CN107317764B - Traffic load balancing method, system, device and computer readable storage medium - Google Patents
Traffic load balancing method, system, device and computer readable storage medium Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/10—Flow control; Congestion control
- H04L47/12—Avoiding congestion; Recovering from congestion
- H04L47/125—Avoiding congestion; Recovering from congestion by balancing the load, e.g. traffic engineering
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Abstract
A method for traffic load balancing in a network comprising a plurality of nodes is provided, wherein the method may comprise: collecting the flow of each node; calculating a difference L between a maximum value and a minimum value in the flow; determining whether the difference L is greater than or equal to a predetermined traffic balancing threshold M; and in response to determining that the difference L is greater than or equal to a predetermined traffic balancing threshold M, switching one or more of the clients connected to the node with the largest traffic to the node with the smallest traffic.
Description
Technical Field
The application relates to the field of computers, in particular to a method and a system for flow load balancing.
Background
With the development of internet technology, data of a unified log system is more and more huge. Network systems often need a forwarding center to forward data volume according to subscription information of users. The forwarding center will carry all data traffic, and a single forwarding center often cannot meet the requirement of receiving and forwarding huge data volume, so that multiple forwarding centers are needed to operate.
Although multiple forwarding centers may solve the above problem, multiple forwarding centers face the problem of how to ensure traffic load balancing. If the traffic load is unbalanced, one of the forwarding centers may also have a problem that it is not sufficient to receive and forward a huge amount of data.
Therefore, various methods of traffic load balancing have been developed, e.g., random, round robin, etc. However, since the traffic transmitted from each client to the forwarding center changes dynamically, the traffic of each client is different, and the difference between different clients may be many times, it is not feasible to perform polling and random determination according to the number of clients. Further, another method for solving the above problem is to make a selection according to the traffic of the forwarding center. The scheme can solve the problem that the flow is statically distributed at a certain moment, and the aim of dynamically realizing flow load balancing cannot be fulfilled. In a scenario where a long connection is used for transmission, this method may cause imbalance of servers actually handling traffic at the backend, resulting in a situation where some servers are very idle and some other servers are very busy, or even possibly down due to handling of huge traffic. The balancing scheme is generally only suitable for the condition that the flow of each client is balanced, and is not suitable for the scene that the flow is large and the flow is frequently changed.
In the prior art, the flow balancing scheme basically cannot achieve the efficiency of flow balancing in the scene of dynamic flow change, and only can balance the number of connections or the number of clients, and cannot guarantee real flow balancing.
In order to solve the problem of dynamic traffic balancing, the application provides a method and a system for traffic load balancing, which dynamically obtain traffic load conditions of each node of a cluster and dynamically schedule according to the actual traffic load conditions dynamically obtained in real time.
Disclosure of Invention
An aspect of the present disclosure is to address at least the above problems and/or disadvantages and to provide at least the advantages described below.
One aspect of the present invention relates to a method for traffic load balancing in a network comprising a plurality of nodes, wherein the method may comprise: collecting the flow of each node; calculating a difference L between a maximum value and a minimum value in the flow; determining whether the difference L is greater than or equal to a predetermined traffic balancing threshold M; and in response to determining that the difference L is greater than or equal to a predetermined traffic balancing threshold M, switching one or more of the clients connected to the node with the largest traffic to the node with the smallest traffic.
Preferably, the method may further comprise: and responding to the fact that the difference value L is smaller than a preset flow balance threshold value M, and continuously collecting the flow of each node.
Preferably, the switching one or more of the clients connected to the node with the largest traffic to the node with the smallest traffic may include: collecting client flow of each client connected with the node with the maximum flow; and selecting one or more clients to switch to the node with the minimum traffic according to the collected client traffic.
