CN112131223B - Traffic classification statistical method, device, computer equipment and storage medium - Google Patents
Traffic classification statistical method, device, computer equipment and storage medium Download PDFInfo
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Abstract
The application relates to a traffic classification statistical method, a traffic classification statistical device, computer equipment and a storage medium. The method comprises the following steps: extracting target classification dimension information and first flow information according to the acquired data packet; the target classification dimension information is used for representing at least one classification dimension corresponding to the data packet, and the first flow information is used for representing the flow size of the data packet; searching a target node in a preset red-black tree based on target classification dimension information; the classification dimension information stored by the target node is consistent with the target classification dimension information; if the target node is not found, inserting a new node into the target finding path of the red-black tree to obtain a new red-black tree; the target searching path is a path for searching a target node, target classification dimension information and first flow information are stored in the new node, and the new red-black tree is used for carrying out flow classification statistics. The flow classification statistical efficiency can be improved by adopting the method.
Description
Technical Field
The present disclosure relates to the field of data communications technologies, and in particular, to a traffic classification statistical method, apparatus, computer device, and storage medium.
Background
In the field of data communications, classification statistics of network traffic is a basic and important technique. The user can know the flow condition under each classification dimension through the flow classification statistical technology.
At present, a common flow classification statistical technology uses a data structure of hash tables and linked lists. And establishing key values in advance according to the classification dimension to be counted, and then creating a hash table to store the key values. When the flow classification statistics is carried out, a hash value is calculated according to the classification dimension information of the flow, and a key value matched with the hash value is searched in a hash table. After the matched key value is found, determining a memory node corresponding to the key value in the hash table, and judging whether the classification dimension information stored in the memory node is consistent with the classification dimension information of the flow. If not, the flow size needs to be stored in the plug-in linked list on the memory node.
The hash table plus linked list data structure has the following disadvantages: under the condition of higher conflict rate, the situation that a link list hung on a certain node in the hash table is long can occur, the time complexity of operations such as searching the link list is uncontrollable, and the statistical efficiency is greatly reduced.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a flow classification statistical method, apparatus, computer device, and storage medium that can improve statistical efficiency.
A traffic classification statistical method, the method comprising:
extracting target classification dimension information and first flow information according to the acquired data packet; the target classification dimension information is used for representing at least one classification dimension corresponding to the data packet, and the first flow information is used for representing the flow size of the data packet;
searching a target node in a preset red-black tree based on target classification dimension information; the classification dimension information stored by the target node is consistent with the target classification dimension information;
if the target node is not found, inserting a new node into the target finding path of the red-black tree to obtain a new red-black tree; the target searching path is a path for searching a target node, target classification dimension information and first flow information are stored in a new node, and the new red black tree is used for carrying out flow classification statistics.
In the above embodiment, the time complexity of the operations such as searching and inserting can be low by adopting the red-black tree structure, so that the efficiency of flow classification statistics can be effectively improved.
In one embodiment, after searching the target node in the preset red-black tree based on the target classification dimension information, the method further includes:
and if the target node is found, updating the second flow information stored in the target node by adopting the first flow information to obtain a new red-black tree.
In the above embodiment, no new node is required to be inserted into the red-black tree, so that storage resources can be saved.
In one embodiment, searching the target node in the preset red-black tree based on the target classification dimension information includes:
determining a target classification dimension value corresponding to the target classification dimension information;
comparing the target classification dimension value with a classification dimension value corresponding to classification dimension information stored by an nth node in the target search path;
if the target classification dimension value is inconsistent with the classification dimension value corresponding to the classification dimension information stored by the nth node, comparing the size of the target classification dimension value with the size of the classification dimension value corresponding to the nth node;
and determining an n+1th node in the target search path from the child nodes of the n node according to the comparison result, and comparing the target classification dimension value with the classification dimension value corresponding to the classification dimension information stored in the n+1th node.
In the embodiment, the searching accuracy can be ensured, and the waste of storage resources can be avoided.
In one embodiment, the method further comprises:
and if the target classification dimension value is consistent with the classification dimension value corresponding to the classification dimension information stored by the nth node, determining the nth node as the target node.
In one embodiment, the method further comprises:
if the target classification dimension value is inconsistent with the classification dimension value corresponding to the classification dimension information stored by the nth node and the nth node does not have a child node, determining that the target node is not found in the red-black tree.
In the embodiment, the searching accuracy can be ensured, and the waste of storage resources can be avoided.
In one embodiment, inserting a new node in the target search path of the red-black tree to obtain a new red-black tree includes:
applying for a new node, and storing the target classification dimension information and the first flow information into the new node;
and taking the node at the tail end of the target search path as a father node, taking the new node as a child node, and connecting the child node with the father node to obtain the new red black tree.
