A load balancing method and system for an internet of things edge

Abstract

The application provides a load balancing method and system for an Internet of Things edge terminal. The load balancing method comprises the following steps: acquiring all devices for uploading data in the Internet of Things; classifying the data uploaded by the devices in units of devices to obtain data categories; summarizing the data generated by all devices in units of data categories, and acquiring the data volume corresponding to each data category and a data category set; distributing servers according to the data volume and the data categories to obtain the servers corresponding to each data category; and sending data to the corresponding servers according to the corresponding relationship between the data categories and the servers. The system comprises modules corresponding to the method steps.

Description

Technical Field

[0001] This invention proposes a load balancing method and system for the edge of the Internet of Things (IoT), belonging to the field of IoT technology. Background Technology

[0002] With the continuous development of sensor and monitoring technologies, IoT systems are increasingly being applied to various monitoring scenarios. Currently, IoT load balancers are mostly deployed on the server side, such as nginx, to distribute a large number of connections. However, in the IoT environment, in addition to customer data, device data also puts significant pressure on the server. Large amounts of data uploaded by devices to the server, if not handled properly, will hinder server concurrency and increase latency. Summary of the Invention

[0003] This invention provides a load balancing method and system for the edge of the Internet of Things (IoT) to solve the problem of data transmission delay caused by the increase in data volume during concurrent IoT data processing. The technical solution adopted is as follows:

[0004] A load balancing method at the edge of an Internet of Things (IoT) device, the load balancing method comprising:

[0005] Acquire all devices within the Internet of Things (IoT) that are uploading data;

[0006] The data generated and uploaded by the devices are classified into data categories, with each device acting as a unit.

[0007] The data generated by all devices is aggregated by data type, and the data volume and data type set corresponding to each data type are obtained.

[0008] Servers are allocated according to the amount and type of data, and a server is obtained for each data type.

[0009] Data is sent to its corresponding server according to the correspondence between data type and server.

[0010] Furthermore, the data generated by all devices is aggregated by data type, and the data volume and data type set corresponding to each data type are obtained, including:

[0011] Extract the data types corresponding to the data generated by each device, and summarize the data volume corresponding to each data type to obtain data type information and multiple data volume information for each device;

[0012] Extract the corresponding data volume of the same type for each device to obtain the total data volume corresponding to the type;

[0013] Sort the data categories in descending order of the total amount of data corresponding to each category to obtain a set of data categories.

[0014] Furthermore, servers are allocated according to data volume and data type to obtain the server corresponding to each data type, including:

[0015] Data types with a data volume greater than a preset first data volume threshold are extracted from the data type set to obtain a first data type cluster, and the first data type cluster is matched with the first server.

[0016] Data types whose data volume does not exceed a preset first data volume threshold but exceeds a second data volume threshold are extracted from the data type set to obtain a second data type cluster, and the second data type cluster is matched with the second server accordingly.

[0017] Data types whose data volume does not exceed a pre-set second data volume threshold are extracted from the data type set to obtain a third data type cluster, and the third data type cluster is matched with a third server.

[0018] Furthermore, according to the correspondence between data type and server, the data is sent to its corresponding server, including:

[0019] Extract the devices contained in each data cluster;

[0020] Extract the amount of data generated by each device in the corresponding data cluster within a pre-set unit of time;

[0021] Configure the port of the server corresponding to the data type cluster according to the amount of data generated in the corresponding data type cluster within the unit time; wherein, the configuration principle of the port configuration is that the device with a larger amount of data corresponds to the port with a larger bandwidth.

[0022] During the long data transmission process, the data generated by each device is uploaded to the corresponding server according to the port configuration.

[0023] A load balancing system at the edge of an Internet of Things (IoT) system, the load balancing system comprising:

[0024] The device acquisition module is used to acquire all devices that upload data within the Internet of Things (IoT).

[0025] The category acquisition module is used to classify the uploaded data generated by the device on a device-by-device basis to obtain the data category;

[0026] The collection acquisition module is used to summarize the data generated by all devices by data type, and obtain the data volume and data type set corresponding to each data type;

[0027] The server allocation module is used to allocate servers according to the data volume and data type, and obtain the server corresponding to each data type.

[0028] The sending module is used to send data to the corresponding server according to the correspondence between data type and server.