Preferably, the selecting one or more clients to switch to the node with the minimum traffic according to the collected client traffic may include: sequencing the client flow of each client from big to small, and extracting the client flow Y of the ith clientiThe initial value of i is 1; the client flow Y of the ith client is measurediComparing with the difference L; in response to client traffic YiIf the difference value is less than or equal to the difference value L, the ith client is switched to be connected with the node with the minimum flow; calculating a difference value L and a client flow YiAnd taking the calculation result as a new difference value L'; determining whether the new difference L' is greater than or equal to a predetermined traffic balancing threshold M; and in response to determining that the new difference L' is greater than or equal to a predetermined traffic balancing threshold M, determining whether client traffic of all clients connected to the node having the maximum traffic value has been traversed, wherein in response to determining that client traffic of all clients has not been traversed, let i be i +1, and return to the extracting operation until all client traffic has been traversed.
Preferably, the method may comprise: in response to determining that the new difference value L' is less than the predetermined traffic balancing threshold M, ending the operation of selecting one or more clients to switch to the node with the least traffic.
Preferably, the method may comprise: responsive to client traffic Y when comparing client traffic Yi to difference LiAnd if the difference value L is larger than the difference value L, jumping to the step of judging whether the client flow of all the clients connected with the node with the maximum flow value is traversed or not.
Preferably, the traffic values of the various nodes in the network are collected at predetermined time intervals.
According to another aspect of the present disclosure, there is provided a system for traffic load balancing in a node-based network, wherein the system may include: a forwarding center cluster consisting of two or more nodes; a plurality of clients configured to be respectively connected to one of the two or more nodes and to communicate; a traffic load balancing scheduler configured to: collecting flow values of each node from the forwarding center cluster; calculating a difference L between a maximum value and a minimum value in the flow; determining whether the difference L is greater than or equal to a predetermined traffic balancing threshold M; and in response to determining that the difference L is greater than or equal to a predetermined traffic balancing threshold M, switching one or more of the clients connected to the node with the largest traffic to the node with the smallest traffic.
Preferably, the traffic load balancing scheduler may be further configured to: and responding to the fact that the difference value L is smaller than a preset flow balance threshold value M, and continuously collecting the flow of each node.
Preferably, the traffic load balancing scheduler may be further configured to: collecting client flow of each client connected with the node with the maximum flow; and selecting one or more clients to switch to the node with the minimum traffic according to the collected client traffic.
According to another aspect of the present disclosure, there is provided a traffic load balancing apparatus, including: a memory; and a processor coupled to the memory, the processor configured to perform the method for traffic load balancing in a network comprising a plurality of nodes as described above based on instructions stored in the memory.
According to another aspect of the present disclosure, a computer-readable storage medium is provided, storing computer instructions which, when executed by a processor, implement a method of traffic load balancing in a network comprising a plurality of nodes as described above.
Drawings
The above and other aspects, features and advantages of example embodiments of the present disclosure will become more apparent from the following description when taken in conjunction with the accompanying drawings, in which:
FIG. 1 shows a schematic diagram of a system for traffic load balancing according to an example embodiment of the present invention;
FIG. 2 shows a flow diagram of a method of traffic load balancing according to an example embodiment of the invention; and
fig. 3 shows a flow chart of a method of traffic load balancing according to another example embodiment of the present invention.
Detailed Description
Fig. 1 shows a schematic diagram of a system for traffic load balancing according to an example embodiment of the present invention.
In fig. 1, a system for traffic load balancing according to an example embodiment of the present invention may include a forwarding center cluster 100, a plurality of clients 200, and a traffic load balancing scheduler 300. Specifically, the forwarding center cluster 100 may include one or more forwarding center nodes 101 and 103 for forwarding data. A plurality of clients 200-1, 200-2, 200-3, etc. may be connected to and communicate data with one of the forwarding center nodes (101, 102, or 103) in the forwarding center cluster 100. The traffic load balancing scheduler 300 is used to obtain the traffic of all nodes dynamically and periodically or at configurable time intervals, and can obtain the client traffic of each client connected to a certain node according to the needs. The traffic load balancing scheduler 300 makes a scheduling policy according to the traffic of each node and/or the client traffic of each client, and issues a scheduling instruction formed by the scheduling policy to the client to be scheduled, so that the client reselects the designated node in forwarding to perform data transmission.
A schematic diagram of a method of traffic load balancing according to an example embodiment of the invention will be described below with reference to fig. 2. Specifically, in step S201, the traffic load balancing scheduler 300 collects traffic of each node. The nodes receive data transmitted by the client links connected with the nodes, wherein the connection between the nodes and the client can be a long connection. For example, N nodes requiring traffic load balancing are deployed in the network, and the traffic load balancing scheduler 300 collects traffic of the N nodes in real time.