In the above embodiment, the new storage resource is applied only when the target node is not found in the red-black tree, that is, the storage resource is allocated as required, and compared with the prior art, the resource allocation is more efficient and reliable.
In one embodiment, after inserting a new node in the target search path of the red-black tree to obtain a new red-black tree, the method further includes:
traversing each node of the new red-black tree, and carrying out statistics on the flow size of the dimension to be counted and classified according to the second flow information stored by each node in the new red-black tree.
The embodiment adopts the red black tree structure to store the classification dimension information of the flow, and when the flow size is counted for the classification dimension to be counted, the time complexity of searching the classification dimension information is lower than that of searching the classification dimension information in the prior art, so that the efficiency of flow classification counting can be improved.
In one embodiment, the extracting the target classification dimension information and the first traffic information according to the acquired data packet includes:
extracting at least one classification dimension corresponding to the data packet from the packet header of the data packet according to the IP layer protocol to obtain target classification dimension information; the classification dimension includes at least one of a source IP address and an application protocol;
and extracting the data length from the packet head of the data packet according to the IP layer protocol, and calculating the flow of the data packet according to the data length to obtain first flow information.
A traffic classification statistical device, the device comprising:
the information extraction module is used for extracting target classification dimension information and first flow information according to the acquired data packet; the target classification dimension information is used for representing at least one classification dimension corresponding to the data packet, and the first flow information is used for representing the flow size of the data packet;
the node searching module is used for searching target nodes in a preset red-black tree based on the target classification dimension information; the classification dimension information stored by the target node is consistent with the target classification dimension information;
the node insertion module is used for inserting a new node into the target searching path of the red-black tree if the target node is not searched, so as to obtain a new red-black tree; the target searching path is a path for searching a target node, target classification dimension information and first flow information are stored in a new node, and the new red black tree is used for carrying out flow classification statistics.
A computer device comprising a memory storing a computer program and a processor which when executing the computer program performs the steps of:
extracting target classification dimension information and first flow information according to the acquired data packet; the target classification dimension information is used for representing at least one classification dimension corresponding to the data packet, and the first flow information is used for representing the flow size of the data packet;
Searching a target node in a preset red-black tree based on target classification dimension information; the classification dimension information stored by the target node is consistent with the target classification dimension information;
if the target node is not found, inserting a new node into the target finding path of the red-black tree to obtain a new red-black tree; the target searching path is a path for searching a target node, target classification dimension information and first flow information are stored in a new node, and the new red black tree is used for carrying out flow classification statistics.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
extracting target classification dimension information and first flow information according to the acquired data packet; the target classification dimension information is used for representing at least one classification dimension corresponding to the data packet, and the first flow information is used for representing the flow size of the data packet;
searching a target node in a preset red-black tree based on target classification dimension information; the classification dimension information stored by the target node is consistent with the target classification dimension information;
if the target node is not found, inserting a new node into the target finding path of the red-black tree to obtain a new red-black tree; the target searching path is a path for searching a target node, target classification dimension information and first flow information are stored in a new node, and the new red black tree is used for carrying out flow classification statistics.
The flow classification statistical method, the flow classification statistical device, the computer equipment and the storage medium are characterized in that a server extracts target classification dimension information and first flow information according to the acquired data packet; searching a target node in a preset red-black tree based on target classification dimension information; if the target node is not found, inserting a new node into the target finding path of the red-black tree to obtain a new red-black tree. In the embodiment of the disclosure, the red black tree structure is adopted to store classification dimension information and flow information, and the time complexity of operations such as searching, inserting and the like increases logarithmically along with the increase of data quantity; in the prior art, the hash table and the linked list are adopted, and the time complexity of operations such as searching, inserting and the like linearly increases along with the increase of the data quantity. Because the logarithmic growth is slower than the linear growth, the time complexity of the disclosed embodiments is lower than that of the prior art, and thus the efficiency of traffic classification statistics can be effectively improved.
Drawings
FIG. 1 is a diagram of an application environment for a traffic classification statistical method in one embodiment;
FIG. 2 is a flow chart of a flow classification statistical method according to one embodiment;
FIG. 3 is a second flow chart of a flow classification statistical method according to one embodiment;
FIG. 4 is a flowchart of a step of searching a preset red-black tree for a target node according to one embodiment;
FIG. 5 is a second flowchart illustrating a step of searching for a target node in a preset red-black tree according to an embodiment;
FIG. 6 is a flow diagram of a step of inserting new nodes in a target lookup path of a red-black tree in one embodiment;
FIG. 7 is a flow chart of a flow classification statistical method according to another embodiment;
FIG. 8 is one of the block diagrams of the traffic classification statistics apparatus in one embodiment;
FIG. 9 is a second block diagram of a traffic classification statistics device in one embodiment;
FIG. 10 is a third block diagram of a traffic classification statistics device in one embodiment;
FIG. 11 is a fourth block diagram of a traffic classification statistics device in one embodiment;
fig. 12 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
The flow classification statistical method provided by the application can be applied to an application environment shown in fig. 1. The application environment includes a terminal 102 and a server 104. Wherein the terminal 102 communicates with the server 104 through a network, the terminal 102 sends a data packet to the server 104, or the server 104 sends a data packet to the terminal 102. Then, the server 104 extracts target classification dimension information and first flow information according to the acquired data packet, and searches for a target node in a preset red-black tree based on the target classification dimension information; if the target node is not found, inserting a new node into the target finding path of the red-black tree to obtain a new red-black tree, and carrying out flow classification statistics according to the new red-black tree. The terminal 102 may be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices, and the server 104 may be implemented as a stand-alone server or a server cluster composed of a plurality of servers.
