Node load self-balancing method, device and electronic equipment

Abstract

The present application provides a method, apparatus, and electronic device for node load self-balancing. The method includes: when the network flow rate of an MP meets a preset flow rate fluctuation condition, obtaining the network flow rate of the MP and the network flow rate of each opposite MP in multiple consecutive time periods; calculating, for each consecutive time period, a first difference between the network flow rate of the MP and a preset network flow rate threshold; for each opposite MP, determining the Pearson coefficient of the network flow rate of each opposite MP based on the multiple first differences and the network flow rate of the opposite MP; selecting the opposite MP with the largest Pearson coefficient and a Pearson coefficient greater than a preset value from the multiple opposite MPs as the target opposite MP, and controlling the MP to send a message to the target opposite MP to switch the backhaul link. The present application calculates the Pearson coefficients of the difference between the flow rate of the opposite MP and the average flow rate over the multiple time periods, determines the opposite MP node that has the greatest impact on the flow rate, and thus effectively performs load balancing operations.

Description

Technical Field

[0001] The present application relates to computer technology, and in particular to a method, device and electronic device for node load self-balancing. Background Art

[0002] Many of the technical features and advantages of wireless mesh networks derive from their mesh connectivity and routing. Routing and forwarding design directly determines the efficiency with which the mesh network utilizes its mesh connectivity, impacting network performance. In wireless mesh networking, routing selection cannot be based solely on the "minimum hop count" but must comprehensively consider multiple performance metrics, load balancing across multiple paths, and maximizing system resource utilization.

[0003] In the prior art, whether the average flow rate within a period exceeds a threshold is calculated to determine whether traffic rerouting is required, thereby achieving Mesh network load balancing.

[0004] However, the existing technology cannot adapt to the over-limit scenario caused by large flow rate fluctuations, and the timing for load balancing is not accurately selected. Summary of the Invention

[0005] The present application provides a method, device and electronic device for node load self-balancing, which is used to solve the technical problem that when the flow rate of the peer MP (Mesh Point, Mesh node) fluctuates greatly, the existing technology cannot correctly find the peer MP that has the greatest impact on the flow rate, which easily causes excessive node load.

[0006] In a first aspect, the present application provides a method for node load self-balancing, wherein a wireless mesh network includes an MP and a peer MP, and the method is applied to a controller, and the method includes:

[0007] When the network flow rate of the MP meets the preset flow rate fluctuation condition, the network flow rate of the MP in multiple consecutive time periods and the network flow rate of each peer MP in multiple consecutive time periods are obtained;

[0008] For each continuous time period, calculating a first difference between the network flow rate of the MP and a preset network flow rate threshold;

[0009] For each peer MP, obtaining a Pearson coefficient of the network flow rate of each peer MP based on the multiple first differences and the network flow rate of the peer MP;

[0010] The opposite-end MP having the largest Pearson coefficient and a Pearson coefficient greater than a preset value is selected from the multiple opposite-end MPs as the target opposite-end MP, and the control MP sends a message for switching the backhaul link to the target opposite-end MP.

[0011] Furthermore, the network flow rate of the MP meets the preset flow rate fluctuation conditions, specifically including:

[0012] The total network traffic of the MP in the total time period is greater than a preset network traffic threshold, and the difference between the average network traffic speed of the MP in the total time period and the standard deviation of the network traffic speed of the MP in the total time period is greater than a preset network traffic threshold; wherein the total time period is the sum of multiple consecutive time periods.

[0013] In a second aspect, the method further comprises:

[0014] Obtain the cumulative network traffic of the MP at the first time point in the total time period, and the cumulative network traffic of the MP at the last time point in the total time period;

[0015] The network traffic of the MP in the total time period is obtained by subtracting the accumulated network traffic of the MP at the first time point from the accumulated network traffic of the MP at the last time point.

[0016] In a third aspect, the method further comprises:

[0017] For each peer MP, repeatedly obtain the cumulative network traffic of the peer MP at the previous time point and the cumulative network traffic of the peer MP at the current time point, calculate the difference between the cumulative network traffic of the peer MP at the previous time point and the cumulative network traffic of the peer MP at the current time point, and obtain the network traffic of the current time period; calculate the ratio between the network traffic of the current time period and the duration of the current time period, and obtain the network flow rate of the peer MP in the current time period, and update the cumulative network traffic at the next time point until the network traffic of the peer MP in multiple time periods is obtained.

[0018] Furthermore, after the control MP sends a message to the target peer MP to switch the backhaul link, the method further includes:

[0019] When the MP continues to receive information returned by the target peer MP through the backhaul link within a limited time period, the control MP performs flow restriction processing on the target peer MP;

[0020] The limited time period starts from the time when the message for switching the backhaul link is sent, and the duration of the limited time period is a preset duration.

