Data offloading method, system and storage medium for edge computing node

Abstract

This disclosure discloses a data offloading method, system, and storage medium for edge computing nodes, comprising: a data acquisition module for acquiring data collected by the edge computing nodes; a data storage module for storing the collected data separately according to the edge computing nodes; a data processing module for determining the data processing priority of the edge computing nodes based on real-time data and historical data; a data analysis module for determining the path priority corresponding to each data transmission path in the data transmission path queue; and an edge scheduling module for determining the data transmission slice length corresponding to the data transmission path, and allocating the data to be transmitted to the corresponding data transmission path for transmission to the cloud computing center according to the data processing priority and path priority. This disclosure realizes data offloading of edge computing nodes, improves data transmission efficiency, thereby shortening the average latency of data transmission, reducing service response latency, and improving user experience.

Description

Technical Field

[0001] This disclosure relates to the field of edge computing technology, and in particular to a data offloading method, system and storage medium for edge computing nodes. Background Technology

[0002] Mobile Edge Computing (MEC) primarily refers to providing IT and cloud computing capabilities to wireless access networks by deploying general-purpose servers on the wireless access side. This enables traditional wireless access networks to achieve business localization and provide low-latency, high-bandwidth transmission capabilities. When network bandwidth is limited, data from edge computing nodes needs to be offloaded to meet data transmission requirements.

[0003] In existing technologies, data splitting methods divide large data streams into multiple smaller data streams and allocate them to different processing units or storage devices according to fixed rules. This allows large amounts of data to be processed in smaller blocks, reducing data storage and transmission overhead. However, in these methods, processing units prioritize data for transmission, and data with the same priority is not further split. This leads to prolonged transmission time along the same path, resulting in low transmission efficiency and a longer average data transmission latency, thus increasing service response time and negatively impacting user experience. Summary of the Invention

[0004] To overcome the problems existing in related technologies, this disclosure provides a data offloading method, system and storage medium for edge computing nodes.

[0005] According to a first aspect of the present disclosure, a data offloading system for edge computing nodes is provided, the system including a cloud computing center, the cloud computing center being communicatively connected to a data acquisition module, a data storage module, a data processing module, a data analysis module and an edge scheduling module;

[0006] The data acquisition module is connected to the cloud computing center in a distributed manner through IoT nodes to acquire data collected by edge computing nodes.

[0007] The data storage module is communicatively connected to the data acquisition module and is used to receive the acquired data transmitted by the data acquisition module and store the acquired data separately according to the edge computing nodes.

[0008] The data processing module is used to obtain real-time data and historical data of all edge computing nodes from the data storage module, and determine the data processing priority of the edge computing nodes based on the real-time data and historical data.

[0009] The data analysis module is used to request a data transmission path queue from the cloud computing center and determine the path priority of each data transmission path in the data transmission path queue.

[0010] The edge scheduling module is used to determine the data transmission slice length corresponding to the data transmission path, and allocate the data to be transmitted to the corresponding data transmission path and transmit it to the cloud computing center according to the data processing priority and the path priority.

[0011] Optionally, in this embodiment of the disclosure, determining the data processing priority of the edge computing node based on the real-time data and historical data includes:

[0012] The data activity of the edge computing node is determined based on the number of data collections within a preset time period;

[0013] Based on the real-time and historical data, determine the type attraction value of the edge computing node;

[0014] The data processing priority of the edge computing node is determined based on the data activity, the type attraction value, and the data size in the real-time data.

[0015] The data processing priority of the edge computing node is determined based on the data processing priority value.

[0016] Optionally, in this embodiment of the disclosure, determining the path priority corresponding to each data transmission path in the data transmission path queue includes:

[0017] Send probe data to each data transmission path in the data transmission path queue, and obtain the start time and end time of the probe data transmission;

[0018] Obtain a graph showing the transmission rate of the probed data as a function of time;

[0019] The real-time transmission rate in the curve is compared with the rate threshold, and the corresponding positive and negative absolute values ​​of deviation are determined.

[0020] Based on the absolute values ​​of the positive and negative deviations, the transmission coefficients of the corresponding data transmission paths are calculated.

[0021] The path priority of each data transmission path is determined based on its transmission coefficient.