[0029] Furthermore, the set acquisition module includes:

[0030] The category extraction module is used to extract the data categories corresponding to the data generated by each device, and to summarize the data volume corresponding to each data category to obtain data category information and multiple data volume information for each device.

[0031] The data volume acquisition module is used to extract the corresponding data volume of the same type of data information for each device to obtain the total data volume corresponding to the type.

[0032] The set generation module is used to sort the data types in descending order of the total amount of data corresponding to the types, and obtain a set of data types.

[0033] Furthermore, the server allocation module includes:

[0034] The first matching module is used to extract data types with a data volume greater than a preset first data volume threshold from the data type set to obtain a first data type cluster, and to match the first data type cluster with the first server.

[0035] The second matching module is used to extract data types whose data volume does not exceed a preset first data volume threshold but is greater than a second data volume threshold from the data type set, obtain a second data type cluster, and match the second data type cluster with the second server.

[0036] The third matching module is used to extract data types whose data volume does not exceed a preset second data volume threshold from the data type set to obtain a third data type cluster, and to match the third data type cluster with the third server.

[0037] Furthermore, the sending module includes:

[0038] The device extraction module is used to extract the devices contained in each data category cluster.

[0039] The category data volume extraction module is used to extract the data volume of each device in the corresponding data category cluster within a preset unit time period;

[0040] The port configuration module is used to configure the ports of the servers corresponding to the data type clusters according to the amount of data generated in the corresponding data type clusters within the unit time; wherein, the configuration principle of the port configuration is that devices with larger data volumes correspond to ports with larger bandwidths.

[0041] The data sending module is used to upload the data generated by each device to the corresponding server according to the port configuration during long data transmission.

[0042] Beneficial effects of this invention:

[0043] The present invention proposes a load balancing method and system for the edge of the Internet of Things (IoT), which can deploy load balancing strategies at the edge of IoT devices. By sending data of different types and sizes to different servers or different ports of the same server, the load on the server can be reduced and the server concurrency can be increased. Attached Figure Description

[0044] Figure 1 This is a flowchart of the method described in this invention;

[0045] Figure 2 This is a system block diagram of the system described in this invention. Detailed Implementation

[0046] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0047] This invention proposes a load balancing method for the edge of the Internet of Things (IoT), such as... Figure 1 As shown, the load balancing method includes:

[0048] S1. Obtain all devices within the Internet of Things that are uploading data;

[0049] S2. Classify the data uploaded by the devices to obtain data categories, taking each device as a unit.

[0050] S3. Summarize the data generated by all devices by data type, and obtain the data volume and data type set corresponding to each data type;

[0051] S4. Allocate servers according to data volume and data type to obtain the server corresponding to each data type;

[0052] S5. Send the data to the corresponding server according to the correspondence between the data type and the server.

[0053] The working principle of the above technical solution is as follows: First, acquire all devices that upload data within the Internet of Things; then, classify the uploaded data generated by the devices into data categories; subsequently, summarize the data generated by all devices into data categories and obtain the data volume and data category set corresponding to each data category; then, allocate servers according to the data volume and data category to obtain the server corresponding to each data category; finally, send the data to its corresponding server according to the correspondence between data category and server.

[0054] The effect of the above technical solution is as follows: The load balancing method for IoT edge devices proposed in this embodiment can deploy load balancing strategies at the edge of IoT devices, and send data of different types and sizes to different servers or different ports of the same server, thereby reducing the pressure on the server and increasing the server's concurrency.

[0055] One embodiment of the present invention involves summarizing the data generated by all devices by data type, and obtaining the data volume and data type set corresponding to each data type, including:

[0056] S301. Extract the data type corresponding to the data generated by each device, and summarize the data volume corresponding to each data type to obtain the data type information and multiple data volume information corresponding to each device.

[0057] S302. Extract the corresponding data quantity of the same type of data type information for each device to obtain the total data quantity corresponding to the type;

[0058] S303. Sort the data types in descending order of the total amount of data corresponding to each type to obtain a set of data types.

[0059] The working principle of the above technical solution is as follows: First, extract the data type corresponding to the data generated by each device, and summarize the data quantity corresponding to each data type to obtain the data type information and multiple data quantity information corresponding to each device; then, extract the corresponding data quantity of the same type of data type information corresponding to each device to obtain the total data quantity corresponding to the type; finally, sort the data types in descending order of the total data quantity corresponding to the type to obtain the data type set.