[0021] In a fourth aspect, the present application provides a node load self-balancing device, wherein a wireless mesh network includes an MP and a peer MP, and the device includes:

[0022] an acquisition module, configured to acquire the network flow rate of the MP in a plurality of consecutive time periods and the network flow rate of each peer MP in a plurality of consecutive time periods when the network flow rate of the MP meets a preset flow rate fluctuation condition;

[0023] a processing module, configured to calculate, for each continuous time period, a first difference between a network flow rate of the MP and a preset network flow rate threshold;

[0024] The processing module is further configured to obtain, for each peer MP, a Pearson coefficient of the network flow rate of each peer MP based on the plurality of first differences and the network flow rate of the peer MP;

[0025] The processing module is further configured to select the opposite MP with the largest Pearson coefficient from multiple opposite MPs and a Pearson coefficient greater than a preset value as the target opposite MP, and control the MP to send a message to switch the backhaul link to the target opposite MP.

[0026] Furthermore, the network flow rate of the MP meets the preset flow rate fluctuation conditions, specifically including:

[0027] The total network traffic of the MP in the total time period is greater than a preset network traffic threshold, and the difference between the average network traffic speed of the MP in the total time period and the standard deviation of the network traffic speed of the MP in the total time period is greater than a preset network traffic threshold; wherein the total time period is the sum of multiple consecutive time periods.

[0028] In a fifth aspect, the present application provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and the processor implements the method of the first aspect when executing the computer program.

[0029] In a sixth aspect, the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the method of the first aspect.

[0030] In a seventh aspect, the present application provides a computer program product, comprising a computer program, which implements the method of the first aspect when executed by a processor.

[0031] The node load self-balancing method, device, and electronic device provided by the present application obtain the network flow rate of the MP in multiple continuous time periods and the network flow rate of each opposite-end MP in multiple continuous time periods when the network flow rate of the MP meets the preset flow rate fluctuation condition; for each continuous time period, calculate the first difference between the network flow rate of the MP and the preset network flow rate threshold; for each opposite-end MP, obtain the Pearson coefficient of the network flow rate of each opposite-end MP based on the multiple first differences and the network flow rate of the opposite-end MP; select the opposite-end MP with the largest Pearson coefficient and the Pearson coefficient greater than the preset value from the multiple opposite-end MPs as the target opposite-end MP, and control the MP to send a message to the target opposite-end MP to switch the backhaul link. The present application calculates the Pearson coefficient of the flow rate of the opposite-end MP in multiple time periods and the average flow rate exceeding the limit difference, determines the opposite-end MP node that has the greatest impact on the flow rate, and thus performs load balancing operations in a targeted manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0033] Figure 1 A flowchart of a node load self-balancing method provided in an embodiment of the present application;

[0034] Figure 2 A flowchart of another node load self-balancing method provided in an embodiment of the present application;

[0035] Figure 3 A flowchart of another node load self-balancing method provided in an embodiment of the present application;

[0036] Figure 4 A flowchart of another node load self-balancing method provided in an embodiment of the present application;

[0037] Figure 5 A flowchart of another node load self-balancing method provided in an embodiment of the present application;

[0038] Figure 6 A schematic diagram of the structure of a node load self-balancing device provided in an embodiment of the present application;

[0039] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0040] The above drawings illustrate specific embodiments of the present disclosure, which will be described in more detail below. These drawings and textual descriptions are not intended to limit the scope of the present disclosure in any way, but rather to illustrate the concepts of the present disclosure to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0041] Exemplary embodiments are described in detail herein, with examples illustrated in the accompanying drawings. When the following description refers to the drawings, identical numerals in different drawings represent identical or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present disclosure.

[0042] Many of the technical features and advantages of wireless mesh networks derive from their mesh connectivity and routing. Routing and forwarding design directly determines the efficiency with which the mesh network utilizes its mesh connectivity, impacting network performance. In wireless mesh networking, routing selection cannot be based solely on the "minimum hop count" but must comprehensively consider multiple performance metrics, load balancing across multiple paths, and maximizing system resource utilization.

[0043] In the prior art, whether the average flow rate within a period exceeds a threshold is calculated to determine whether traffic rerouting is required, thereby achieving Mesh network load balancing.

[0044] However, the existing technology cannot adapt to the over-limit scenario caused by large flow rate fluctuations, and the timing for load balancing is not accurately selected.