[0022] Optionally, in this embodiment of the disclosure, determining the data transmission slice length corresponding to the data transmission path includes: matching the transmission coefficient corresponding to the data transmission path with a data transmission slice length lookup table to determine the data transmission slice length corresponding to the data transmission path.

[0023] Optionally, in this embodiment of the disclosure, the step of allocating the data to be transmitted to the corresponding data transmission path and transmitting it to the cloud computing center according to the data processing priority and the path priority includes:

[0024] Based on the descending order of data processing priorities of each edge computing node, the data to be transmitted is allocated to data transmission paths arranged in descending order of path priority;

[0025] If high-priority data is not transmitted within the data transmission slice length of the current data transmission path, the untransmitted data will be allocated to the next higher priority data transmission path for transmission until the data allocation is completed.

[0026] Optionally, in this embodiment of the disclosure, the edge scheduling module is further configured to: determine the data round-trip delay time of the data transmission path, and send the data round-trip delay time to the cloud computing center.

[0027] Optionally, in this embodiment of the disclosure, the cloud computing center is used to: determine whether to adjust the data transmission slice length of the data transmission path based on the data round-trip delay time.

[0028] Optionally, in this embodiment of the disclosure, determining whether to adjust the data transmission slice length of the data transmission path based on the data round-trip delay time includes:

[0029] The data round-trip delay time is compared with a preset delay threshold.

[0030] If the data round-trip delay time is greater than or equal to the preset delay threshold, then it is determined that the data transmission slice length of the data transmission path will not be adjusted, and the data transmission slice length of the data transmission path is marked as the data transmission slice quota of the data transmission path.

[0031] If the data round-trip delay time is less than the preset delay threshold, then the data transmission slice length of the data transmission path is increased until the data transmission slice length of the data transmission path reaches the data transmission slice quota.

[0032] According to a second aspect of the present disclosure, a data offloading method for edge computing nodes is provided, the method comprising:

[0033] Acquire real-time and historical data from edge computing nodes;

[0034] The data processing priority of the edge computing node is determined based on the real-time data and historical data.

[0035] In response to the edge computing node transmitting data to the cloud computing center, it requests a data transmission path queue from the cloud computing center and determines the path priority corresponding to each data transmission path in the data transmission path queue.

[0036] The data transmission slice length corresponding to the data transmission path is determined, and the data to be transmitted is allocated to the corresponding data transmission path and transmitted to the cloud computing center according to the data processing priority and the path priority.

[0037] According to a third aspect of the present disclosure, an electronic device is provided, comprising:

[0038] processor;

[0039] Memory used to store processor-executable instructions;

[0040] The processor is configured to implement the method described in the first aspect above.

[0041] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium is provided, the computer storage medium storing computer-executable instructions; the computer-executable instructions, when executed by a processor, are capable of implementing the method described in the first aspect above.

[0042] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects:

[0043] This disclosure discloses a data offloading method, system, and storage medium for edge computing nodes. The system includes a cloud computing center, which is communicatively connected to a data acquisition module, a data storage module, a data processing module, a data analysis module, and an edge scheduling module. The data acquisition module is distributedly connected to the cloud computing center via IoT nodes to acquire data collected by the edge computing nodes. The data storage module is communicatively connected to the data acquisition module to receive the acquired data transmitted by the data acquisition module and stores the acquired data separately according to the edge computing nodes. The data processing module is used to acquire real-time and historical data of all edge computing nodes from the data storage module and determine the data processing priority of the edge computing nodes based on the real-time and historical data. The data analysis module is used to request a data transmission path queue from the cloud computing center and determine the path priority corresponding to each data transmission path in the data transmission path queue. The edge scheduling module is used to determine the data transmission slice length corresponding to the data transmission path and allocate the data to be transmitted to the corresponding data transmission path for transmission to the cloud computing center according to the data processing priority and path priority. Therefore, this disclosure can allocate the data to be transmitted to the corresponding data transmission path according to the data processing priority of each edge computing node and the path priority of each data transmission path, thereby realizing data diversion of edge computing nodes, improving data transmission efficiency, shortening the average latency of data transmission, reducing service response latency, and improving user experience.

