CN117081985B - Network traffic redundancy transmission method and device - Google Patents
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Abstract
The application discloses a network traffic redundancy transmission method and device, which solve the problems of high network overhead and reduced network reliability due to multi-link faults caused by redundant transmission of a network. The network traffic redundancy transmission method comprises the following steps: calculating a shortest path from a start point to an end point by using a shortest path algorithm; links with failure rates greater than a set threshold exist on the shortest path; calculating the sum of path costs of a plurality of paths from the starting point to the end point every two; selecting two paths with the minimum sum of path costs and junction nodes as redundant transmission paths to respectively transmit the same data frame; the factor for calculating the path cost at least comprises the sum of the hops of each intersection node and the destination node of the path. Based on a completely centralized network architecture, the network link fault prediction model is constructed by observing the state of a network link and collecting data and using a machine learning method.
Description
Technical Field
The present disclosure relates to the field of communications technologies, and in particular, to a method and an apparatus for redundant transmission of network traffic.
Background
In the conventional ethernet, data is transmitted in a best effort manner, congestion and packet loss often occur in the network due to arrival of burst traffic, and in addition, a node or a link of the network fails, which also causes that a receiving end cannot normally receive the data packet. In industrial networks, many scenarios have extremely high requirements on the reliability of data, zero tolerance for loss of data packets, and conventional ethernet best effort transmission modes are obviously not suitable for application scenarios with ultra-high reliability requirements. In view of the above, time-sensitive network protocols use methods of frame duplication and frame erasure to ensure reliable transmission of the network. Frame duplication and frame erasure improve the reliability of a network by performing redundant transmission of the same data frame over multiple different paths, and even if one of the paths is lost or erroneous, the data frame can still smoothly reach the destination node along the other path. When the repeated data frames reach the intersection node, the intersection node identifies and duplicates the repeated data frames.
The conventional frame copying and frame eliminating mechanism has the following three problems: firstly, multi-path redundant transmission is carried out on all data frames with high reliability requirements, a great amount of additional expenses are brought to a network, the resource waste is more remarkable when the network scale is enlarged, and the network efficiency is greatly reduced; secondly, a plurality of discontinuous nodes or links in the network occasionally fail, and if two paths of data frame transmission fail, the data frame still cannot reach the end smoothly; third, if there is no completely disjoint path in the network when performing redundant transmission, the data frame copies will be eliminated at the junction node, and once the transmission path between the junction node and the destination fails, the data transmission will be greatly affected.
Disclosure of Invention
The application provides a network traffic redundancy transmission method and device, which solve the problems of high network overhead and reduced network reliability due to multi-link faults caused by redundant transmission of a network.
In a first aspect, an embodiment of the present application provides a network traffic redundancy transmission method, including the steps of:
calculating a shortest path from a start point to an end point by using a shortest path algorithm; links with failure rates greater than a set threshold exist on the shortest path;
calculating the sum of path costs of a plurality of paths from the starting point to the end point every two;
selecting two paths with the minimum sum of path costs and junction nodes as redundant transmission paths to respectively transmit the same data frame; the factor for calculating the path cost at least comprises the sum of the hops of each intersection node and the destination node of the path.
Further, the selecting of the two paths includes the steps of:
determining a plurality of alternative paths from a starting point to an end point through a KSP algorithm to form an alternative path set;
the sum of path costs is calculated for each path in the alternative path set, and two paths with the minimum sum of path costs are determined.
In one embodiment, the factors also include network overhead. The sum of the data frame sizes transmitted by each link on the path determines the cost of the network overhead.
In one embodiment, the factors also include redundant path lengths. The delay and difference weighted sum of the two path transmissions determines the cost of the length of the redundant path.
Preferably, the path cost for each path is a weighted sum of the costs of all factors.
Preferably, a preset network failure model is used to predict failure rates of all links of the shortest path during the transmission period.