Subsequently, in step S202, a difference L between the maximum value and the minimum value in the flow rate is calculated. There are various ways for calculating the difference L, for example, the traffic load balancing scheduler may sort the collected N traffic flows from small to large, and then the first is the traffic flow corresponding to the node with the smallest traffic flow, and the last is the traffic flow corresponding to the node with the largest traffic flow, so that the difference L may be calculated. It should be noted that the order from small to large is only an example, and the traffic may be sorted in the order from large to small. Or instead of sorting the N collected flows, the difference L between the two may be calculated by an algorithm that seeks a maximum and a minimum.
Next, in step S203, it is determined whether the difference L is greater than or equal to a predetermined traffic balancing threshold M, where M may be preset by a user or default by the system. When the difference L is greater than or equal to the predetermined traffic balancing threshold M, it indicates that the traffic loads between the nodes are relatively unbalanced, and traffic load balancing processing needs to be performed. Conversely, when the difference L is smaller than the predetermined traffic balancing threshold M, it indicates that the traffic load between the nodes is more balanced, and the traffic load balancing process is not required.
In response to determining that the difference L is greater than or equal to the predetermined traffic balancing threshold M, that is, traffic load balancing is required, in step S204, one or more of the clients connected to the node with the largest traffic are switched to be connected to the node with the smallest traffic. Specifically, in the present disclosure, the operation of switching the client from the state of being connected to one node to the state of being connected to another node may be realized by: and the load balancing scheduler sends a scheduling instruction to the corresponding client, and the scheduling instruction enables the client to be linked to the appointed forwarding center node. After receiving the scheduling instruction, the client ends transmission of the current data and disconnects the link with the original forwarding center node, and then links to the forwarding center node designated by the traffic load balancing scheduler 300. Furthermore, if the difference L is less than the predetermined traffic balancing threshold M, i.e., no traffic load balancing is needed at this time, the traffic load balancing scheduler 300 may continue to collect the traffic of each node.
A flow chart of a method of traffic load balancing according to an example embodiment of the invention is described above. A flow chart of a method for traffic load balancing according to another exemplary embodiment of the present invention will be described below with reference to specific implementation manners.
Fig. 3 shows a flow chart of a method of traffic load balancing according to another example embodiment of the present invention. Steps S301-S303 in fig. 3 are similar to steps S201-S203 in fig. 2 and will therefore not be described again.
Unlike fig. 2, in fig. 3, in response to determining that the difference L is greater than or equal to the predetermined traffic balancing threshold M, that is, traffic load balancing is required, the traffic load balancing scheduler 300 may collect, in step 304, client traffic of each client connected to the node with the largest traffic, and select one or more clients to switch to connect to the node with the smallest traffic according to the collected client traffic. Specifically, in step 305, the client traffic of each client is sorted from large to small, and the client traffic Y of the ith client is extractedi. It should be noted that although the description herein describes client traffic YiThe ordering is from large to small, however the invention may also be in other orderings (e.g., small to large, or randomly). Further, if the sorting has already been performed at step S302, the sorting may be omitted here, or a subsequent operation may be performed based on the sorting performed at step S302. Compared with other sorting modes, sorting from large to small can ensure that fewer clients are switched at a higher speed, so that flow load balancing is achieved more massively. In the case of sorting client traffic from large to small, the initial value of i is preferably equal to 1.