[0045] In response to the above problems, the node load self-balancing method, device and electronic device provided by the present application are intended to solve the technical problem that when the flow rate of the opposite MP fluctuates greatly, the existing technology cannot correctly find the opposite MP that has the greatest impact on the flow rate, which easily causes excessive node load. The technical concept of the present application is: when the network flow rate of the MP meets the preset flow rate fluctuation condition, the network flow rate of the MP in multiple continuous time periods and the network flow rate of each opposite MP in multiple continuous time periods are obtained; for each continuous time period, the first difference between the network flow rate of the MP and the preset network flow rate threshold is calculated; for each opposite MP, the Pearson coefficient of the network flow rate of each opposite MP is obtained based on the multiple first differences and the network flow rate of the opposite MP; the opposite MP with the largest Pearson coefficient and the Pearson coefficient greater than the preset value is selected from the multiple opposite MPs as the target opposite MP, and the MP is controlled to send a message to the target opposite MP to switch the backhaul link. The present application calculates the Pearson coefficient of the flow rate of the opposite MP in multiple time periods and the average flow rate exceeding the limit difference, determines the opposite MP node that has the greatest impact on the flow rate, and thus performs load balancing operations in a targeted manner.

[0046] The following specific embodiments describe in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0047] Figure 1 A flow chart of a node load self-balancing method provided in an embodiment of the present application is shown as follows: Figure 1 As shown, the method includes:

[0048] S101 : When the network flow rate of an MP meets a preset flow rate fluctuation condition, obtain the network flow rate of the MP in multiple consecutive time periods and the network flow rate of each peer MP in multiple consecutive time periods.

[0049] In this step, the execution subject of this embodiment can be an electronic device, or a controller, or a node load self-balancing device or equipment, or other devices or equipment that can execute this embodiment, without limitation. This embodiment is described with the execution subject being the controller.

[0050] Wireless mesh networks are a new type of wireless local area network (WLAN). Many of their technical features and advantages stem from their mesh connectivity and routing. Routing and forwarding design directly determines the efficiency with which the mesh network utilizes its mesh connectivity, impacting network performance. In wireless mesh networking, it's important to comprehensively consider various performance metrics, implement load balancing across multiple paths, and maximize system resource utilization.

[0051] Exemplarily, the preset flow rate fluctuation condition may be that the total network flow of the MP in the total time period is greater than the preset network flow threshold, and the difference between the average network flow rate of the MP in the total time period and the standard deviation of the network flow rate of the MP in the total time period is greater than the preset network flow threshold.

[0052] In response to the MP's network flow rate meeting the preset flow rate fluctuation condition, the MP starts to periodically record the total network traffic forwarded in the most recent multiple continuous time periods after startup, in bytes, and the time period is 2 seconds; the MP also periodically records the total network traffic returned by the opposite MP in the most recent multiple continuous time periods, in bytes, and the time period is 2 seconds; the total network traffic of the MP is divided by the time of the time period to obtain the network flow rate of the MP in the multiple continuous time periods; the total network traffic returned by the multiple opposite MPs is divided by the time of the time period to obtain the network flow rate of each opposite MP in the multiple continuous time periods.

[0053] It should be noted that the time period is 2 seconds, which can be set according to needs and is not limited here.

[0054] S102: For each continuous time period, calculate a first difference between the network flow rate of the MP and a preset network flow rate threshold.

[0055] In this step, the preset network flow rate threshold is a flow rate threshold defined by the system, taking into account the system's performance indicators. After obtaining the network flow rate of the MP for multiple consecutive time periods, the network flow rate of the MP is subtracted from the preset network flow rate threshold to obtain a first difference between the network flow rate of the MP and the preset network flow rate threshold. For example, the formula can be expressed as:

[0056]

[0057] Among them, v(x) is the network flow rate of MP, is the preset network flow rate threshold, and x is multiple consecutive time periods.

[0058] S103 : For each peer MP, obtain the Pearson coefficient of the network flow rate of each peer MP according to the multiple first differences and the network flow rate of the peer MP.

[0059] In this step, the Pearson coefficient of the network flow rate of each peer MP is calculated according to the following expression:

[0060]

[0061] Where D(x) represents the difference between the network flow rate of each peer MP and the preset network flow rate threshold in multiple consecutive time periods, v i (x) represents the network flow rate of each peer MP, r i Pearson coefficient of the network flow rate of each peer MP.

[0062] S104 : Select the opposite-end MP with the largest Pearson coefficient from the multiple opposite-end MPs and the one corresponding to the Pearson coefficient greater than a preset value as the target opposite-end MP, and control the MP to send a message for switching the backhaul link to the target opposite-end MP.

[0063] In this step, the Pearson coefficients of multiple peer MPs are first sorted, and the largest peer MP's Pearson coefficient is selected. It is then determined whether the largest peer MP's Pearson coefficient is greater than a preset value, such as 0.6. It should be noted that the preset value can be set as needed and is not limited here. If the largest peer MP's Pearson coefficient is greater than the preset value, the peer MP with the largest Pearson coefficient and a Pearson coefficient greater than the preset value is selected as the target peer MP. The controller controls the MP to send a message to the target peer MP via the access link.