[0044] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0045] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0046] Figure 1 This is a schematic diagram illustrating the structure of a data offloading system for edge computing nodes according to some embodiments of the present disclosure;

[0047] Figure 2 This is a flowchart illustrating a data offloading method for edge computing nodes according to some embodiments of the present disclosure;

[0048] Figure 3 This is a block diagram illustrating an electronic device suitable for performing a data offloading method for edge computing nodes, according to some embodiments of the present disclosure. Detailed Implementation

[0049] Some embodiments of this disclosure will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. Various changes, modifications, and equivalents of the methods, apparatus, and / or systems described herein will become apparent upon understanding this disclosure. For example, the order of operations described herein is merely illustrative and is not limited to those orders set forth herein, but can be changed as will become apparent upon understanding this disclosure, except for operations that must be performed in a particular order. Furthermore, for clarity and brevity, descriptions of features known in the art may be omitted.

[0050] The embodiments described in the following examples of this disclosure are not representative of all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0051] Figure 1 This is a schematic diagram illustrating the structure of a data offloading system for edge computing nodes according to some embodiments of this disclosure, such as... Figure 1 As shown, the system includes a cloud computing center, which is connected to a data acquisition module, a data storage module, a data processing module, a data analysis module, and an edge scheduling module.

[0052] The data acquisition module is connected to the cloud computing center in a distributed manner through IoT nodes to acquire data collected by edge computing nodes;

[0053] The data storage module is communicatively connected to the data acquisition module, and is used to receive the acquired data transmitted by the data acquisition module and store the acquired data separately according to the edge computing nodes;

[0054] The data processing module is used to obtain real-time and historical data of all edge computing nodes from the data storage module, and determine the data processing priority of the edge computing nodes based on the real-time and historical data.

[0055] The data analysis module is used to request the data transmission path queue from the cloud computing center and determine the path priority of each data transmission path in the data transmission path queue.

[0056] The edge scheduling module is used to determine the length of the data transmission slice corresponding to the data transmission path, and allocate the data to be transmitted to the corresponding data transmission path and transmit it to the cloud computing center according to the data processing priority and path priority.

[0057] In this embodiment of the disclosure, the data collected by the data acquisition module may include information about the edge computing node, the acquisition period, the acquisition time, the data type, and the data size. The information about the edge computing node may include the name, IP address, authentication certificate, and identification information of the edge computing node.

[0058] Furthermore, in this embodiment of the disclosure, before the data acquisition module and the data storage module establish a communication connection, the data storage module needs to authenticate the data acquisition module. Specifically, in this embodiment of the disclosure, the data acquisition module sends the information of the edge computing nodes it collects to the data storage module. The data storage module determines whether the information of the edge computing node is authorized data. If it determines that the information of the edge computing node is authorized data, it allows the data acquisition module of the edge computing node to communicate with the data storage module; otherwise, it does not allow the data acquisition module of the edge computing node to communicate with the data storage module. This realizes the management of edge computing nodes and the scheduling of resources, and at the same time realizes the management of user permissions.

[0059] Furthermore, in this embodiment of the present disclosure, the data storage module is provided with a temporary database and a historical database. The temporary database is used to store the data collected in the current collection cycle and to mark the data information collected in the current collection cycle as real-time data. When the temporary database detects that new data is collected in the next collection cycle, it sends the data collected in the current collection cycle to the historical database for storage and marks the data sent to the historical database as historical data.

[0060] Furthermore, in this embodiment of the disclosure, the method for determining the data processing priority of edge computing nodes based on real-time data and historical data may include the following steps:

[0061] Step 1: Determine the data activity of edge computing nodes based on the number of data collections within a preset time period;

[0062] Step 2: Determine the type attraction value of the edge computing node based on real-time and historical data;

[0063] Step 3: Determine the data processing priority of the edge computing nodes based on data activity, type attraction value, and data size in real-time data;

[0064] Step 4: Determine the data processing priority of the edge computing nodes based on the data processing priority value.

[0065] In this embodiment of the disclosure, the method for determining the data activity of an edge computing node based on the number of data collections within a preset time period may specifically include: determining the data activity of the edge computing node using a first formula based on the number of data collections of the edge computing node and the total number of data collections of all edge computing nodes within the preset time period, wherein the first formula is:

[0066]

[0067] in, For the data activity of edge computing node i, This indicates the number of times data is collected from edge computing node i within a preset time period. This indicates the number of times data was collected from all edge computing nodes within a preset time period.