In step S306, the client flow Y of the ith client is determinediAnd compared to the difference L. In response to client traffic YiIf the difference value is greater than the difference value L, the process jumps to step S311 to determine whether the client flow Y of all the clients connected with the node with the maximum flow has been traversedi. And if so, ending the flow balancing processing. If the client flow Y of all the clients connected with the node with the maximum flow is not traversediIn S312, let i be i +1, so as to extract the client traffic Y of the next clienti+1. However, if client traffic is determined at step 306YiIf the difference is less than or equal to the difference L, the traffic load balancing regulator 300 may send a scheduling command to switch the ith client to be connected to the node with the smallest traffic, that is, to switch the ith client from being connected to the node with the largest traffic to being connected to the node with the smallest traffic. Subsequently, in step S308, the difference L and the client traffic Y are calculatediI.e. the difference in the flow between the two nodes at that time, and the calculation result is taken as a new difference L', where L ═ L-2 × Yi. In step S309, it is determined whether the new difference L' is greater than or equal to a predetermined traffic balancing threshold M. In the case that it is determined that the new difference L' is greater than or equal to the predetermined traffic balancing threshold M, that is, after the ith client is switched to be connected to the node with the smallest traffic, the traffic load between the nodes is still unbalanced, and traffic balancing still needs to be performed. At this time, in step S310, a new difference L' is given to the difference L, and in step S311, it is determined whether the client traffic of all the clients connected to the node having the maximum traffic value is traversed. In response to determining that the client traffic of all the clients has not been traversed (S311 — no), i is made i +1 in step S312, and the extraction operation is returned (i.e., returned to step S305), a new client traffic Y is comparedi+1And the difference value L of the new assignment until all client traffic is traversed. In contrast, if it is determined that the new difference value L' is smaller than the predetermined traffic balance threshold value M (S311 — yes), that is, traffic balance is achieved after the ith client is switched to be connected to the node with the smallest traffic, the operation of selecting one or more clients to switch to be connected to the node with the smallest traffic is ended. After the operation is finished, the result that the difference value between the maximum traffic node and the minimum traffic node is minimum is finally obtained, namely the two traffic are in the closest state, namely, the traffic balance is realized. It should be noted that the method according to the exemplary embodiment of the present invention may be repeatedly performed every predetermined time.
According to the traffic load balancing method and system provided by the embodiment of the disclosure, the traffic load condition of each node of the cluster is dynamically acquired, so that dynamic scheduling is performed according to the actual traffic load condition dynamically acquired in real time. For example, the method can be applied to a unified log system, and the flow of each forwarding center is basically in a balanced state at any time by adopting the scheme, so that the utilization rate of the server of each forwarding center node is improved, and the stability of the whole system is ensured due to a good balancing effect. In addition, the method can also be applied to the process of transmitting the message in the distributed message middleware, and the scheme can be adopted to dynamically change the subscription relationship of the message, so that the dynamic balance of the flow of each node in the message middleware is ensured. Besides the above applications, the method according to the present disclosure may also be applied to other network communication systems for data transmission, and is particularly suitable for a communication system that uses long connections for data transmission, so as to solve the problem of traffic load imbalance and perform real-time dynamic traffic balancing.
The above-described method may be implemented in the form of executable program commands by various computer devices and recorded in a computer-readable recording medium. In this case, the computer-readable recording medium may include a program command, a data file, a data structure, or a combination thereof alone. Meanwhile, the program command recorded in the recording medium may be specially designed or configured for the present disclosure, or may be known to those skilled in the computer software field for application. The computer-readable recording medium includes a magnetic medium such as a hard disk, a floppy disk, or a magnetic tape, an optical medium such as a compact disc read only memory (CD-ROM) or a Digital Versatile Disc (DVD), a magneto-optical medium such as a magneto-optical floppy disk, and a hardware device such as a ROM, a RAM, a flash memory, etc. which stores and executes a program command. Further, the program command includes a machine language code formed by a compiler and a high-level language executable by a computer by using an interpreter. The foregoing hardware devices may be configured to operate as at least one software module to perform the operations of the present disclosure, and vice versa.
Although the operations of the methods herein are shown and described in a particular order, the order of the operations of each method may be changed so that the particular operations may be performed in a reverse order or so that the particular operations may be performed at least partially concurrently with other operations. Furthermore, the present disclosure is not limited to the above-described exemplary embodiments, and it may include one or more other components or operations or omit one or more other components or operations without departing from the spirit and scope of the present disclosure.
The present disclosure has been shown in connection with preferred embodiments thereof, but it will be understood by those skilled in the art that various modifications, substitutions and changes may be made thereto without departing from the spirit and scope of the present disclosure. Accordingly, the present disclosure should not be limited by the above-described embodiments, but should be defined by the appended claims and their equivalents.
