quantification score of the link as attribute information for the link in the third network topology.

[0096] In some embodiments, the processing module 602 is used to determine multiple paths between a first node and a second node based on a third network topology, and to determine the path score for each of these paths. The path score for a path is obtained based on the quantified scores of all links that make up the path. The first node and the second node are any two nodes in the third network topology. From the multiple paths, the top N paths with the minimum path score are determined as multiple candidate paths between the second node and the first node, where N is a positive integer greater than 1.

[0097] In some implementations, multiple paths between the first and second nodes are defined as the first path, which is the shortest path from the first node to the second node, and the third path, which is any node in the first path other than the first and second nodes. The The path, and from the third node to the second node shortest It includes a second route composed of a first route.

[0098] In some embodiments, the acquisition module 601 is multiple Candidate routes Each The transmission parameters of all links constituting the candidate path are obtained and used to determine the transmission parameters of the candidate path based on the transmission parameters of all links constituting the candidate path. The processing module 602 is used to determine a target path that satisfies the path selection constraints from among the multiple candidate paths based on the transmission parameters of each of the multiple candidate paths.

[0099] In some embodiments, for the concavity parameter, the value of the concavity parameter of a candidate path is equal to the minimum value of the concavity parameters of all the links constituting the candidate path; for the additivity parameter, the value of the additivity parameter of a candidate path is obtained by performing an addition operation based on the values ​​of the additivity parameters of all the links constituting the candidate path; and for the multiplicative parameter, the value of the multiplicative parameter of a candidate path is obtained by performing a multiplication operation based on the values ​​of the multiplicative parameters of all the links constituting the candidate path.

[0100] Note that the modules in Figure 6 may also be called units; for example, the acquisition module may be called an acquisition unit. Also, in the embodiment shown in Figure 6, the names of each module do not have to be those shown in the figure; for example, the acquisition module may be called a communication module.

[0101] Each unit in Figure 6 may be implemented as a software function module and, if sold or used as an independent product, may be stored on a computer-readable storage medium. Based on this understanding, the inventions relating to the embodiments of this disclosure may essentially, in other words, be embodied in the form of a software product stored on a storage medium that includes several instructions for causing a computer device (such as a personal computer, server, or network device) or processor to execute all or part of the steps of the methods of the various embodiments of this disclosure. Storage media for storing computer software products include various media capable of storing program code, such as USB memory, removable hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0102] The embodiments of this disclosure further provide schematic diagrams of a routing device when the functions of the above-mentioned integrated module are implemented in hardware form. As shown in Figure 7, the routing device 70 includes a processor 702, a communication interface 703, and a bus 704. In some embodiments, Route determination The device 70 may further include a memory 701.

[0103] The processor 702 can implement or run various exemplary logic blocks, modules, and circuits described in relation to this disclosure. The processor 702 may be a central processor, a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field-programmable gate array, or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. The processor 702 may implement or run various exemplary logic blocks, modules, and circuits described in relation to this disclosure. The processor 702 may be a combination that implements computing functions, for example, a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0104] The communication interface 703 is for connecting to other devices via a communication network. This communication network may be Ethernet®, a wireless access network, a wireless local area network (WLAN), or the like.

[0105] Memory 701 is, but is not limited to, read-only memory (ROM) or other types of static storage devices capable of storing static information and instructions, random-access memory (RAM) or other types of dynamic storage devices capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and being accessible by a computer.

[0106] As one implementation configuration, the memory 701 may exist independently of the processor 702, or the memory 701 may be connected to the processor 702 via the bus 704 to store instructions or program code. The processor 702 calls and executes the instructions or program code stored in the memory 701, thereby achieving the embodiment of the present disclosure. route A decision-making method can be implemented.

[0107] In another embodiment, the memory 701 can be integrated with the processor 702.

[0108] Bus 704 may be an extended industry standard architecture (EISA) bus, etc. Bus 704 can be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in Figure 7, but this does not mean that there is only one bus or only one type of bus.

[0109] From the above description of the embodiments, it will be apparent to those skilled in the art that, for the sake of convenience and brevity of explanation, the division of each functional module described above is merely illustrative. In actual applications, the above functions may be assigned to different functional modules as needed, that is, the internal structure of the equipment or apparatus may be divided into different functional modules to achieve all or part of the above functions.

[0110] Embodiments of this disclosure further provide a computer-readable storage medium. Computer instructions are provided so that all or part of the flow relating to the embodiments of the above-described method is executed by the relevant hardware. or programThis can be instructed. The program may be stored in the computer-readable storage medium described above. The program may include the flows of the embodiments of each of the above methods at runtime. The computer-readable storage medium may be any of the memories described in the embodiments above. The computer-readable storage medium may be an external storage device of the device or apparatus, for example, a plug-in hard disk, a SmartMedia card (SMC), a Secure Digital (SD) card, a flash card, etc., provided in the device or apparatus. Furthermore, the computer-readable storage medium may include both the internal storage unit and the external storage device of the device or apparatus. The computer-readable storage medium stores the computer program, as well as any other programs and data required by the device or apparatus. The computer-readable storage medium may be used to temporarily store data that has been output or is scheduled to be output. The readable storage medium includes non-temporary computer-readable storage medium.