[0064] In an embodiment of the present application, when the network flow rate of an MP meets a preset flow rate fluctuation condition, the network flow rate of the MP and the network flow rates of each peer MP in multiple consecutive time periods are obtained; for each consecutive time period, a first difference between the network flow rate of the MP and a preset network flow rate threshold is calculated; for each peer MP, the Pearson coefficient of the network flow rate of each peer MP is calculated based on the multiple first differences and the network flow rate of the peer MP; the peer MP with the largest Pearson coefficient and a Pearson coefficient greater than a preset value is selected from the multiple peer MPs as the target peer MP, and the MP is controlled to send a message to the target peer MP to switch the backhaul link. This determines the peer MP node that has the greatest impact on the flow rate, thereby performing targeted load balancing operations.

[0065] Figure 2 A flow chart of another node load self-balancing method provided in an embodiment of the present application is shown as follows: Figure 2 As shown, the method includes:

[0066] S201. The total network traffic of the MP in the total time period is greater than a preset network traffic threshold, and the difference between the average network traffic speed of the MP in the total time period and the standard deviation of the network traffic speed of the MP in the total time period is greater than a preset network traffic threshold; wherein the total time period is the sum of multiple consecutive time periods.

[0067] In this step, the total time period is the sum of multiple consecutive time periods. For example, if the multiple consecutive time periods are 3 o'clock to 4 o'clock, 4 o'clock to 5 o'clock, 5 o'clock to 6 o'clock, and 6 o'clock to 7 o'clock, the total time period is 3 o'clock to 7 o'clock.

[0068] The MP counts the total network traffic in the total time period when the total network traffic exceeds the system-preset network traffic threshold, and the difference between the MP's average network traffic speed in the total time period and the standard deviation of the MP's network traffic speed in the total time period exceeds the preset network traffic speed threshold; wherein, the average network traffic speed is equal to the total network traffic divided by the total time period.

[0069] S202: Obtain the network flow rate of the MP in multiple consecutive time periods and the network flow rate of each peer MP in multiple consecutive time periods.

[0070] This step has been described in detail in step S101 and will not be repeated here.

[0071] S203: For each continuous time period, calculate a first difference between the network flow rate of the MP and a preset network flow rate threshold.

[0072] This step has been described in detail in step S102 and will not be repeated here.

[0073] S204 : For each peer MP, obtain the Pearson coefficient of the network flow rate of each peer MP according to the multiple first differences and the network flow rate of the peer MP.

[0074] This step has been described in detail in step S103 and will not be repeated here.

[0075] S205 : Select the opposite-end MP with the largest Pearson coefficient from the multiple opposite-end MPs and the opposite-end MP corresponding to the Pearson coefficient greater than a preset value as the target opposite-end MP, and control the MP to send a message for switching the backhaul link to the target opposite-end MP.

[0076] This step has been described in detail in step S104 and will not be repeated here.

[0077] In an embodiment of the present application, the total network traffic of the MP in the total time period is greater than a preset network traffic threshold, and the difference between the average network traffic speed of the MP in the total time period and the standard deviation of the network traffic speed of the MP in the total time period is greater than a preset network traffic threshold; wherein the total time period is the sum of multiple continuous time periods. The network traffic speed of the MP in multiple continuous time periods and the network traffic speed of each opposite-end MP in multiple continuous time periods are obtained; for each continuous time period, the first difference between the network traffic speed of the MP and the preset network traffic speed threshold is calculated; for each opposite-end MP, the Pearson coefficient of the network traffic speed of each opposite-end MP is obtained based on the multiple first differences and the network traffic speed of the opposite-end MP; the opposite-end MP with the largest Pearson coefficient and the Pearson coefficient greater than the preset value is selected from the multiple opposite-end MPs as the target opposite-end MP, and the MP is controlled to send a message to the target opposite-end MP to switch the backhaul link. Thus, targeted load balancing operations can be achieved.

[0078] Figure 3 A flow chart of another node load self-balancing method provided in an embodiment of the present application is shown as follows: Figure 3 As shown, the method includes:

[0079] S301: Obtain the cumulative network traffic of the MP at the first time point in the total time period and the cumulative network traffic of the MP at the last time point in the total time period.