In one embodiment, as shown in fig. 2, a traffic classification statistical method is provided, and the method is applied to the server in fig. 1 for illustration, and includes the following steps:
step 201, extracting target classification dimension information and first flow information according to the acquired data packet.
The target classification dimension information is used for representing at least one classification dimension corresponding to the data packet, and the first flow information is used for representing the flow size of the data packet. The classification dimension may include at least one of a source IP (Internet Protocol ) address and an application protocol. The embodiment of the disclosure does not limit the classification dimension, and can be set according to actual conditions.
The terminal sends the data packet to the server, and the server extracts the source IP address, the application protocol and the like of the data packet from the data packet sent by the terminal to obtain the target classification dimension information of the data packet. Meanwhile, the server determines the flow size of the data packet and obtains the first flow information of the data packet.
For example, the server extracts the destination classification dimension information from the data packet, where the destination classification dimension information includes a source IP address a, and the first traffic information includes a traffic size x1.
Or the terminal sends a data packet to the server, and the server processes the received data packet to obtain stream abstract information; then, the server extracts the source IP address and the application protocol from the stream summary information, and determines the traffic size. The embodiments of the present disclosure do not limit the manner in which the target classification dimension information and the first traffic information are extracted.
Step 202, searching a target node in a preset red-black tree based on the target classification dimension information.
The classification dimension information stored by the target node is consistent with the target classification dimension information.
The server presets a red black tree, and each node of the red black tree stores classification dimension information and flow information. After the server acquires the target classification dimension information of the data packet, searching a target node from the red-black tree according to the target classification dimension information, wherein the classification dimension information stored by the target node is consistent with the target classification dimension information.
For example, the target classification dimension information includes a source IP address a, and the server determines whether each node of the red-black tree is a target node according to the source IP address a. If the classification dimension information stored by the node 1 of the red and black tree comprises a source IP address A, determining that the classification dimension information stored by the node 1 is consistent with the target classification dimension information, wherein the node 1 is a target node; if the classification dimension information stored by the node 1 includes a source IP address B, determining that the classification dimension information stored by the node 1 is inconsistent with the target classification dimension information, the node 1 is not the target node. Then, whether the child node of the node 1 is the target node is judged according to the source IP address A.
If the target node is not found, a new node is inserted into the target search path of the red-black tree, so as to obtain a new red-black tree.
The target searching path is a path for searching a target node, target classification dimension information and first flow information are stored in a new node, and the new red black tree is used for carrying out flow classification statistics.
If the server searches nodes one by one along the target searching path in the red-black tree, but no target node is found, storing the target classification dimension information and the first flow information into a new node, and inserting the new node into the target searching path of the red-black tree to obtain a new red-black tree. And then, the server performs statistics on the flow size of one or more classification dimensions according to the classification dimension information and the flow information stored by each node in the red-black tree.
For example, traffic sizes of the same source IP address are counted, or traffic sizes of a plurality of source IP addresses are counted. The flow size can be further divided into an uplink flow size and a downlink flow size. The embodiment of the disclosure does not limit the statistical mode, and can be selected according to actual conditions.
In the flow classification statistical method, a server extracts target classification dimension information and first flow information according to the acquired data packet; searching a target node in a preset red-black tree based on target classification dimension information; if the target node is not found, inserting a new node into the target finding path of the red-black tree to obtain a new red-black tree. In the embodiment of the disclosure, the red black tree structure is adopted to store classification dimension information and flow information, and the time complexity of operations such as searching, inserting and the like increases logarithmically along with the increase of data quantity; in the prior art, the hash table and the linked list are adopted, and the time complexity of operations such as searching, inserting and the like linearly increases along with the increase of the data quantity. Because the logarithmic growth is slower than the linear growth, the time complexity of the disclosed embodiments is lower than that of the prior art, and thus the efficiency of traffic classification statistics can be effectively improved.
In one embodiment, as shown in fig. 3, after the step of searching for the target node in the preset red-black tree based on the target classification dimension information, the method may further include:
and step 204, if the target node is found, updating the second traffic information stored in the target node by adopting the first traffic information to obtain a new red-black tree.