Claims (10)
1. A method for traffic load balancing in a network comprising a plurality of nodes, for a unified logging system, performed by a traffic load balancing scheduler, wherein the method comprises:
collecting the flow of each node in a forwarding center cluster, wherein the forwarding center cluster comprises a server cluster, the nodes comprise server nodes, and the nodes are used for forwarding the data volume according to the subscription information of a user;
calculating a difference L between a maximum value and a minimum value in the flow;
determining whether the difference L is greater than or equal to a predetermined traffic balancing threshold M; and
in response to determining that the difference L is greater than or equal to a predetermined traffic balancing threshold M, switching one or more of the clients connected to the node with the largest traffic to the node with the smallest traffic in a long connection, including:
collecting client flow of each client connected with the node with the maximum flow, wherein the client flow of each client is different; and
selecting one or more clients to switch to connect with the node with the smallest traffic according to the collected client traffic, comprising:
sequencing the client flow of each client from big to small, and extracting the client flow of the ith clientYiThe initial value of i is 1;
the client flow Y of the ith client is measurediComparing with the difference L;
in response to client traffic YiAnd if the difference value is less than or equal to the difference value L, switching the ith client to be connected with the node with the minimum flow, wherein the switching comprises sending a scheduling instruction to the one or more clients, and the scheduling instruction enables the one or more clients to be connected with the node with the minimum flow.
2. The method of claim 1, further comprising: and responding to the fact that the difference value L is smaller than a preset flow balance threshold value M, and continuously collecting the flow of each node.
3. The method of claim 1, wherein selecting one or more clients to switch to connecting to the node with the least traffic based on the collected client traffic further comprises:
calculating a difference value L and a client flow YiAnd taking the calculation result as a new difference value L';
determining whether the new difference L' is greater than or equal to a predetermined traffic balancing threshold M; and
and in response to determining that the new difference L' is greater than or equal to a predetermined traffic balancing threshold M, determining whether client traffic of all clients connected to the node having the maximum traffic value has been traversed, wherein in response to determining that client traffic of all clients has not been traversed, let i be i +1, and return to the extracting operation until all client traffic has been traversed.
4. The method of claim 3, wherein the method comprises: in response to determining that the new difference value L' is less than the predetermined traffic balancing threshold M, ending the operation of selecting one or more clients to switch to the node with the least traffic.
5. The method of claim 3, wherein when the guest is going to be takenFlow rate of the user terminal YiResponsive to client traffic Y when compared to the difference LiAnd if the difference value L is larger than the difference value L, jumping to the step of judging whether the client flow of all the clients connected with the node with the maximum flow value is traversed or not.
6. The method of claim 1, wherein the traffic values for each node in the network are collected at predetermined time intervals.
7. A system for traffic load balancing in a node-based network for a unified logging system, wherein the system comprises:
the forwarding center cluster is composed of two or more nodes, wherein the forwarding center cluster comprises a server cluster, the nodes comprise server nodes, and the nodes are used for forwarding data volume according to subscription information of users;
a plurality of clients configured to be respectively connected to and communicate with one of the two or more nodes in a long connection manner,
a traffic load balancing scheduler configured to:
collecting flow values of each node from the forwarding center cluster;
calculating a difference L between a maximum value and a minimum value in the flow;
determining whether the difference L is greater than or equal to a predetermined traffic balancing threshold M; and
in response to determining that the difference L is greater than or equal to a predetermined traffic balancing threshold M, switching one or more of the clients connected to the node with the largest traffic to connect to the node with the smallest traffic, including:
collecting client flow of each client connected with the node with the maximum flow, wherein the client flow of each client is different; and
selecting one or more clients to switch to connect with the node with the smallest traffic according to the collected client traffic, comprising:
from big to small for each client's clientSorting the flow, extracting the client flow Y of the ith clientiThe initial value of i is 1;
the client flow Y of the ith client is measurediComparing with the difference L;
in response to client traffic YiAnd if the difference value is less than or equal to the difference value L, switching the ith client to be connected with the node with the minimum flow, wherein the switching comprises sending a scheduling instruction to the one or more clients, and the scheduling instruction enables the one or more clients to be connected with the node with the minimum flow.