[0080] In this step, the first time point in the total time period, for example, the total time period is from 3 o'clock to 9 o'clock, then the first time point in the total time period is 3 o'clock, and the controller obtains the cumulative network traffic of the MP statistics at the first time point in the total time period by sending a control command; the last time point in the total time period, for example, the total time period is from 3 o'clock to 9 o'clock, then the last time point in the total time period is 9 o'clock, and the controller obtains the cumulative network traffic of the MP statistics at the last time point in the total time period by sending a control command.

[0081] S302: Subtract the accumulated network traffic of the MP at the first time point from the accumulated network traffic of the MP at the last time point to obtain the network traffic of the MP in the total time period.

[0082] In this step, for example, if the total time period is from 3 o'clock to 9 o'clock, then the first time point in the total time period is 3 o'clock, and the last time point in the total time period is 9 o'clock. The cumulative network traffic of the MP at 3 o'clock is subtracted from the cumulative network traffic of the MP at 9 o'clock to obtain the network traffic of the MP in the total time period.

[0083] S303: The total network traffic of the MP in the total time period is greater than a preset network traffic threshold, and the difference between the average network traffic speed of the MP in the total time period and the standard deviation of the network traffic speed of the MP in the total time period is greater than a preset network traffic threshold; wherein the total time period is the sum of multiple consecutive time periods.

[0084] This step has been described in detail in step S201 and will not be repeated here.

[0085] S304: Obtain the network flow rate of the MP in multiple consecutive time periods and the network flow rate of each peer MP in multiple consecutive time periods.

[0086] This step has been described in detail in step S101 and will not be repeated here.

[0087] S305 : For each continuous time period, calculate a first difference between the network flow rate of the MP and a preset network flow rate threshold.

[0088] This step has been described in detail in step S102 and will not be repeated here.

[0089] S306 : For each peer MP, obtain the Pearson coefficient of the network flow rate of each peer MP according to the multiple first differences and the network flow rate of the peer MP.

[0090] This step has been described in detail in step S103 and will not be repeated here.

[0091] S307 : Select the opposite-end MP with the largest Pearson coefficient from the multiple opposite-end MPs and the opposite-end MP corresponding to the Pearson coefficient greater than a preset value as the target opposite-end MP, and control the MP to send a message for switching the backhaul link to the target opposite-end MP.

[0092] This step has been described in detail in step S104 and will not be repeated here.

[0093] In an embodiment of the present application, the cumulative network traffic of the MP at the first time point within a total time period and the cumulative network traffic of the MP at the last time point within the total time period are obtained; the cumulative network traffic of the MP at the first time point is subtracted from the cumulative network traffic of the MP at the last time point to obtain the network traffic of the MP within the total time period. The total network traffic of the MP within the total time period is greater than a preset network traffic threshold, and the difference between the average network traffic speed of the MP within the total time period and the standard deviation of the network traffic speed of the MP within the total time period is greater than a preset network traffic threshold; wherein the total time period is the sum of multiple consecutive time periods. The network traffic speeds of the MP within the multiple consecutive time periods and the network traffic speeds of each opposite-end MP within the multiple consecutive time periods are obtained; for each consecutive time period, a first difference between the network traffic speed of the MP and the preset network traffic threshold is calculated; for each opposite-end MP, the Pearson coefficient of the network traffic speed of each opposite-end MP is obtained based on the multiple first differences and the network traffic speed of the opposite-end MP; the opposite-end MP with the largest Pearson coefficient and a Pearson coefficient greater than a preset value is selected from the multiple opposite-end MPs as the target opposite-end MP, and the MP is controlled to send a message to the target opposite-end MP to switch the backhaul link. Implement targeted load balancing operations to improve the performance efficiency and resource utilization of the Mesh network.

[0094] Figure 4 A flow chart of another node load self-balancing method provided in an embodiment of the present application is shown as follows: Figure 4 As shown, the method includes:

[0095] S401. For each peer MP, repeatedly obtain the cumulative network traffic of the peer MP at the previous time point and the cumulative network traffic of the peer MP at the current time point, calculate the difference between the cumulative network traffic of the peer MP at the previous time point and the cumulative network traffic of the peer MP at the current time point, and obtain the network traffic of the current time period; calculate the ratio between the network traffic of the current time period and the duration of the current time period, and obtain the network flow rate of the peer MP in the current time period, and update the cumulative network traffic at the next time point until the network traffic of the peer MP in multiple time periods is obtained.