In a second aspect, an embodiment of the present application further provides a network traffic redundancy path selection apparatus, configured to implement the method described in any one embodiment of the first aspect, including: and the link fault prediction module is used for collecting and training the link information and improving the accuracy of the fault prediction model according to the historical data. And the path calculation module is used for calculating the path of the redundant transmission. And the decision issuing module is used for issuing the redundant transmission path to each network node.
Further, the path calculation module includes: and the acquisition unit is used for acquiring the network traffic data. A determining unit, configured to determine factors of an alternative path and path cost; but also for determining two paths for which the sum of path costs is minimal. And a calculation unit for calculating the sum of the path costs.
In a third aspect, embodiments of the present application further provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method according to any of the embodiments of the first aspect.
In a fourth aspect, embodiments of the present application further provide an electronic device, including a memory, a processor and a computer program stored on the memory and executable by the processor, the processor implementing the method according to any of the embodiments of the first aspect when the processor executes the computer program.
The above-mentioned at least one technical scheme that this application embodiment adopted can reach following beneficial effect:
based on a completely centralized network architecture, the network link fault prediction model is constructed by observing the state of a network link and collecting data and using a machine learning method. Based on the model, the traditional flow redundancy transmission method is improved, differentiated redundancy transmission is carried out on data according to the predicted link failure probability in the flow transmission time period, and a redundant path is not planned for a path with good link state, so that the communication cost of a network is greatly reduced; in addition, for the flow needing redundant transmission, the positions of the intersection nodes of the transmission paths and the time delay of the transmission paths are comprehensively considered in consideration of multi-link faults, the flow is constructed as a function, and the minimized path cost function is used as an optimization target, so that the reliability of the network redundant transmission is improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:
fig. 1 is a flowchart of a network traffic redundancy transmission method according to an embodiment of the present application;
FIG. 2 is a flowchart of another network traffic redundancy transmission method according to an embodiment of the present application;
FIG. 3 is a diagram illustrating a network traffic redundancy path selection device according to an embodiment of the present application;
fig. 4 is a block diagram of a path calculation module according to an embodiment of the present application.
Detailed Description
For the purposes, technical solutions and advantages of the present application, the technical solutions of the present application will be clearly and completely described below with reference to specific embodiments of the present application and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
The following describes in detail the technical solutions provided by the embodiments of the present application with reference to the accompanying drawings.
Fig. 1 is a flowchart of a network traffic redundancy transmission method according to an embodiment of the present application.
The embodiment of the application provides a network traffic redundancy transmission method, which comprises the following steps 110-130:
step 110, calculating the shortest path from the starting point to the end point by using a shortest path algorithm; links with failure rates greater than a set threshold exist on the shortest path.
Preferably, a preset network failure model is used to predict failure rates of all links of the shortest path during the transmission period.
For example, the network can monitor link state information based on a fully centralized architecture. Constructing a training set of network link fault information based on accumulated historical information of the early-stage link state information, learning training set data in a network centralized controller by using a machine learning method, thereby constructing a fault model of the whole network, and being capable of carrying out fault probability on different links in different time periodsAnd (5) accurately predicting.
For example, the construction of the fault model includes the following steps:
1) Continuously monitoring network state, and collecting information such as delay, jitter and packet loss of data flow;
2) Abnormal detection is carried out on index information such as time delay, jitter and packet loss, path information of the data flow with abnormal index in the same time period is obtained, and fault positions are judged in a mode that different paths take intersections (for example, the data flow passing through the same link/node in the same time period has packet loss phenomenon, and the link/node is indicated to have packet loss fault);
3) Through the continuous accumulation of historical data, the original data is trained by using machine learning, and a time-fault probability prediction graph is drawn for each node and each link.
As another example, the network may use a directed graphWhere V represents the set of vertices and E represents the set of edges. Node->，/>Is a directed edge from u to v. Path P is defined as a set of node sequences, i.eThe nodes are contained in vertex set V, wherein the edges of any two adjacent nodes are contained in edge set E. Therefore, set +.>Containing the set of possible k combinations of paths in graph G.