[0060] The effects of the above technical solution are as follows: the above method can effectively improve the efficiency of data classification. At the same time, the data classification and sorting performed through the above data classification logic can effectively improve the rationality of data classification, thereby improving the rationality of subsequent server allocation.

[0061] One embodiment of the present invention involves allocating servers according to data volume and data type to obtain a server corresponding to each data type, including:

[0062] S401. Extract data types whose data volume exceeds a preset first data volume threshold from the data type set to obtain a first data type cluster, and match the first data type cluster with the first server.

[0063] S402. Extract data types whose data volume does not exceed a preset first data volume threshold but is greater than a second data volume threshold from the data type set to obtain a second data type cluster, and match the second data type cluster with the second server accordingly.

[0064] S403. Extract data types whose data volume does not exceed a preset second data volume threshold from the data type set to obtain a third data type cluster, and match the third data type cluster with a third server.

[0065] The server configuration parameters of the first server, the second server, and the third server are different.

[0066] The working principle of the above technical solution is as follows: First, data types with a data volume greater than a preset first data volume threshold are extracted from the data type set to obtain a first data type cluster, and the first data type cluster is matched with a first server; then, data types with a data volume not exceeding the preset first data volume threshold but greater than a second data volume threshold are extracted from the data type set to obtain a second data type cluster, and the second data type cluster is matched with a second server; finally, data types with a data volume not exceeding the preset second data volume threshold are extracted from the data type set to obtain a third data type cluster, and the third data type cluster is matched with a third server.

[0067] The effect of the above technical solution is that by setting the above two data thresholds and matching the first data type cluster, the second data type cluster, and the third data type cluster with their corresponding servers, the rationality of the division of the first data type cluster, the second data type cluster, and the third data type cluster, as well as the rationality and allocation efficiency of the corresponding configuration of each data type cluster with the server are effectively improved.

[0068] One embodiment of the present invention involves sending data to its corresponding server according to the correspondence between data type and server, including:

[0069] S501. Extract the devices contained in each data category cluster;

[0070] S502. Extract the amount of data generated by each device in the corresponding data type cluster within a preset unit time period;

[0071] S503. Configure the port of the server corresponding to the data type cluster according to the size of the data volume in the corresponding data type cluster generated per unit time; wherein, the configuration principle of the port configuration is that the device with a larger data volume corresponds to the port with a larger bandwidth.

[0072] S504. During long-distance data transmission, data generated by each device is uploaded to the corresponding server according to the port configuration relationship.

[0073] The working principle of the above technical solution is as follows: First, extract the devices contained in each data type cluster; then, extract the amount of data generated by each device in the corresponding data type cluster within a pre-set unit time; subsequently, configure the port of the server corresponding to the data type cluster according to the amount of data generated in the corresponding data type cluster within the unit time; wherein, the configuration principle of the port configuration is that devices with larger data volumes correspond to ports with larger bandwidths; finally, during the long data transmission process, upload the data generated by each device to the corresponding server according to the port configuration correspondence.

[0074] The effect of the above technical solution is to reduce the pressure on the server and thus increase the server's concurrency by sending data of different types and sizes to different servers or different ports of the same server.

[0075] This invention proposes a load balancing system for the edge of the Internet of Things (IoT), such as... Figure 2 As shown, the load balancing system includes:

[0076] The device acquisition module is used to acquire all devices that upload data within the Internet of Things (IoT).

[0077] The category acquisition module is used to classify the uploaded data generated by the device on a device-by-device basis to obtain the data category;

[0078] The collection acquisition module is used to summarize the data generated by all devices by data type, and obtain the data volume and data type set corresponding to each data type;

[0079] The server allocation module is used to allocate servers according to the data volume and data type, and obtain the server corresponding to each data type.

[0080] The sending module is used to send data to the corresponding server according to the correspondence between data type and server.

[0081] The working principle of the above technical solution is as follows: First, the device acquisition module acquires all devices that upload data within the Internet of Things; then, the category acquisition module classifies the uploaded data generated by the devices into categories based on the devices themselves; subsequently, the set acquisition module aggregates the data generated by all devices by data category and obtains the data volume and data category set corresponding to each data category; then, the server allocation module allocates servers according to the data volume and data category to obtain the server corresponding to each data category; finally, the sending module sends the data to its corresponding server according to the correspondence between data category and server.