[0096] In this step, for example, the total time period is from 3 o'clock to 7 o'clock, and multiple consecutive time periods are from 3 o'clock to 4 o'clock, 4 o'clock to 5 o'clock, 5 o'clock to 6 o'clock, and 6 o'clock to 7 o'clock. For each peer MP, repeatedly obtain the cumulative network traffic of the peer MP at the previous time point and the cumulative network traffic of the peer MP at the current time point, calculate the difference between the cumulative network traffic of the peer MP at the previous time point and the cumulative network traffic of the peer at the current time point, and obtain the network traffic of the current time period; then calculate the ratio between the network traffic of the current time period and the duration of the current time period, and obtain the network flow rate of the peer MP from 3 o'clock to 4 o'clock in the time period, and update the cumulative network traffic from the next time point from 4 o'clock to 5 o'clock, until the network traffic of the peer MP from 3 o'clock to 4 o'clock, 4 o'clock to 5 o'clock, 5 o'clock to 6 o'clock, and 6 o'clock to 7 o'clock is obtained.

[0097] S402: When the network flow rate of the MP meets a preset flow rate fluctuation condition, obtain the network flow rate of the MP in multiple consecutive time periods and the network flow rate of each peer MP in multiple consecutive time periods.

[0098] This step has been described in detail in step S101 and will not be repeated here.

[0099] S403: For each continuous time period, calculate a first difference between the network flow rate of the MP and a preset network flow rate threshold.

[0100] This step has been described in detail in step S102 and will not be repeated here.

[0101] S404 : For each peer MP, obtain the Pearson coefficient of the network flow rate of each peer MP according to the multiple first differences and the network flow rate of the peer MP.

[0102] This step has been described in detail in step S103 and will not be repeated here.

[0103] S405 : Select the opposite-end MP with the largest Pearson coefficient from the multiple opposite-end MPs and the opposite-end MP corresponding to the Pearson coefficient greater than a preset value as the target opposite-end MP, and control the MP to send a message for switching the backhaul link to the target opposite-end MP.

[0104] This step has been described in detail in step S104 and will not be repeated here.

[0105] In an embodiment of the present application, for each peer MP, the cumulative network traffic of the peer MP at the previous time point and the cumulative network traffic of the peer MP at the current time point are repeatedly obtained, the difference between the cumulative network traffic of the peer MP at the previous time point and the cumulative network traffic of the peer MP at the current time point is calculated, and the network traffic of the current time period is obtained; the ratio between the network traffic of the current time period and the duration of the current time period is calculated to obtain the network flow rate of the peer MP in the current time period, and the cumulative network traffic at the next time point is updated until the network traffic of the peer MP in multiple time periods is obtained. When the MP's network flow rate meets a preset flow rate fluctuation condition, the system obtains the MP's network flow rate for multiple consecutive time periods and the network flow rates of each peer MP for multiple consecutive time periods. For each consecutive time period, the system calculates a first difference between the MP's network flow rate and a preset network flow rate threshold. For each peer MP, the system calculates the Pearson coefficient of the network flow rate of each peer MP based on the multiple first differences and the peer MP's network flow rate. The system then selects the peer MP with the largest Pearson coefficient, whose Pearson coefficient is greater than a preset value, as the target peer MP. The system then controls the MP to send a message to the target peer MP to switch backhaul links. This allows for targeted load balancing, improving the performance efficiency and resource utilization of the Mesh network.

[0106] Figure 5 A flow chart of another node load self-balancing method provided in an embodiment of the present application is shown as follows: Figure 5 As shown, the method includes:

[0107] S501: When the network flow rate of the MP meets a preset flow rate fluctuation condition, obtain the network flow rate of the MP in multiple consecutive time periods and the network flow rate of each peer MP in multiple consecutive time periods.

[0108] This step has been described in detail in step S101 and will not be repeated here.

[0109] S502: For each continuous time period, calculate a first difference between the network flow rate of the MP and a preset network flow rate threshold.

[0110] This step has been described in detail in step S102 and will not be repeated here.

[0111] S503 : For each peer MP, obtain the Pearson coefficient of the network flow rate of each peer MP according to the multiple first differences and the network flow rate of the peer MP.

[0112] This step has been described in detail in step S103 and will not be repeated here.

[0113] S504 : Select the opposite-end MP with the largest Pearson coefficient from the multiple opposite-end MPs and the opposite-end MP corresponding to the Pearson coefficient greater than a preset value as the target opposite-end MP, and control the MP to send a message for switching the backhaul link to the target opposite-end MP.

[0114] This step has been described in detail in step S104 and will not be repeated here.

[0115] S505: When the MP continues to receive information returned by the target peer MP through the backhaul link within the limited time period, the MP is controlled to perform flow restriction processing on the target peer MP.

[0116] In this step, the limited time period begins after the message requesting the backhaul link switch is sent, and the duration of the limited time period is preset. If the MP continues to receive information from the target peer MP via the backhaul link during the limited time period, the MP is controlled to actively switch traffic to the backhaul link for the target peer MP. If the MP does not actively switch the backhaul link within the limited time period, the MP limits the flow of data on the backhaul link, forcing the peer MP to passively switch to the backhaul link.