For a network traffic instance, assume that its starting point is node S, its ending point is node D, and both S and D are nodes in set V. The shortest path P from node S to node D is first found using a shortest path algorithm, such as Dijkstra' S algorithm. For each link that constitutes path P, a pre-trained network failure model is used to predict its failure rate during the link transmission period.
For example, a threshold value of failure rate is setThis value may be modified according to the specific needs of the network. If any link on path P predicted by model +.>Is>All smaller than the threshold F, namely:
equation 1
The method has the advantages that all the links on the path P are good in state, and the probability of failure on the path P is extremely low, so that the controller decides to transmit the network traffic by using only one path of the path P, other redundant paths do not need to be planned, and network overhead caused by redundant transmission can be greatly reduced.
If there is a link on the path PIts predictive failure rate->Greater than threshold F, namely:
equation 2
It is stated that traffic may be erroneous or lost on the link as it is transmitted on path P and therefore the controller needs to choose other redundant paths to transmit.
Step 120, calculating the sum of path costs for a plurality of paths from the start point to the end point.
Step 130, selecting two paths with the minimum sum of path costs and with intersection nodes as redundant transmission paths to respectively transmit the same data frame (i.e. repeated data frames); the factor for calculating the path cost at least comprises the sum of the hops of each intersection node and the destination node of the path.
The number of the intersection nodes is 0, and the path cost is not calculated.
The number of the intersection nodes is larger than 0, and the path cost of the distance between the intersection nodes and the destination node (namely the destination) is the sum of the hop count (distance) of each intersection node and the destination node (namely the destination) of the path.
The impact of the junction (except the starting point) on the path quality is mainly reflected in two aspects: the number of the intersection nodes and the positions of the intersection nodes. The specific explanation is as follows:
1) Number of junction nodes. At each intersection node, whether to copy and eliminate the data frames can be configured, along with the increase of the number of the intersection nodes, the configuration of the intersection nodes needs to be decided according to specific conditions, and meanwhile, the controller independently configures each intersection node. Thus, the greater the number of junction nodes, the more complex the configuration of the network.
2) Number of hops from the junction to the endpoint. Assuming that the data frame stops copying after eliminating the redundant frame at the nth intersection node N (the behavior is decided by the network controller), the data frame is finally transmitted along one path only after the intersection node, and the transmission success is provided that all links behind the intersection node work normally, and the failure rate of each link between the intersection node N and the destination node D is set as followsThe probability of successful transmission is +.>Due to->(0, 1), the probability of successful transmission follows +.>Increasing and decreasing.
For example, whether or not two redundant paths selected have intersecting nodes and the location of the intersecting nodes (if any). Assuming that the two paths selected have n junction points (n is a natural number), if n=0, it is stated that the two paths have no junction points, in which case the influence of this factor on the path cost function is 0, i.e.>The method comprises the steps of carrying out a first treatment on the surface of the If n>0, it indicates that there is a junction node between the two paths, and the transmission reliability is higher as the junction node is closer to the destination node, at this time, the junction node is +.>Equal to the sum of the number of hops (distance) of each junction node from the destination node.
For measuring the condition of the junction of two selected redundant paths, there is provided +.>Junction nodes, all junction nodes are assembled by +.>The expression is:
differentiated redundant transmission is performed based on network link failure prediction, and unnecessary communication overhead is reduced.
Fig. 2 is a flowchart of another network traffic redundancy transmission method according to an embodiment of the present application, including steps 110 to 230.
Step 110, calculating the shortest path from the starting point to the end point by using a shortest path algorithm; links with failure rates greater than a set threshold exist on the shortest path.
Steps 210 to 220 are calculating the sum of path costs for a plurality of paths from the start point to the end point.
Step 210, determining a plurality of alternative paths from a starting point to an end point through a KSP algorithm to form an alternative path set.
For example, k shortest paths from the start point S to the end point D are found using the KSP algorithm, constituting a set of alternative paths.
Step 220, calculating the sum of path costs for each path in the alternative path set.
For example, two paths are selected from the candidate path set, and a path cost function is constructed to measure the merits of the selected paths.
Factors that calculate the path cost include: network overhead, junction-to-endpoint distance, length of redundant paths.
