CN115914071A - Message transmission analysis method combining SRv6 with k nearest neighbor algorithm - Google Patents
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Abstract
The invention discloses a message transmission analysis method combining SRv6 with a k nearest neighbor algorithm. The method comprises the following steps: s11, an address identifier generating module is initially allocated to the Srv6 network node; and S12, constructing a k-nearest neighbor algorithm model, an early warning model and generating an intelligent distribution identification module. The method combines the SRv6 message environment with the optimal path method in the SRv6 network, and improves the accuracy and flexible configuration of data transmission by adopting different distribution algorithms for an SRv6 message routing distribution mechanism.
Description
Technical Field
The invention belongs to the field of network technology and security, and particularly relates to a message transmission analysis method of an SRv6 combined k-nearest neighbor algorithm.
Background
With the rapid development of computer technology, information networks have become an important guarantee of social development, under the large background of the cloud network convergence era, the flexible and agile network service capability directly affects the competitiveness of operators, SR (Segment Routing) is a source Routing technology, SRv6 is the application of SR technology in IPv6 networks, SRv6 is a huge innovation, programmable networks are enabled by combining with SDN technology, and innovative soil is provided for network basic services and value-added network services in the cloud network era.
Chinese invention patent CN201911329833.1 discloses a SID distribution method and device based on SRv6 network, including: acquiring at least one network segment, wherein each network segment comprises at least one IPv6 address; dividing each network segment into a plurality of SID segments which are different in type and have no intersection with each other; determining a corresponding distribution mode according to the type of each SID segment; and distributing the corresponding SID for the routing information of the SR equipment by each SID segment according to the corresponding distribution mode, so that the SR equipment generates a routing table according to the distributed SID. However, the invention only guarantees the uniqueness of the SID and cannot ensure the optimal path.
Chinese patent CN202010346913.4 discloses a data processing method based on SRv6 and related equipment, comprising: the controller generates a segment identifier, wherein the segment identifier comprises position information, an instruction and a path identifier, and the path identifier is used for indicating a forwarding path of the SRv6 message between network devices in the SR network; and the controller sends the segment identifier to network equipment, so that the network equipment forwards the SRv6 message according to the segment identifier. But this invention does not allow intelligent allocation of address identities.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a message transmission analysis method of SRv6 combined with k nearest neighbor algorithm, which highlights the position of the artificial intelligence in the scene of combining the optimal path method in the SRv6 network with the SRv6 message environment, and improves the accuracy and flexible configuration of data transmission by adopting different distribution algorithms for the SRv6 message routing distribution mechanism.
In order to achieve the purpose, the invention adopts the following technical scheme:
a message transmission analysis method of SRv6 combined with k nearest neighbor algorithm includes the following steps:
s11, an address identifier generating module is initially allocated to the Srv6 network node;
s12, constructing a k nearest neighbor algorithm model and an early warning model and generating an intelligent distribution identification module;
the step S12 includes the steps of:
s121, obtaining the fault rates of all next-hop SR nodes related to the initiating node through a k neighbor algorithm model, and predicting the alarm probability of all next-hop SR nodes through an early warning model;
s122, calculating the fault rate and the alarm probability, and judging the optimal next hop SR node until the optimal SR node of each hop reaching the target SR node is acquired and connected in series to form an optimal address identifier;
and S123, if the optimal address identification of each hop of SR node is superior to the initial address identification of each hop of SR node, updating the alignment initial address identification of the SRH part of the IPv6 extension head.
The SRv6 is a network forwarding technology, SR refers to Segment Routing technology, v6 refers to native IPv6, and SRv6 is IPv6+ Segment Routing; the initial distribution identification is an initial address identification generated by connecting SIDs of all path Segment Lists from the current SR node to the target SR node in series from bottom to top and spacing by # # # # in the middle; the optimal address identification is obtained by constructing a k nearest neighbor algorithm model and an early warning model, AI fault prejudgment and intelligent path optimization are added, initial distribution identification is replaced after preference comparison, a new solution is provided for complex service message forwarding in an ultra-large group network, resource consumption of message forwarding is reduced, and accuracy and flexible configuration of transmitted data are improved.