[0117] Exemplarily, the controller may select the “minimum hops” shortest path principle to enable the peer MP to switch the backhaul link.

[0118] Exemplarily, the controller may also select a neighboring MP node principle to enable the opposite MP to switch the backhaul link.

[0119] Exemplarily, the controller may also select the principle of the strongest signal strength of the opposite MP to enable the opposite MP to switch the backhaul link.

[0120] In an embodiment of the present application, when the network flow rate of an MP meets a preset flow rate fluctuation condition, the network flow rate of the MP in multiple continuous time periods and the network flow rate of each opposite-end MP in multiple continuous time periods are obtained; for each continuous time period, the first difference between the network flow rate of the MP and the preset network flow rate threshold is calculated; for each opposite-end MP, the Pearson coefficient of the network flow rate of each opposite-end MP is obtained based on the multiple first differences and the network flow rate of the opposite-end MP; the opposite-end MP with the largest Pearson coefficient and a Pearson coefficient greater than the preset value is selected from the multiple opposite-end MPs as the target opposite-end MP, and the MP is controlled to send a message to the target opposite-end MP to switch the backhaul link. When the MP continues to receive information returned by the target opposite-end MP through the backhaul link within a limited time period, the MP is controlled to perform flow restriction processing on the target opposite-end MP; wherein the limited time period is counted from the time the message of switching the backhaul link is sent, and the duration of the limited time period is the preset duration. Targeted load balancing operations are implemented to improve the performance efficiency and resource utilization of the Mesh network.

[0121] Figure 6A schematic diagram of a node load self-balancing device 600 provided in an embodiment of the present application is shown in FIG. Figure 6 As shown, the apparatus 600 includes:

[0122] An acquisition module 601 is configured to acquire the network flow rate of the MP in a plurality of consecutive time periods and the network flow rate of each peer MP in a plurality of consecutive time periods when the network flow rate of the MP meets a preset flow rate fluctuation condition;

[0123] A processing module 602 is configured to calculate, for each continuous time period, a first difference between the network flow rate of the MP and a preset network flow rate threshold;

[0124] The processing module 602 is further configured to obtain, for each peer MP, a Pearson coefficient of the network flow rate of each peer MP based on the multiple first differences and the network flow rate of the peer MP;

[0125] The processing module 602 is further configured to select the opposite MP with the largest Pearson coefficient from the multiple opposite MPs and a Pearson coefficient greater than a preset value as the target opposite MP, and control the MP to send a message to switch the backhaul link to the target opposite MP.

[0126] In one embodiment, the acquisition module 601 is further specifically used to determine that the total network traffic of the MP in the total time period is greater than a preset network traffic threshold, and the difference between the average network traffic speed of the MP in the total time period and the standard deviation of the network traffic speed of the MP in the total time period is greater than a preset network traffic threshold; wherein the total time period is the sum of multiple consecutive time periods.

[0127] In one embodiment, the acquisition module 601 is further configured to acquire the cumulative network traffic of the MP at the first time point within the total time period, and the cumulative network traffic of the MP at the last time point within the total time period;

[0128] The processing module 602 is further configured to obtain the network traffic of the MP in the time period by subtracting the accumulated network traffic of the MP at the first time point from the accumulated network traffic of the MP at the last time point.

[0129] In one embodiment, the processing module 602 is further used to repeatedly execute, for each peer MP, obtaining the cumulative network traffic of the peer MP at the previous time point and the cumulative network traffic of the peer MP at the current time point, calculating the difference between the cumulative network traffic of the peer MP at the previous time point and the cumulative network traffic of the peer MP at the current time point, and obtaining the network traffic of the current time period; calculating the ratio between the network traffic of the current time period and the duration of the current time period, and obtaining the network flow rate of the peer MP in the current time period, and updating the cumulative network traffic at the next time point, until the network traffic of the peer MP in multiple time periods is obtained.

[0130] In one embodiment, the processing module 602 is further configured to:

[0131] When the MP continues to receive information returned by the target peer MP through the backhaul link within a limited time period, the control MP performs flow restriction processing on the target peer MP;

[0132] The limited time period starts from the time when the message for switching the backhaul link is sent, and the duration of the limited time period is a preset duration.

[0133] The device of this embodiment can execute the technical solution in the above method. Its specific implementation process and technical principles are the same and will not be repeated here.

[0134] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application is shown in FIG. Figure 7 As shown, the electronic device 700 includes: a memory 701 and a processor 702;

[0135] The memory 701 is used to store computer instructions executable by the processor;

[0136] The processor 702 implements each step of the method in the above embodiment when executing the computer instructions. For details, please refer to the relevant description in the above method embodiment.