8. The system of claim 7, wherein the traffic load balancing scheduler is further configured to: and responding to the fact that the difference value L is smaller than a preset flow balance threshold value M, and continuously collecting the flow of each node.
9. A traffic load balancing apparatus, comprising:
a memory; and
a processor coupled to the memory, the processor configured to perform the method for traffic load balancing in a network comprising a plurality of nodes of any one of claims 1 to 6 based on instructions stored in the memory.
10. A computer readable storage medium storing computer instructions which, when executed by a processor, implement a method of traffic load balancing in a network comprising a plurality of nodes as claimed in any one of claims 1 to 6.
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|---|---|---|---|---|
| CN107835131A (en) * | 2017-12-15 | 2018-03-23 | 北京星河星云信息技术有限公司 | Network traffics dispatching method, device and storage medium |
| CN108173689B (en) * | 2017-12-27 | 2021-07-23 | 杭州迪普科技股份有限公司 | Output system of load balancing data |
| WO2019232680A1 (en) * | 2018-06-04 | 2019-12-12 | Beijing Thomson Commerce Co., Ltd. | Method and device for providing load balancing |
| CN109981419A (en) * | 2019-04-11 | 2019-07-05 | 苏州浪潮智能科技有限公司 | Test method, device, system, equipment and the storage medium of load balancing characteristic |
| CN110515729B (en) * | 2019-08-19 | 2022-05-24 | 中国人民解放军国防科技大学 | Graph computing node vector load balancing method and device based on graph processor |
| CN110532091B (en) * | 2019-08-19 | 2022-02-22 | 中国人民解放军国防科技大学 | Graph computation edge vector load balancing method and device based on graph processor |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1111863A (en) * | 1993-06-02 | 1995-11-15 | 三星电子株式会社 | Alogorithmic controlling method of blind balancing system |
| CN101772078A (en) * | 2008-12-31 | 2010-07-07 | 大唐移动通信设备有限公司 | Load control method and device |
| CN101965012A (en) * | 2009-07-22 | 2011-02-02 | 中兴通讯股份有限公司 | Load balancing method and device |
| CN102111813A (en) * | 2010-03-22 | 2011-06-29 | 电信科学技术研究院 | Method and equipment for balancing loads of carriers |
| CN102149136A (en) * | 2010-02-10 | 2011-08-10 | 杭州华三通信技术有限公司 | Method and system for switching channel of access point and access point |
| CN103428100A (en) * | 2013-08-20 | 2013-12-04 | 北京京东尚科信息技术有限公司 | Dynamic flow control method and device based on data classification |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103118136B (en) * | 2013-02-28 | 2015-08-12 | 杭州电子科技大学 | A kind of method based on block rank cloud memory load balance optimizing |
| CN103368862B (en) * | 2013-06-21 | 2015-01-21 | 北京京东尚科信息技术有限公司 | Load balance dispatching method and load balance dispatching device |
| CN104023083B (en) * | 2014-06-23 | 2017-12-12 | 广东睿江云计算股份有限公司 | The method and device of log collection cluster load balance |
-
2016
- 2016-04-26 CN CN201610264986.2A patent/CN107317764B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1111863A (en) * | 1993-06-02 | 1995-11-15 | 三星电子株式会社 | Alogorithmic controlling method of blind balancing system |
| CN101772078A (en) * | 2008-12-31 | 2010-07-07 | 大唐移动通信设备有限公司 | Load control method and device |
| CN101965012A (en) * | 2009-07-22 | 2011-02-02 | 中兴通讯股份有限公司 | Load balancing method and device |
| CN102149136A (en) * | 2010-02-10 | 2011-08-10 | 杭州华三通信技术有限公司 | Method and system for switching channel of access point and access point |
| CN102111813A (en) * | 2010-03-22 | 2011-06-29 | 电信科学技术研究院 | Method and equipment for balancing loads of carriers |
| CN103428100A (en) * | 2013-08-20 | 2013-12-04 | 北京京东尚科信息技术有限公司 | Dynamic flow control method and device based on data classification |
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