[0082] The effect of the above technical solution is that the load balancing system at the edge of the Internet of Things proposed in this embodiment can deploy load balancing strategies at the edge of the Internet of Things devices, and send data of different types and sizes to different servers or different ports of the same server, thereby reducing the pressure on the server and increasing the server's concurrency.

[0083] In one embodiment of the present invention, the set acquisition module includes:

[0084] The category extraction module is used to extract the data categories corresponding to the data generated by each device, and to summarize the data volume corresponding to each data category to obtain data category information and multiple data volume information for each device.

[0085] The data volume acquisition module is used to extract the corresponding data volume of the same type of data information for each device to obtain the total data volume corresponding to the type.

[0086] The set generation module is used to sort the data types in descending order of the total amount of data corresponding to the types, and obtain a set of data types.

[0087] The working principle of the above technical solution is as follows: First, the data type corresponding to the data generated by each device is extracted by the type extraction module, and the data quantity corresponding to each data type is summarized to obtain the data type information and multiple data quantity information corresponding to each device; then, the data quantity acquisition module extracts the corresponding data quantity of the same type of data information corresponding to each device to obtain the total data quantity corresponding to the type; finally, the set generation module sorts the data types in descending order of the total data quantity corresponding to the type to obtain the data type set.

[0088] The effects of the above technical solution are as follows: the above method can effectively improve the efficiency of data classification. At the same time, the data classification and sorting performed through the above data classification logic can effectively improve the rationality of data classification, thereby improving the rationality of subsequent server allocation.

[0089] In one embodiment of the present invention, the server allocation module includes:

[0090] The first matching module is used to extract data types with a data volume greater than a preset first data volume threshold from the data type set to obtain a first data type cluster, and to match the first data type cluster with the first server.

[0091] The second matching module is used to extract data types whose data volume does not exceed a preset first data volume threshold but is greater than a second data volume threshold from the data type set, obtain a second data type cluster, and match the second data type cluster with the second server.

[0092] The third matching module is used to extract data types whose data volume does not exceed a preset second data volume threshold from the data type set to obtain a third data type cluster, and to match the third data type cluster with the third server.

[0093] The working principle of the above technical solution is as follows: First, the first matching module extracts data types with a data volume greater than a preset first data volume threshold from the data type set to obtain a first data type cluster, and the first data type cluster is matched with the first server; then, the second matching module extracts data types with a data volume not exceeding the preset first data volume threshold but greater than the second data volume threshold from the data type set to obtain a second data type cluster, and the second data type cluster is matched with the second server; finally, the third matching module extracts data types with a data volume not exceeding the preset second data volume threshold from the data type set to obtain a third data type cluster, and the third data type cluster is matched with the third server.

[0094] The effect of the above technical solution is that by setting the above two data thresholds and matching the first data type cluster, the second data type cluster, and the third data type cluster with their corresponding servers, the rationality of the division of the first data type cluster, the second data type cluster, and the third data type cluster, as well as the rationality and allocation efficiency of the corresponding configuration of each data type cluster with the server are effectively improved.

[0095] In one embodiment of the present invention, the sending module includes:

[0096] The device extraction module is used to extract the devices contained in each data category cluster.

[0097] The category data volume extraction module is used to extract the data volume of each device in the corresponding data category cluster within a preset unit time period;

[0098] The port configuration module is used to configure the ports of the servers corresponding to the data type clusters according to the amount of data generated in the corresponding data type clusters within the unit time; wherein, the configuration principle of the port configuration is that devices with larger data volumes correspond to ports with larger bandwidths.

[0099] The data sending module is used to upload the data generated by each device to the corresponding server according to the port configuration during long data transmission.

[0100] The working principle of the above technical solution is as follows: First, the device extraction module extracts the devices contained in each data category cluster. Then, the data volume extraction module extracts the data volume generated by each device in the corresponding data category cluster within a pre-set unit time. Next, the port configuration module configures the port of the server corresponding to the data category cluster according to the size of the data volume generated in the corresponding data category cluster within the unit time. The configuration principle of the port is that devices with larger data volumes correspond to ports with larger bandwidth. Finally, the data sending module uploads the data generated by each device to the corresponding server according to the port configuration correspondence during the long data transmission process.