If the server finds the target node in the red-black tree, combining the first flow information and the second flow information stored in the target node, and updating the second flow information stored in the target node by adopting the combined flow information to obtain a new red-black tree.
For example, if the first traffic information includes a traffic size x1 and the second traffic information stored in the target node includes a traffic size x2, the second traffic information stored in the target node is updated to a traffic size x1+x2. The embodiment of the disclosure does not limit the updating mode, and can be set according to actual conditions.
Understandably, the server searches for a target node in a preset red-black tree based on target classification dimension information; and if the target node is found, updating the second flow information stored in the target node by adopting the first flow information. In this way, no new nodes need to be inserted in the red-black tree, and therefore memory resources can be saved.
In one embodiment, as shown in fig. 4, the step of searching for the target node in the preset red-black tree based on the target classification dimension information may include:
step 301, determining a target classification dimension value corresponding to the target classification dimension information.
The server may preset a comparison function for determining a corresponding classification dimension value according to the classification dimension information. For target classification dimension information, the comparison function may determine a corresponding target classification dimension value. For example, inputting the target classification dimension information, including the source IP address a, into a comparison function, the comparison function may determine the corresponding target classification dimension value as a.
Step 302, for the nth node in the target search path, comparing the target classification dimension value with the classification dimension value corresponding to the classification dimension information stored in the nth node.
In the process of searching the target node, the classification dimension information stored in the nth node in the target searching path is also input into a comparison function, and the comparison function can determine the corresponding classification dimension value. After the target classification dimension value and the classification dimension value corresponding to the nth node are obtained, the comparison function compares the target classification dimension value with the classification dimension value corresponding to the nth node.
Step 303, if the target classification dimension value is inconsistent with the classification dimension value corresponding to the classification dimension information stored in the nth node, comparing the size of the target classification dimension value with the size of the classification dimension value corresponding to the nth node.
The comparison function defines the size judgment condition of the classification dimension value corresponding to each node in the red and black tree in advance. If the target classification dimension value is inconsistent with the classification dimension value corresponding to the nth node, the comparison function compares the target classification dimension value with the classification dimension value corresponding to the nth node to obtain a comparison result. The comparison result may include that the target classification dimension value is greater than the classification dimension value size corresponding to the nth node, or that the target classification dimension value is less than the classification dimension value size corresponding to the nth node.
And step 304, determining the n+1th node in the target search path from the child nodes of the n node according to the comparison result, and comparing the target classification dimension value with the classification dimension value corresponding to the classification dimension information stored in the n+1th node.
Each node in the red-black tree can comprise a left sub node and a right sub node, and if the comparison result is that the target classification dimension value is larger than the classification dimension value corresponding to the nth node, the left node of the nth node is determined to be the (n+1) th node in the target search path; and if the comparison result is that the target classification dimension value is smaller than the classification dimension value corresponding to the nth node, determining the right node of the nth node as the (n+1) th node in the target searching path. Or if the comparison result is that the target classification dimension value is larger than the classification dimension value corresponding to the nth node, determining the right node of the nth node as the (n+1) th node in the target search path; and if the comparison result is that the target classification dimension value is smaller than the classification dimension value corresponding to the nth node, determining the left node of the nth node as the (n+1) th node in the target searching path. The specific determination manner is set according to the actual situation, and the embodiment of the present disclosure is not limited in detail.
After determining the (n+1) th node in the target search path, comparing the target classification dimension value with the classification dimension value corresponding to the classification dimension information stored in the (n+1) th node by adopting a comparison function, and determining the (n+1) th node as the target node if the target classification dimension value is consistent with the classification dimension value corresponding to the (n+1) th node. If the target classification dimension value is not consistent with the classification dimension value corresponding to the n+1th node, the target node continues to be searched with reference to steps 303 and 304.
It can be understood that, the target node is searched according to the classification dimension value corresponding to the classification dimension information of each node and the target classification dimension information, so that the searching accuracy can be ensured, and various classification dimensions can be set according to the user requirements. In the prior art, a hash table and a linked list are adopted, if more classification dimensions are required to be set, a large enough hash table is required to be set, but the waste of storage resources is easily caused by overlarge hash table setting. The embodiment of the disclosure adopts the structure of the red and black tree, so that the waste of storage resources can be avoided.
As shown in fig. 5, after step 302, it may further include:
in step 305, if the target classification dimension value is consistent with the classification dimension value corresponding to the classification dimension information stored in the nth node, the nth node is determined as the target node.
If the server determines that the target classification dimension value is consistent with the classification dimension value corresponding to the nth node, the classification dimension information stored by the nth node is consistent with the target classification dimension information, and therefore the nth node is determined to be the target node.