[0137] Optionally, the memory 701 can be independent or integrated with the processor 702. When the memory 701 is independent, the test device further includes a bus for connecting the memory 701 and the processor 702.

[0138] An embodiment of the present application also provides a non-transitory computer-readable storage medium. When the instructions in the storage medium are executed by a processor of an electronic device, the electronic device can execute the method provided in the above embodiment.

[0139] An embodiment of the present application also provides a computer program product, which includes: a computer program, which is stored in a readable storage medium, and at least one processor of an electronic device can read the computer program from the readable storage medium, and at least one processor executes the computer program so that the electronic device executes the solution provided by any of the above embodiments.

[0140] Other embodiments of the present disclosure will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the following claims.

[0141] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A method for node load self-balancing, characterized in that: A wireless mesh network includes an MP and a peer MP. The method is applied to a controller and includes: When the network flow rate of the MP meets a preset flow rate fluctuation condition, obtaining the network flow rate of the MP in multiple consecutive time periods and the network flow rate of each peer MP in multiple consecutive time periods; For each continuous time period, calculating a first difference between the network flow rate of the MP and a preset network flow rate threshold; For each peer MP, obtaining a Pearson coefficient of the network flow rate of each peer MP according to the plurality of first differences and the network flow rate of the peer MP; The opposite-end MP having the largest Pearson coefficient and a Pearson coefficient greater than a preset value is selected from the plurality of opposite-end MPs as the target opposite-end MP, and the MP is controlled to send a message for switching the backhaul link to the target opposite-end MP.

2. The method according to claim 1, characterized in that The network flow rate of the MP satisfies a preset flow rate fluctuation condition, specifically including: The total network traffic of the MP in the total time period is greater than a preset network traffic threshold, and the difference between the average network traffic speed of the MP in the total time period and the standard deviation of the network traffic speed of the MP in the total time period is greater than the preset network traffic speed threshold; wherein, the total time period is the sum of the multiple consecutive time periods.

3. The method according to claim 2, characterized in that The method further comprises: Obtaining the cumulative network traffic of the MP at the first time point within the total time period, and the cumulative network traffic of the MP at the last time point within the total time period; The network traffic of the MP in the total time period is obtained by subtracting the accumulated network traffic of the MP at the first time point from the accumulated network traffic of the MP at the last time point.

4. The method according to claim 1, wherein The method further comprises: For each peer MP, repeatedly obtain the cumulative network traffic of the peer MP at the previous time point and the cumulative network traffic of the peer MP at the current time point, calculate the difference between the cumulative network traffic of the peer MP at the previous time point and the cumulative network traffic of the peer at the current time point, and obtain the network traffic of the current time period; calculate the ratio between the network traffic of the current time period and the duration of the current time period, obtain the network flow rate of the peer MP in the current time period, update the cumulative network traffic at the next time point, until the network traffic of the peer MP in multiple time periods is obtained.

5. The method according to claim 1, wherein After controlling the MP to send a message for switching the backhaul link to the target peer MP, the method further includes: When the MP continues to receive information returned by the target peer MP through the backhaul link within a limited time period, controlling the MP to perform flow restriction processing on the target peer MP; The limited time period starts from the time when the message for switching the backhaul link is sent, and the duration of the limited time period is a preset duration.

6. A node load self-balancing device, characterized in that: The wireless mesh network includes an MP and a peer MP, and the device includes: an acquisition module, configured to acquire the network flow rates of the MP in a plurality of consecutive time periods and the network flow rates of each peer MP in a plurality of consecutive time periods when the network flow rate of the MP meets a preset flow rate fluctuation condition; a processing module, configured to calculate, for each continuous time period, a first difference between a network flow rate of the MP and a preset network flow rate threshold; The processing module is further configured to obtain, for each peer MP, a Pearson coefficient of the network flow rate of each peer MP based on the plurality of first differences and the network flow rate of the peer MP; The processing module is further configured to select the opposite-end MP having the largest Pearson coefficient and a Pearson coefficient greater than a preset value from the plurality of opposite-end MPs as the target opposite-end MP, and control the MP to send a message for switching the backhaul link to the target opposite-end MP.

7. The device according to claim 6, characterized in that The network flow rate of the MP satisfies a preset flow rate fluctuation condition, specifically including: The total network traffic of the MP in the total time period is greater than a preset network traffic threshold, and the difference between the average network traffic speed of the MP in the total time period and the standard deviation of the network traffic speed of the MP in the total time period is greater than the preset network traffic speed threshold; wherein, the total time period is the sum of the multiple consecutive time periods.

8. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, the method according to any one of claims 1 to 5 is implemented.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 5 when executed by a processor.

10. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 5 when executed by a processor.

Images