[0068] Furthermore, in this embodiment of the disclosure, the aforementioned preset time period can be the current data collection cycle.

[0069] Furthermore, in this embodiment of the disclosure, the method for determining the type attraction value of an edge computing node based on real-time data and historical data may specifically include the following steps:

[0070] Step a: Determine the data type collected by edge computing node i in the current collection cycle. And determine the current collection period Number of times the data types overlap with those in the historical database ;

[0071] Step b: Determine the number of times all data types in the current collection period overlap with the data types in the historical database. ;

[0072] Step c: Calculate and determine the type attraction value of the edge computing node using the second formula, where the second formula is:

[0073]

[0074] in, Calculate the type attraction value for node i at the edge. for The weighting coefficients, for The weighting coefficients.

[0075] Furthermore, in this embodiment of the disclosure, after obtaining the data activity and type attraction value through the above steps, the data processing priority value of the edge computing node can be determined according to the data activity, type attraction value, and data size in the real-time data using a third formula, wherein the third formula is:

[0076]

[0077] in, For edge computing i, the data processing priority value is... The size of the data in the real-time data. for The weighting coefficients, for The weighting coefficients, for The weighting coefficients.

[0078] Furthermore, in this embodiment of the disclosure, after obtaining the data processing priority value of each edge computing node through the above steps, the data processing priority values ​​of each edge computing node can be sorted in descending order, and the sorting result obtained by descending order is determined as the data processing priority of the edge computing node. That is, the edge computing node with the larger data processing priority value has a higher data processing priority.

[0079] Furthermore, in this embodiment of the present disclosure, in response to the edge computing node transmitting data to the cloud computing center, after the data analysis module requests the data transmission path queue from the cloud computing center, it needs to determine the path priority corresponding to each data transmission path in the data transmission path queue so that a path can be allocated to the edge computing node according to the path priority corresponding to the data transmission path.

[0080] Specifically, in this embodiment of the disclosure, the method for determining the path priority corresponding to each data transmission path in the data transmission path queue may include the following steps:

[0081] Step 1: Send probe data to each data transmission path in the data transmission path queue, and obtain the start time and end time of probe data transmission.

[0082] Step 2: Obtain a graph showing the transmission rate of the probe data over time;

[0083] Step 3: Compare the real-time transmission rate in the curve with the rate threshold, and determine the corresponding absolute values ​​of positive and negative deviations.

[0084] Step 4: Calculate the transmission coefficient of the corresponding data transmission path based on the absolute values ​​of the positive and negative deviations.

[0085] Step 5: Determine the path priority of each data transmission path based on the transmission coefficient of each data transmission path.

[0086] In this embodiment of the disclosure, when comparing the real-time transmission rate in the graph with a rate threshold, if the real-time transmission rate is greater than or equal to the rate threshold, the absolute value of the positive deviation between the real-time transmission rate and the rate threshold within the corresponding time period is obtained; if the real-time transmission rate is less than the rate threshold, the absolute value of the negative deviation between the real-time transmission rate and the rate threshold within the corresponding time period is obtained. Furthermore, in this embodiment of the disclosure, the aforementioned corresponding time period can be the average of the transmission start time and transmission end time corresponding to the graph, divided into the same time interval. It should be noted that in this embodiment of the disclosure, when the real-time transmission rate in the graph is always greater than or equal to the rate threshold, the absolute value of the negative deviation is zero; when the real-time transmission rate is always less than the rate threshold, the absolute value of the positive deviation is zero.

[0087] Furthermore, in this embodiment of the disclosure, after obtaining the absolute values ​​of the positive and negative deviations through the above steps, the transmission coefficient of the corresponding data transmission path is calculated using the fourth formula, wherein the fourth formula is:

[0088]

[0089] in, Let j be the transmission coefficient corresponding to the data transmission path. Let j be the absolute value of the positive deviation corresponding to data transmission path j. This represents the absolute value of the negative deviation corresponding to data transmission path j. The weighting coefficient corresponds to the absolute value of the positive deviation. This represents the weighting coefficient corresponding to the absolute value of the negative deviation.