As shown in fig. 5, after step 302, it may further include:
step 306, if the target classification dimension value is inconsistent with the classification dimension value corresponding to the classification dimension information stored in the nth node, and the nth node does not have a child node, determining that the target node is not found in the red-black tree.
If the server determines that the target classification dimension value is inconsistent with the classification dimension value corresponding to the nth node and the nth node does not have any child node, the server indicates that the classification dimension information stored in each node in the red-black tree is inconsistent with the target classification dimension information, and therefore the target node does not exist in the red-black tree.
In the above embodiment, the server determines the target classification dimension value corresponding to the target classification dimension information; comparing the target classification dimension value with a classification dimension value corresponding to classification dimension information stored by an nth node in the target search path; if the target classification dimension value is inconsistent with the classification dimension value corresponding to the classification dimension information stored by the nth node, comparing the size of the target classification dimension value with the size of the classification dimension value corresponding to the nth node; and determining an n+1th node in the target search path from the child nodes of the n node according to the comparison result, and comparing the target classification dimension value with the classification dimension value corresponding to the classification dimension information stored in the n+1th node. According to the embodiment of the disclosure, the searching accuracy can be ensured, and the waste of storage resources can be avoided.
In one embodiment, as shown in fig. 6, the step of inserting a new node in the target search path of the red-black tree to obtain a new red-black tree may include:
step 401, applying for a new node, and storing the target classification dimension information and the first traffic information into the new node.
If the server does not find the target node in the red-black tree, a new node is applied for in the memory, and then the target classification dimension information and the first flow information are stored in the new node.
And step 402, taking a node at the tail end of the target search path as a father node, taking a new node as a child node, and connecting the child node with the father node to obtain a new red-black tree.
The server obtains a target searching path in the process of searching the target node, and after storing the target classification dimension information and the first flow information into a new node, the server takes the node at the tail end of the target searching path as a father node, and takes the new node as a child node to be connected to the father node, so that a new red-black tree is obtained. In practical application, nodes except for the tail end of the target search path can be used as father nodes, other nodes in the target search path can also be used as father nodes, and the selection of the father nodes is determined according to practical conditions.
Inserting a new node into the target search path of the red-black tree, and if the target node is not found in the red-black tree, applying for the new node, and storing target classification dimension information and first flow information into the new node; and then taking the node at the tail end of the target search path as a father node, taking the new node as a child node, and connecting the child node with the father node to obtain a new red-black tree. In the embodiment of the disclosure, a new storage resource is applied under the condition that a target node is not found in the red-black tree, namely, the storage resource is allocated on demand; in the prior art, the size of the hash table needs to be set in advance, so that compared with the prior art, the resource allocation is more efficient and reliable in the embodiment of the disclosure.
In one embodiment, as shown in fig. 7, a traffic classification statistical method is provided, and the method is applied to the server in fig. 1 for illustration, and includes the following steps:
step 501, extracting at least one sort dimension corresponding to the data packet from the header of the data packet according to the IP layer protocol, and obtaining the target sort dimension information.
Wherein the classification dimension includes at least one of a source IP address and an application protocol.
After the server acquires the data packet, stripping a part of the packet header from the data packet, extracting a source IP address corresponding to the data packet from a first preset position of the packet header according to an IP layer protocol, and extracting an application protocol corresponding to the data packet from a second preset position of the packet header; and obtaining the target classification dimension information of the data packet according to the source IP address, the application protocol and the like corresponding to the data packet.
Wherein, according to the source IP address, whether the data packet is uplink traffic or downlink traffic can be determined.
Step 502, extracting the data length from the header of the data packet according to the IP layer protocol, and calculating the flow size of the data packet according to the data length to obtain the first flow information.
The server extracts the data length of the data packet from the third preset position of the packet head, and then calculates the flow size of the data packet according to the data length. For example, the flow size of the data packet is calculated to be x1 according to the data length.
Embodiments of the present disclosure do not limit the order of steps 501 and 502.
Step 503, determining a target classification dimension value corresponding to the target classification dimension information.
Step 504, for the nth node in the target search path, comparing the target classification dimension value with the classification dimension value corresponding to the classification dimension information stored in the nth node.
And executing one of the steps 505, 507 and 510 according to whether the target classification dimension value is consistent with the classification dimension value corresponding to the classification dimension information stored by the nth node and whether the nth node has a child node.
In step 505, if the target classification dimension value is inconsistent with the classification dimension value corresponding to the classification dimension information stored in the nth node, comparing the size of the target classification dimension value with the size of the classification dimension value corresponding to the nth node.
Step 506, determining the n+1th node in the target search path from the child nodes of the n-th node according to the comparison result, and comparing the target classification dimension value with the classification dimension value corresponding to the classification dimension information stored in the n+1th node.