The SR is to Segment the packet forwarding path into different segments, and insert Segment information into the packet at the starting point of the path, and the intermediate node only needs to forward according to the Segment information carried in the packet, such a path Segment is called "Segment" and identified by a SID (Segment Identifier);
as a further description of the message transmission analysis method of the SRv6 combined with the k-nearest neighbor algorithm of the present invention, said step S11 includes the following steps:
s111, acquiring all path Segment Lists from the current SR node to the target SR node;
s112, extracting all SIDs of the Segment List, connecting the SIDs in series from bottom to top, and generating an initial address identifier by using a # # number in the middle;
s113, before the current SR node transmits to the next-hop SR node, the initial address identifier is put into the alignment of the SRH part of the IPv6 extended header.
The Segment List is all paths which are automatically distributed by a networking route and automatically reach a target SR node when the current SR node in the SRv6 network carries out IPv6 message transmission to the target SR node, and the segments List is stored in an SRv6 message protocol SID; the IPv6 extension header SRH mainly consists of Locator, function, and artifact, where Locator = location information reachability, function = service Function definition, and artifact = enhancement; the address identification facilitates communication object location.
As a further description of the message passing analysis method of the SRv6 combined with the k-nearest neighbor algorithm of the present invention, the step S121 k-nearest neighbor algorithm includes the following steps:
s1211, preparing data, and sequentially arranging the data according to dimensions;
s1212, calculating the distance from the test sample point to each other sample point;
s1213, sorting each distance, and then selecting K points with the minimum distance;
s1214, the categories of the K points are compared, and the test sample points are classified into the category with the highest proportion among the K points according to the principle that a minority obeys majority.
The K-nearest neighbor algorithm is used for data mining classification, and is a method for classifying each record in a data set, wherein K-nearest neighbors mean K nearest neighbors, and each sample can be represented by the K nearest neighbors.
As a further description of the message transmission analysis method of the SRv6 combined k-nearest neighbor algorithm of the present invention, the k-nearest neighbor algorithm model formula is:
in the formula: x is a radical of a fluorine atom 1 x 2 ... x n Is n-dimensional data of sample X; y is 1 y 2 ... y n N-dimensional data for sample Y; x is time, Y is network delay millisecond (ms); d (X, Y) is the distance of sample X, Y, in the present invention, 24 hours delay millisecond for the current network node<=50 associated nodes.
As a further description of the SRv6 combined k-nearest neighbor algorithm message transmission analysis method, the early warning model is constructed by adopting a Markov chain, and the formula is as follows:
X(k+1)＝X(k)×P
in the formula: x (k) represents a state vector of the trend analysis and prediction object at the time t = k, P represents a one-step transition probability matrix, and X (k + 1) represents a state vector of the trend analysis and prediction object at the time t = k + 1.
As a further description of the message transmission analysis method of the SRv6 combined with the k-nearest neighbor algorithm of the present invention, the calculation method of the failure rate and the alarm probability in step S13 is a weighted average. The weighted average is used to calculate the raw data in reasonable proportions.
The invention has the following beneficial effects:
when IPv6 nodes in an SRv6 network are accessed to the network, the invention applies a k nearest neighbor algorithm to obtain the probability of fault nodes in all network nodes which can be used as a first hop, thereby screening out healthy network nodes, then adopts a Markov chain to analyze the probability of fault of each Sid network node by taking historical flow alarm data as an analysis basis, and allocates probability value, and provides a new solution for forwarding complex service messages in a super-large group network by increasing AI fault prejudgment and intelligent path optimization, reduces the resource consumption of message forwarding, and simultaneously improves the accuracy and flexible configuration of transmission data.
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Fig. 1 is a flowchart of an overall scheme of a message transmission analysis method of the SRv6 combined with the k-nearest neighbor algorithm of the present invention.
Fig. 2 is a flow chart of an initial assignment address identifier generation module in fig. 1.