[0090] Furthermore, in this embodiment of the disclosure, after obtaining the transmission coefficients of each data transmission path through the above steps, the transmission coefficients can be sorted in descending order, and the sorting result obtained in descending order can be determined as the path priority of the data transmission path. That is, the data transmission path with a larger transmission coefficient has a higher path priority.

[0091] Furthermore, in this embodiment of the present disclosure, after obtaining the transmission coefficients of each data transmission path through the above steps, the edge scheduling module can determine the data transmission slice length corresponding to each data transmission path based on the transmission coefficients of each data transmission path. Specifically, in this embodiment of the present disclosure, the method for determining the data transmission slice length corresponding to the data transmission path may include: matching the transmission coefficients corresponding to the data transmission path with a data transmission slice length lookup table to determine the data transmission slice length corresponding to the data transmission path.

[0092] Furthermore, in this embodiment of the present disclosure, after obtaining the data processing priority of the edge computing node and the path priority of the data transmission path through the above steps, the edge scheduling module can allocate the data to be transmitted to the cloud computing center according to the data processing priority and the path priority.

[0093] Specifically, in this embodiment of the disclosure, the method of allocating the data to be transmitted to the corresponding data transmission path and transmitting it to the cloud computing center according to the data processing priority and the path priority may include the following steps:

[0094] Step S1: Based on the descending order of data processing priorities of each edge computing node, allocate the data to be transmitted to the data transmission paths arranged in descending order of path priority;

[0095] Step S2: If data with high processing priority is not transmitted within the data transmission slice length of the current data transmission path, the untransmitted data is allocated to the next priority data transmission path for transmission until the data allocation is completed.

[0096] For example, in this embodiment of the disclosure, it is assumed that the data transmission time required for edge computing node A to collect the data is 3 minutes, the data transmission time required for edge computing node B to collect the data is 10 minutes, and the data transmission time required for edge computing node C to collect the data is 30 minutes, and the data processing priority of edge computing nodes A, B and C is edge computing node A < edge computing node B < edge computing node C.

[0097] Furthermore, in this embodiment of the disclosure, it is assumed that edge computing node A, edge computing node B, and edge computing node C use the same data transmission path. If data is transmitted in a prioritized manner, then edge computing node C will be prioritized through the data transmission path. Data is transmitted, and then edge computing node B transmits data through the data transmission path. Data is transmitted, and finally, edge computing node A transmits data through the data transmission path. Data transmission. Based on this, the average latency for data transmission across all edge computing nodes using the above transmission method is calculated. = (0 + 30 + 40) / 3 = 23.33 minutes.

[0098] Furthermore, in this embodiment of the disclosure, it is assumed that three data transmission paths are allocated. , and Their transmission coefficients are sorted as follows < < The length of the data transmission slice on each data transmission path is respectively =20 minutes =9 minutes =3 minutes, assign edge computing node A, edge computing node B and edge computing node C to the data transmission path , and Transmit the data.

[0099] Specifically, in this embodiment of the disclosure, the data processing priority is: Edge computing node C prioritizes data transmission, and edge computing node C is on the data transmission path. Internal data transfer slice length =After 3 minutes of data transmission, the remaining data will be allocated to the data transmission path. Internal data transfer slice length =After 9 minutes of data transmission, the remaining data will be allocated to the data transmission path. The latency required for data transmission to be completed within the edge computing node C is [not specified]. =0 minutes; data processing priority is Edge computing node B then performs data transmission, and edge computing node B is on the data transmission path. Internal data transfer slice length =After 3 minutes of data transmission, the remaining data will be allocated to the data transmission path. Inside and in slice length =Data transmission completed within a 9-minute data transmission slice; the time required for data transmission collected by edge computing node B to complete. = (3 + 9) = 12 minutes; data processing priority is Edge computing node A then performs data transmission, and edge computing node A is on the data transmission path. Internal data transfer slice length =3 minutes of data transmission; the time required for data transmission collected by edge computing node A to complete. = (3+3) = 6 minutes; therefore, the average latency for completing data transmission across all edge computing nodes is 6 minutes. = (0 + 12 + 6) / 3 = 6 minutes.

[0100] Based on this, we can obtain the following from the above description: > In other words, the above methods improve data transmission efficiency, thereby shortening the average latency of data transmission, reducing service response latency, and enhancing user experience.