Comparing the target classification dimension value with the classification dimension value corresponding to the classification dimension information stored by the n+1th node to obtain a result of whether the target classification dimension value is consistent with the classification dimension value corresponding to the n+1th node, and continuing to search the target node until the target node is found or the target node is not found is determined according to the step 504.
In step 507, if the target classification dimension value is inconsistent with the classification dimension value corresponding to the classification dimension information stored in the nth node, and the nth node does not have a child node, it is determined that the target node is not found in the red-black tree.
Step 508, apply for the new node, and store the target classification dimension information and the first traffic information into the new node.
Step 509, taking the node at the tail end of the target search path as a father node, taking the new node as a child node, and connecting the child node with the father node to obtain a new red-black tree.
In step 510, if the target classification dimension value is consistent with the classification dimension value corresponding to the classification dimension information stored in the nth node, the nth node is determined as the target node.
In step 511, the first traffic information is used to update the second traffic information stored in the target node, so as to obtain a new red-black tree.
Step 512, traversing each node of the new red-black tree, and counting the flow size of the dimension to be counted and classified according to the second flow information stored in each node of the new red-black tree.
In the process of carrying out flow classification statistics, a server traverses each node of a new red-black tree, and determines classification dimension information and flow information stored in each node; then, the server searches out nodes to be counted according to the dimension of classification to be counted; the node to be counted stores classification dimension information which is matched with a preset classification dimension. And then, the server combines the flow sizes stored by the nodes to be counted, so that the flow sizes corresponding to the classification dimensions to be counted can be counted.
For example, the dimension to be counted is a source IP address a, and the classification dimension information stored in the node 1 to be counted is found out from the new red and black tree according to the dimension to be counted, wherein the classification dimension information comprises the source IP address a, and the stored traffic information comprises the traffic size x1; the classification dimension information stored by the node 2 to be counted comprises a source IP address A, and the stored traffic information comprises a traffic size x2. According to the statistics, the traffic size corresponding to the source IP address a is x1+x2.
By adopting the structure of the red black tree, the embodiment of the disclosure can not only reduce the time complexity of searching, inserting and other operations and improve the efficiency of flow classification statistics, but also allocate storage resources as required, thereby avoiding wasting the storage resources.
It should be understood that, although the steps in the flowcharts of fig. 2-7 are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least a portion of the steps of fig. 2-7 may include multiple steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor does the order in which the steps or stages are performed necessarily occur sequentially, but may be performed alternately or alternately with at least a portion of the steps or stages in other steps or other steps.
In one embodiment, as shown in fig. 8, there is provided a traffic classification statistical device, including:
an information extraction module 601, configured to extract target classification dimension information and first traffic information according to the acquired data packet; the target classification dimension information is used for representing at least one classification dimension corresponding to the data packet, and the first flow information is used for representing the flow size of the data packet;
the node searching module 602 is configured to search a target node in a preset red-black tree based on the target classification dimension information; the classification dimension information stored by the target node is consistent with the target classification dimension information;
the node inserting module 603 is configured to insert a new node into the target search path of the red-black tree if the target node is not found, so as to obtain a new red-black tree; the target searching path is a path for searching a target node, target classification dimension information and first flow information are stored in a new node, and the new red black tree is used for carrying out flow classification statistics.
In one embodiment, as shown in fig. 9, the apparatus further comprises:
and the information updating module 604 is configured to update the second traffic information stored in the target node by using the first traffic information if the target node is found, so as to obtain a new red-black tree.
In one embodiment, the node searching module 602 is specifically configured to determine a target classification dimension value corresponding to the target classification dimension information; comparing the target classification dimension value with a classification dimension value corresponding to classification dimension information stored by an nth node in the target search path; if the target classification dimension value is inconsistent with the classification dimension value corresponding to the classification dimension information stored by the nth node, comparing the size of the target classification dimension value with the size of the classification dimension value corresponding to the nth node; and determining an n+1th node in the target search path from the child nodes of the n node according to the comparison result, and comparing the target classification dimension value with the classification dimension value corresponding to the classification dimension information stored in the n+1th node.
In one embodiment, as shown in fig. 10, the apparatus further comprises:
the first node determining module 605 is configured to determine the nth node as the target node if the target classification dimension value is consistent with the classification dimension value corresponding to the classification dimension information stored by the nth node.
In one embodiment, as shown in fig. 10, the apparatus further comprises:
the second node determining module 606 is configured to determine that the target node is not found in the red-black tree if the target classification dimension value is inconsistent with the classification dimension value corresponding to the classification dimension information stored in the nth node and the nth node does not have a child node.
In one embodiment, the node inserting module 603 is specifically configured to apply for a new node, and store the target classification dimension information and the first traffic information in the new node; and taking the node at the tail end of the target search path as a father node, taking the new node as a child node, and connecting the child node with the father node to obtain the new red black tree.