Fig. 3 is a flowchart of the k-nearest neighbor algorithm model, the early warning model and the intelligent distribution identification module generated in fig. 1.
Fig. 4 is a diagram of the SRv6 protocol architecture.
Fig. 5 is a flow chart of the k-nearest neighbor algorithm.
Fig. 6 is a schematic diagram of the k-nearest neighbor algorithm model.
Fig. 7 is an overall framework diagram of the message transmission analysis method of the SRv6 combined with the k-nearest neighbor algorithm of the present invention.
Detailed Description
The invention will be further explained with reference to the drawings.
A message transmission analysis method of SRv6 combined with k-nearest neighbor algorithm, as shown in fig. 1, includes the following steps:
s11, an address identifier generating module is initially allocated to the Srv6 network node;
as shown in fig. 2, the method specifically includes the following steps:
s111, acquiring all path Segment Lists from the current SR node to the target SR node; when the current SR node in SRv6 network transmits IPv6 message to the target SR node, the networking route will automatically distribute a path automatically reaching the target SR node, which is called Segment List for short, and it is stored in SRv6 message protocol SID;
s112, extracting all SIDs of the Segment List, connecting the SIDs in series from bottom to top, and generating an initial address identifier by using a # # number in the middle;
s113, before the current SR node transmits to the next-hop SR node, the initial address identifier is put into the alignment of the SRH part of the IPv6 extended header.
S12, constructing a k nearest neighbor algorithm model and an early warning model and generating an intelligent distribution identification module;
as shown in fig. 3, the method specifically includes the following steps:
s121, obtaining the fault rates of all next-hop SR nodes related to the initiating node through a k neighbor algorithm model, and predicting the alarm probability of all next-hop SR nodes through an early warning model;
s122, calculating the fault rate and the alarm probability, and judging the optimal next hop SR node until the optimal SR node of each hop reaching the target SR node is acquired and connected in series to form an optimal address identifier; the failure rate and the alarm probability are calculated by weighted average;
and S123, if the optimal address identification of each hop of SR node is superior to the initial address identification of each hop of SR node, updating the alignment initial address identification of the SRH part of the IPv6 extension head.
The SRv6 protocol structure is as shown in fig. 4, where an SRH (Segment Routing Header) extension Header is newly added to an IPv6 Routing extension Header, where the extension Header specifies an IPv6 explicit path and stores Segment List information of IPv6, where the Segment List is a forwarding path obtained by sequentially arranging segments and network nodes; the programmable capability of the SRv6 comes from the application of 128 bits of SRv6SID, the SRv6 Segment defines a network instruction in SRv6 network programming, indicates which and how to go, the ID for identifying the SRv6 Segment is called as the SRv6SID, the SRv6SID is a 128-bit value, is in an IPv6 address form and consists of a Locator, a Function and an augmentors; SRv6 Segment structure Locator: the method has a positioning function, provides the routing capability of IPv6, realizes addressing and forwarding of the message through the field, and also ensures that the route corresponding to the Locator can be aggregated; function: the forwarding actions are used for expressing the forwarding actions to be executed by the equipment instructions, and different forwarding actions are expressed by different functions; arguments: an optional field, which is a complement to the Function, and is a parameter corresponding to the instruction when executed, and the parameters may include a flow, a service, or any other relevant information; each Segment of the SRv6 is 128 bits, can be flexibly divided into multiple segments, and each Segment can be customized in function and length, so that the flexible programming capability is realized, namely, the service can be edited; through the programming space, the SRv6 has stronger network programming capability, can better meet the requirements of different network paths, is perfectly integrated with the SDN technology, realizes the interaction between the network and the application, and enables a service-driven programmable network.