Step 230, determining two paths with minimum sum of path costs through heuristic algorithm.
Step 130, selecting two paths with the minimum sum of path costs and with intersection nodes as redundant transmission paths to respectively transmit the same data frame; the factor for calculating the path cost at least comprises the sum of the hops of each intersection node and the destination node of the path.
The method comprises the following steps that the path cost of each path is preferably weighted sum of the costs of all factors.
The path costs of the above factors are summed:
equation 4
Wherein,any one of the paths representing the path cost to be calculated, and the factors determining the path cost include network overhead factor +.>Junction and endpoint distance factor +.>Length factor->，/>、/>、/>For the weight coefficient, the sum of the weight coefficients is 1, i.e. satisfy +.>+/>The specific value of the network interface can be dynamically adjusted according to the network requirements.
Using heuristic algorithms, e.g. geneticThe optimization solution is carried out on the algorithm, the ant lion algorithm and the like, and finally the algorithm is integratedTwo optimal paths are selected as the decision of the final redundant transmission, and the final optimized objective function is +.>。
On the basis of differential redundant transmission, a KSP algorithm is used for calculating a set of k shortest paths to form an alternative path setMeanwhile, a path cost function is constructed, information such as network overhead, multi-path junction node position, multi-path transmission delay and the like is comprehensively considered, the path cost function is minimized, and the path cost function is selected from an alternative path set +.>The optimal two paths are selected as the decision of the final redundant transmission.
Preferably, for example, factors affecting path cost are mainly the following three parts:
first, the condition of the junction point, namely the distance between the junction point and the destination point, is specifically the sum of the hops of each junction point and the destination point of the path.
Second, the factors also include network overhead. The sum of the data frame sizes transmitted by each link on the path determines the cost of the network overhead.
For example, network overhead caused by data transmission on these two paths. Namely: to complete the transmission of this data, the sum of the data frame sizes required to be transmitted for each link on the two paths selected.
Third, in one embodiment, the factors further include redundant path lengths. The delay and difference weighted sum of the two path transmissions determines the cost of the length of the redundant path.
For example, the length factor of the two redundant paths selected. The shorter the path is, the smaller the transmission delay difference between the two paths is, and the better the real-time performance and the reliability of the transmission are. Use->And->Representing the two paths selected, there are:
equation 5
Wherein,、/>respectively representing the time delays of the two paths transmission,representing the sum of the delays of the two transmission paths,representing the delay difference of the two transmission paths. />、/>For the weight coefficient, the sum of the weight coefficients is 1, i.e. satisfy +.>+/>The +.f. can be dynamically adjusted according to the requirement of the time delay difference of the two paths>And->Is of a size of (a) and (b).
It should be noted that there is no sequence relationship among the first, second and third portions, and after the path costs of the factors are obtained, the sum of the path costs of the steps 220 to 230 may be calculated.
Fig. 3 is a block diagram of a network traffic redundancy path selection device according to an embodiment of the present application. An embodiment of the present application further provides a network traffic redundancy path selection apparatus, configured to implement the method described in any one embodiment of the first aspect, including:
the link failure prediction module 31 is configured to collect and train link information, and improve accuracy of a failure prediction model according to historical data.
The path calculation module 32 is configured to calculate a path of the redundant transmission.
The decision issuing module 33 is configured to issue the path of the redundant transmission to each network node.
As shown in fig. 3, the redundant transmission of the network traffic is calculated and decided by the redundant transmission policy device located in the centralized control thereof. The redundant transmission strategy device mainly comprises three modules, namely a link failure prediction model, path calculation and decision issuing. The link failure prediction model can collect and train link information, and the accuracy of the failure prediction model is continuously improved according to historical data. The path calculation module can calculate a required path set by using various routing algorithms according to the network topology. The decision issuing module can calculate a final redundant transmission decision according to the link fault prediction information and the information fed back by the path calculation module, and issues the final redundant transmission decision to each network node.
Fig. 4 is a block diagram of a path calculation module according to an embodiment of the present application. Further, based on the embodiment shown in fig. 3, the path calculation module includes:
an acquiring unit 41, configured to acquire network traffic data.