In one embodiment, as shown in fig. 11, the apparatus further comprises:
and a statistics module 607, configured to traverse each node of the new red-black tree, and perform statistics on the traffic size of the dimension to be statistically classified according to the second traffic information stored in each node of the new red-black tree.
In one embodiment, the information extraction module is specifically configured to extract at least one classification dimension corresponding to the data packet from a packet header of the data packet according to an IP layer protocol, so as to obtain target classification dimension information; the classification dimension includes at least one of a source IP address and an application protocol; and extracting the data length from the packet head of the data packet according to the IP layer protocol, and calculating the flow of the data packet according to the data length to obtain first flow information.
For specific limitations of the flow classification statistics device, reference may be made to the above limitations of the flow classification statistics method, and no further description is given here. The various modules in the traffic classification statistics device described above may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, and the internal structure of which may be as shown in fig. 12. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used to store traffic classification statistics. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a traffic classification statistical method.
It will be appreciated by those skilled in the art that the structure shown in fig. 12 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of:
extracting target classification dimension information and first flow information according to the acquired data packet; the target classification dimension information is used for representing at least one classification dimension corresponding to the data packet, and the first flow information is used for representing the flow size of the data packet;
searching a target node in a preset red-black tree based on target classification dimension information; the classification dimension information stored by the target node is consistent with the target classification dimension information;
if the target node is not found, inserting a new node into the target finding path of the red-black tree to obtain a new red-black tree; the target searching path is a path for searching a target node, target classification dimension information and first flow information are stored in a new node, and the new red black tree is used for carrying out flow classification statistics.
In one embodiment, the processor when executing the computer program further performs the steps of:
and if the target node is found, updating the second flow information stored in the target node by adopting the first flow information to obtain a new red-black tree.
In one embodiment, the processor when executing the computer program further performs the steps of:
determining a target classification dimension value corresponding to the target classification dimension information;
comparing the target classification dimension value with a classification dimension value corresponding to classification dimension information stored by an nth node in the target search path;
if the target classification dimension value is inconsistent with the classification dimension value corresponding to the classification dimension information stored by the nth node, comparing the size of the target classification dimension value with the size of the classification dimension value corresponding to the nth node;
and determining an n+1th node in the target search path from the child nodes of the n node according to the comparison result, and comparing the target classification dimension value with the classification dimension value corresponding to the classification dimension information stored in the n+1th node.
In one embodiment, the processor when executing the computer program further performs the steps of:
and if the target classification dimension value is consistent with the classification dimension value corresponding to the classification dimension information stored by the nth node, determining the nth node as the target node.
In one embodiment, the processor when executing the computer program further performs the steps of:
if the target classification dimension value is inconsistent with the classification dimension value corresponding to the classification dimension information stored by the nth node and the nth node does not have a child node, determining that the target node is not found in the red-black tree.
In one embodiment, the processor when executing the computer program further performs the steps of:
applying for a new node, and storing the target classification dimension information and the first flow information into the new node;
and taking the node at the tail end of the target search path as a father node, taking the new node as a child node, and connecting the child node with the father node to obtain the new red black tree.
In one embodiment, the processor when executing the computer program further performs the steps of:
traversing each node of the new red-black tree, and carrying out statistics on the flow size of the dimension to be counted and classified according to the second flow information stored by each node in the new red-black tree.
In one embodiment, the processor when executing the computer program further performs the steps of:
extracting at least one classification dimension corresponding to the data packet from the packet header of the data packet according to the IP layer protocol to obtain target classification dimension information; the classification dimension includes at least one of a source IP address and an application protocol;
and extracting the data length from the packet head of the data packet according to the IP layer protocol, and calculating the flow of the data packet according to the data length to obtain first flow information.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
Extracting target classification dimension information and first flow information according to the acquired data packet; the target classification dimension information is used for representing at least one classification dimension corresponding to the data packet, and the first flow information is used for representing the flow size of the data packet;
searching a target node in a preset red-black tree based on target classification dimension information; the classification dimension information stored by the target node is consistent with the target classification dimension information;
if the target node is not found, inserting a new node into the target finding path of the red-black tree to obtain a new red-black tree; the target searching path is a path for searching a target node, target classification dimension information and first flow information are stored in a new node, and the new red black tree is used for carrying out flow classification statistics.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and if the target node is found, updating the second flow information stored in the target node by adopting the first flow information to obtain a new red-black tree.
In one embodiment, the computer program when executed by the processor further performs the steps of:
determining a target classification dimension value corresponding to the target classification dimension information;
Comparing the target classification dimension value with a classification dimension value corresponding to classification dimension information stored by an nth node in the target search path;
if the target classification dimension value is inconsistent with the classification dimension value corresponding to the classification dimension information stored by the nth node, comparing the size of the target classification dimension value with the size of the classification dimension value corresponding to the nth node;
and determining an n+1th node in the target search path from the child nodes of the n node according to the comparison result, and comparing the target classification dimension value with the classification dimension value corresponding to the classification dimension information stored in the n+1th node.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and if the target classification dimension value is consistent with the classification dimension value corresponding to the classification dimension information stored by the nth node, determining the nth node as the target node.