The SR technology designs two implementation modes for a data plane, one is SR-MPLS multiplexing MPLS data plane, and the other is SRv6; SRv6 uses IPv6 data plane, expand on the basis of IPv6 route extension head, this part expands and does not destroy the IPv6 header of the standard, and, only SRv6 node need carry on the extra processing to the extension head, have no influence on other ordinary IPv6 nodes, this makes SRv6 compatible with existing IPv6 network seamlessly, let the forwarding level reach the extremely simple forwarding of pure IPv6 even more; SR-MPLS uses 4 bytes label identification path information, MPLS label can only identify three information of label value, TTL and label stack bottom, without expanding information ability. Unlike the Segment of SR MPLS, the ID of Segment of SRv6 is called SRv6SID, which is a 128-bit value and is divided into three parts:
locator (position indication): the identity assigned to a network node in the network may be used to route and forward packets. The Locator has two important attributes, routable and aggregated. In SRv6SID, locator is a variable length part used to adapt to networks of different sizes.
Function: the device assigns an ID value to the local forwarding instruction that can be used to express the forwarding action that the device is required to perform, corresponding to the opcode of the computer instruction. In SRv6 network programming, different forwarding behaviors are expressed by different function IDs. To some extent the functional ID is similar to the MPLS label and is used to identify VPN forwarding instances, etc.
Args (variables): parameters required by the forwarding instructions when executed may include flow, service, or any other relevant variable information.
In a word, the SRv6 has two forwarding attributes of routing and MPLS, has TE traffic engineering capability, expandability capability, and compatibility with IPv6, is also convenient for future fixed-mobile convergence, and realizes the unification of IP forwarding technologies.
Further, as shown in fig. 5, the step S121 k neighbor algorithm includes the following steps:
s1211, preparing data, and sequentially arranging the data according to dimensions;
s1212, calculating the distance from the test sample point to each other sample point;
s1213, sorting each distance, and then selecting K points with the minimum distance;
s1214, the categories of the K points are compared, and the test sample points are classified into the category with the highest proportion among the K points according to the principle that a minority obeys majority.
The working principle of the k-nearest neighbor algorithm is as follows: a sample data set and a data label exist, and the corresponding relation between the sample and the label is known; inputting data without labels, and comparing each characteristic of the new data with the characteristic corresponding to the data in the sample set; and extracting the classification label of the data with the most similar characteristics in the sample set, and only selecting the first k most similar data, wherein k is generally less than 20.
Further, the k-nearest neighbor algorithm model formula is as follows:
in the formula: x is a radical of a fluorine atom 1 x 2 ... x n Is n-dimensional data of a sample X; y is 1 y 2 ... y n N-dimensional data for sample Y; x is time, Y is network delay millisecond (ms); d (X, Y) is the distance of sample X, Y, in the present invention, 24 hours delay millisecond for the current network node<=50 associated nodes. .
The principle of the k-nearest neighbor algorithm model is schematically shown in fig. 6.
The calculation of the failure rate is illustrated by the following example:
after the current SID node obtains 10 network node data associated with the current SID node through log analysis and calculates, the alarm state is determined through the alarm states of 10 nodes (0 = no alarm, 1= alarm). Thereby completing the failure prediction for the current sample.
And (3) model operation results: if 10 associated node alarm states delaying millisecond < =50 within 24 hours of the current network node have 3 alarms, the network quality failure rate of the current node is 30%.
Further, the early warning model is constructed by adopting a Markov chain, and the formula is as follows:
X(k+1)＝X(k)×P
in the formula: x (k) represents a state vector of the trend analysis and prediction object at the time t = k, P represents a one-step transition probability matrix, and X (k + 1) represents a state vector of the trend analysis and prediction object at the time t = k + 1.
The calculation of the alarm probability is illustrated by the following example:
the current node forwarding historical probabilities [ 0.3, 0.7 ];
the probability of the current node to carry out the alarm to the normal transition is 0.6 and 0.4;
the probability of the current node normally transferring to the alarm is (0.3, 0.7);
calculating by a model formula to obtain:
the priority forwarding probability =0.3x0.6+0.3x0.7=0.39;
this time non-preferential forwarding probability =0.3x0.4+0.7x0.7=0.61.