[0111] When the embodiments of this disclosure are executed by a computer, the embodiments relating to the above embodiments route The present invention further provides a computer program product that includes a computer program that causes the computer to execute one of the decision-making methods.

[0112] While this specification has illustrated the present disclosure with various embodiments, a person skilled in the art can understand and implement other variations of the disclosed embodiments by referring to the accompanying drawings, the content of the disclosure, and the claims in the implementation of the present disclosure as described in the claims. In the claims, the term “comprising” does not exclude other components or steps, and “one” or “one” does not exclude multiple cases. A single processor or other unit can implement some of the functions enumerated in the claims. Although some means are described in different dependent claims, this does not mean that good results cannot be obtained by combining these means.

[0113] While this disclosure is described by detailed features and embodiments, it is evident that various modifications and combinations are possible without departing from the spirit and scope of this disclosure. Therefore, this specification and the accompanying drawings are merely illustrative descriptions of the disclosure as defined by the accompanying claims and are deemed to encompass any and all modifications, changes, combinations or equivalents within the scope of this disclosure. Clearly, a person skilled in the art can make various modifications and variations based on this disclosure without departing from the spirit and scope of this disclosure. Thus, if these modifications and variations of this disclosure fall within the scope of the claims and equivalent technologies of this disclosure, this disclosure is also intended to include these modifications and variations.

[0114] The above are merely specific embodiments of the present disclosure, but the scope of protection of the present disclosure is not limited thereto, and any modification or substitution within the technical scope disclosed herein shall be included within the scope of protection of the present disclosure. Accordingly, the scope of protection of the present disclosure shall be subject to the claims.

Claims

1. A route determination method, wherein the method is executed by a control node, A step of obtaining a first network topology of an audio-video system, wherein the attribute information of the links in the first network topology includes the transmission parameters of the links, and the transmission parameters of the links include a concave parameter, an additivity parameter, and a multiplicative parameter. The steps include determining links that satisfy the concave parameter constraint from the first network topology and generating a second network topology using the links that satisfy the concave parameter constraint, A step of generating a third network topology based on key parameters of links in the second network topology, wherein the key parameters are the parameters among the additive and multiplicative parameters that have the greatest influence on the target traffic type, and the attribute information of links in the third network topology includes a quantified score determined by the key parameters. The steps include determining a plurality of candidate paths based on the third network topology, The step includes determining a target path that satisfies the path selection constraints from the aforementioned plurality of candidate paths, The method wherein the second network topology is a simplified network topology obtained based on the first network topology, and the concave parameter constraints are pre-set in the simplified model.

2. The concave parameter includes bandwidth, the additive parameter includes cost and / or delay, and the multiplicative parameter includes packet loss rate. The method according to claim 1.

3. If the concavity parameter includes bandwidth, the constraint condition for the concavity parameter includes that the bandwidth is less than a pre-set threshold. The method according to claim 1.

4. The step of generating a third network topology based on the key parameters of the links in the second network topology is: A step of obtaining a quantitative score for the link based on the key parameters of the link, The process includes the step of generating the third network topology by using the structure of the second network topology as the structure of the third network topology and using the quantification score of the links as attribute information of the links in the third network topology. The method according to claim 1.

5. The step of determining multiple candidate paths based on the third network topology is: A step of determining a plurality of paths between a first node and a second node based on the third network topology, and determining the path score for each of the plurality of paths, wherein the path score for the plurality of paths is obtained based on the quantified scores of all links constituting the plurality of paths, and the first node and the second node are any two nodes in the third network topology, The step of determining the top N paths with the minimum path score from the plurality of paths as a plurality of candidate paths between the second node and the first node, wherein N is a positive integer greater than 1, The method according to claim 1.

6. The plurality of paths between the first node and the second node include a first path which is the shortest path from the first node to the second node, and a second path which is composed of a path from the first node to a divergent node and the shortest path from the divergent node to the second node, wherein the divergent node is any node in the first path other than the first node and the second node. The method according to claim 5.

7. The step of determining a target path that satisfies the path selection constraints from the aforementioned multiple candidate paths is: The steps include: obtaining the transmission parameters of all links constituting each of the plurality of candidate paths, and determining the transmission parameters of the candidate path based on the transmission parameters of all links constituting the candidate path; The process includes the step of determining a target path that satisfies the path selection constraints from among the multiple candidate paths, based on the transmission parameters of each of the multiple candidate paths. The method according to claim 1.

8. Regarding the concavity parameter, the value of the concavity parameter of the candidate path is equal to the minimum value of the concavity parameter of all links constituting the candidate path. Regarding the additivity parameter, the value of the additivity parameter of the candidate path is obtained by performing an addition operation based on the values ​​of the additivity parameters of all links constituting the candidate path. Regarding the multiplicity parameter, the value of the multiplicity parameter of the candidate path is obtained by performing a multiplication operation based on the values ​​of the multiplicity parameters of all links constituting the candidate path. The method according to claim 7.

9. A communication device including memory and a processor, A memory and a processor are coupled, the memory is for storing instructions that can be executed by the processor, and when the processor executes the instructions, it performs the method according to any one of claims 1 to 8. Communication device.

10. A computer-readable storage medium, The computer-readable storage medium stores computer instructions, and when a computer instruction is executed by the communication device, the communication device performs the method according to any one of claims 1 to 8. Computer-readable storage medium.

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