[0101] The effect of the above technical solution is to reduce the pressure on the server and thus increase the server's concurrency by sending data of different types and sizes to different servers or different ports of the same server.

[0102] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A load balancing method of an Internet of Things edge, characterized in that, The load balancing method includes: Acquire all devices within the Internet of Things (IoT) that are uploading data; The data generated and uploaded by the devices are classified into data categories, with each device acting as a unit. The data generated by all devices is aggregated by data type, and the data volume and data type set corresponding to each data type are obtained. Servers are allocated according to the data volume and data type to obtain the server corresponding to each data type; Data is sent to its corresponding server according to the correspondence between data type and server; This involves allocating servers according to data volume and data type, obtaining the server corresponding to each data type, including: Data types with a data volume greater than a preset first data volume threshold are extracted from the data type set to obtain a first data type cluster, and the first data type cluster is matched with the first server. Data types whose data volume does not exceed a preset first data volume threshold but exceeds a second data volume threshold are extracted from the data type set to obtain a second data type cluster, and the second data type cluster is matched with the second server accordingly. Data types whose data volume does not exceed a pre-set second data volume threshold are extracted from the data type set to obtain a third data type cluster, and the third data type cluster is matched with a third server.

2. The load balancing method of claim 1, wherein, All data generated by all devices is aggregated by data type, and the data volume and data type set corresponding to each data type are obtained, including: Extract the data types corresponding to the data generated by each device, and summarize the data volume corresponding to each data type to obtain data type information and multiple data volume information for each device; Extract the corresponding data volume of the same type for each device to obtain the total data volume corresponding to the type; Sort the data categories in descending order of the total amount of data corresponding to each category to obtain a set of data categories.

3. The load balancing method of claim 1, wherein, Data is sent to its corresponding server according to the mapping relationship between data type and server, including: Extract the devices contained in each data cluster; Extract the amount of data generated by each device in the corresponding data cluster within a pre-set unit of time; Configure the port of the server corresponding to the data type cluster according to the size of the data volume in the corresponding data type cluster generated within the unit time. During the long data transmission process, the data generated by each device is uploaded to the corresponding server according to the port configuration.

4. A load balancing system for an Internet of Things edge, the system comprising: The load balancing system includes: The device acquisition module is used to acquire all devices that upload data within the Internet of Things (IoT). The category acquisition module is used to classify the uploaded data generated by the device on a device-by-device basis to obtain the data category; The collection acquisition module is used to summarize the data generated by all devices by data type, and obtain the data volume and data type set corresponding to each data type; The server allocation module is used to allocate servers according to the data volume and data type, and obtain the server corresponding to each data type. The sending module is used to send data to the corresponding server according to the correspondence between data type and server; The server allocation module includes: The first matching module is used to extract data types with a data volume greater than a preset first data volume threshold from the data type set to obtain a first data type cluster, and to match the first data type cluster with the first server. The second matching module is used to extract data types whose data volume does not exceed a preset first data volume threshold but is greater than a second data volume threshold from the data type set, obtain a second data type cluster, and match the second data type cluster with the second server. The third matching module is used to extract data types whose data volume does not exceed a preset second data volume threshold from the data type set to obtain a third data type cluster, and to match the third data type cluster with the third server.

5. The load balancing system of claim 4, wherein, The collection acquisition module includes: The category extraction module is used to extract the data categories corresponding to the data generated by each device, and to summarize the data volume corresponding to each data category to obtain data category information and multiple data volume information for each device. The data volume acquisition module is used to extract the corresponding data volume of the same type of data information for each device to obtain the total data volume corresponding to the type. The set generation module is used to sort the data types in descending order of the total amount of data corresponding to the types, and obtain a set of data types.

6. The load balancing system of claim 4, wherein, The sending module includes: The device extraction module is used to extract the devices contained in each data category cluster. The category data volume extraction module is used to extract the data volume of each device in the corresponding data category cluster within a preset unit time period; The port configuration module is used to configure the port of the server corresponding to the data type cluster according to the size of the data volume in the corresponding data type cluster generated within the unit time. The data sending module is used to upload the data generated by each device to the corresponding server according to the port configuration during long data transmission.

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