The overall structure of the present invention obtained by the above embodiments is as shown in fig. 7, and the artificial intelligence position is creatively highlighted in the SRv6 network message forwarding. Firstly, the Srv6 network starting node extracts all SIDs of the Segment List, the SIDs are connected in series from bottom to top, and the interval is marked with # #, so as to generate an initial address identifier. And secondly, obtaining the fault rates of all next-hop SR nodes related to the initiating node through a k-nearest neighbor algorithm model. And then, carrying out alarm probability prediction on all next-hop SR nodes through an early warning model. If the optimal address identifies each hop of SR node better than the initial address identifies each hop of SR node. Then the alignment initial address identification of the IPv6 extension header SRH part is updated. Compared with the existing Srv6 message forwarding mechanism, the method increases AI fault prejudgment and intelligent path optimization, provides a new solution for forwarding complex service messages in the ultra-large group network, reduces the resource consumption of message forwarding, and improves the accuracy and flexible configuration of transmitted data.
The above embodiments are only exemplary embodiments of the present invention, and are not intended to limit the present invention, and the scope of the present invention is defined by the claims. Various modifications and equivalents may be made thereto by those skilled in the art within the spirit and scope of the present invention, and such modifications and equivalents should be considered as falling within the scope of the present invention.
Claims (6)
1. A message transmission analysis method of SRv6 combined with k nearest neighbor algorithm is characterized by comprising the following steps:
s11, an address identifier generating module is initially allocated to the Srv6 network node;
s12, constructing a k nearest neighbor algorithm model, an early warning model and generating an intelligent distribution identification module;
the step S12 includes the steps of:
s121, obtaining the fault rates of all next-hop SR nodes related to the initiating node through a k neighbor algorithm model, and predicting the alarm probability of all next-hop SR nodes through an early warning model;
s122, calculating the fault rate and the alarm probability, and judging the optimal next hop SR node until the optimal SR node of each hop reaching the target SR node is acquired and connected in series to form an optimal address identifier;
and S123, if the optimal address identification of each hop of SR node is superior to the initial address identification of each hop of SR node, updating the alignment initial address identification of the SRH part of the IPv6 extension head.
2. The SRv6 message transmission analysis method in combination with k-nearest neighbor algorithm according to claim 1, wherein: the step S11 includes the steps of:
s111, acquiring all path Segment Lists from the current SR node to the target SR node;
s112, extracting all SIDs of the Segment List, connecting the SIDs in series from bottom to top, and generating an initial address identifier by spacing with # # #;
s113, before the current SR node transmits to the next-hop SR node, the initial address identifier is put into the alignment of the SRH part of the IPv6 extended header.
3. The SRv6 message transmission analysis method in combination with k-nearest neighbor algorithm according to claim 1, wherein: the step S121 k nearest neighbor algorithm includes the steps of:
s1211, preparing data, and sequentially arranging the data according to dimensions;
s1212, calculating the distance from the test sample point to each other sample point;
s1213, sorting each distance, and then selecting K points with the minimum distance;
s1214, the categories of the K points are compared, and the test sample points are classified into the category with the highest proportion among the K points according to the principle that a minority obeys majority.
4. The SRv6 message transmission analysis method in combination with k-nearest neighbor algorithm according to claim 3, wherein: the k nearest neighbor algorithm model formula is as follows:
in the formula: x is a radical of a fluorine atom 1 x 2 ... x n Is n-dimensional data of sample X; y is 1 y 2 ... y n N-dimensional data for sample Y; x is time, Y is network delay millisecond (ms); d (X, Y) is the distance of sample X, Y, in the present invention, 24 hours delay millisecond for the current network node<=50 associated nodes.
5. The SRv6 message transmission analysis method combined with k-nearest neighbor algorithm according to claim 4, wherein: the early warning model is constructed by adopting a Markov chain, and the formula is as follows:
X(k+1)＝X(k)×P
in the formula: x (k) represents a state vector of the trend analysis and prediction object at the time t = k, P represents a one-step transition probability matrix, and X (k + 1) represents a state vector of the trend analysis and prediction object at the time t = k + 1.
6. The SRv6 message transmission analysis method in combination with k-nearest neighbor algorithm according to claim 1, wherein: the calculation method of the failure rate and the alarm probability in step S122 is a weighted average.
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