In one embodiment, the computer program when executed by the processor further performs the steps of:
if the target classification dimension value is inconsistent with the classification dimension value corresponding to the classification dimension information stored by the nth node and the nth node does not have a child node, determining that the target node is not found in the red-black tree.
In one embodiment, the computer program when executed by the processor further performs the steps of:
applying for a new node, and storing the target classification dimension information and the first flow information into the new node;
and taking the node at the tail end of the target search path as a father node, taking the new node as a child node, and connecting the child node with the father node to obtain the new red black tree.
In one embodiment, the computer program when executed by the processor further performs the steps of:
traversing each node of the new red-black tree, and carrying out statistics on the flow size of the dimension to be counted and classified according to the second flow information stored by each node in the new red-black tree.
In one embodiment, the computer program when executed by the processor further performs the steps of:
extracting at least one classification dimension corresponding to the data packet from the packet header of the data packet according to the IP layer protocol to obtain target classification dimension information; the classification dimension includes at least one of a source IP address and an application protocol;
and extracting the data length from the packet head of the data packet according to the IP layer protocol, and calculating the flow of the data packet according to the data length to obtain first flow information.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, or the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.
Claims (10)
1. A method of traffic classification statistics, the method comprising:
extracting target classification dimension information and first flow information according to the acquired data packet; the target classification dimension information is used for representing at least one classification dimension corresponding to the data packet, and the first flow information is used for representing the flow size of the data packet;
searching a target node in a preset red-black tree based on the target classification dimension information; the classification dimension information stored by the target node is consistent with the target classification dimension information;
If the target node is not found, inserting a new node into the target search path of the red black tree to obtain a new red black tree; the target searching path is a path for searching the target node, the target classification dimension information and the first flow information are stored in the new node, and the new mangrove is used for carrying out flow classification statistics.
2. The method of claim 1, wherein after searching for a target node in a preset red-black tree based on the target classification dimension information, the method further comprises:
and if the target node is found, updating the second flow information stored in the target node by adopting the first flow information to obtain the new red-black tree.
3. The method of claim 1, wherein searching for the target node in the preset red-black tree based on the target classification dimension information comprises:
determining a target classification dimension value corresponding to the target classification dimension information;
comparing the target classification dimension value with a classification dimension value corresponding to classification dimension information stored by an nth node in the target search path;
If the target classification dimension value is inconsistent with the classification dimension value corresponding to the classification dimension information stored by the nth node, comparing the size of the target classification dimension value with the size of the classification dimension value corresponding to the nth node;
and determining an n+1th node in the target search path from the child nodes of the n node according to the comparison result, and comparing the target classification dimension value with a classification dimension value corresponding to classification dimension information stored in the n+1th node.
4. A method according to claim 3, characterized in that the method further comprises:
and if the target classification dimension value is consistent with the classification dimension value corresponding to the classification dimension information stored by the nth node, determining the nth node as the target node.
5. A method according to claim 3, characterized in that the method further comprises:
and if the target classification dimension value is inconsistent with the classification dimension value corresponding to the classification dimension information stored by the nth node and the nth node does not have a child node, determining that the target node is not found in the red black tree.
6. The method according to claim 1, wherein inserting new nodes in the target search path of the red-black tree to obtain a new red-black tree comprises:
Applying for the new node, and storing the target classification dimension information and the first traffic information into the new node;
and taking the node at the tail end of the target search path as a father node, taking the new node as a child node, and connecting the child node with the father node to obtain the new red-black tree.
7. The method of claim 1, wherein after inserting a new node in the target lookup path of the red-black tree to obtain a new red-black tree, the method further comprises:
traversing each node of the new red-black tree, and carrying out statistics on the flow size of the dimension to be counted and classified according to the second flow information stored in each node of the new red-black tree.
8. A traffic classification statistical device, the device comprising:
the information extraction module is used for extracting target classification dimension information and first flow information according to the acquired data packet; the target classification dimension information is used for representing at least one classification dimension corresponding to the data packet, and the first flow information is used for representing the flow size of the data packet;
the node searching module is used for searching target nodes in a preset red-black tree based on the target classification dimension information; the classification dimension information stored by the target node is consistent with the target classification dimension information;
The node insertion module is used for inserting a new node into the target search path of the red-black tree if the target node is not found, so as to obtain a new red-black tree; the target searching path is a path for searching the target node, the target classification dimension information and the first flow information are stored in the new node, and the new mangrove is used for carrying out flow classification statistics.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
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