A determining unit 42 for determining factors of the alternative path, path cost function; but also for determining two paths for which the sum of path costs is minimal.
A calculating unit 43 for calculating the sum of path costs, including the processes of shortest path calculation, k shortest path calculation and cost function construction.
Specific methods for implementing the functions of the acquiring unit, the determining unit and the calculating unit are described in the embodiments of the methods of the first aspect of the present application, and are not described herein again.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Accordingly, the present application also proposes a computer readable storage medium, on which a computer program is stored, which program, when being executed by a processor, implements a method as described in any of the embodiments of the present application.
Further, the application also proposes an electronic device comprising a memory, a processor and a computer program stored on the memory and executable by the processor, said processor implementing a method according to any of the embodiments of the application when executing said computer program.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory. The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and changes may be made to the present application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present application are intended to be included within the scope of the claims of the present application.
Claims (7)
1. The network traffic redundancy transmission method is characterized by comprising the following steps:
calculating a shortest path from a start point to an end point by using a shortest path algorithm; links with failure rates greater than a set threshold exist on the shortest path;
calculating the sum of path costs of a plurality of paths from the starting point to the end point every two;
selecting two paths with the minimum sum of path costs and junction nodes as redundant transmission paths to respectively transmit the same data frame; the factors for calculating the path cost at least comprise the sum of the hop counts of each intersection node and the destination node of the path, network overhead and redundant path length;
the cost of the network overhead is determined by summing the sizes of the data frames transmitted by each link on the path;
the cost of the redundant path length is determined by the sum of the time delay and the difference weight of the two path transmission;
the path cost for each path is a cost weighted sum of all factors.
2. The network traffic redundancy transmission method according to claim 1, wherein the selection of the two paths comprises the steps of:
determining a plurality of alternative paths from a starting point to an end point through a KSP algorithm to form an alternative path set;
the sum of path costs is calculated for each path in the alternative path set, and two paths with the minimum sum of path costs are determined.
3. The network traffic redundancy transmission method of claim 1, wherein the failure rate of all links of the shortest path during the transmission period is predicted using a preset network failure model.
4. A network traffic redundancy transmission apparatus for implementing the network traffic redundancy transmission method of any one of claims 1 to 3, comprising:
the link fault prediction module is used for collecting and training the link information and improving the accuracy of a fault prediction model according to the historical data;
the path calculation module is used for calculating the path of redundant transmission;
and the decision issuing module is used for issuing the redundant transmission path to each network node.
5. The network traffic redundant transmission apparatus of claim 4 wherein the path computation module comprises:
the acquisition unit is used for acquiring network traffic data;
a determining unit, configured to determine factors of an alternative path and path cost; the method is also used for determining two paths with minimum sum of path costs;
and a calculation unit for calculating the sum of the path costs.
6. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any of claims 1-3.
7. An electronic device comprising a memory, a processor and a computer program stored on the memory and running on the processor, characterized in that the processor implements the method according to any of claims 1-3 when executing the computer program.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113489643A (en) * | 2021-07-13 | 2021-10-08 | 腾讯科技（深圳）有限公司 | Cloud-edge cooperative data transmission method and device, server and storage medium |
| CN115630367A (en) * | 2022-10-19 | 2023-01-20 | 南京航空航天大学 | XGboost-based routing algorithm key fault point identification method |
| CN116828623A (en) * | 2023-07-11 | 2023-09-29 | 北京交通大学 | Multi-path scheduling device and method for data packet |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113489643A (en) * | 2021-07-13 | 2021-10-08 | 腾讯科技（深圳）有限公司 | Cloud-edge cooperative data transmission method and device, server and storage medium |
| CN115630367A (en) * | 2022-10-19 | 2023-01-20 | 南京航空航天大学 | XGboost-based routing algorithm key fault point identification method |
| CN116828623A (en) * | 2023-07-11 | 2023-09-29 | 北京交通大学 | Multi-path scheduling device and method for data packet |
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