[0101] Furthermore, in this embodiment of the present disclosure, after the edge scheduling module allocates the data to be transmitted to the corresponding data transmission path and transmits it to the cloud computing center, the edge scheduling module can also determine the data round-trip delay time of the data transmission path and send the data round-trip delay time to the cloud computing center.

[0102] Furthermore, in this embodiment of the present disclosure, after the cloud computing center receives the data round-trip delay time sent by the edge scheduling module, it can determine whether to adjust the data transmission slice length of the data transmission path based on the data round-trip delay time, so as to make full use of the idle resources of the data transmission path and effectively improve the data transmission efficiency.

[0103] Specifically, in this embodiment of the present disclosure, the method for determining whether to adjust the data transmission slice length of the data transmission path based on the data round-trip delay time may include: comparing the data round-trip delay time with a preset delay threshold; if the data round-trip delay time is greater than or equal to the preset delay threshold, then determining not to adjust the data transmission slice length of the data transmission path, and marking the data transmission slice length of the data transmission path as the data transmission slice quota of the data transmission path; if the data round-trip delay time is less than the preset delay threshold, then determining to increase the data transmission slice length of the data transmission path until the data transmission slice length of the data transmission path reaches the data transmission slice quota.

[0104] This disclosure proposes a data offloading system for edge computing nodes. The system includes a cloud computing center, which is communicatively connected to a data acquisition module, a data storage module, a data processing module, a data analysis module, and an edge scheduling module. The data acquisition module is distributedly connected to the cloud computing center via IoT nodes to acquire data collected by the edge computing nodes. The data storage module is communicatively connected to the data acquisition module to receive the acquired data and store it separately according to the edge computing nodes. The data processing module retrieves real-time and historical data from all edge computing nodes from the data storage module and determines the data processing priority of the edge computing nodes based on the real-time and historical data. The data analysis module requests a data transmission path queue from the cloud computing center and determines the path priority of each data transmission path in the queue. The edge scheduling module determines the data transmission slice length corresponding to the data transmission path and allocates the data to be transmitted to the corresponding data transmission path for transmission to the cloud computing center according to the data processing priority and path priority. Therefore, this disclosure can allocate the data to be transmitted to the corresponding data transmission path according to the data processing priority of each edge computing node and the path priority of each data transmission path, thereby realizing data diversion of edge computing nodes, improving data transmission efficiency, shortening the average latency of data transmission, reducing service response latency, and improving user experience.

[0105] Figure 2 This is a data offloading method for edge computing nodes illustrated according to some embodiments of the present disclosure, such as... Figure 2 As shown, the method may include the following steps:

[0106] Step 201: Acquire real-time and historical data from edge computing nodes;

[0107] Step 202: Determine the data processing priority of edge computing nodes based on real-time data and historical data;

[0108] Step 203: In response to the edge computing node transmitting data to the cloud computing center, request the data transmission path queue from the cloud computing center and determine the path priority corresponding to each data transmission path in the data transmission path queue;

[0109] Step 204: Determine the data transmission slice length corresponding to the data transmission path, and allocate the data to be transmitted to the corresponding data transmission path and transmit it to the cloud computing center according to the data processing priority and path priority.

[0110] In one or more embodiments of this disclosure, real-time and historical data of edge computing nodes are acquired; the data processing priority of the edge computing nodes is determined based on the real-time and historical data; in response to the edge computing nodes transmitting data to the cloud computing center, a data transmission path queue is requested from the cloud computing center, and the path priority corresponding to each data transmission path in the data transmission path queue is determined; the data transmission slice length corresponding to the data transmission path is determined, and the data to be transmitted is allocated to the corresponding data transmission path for transmission to the cloud computing center according to the data processing priority and the path priority. Therefore, this disclosure can allocate the data to be transmitted to the corresponding data transmission path according to the data processing priority of each edge computing node and the path priority of each data transmission path, realizing data diversion of edge computing nodes, improving data transmission efficiency, thereby shortening the average latency of data transmission, reducing service response latency, and improving user experience.

[0111] like Figure 3 As shown, the electronic device 300 includes a computing unit 301, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 302 or a computer program loaded from a storage unit 308 into a random access memory (RAM) 303. The RAM 303 may also store various programs and data required for the operation of the electronic device 300. The computing unit 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0112] Multiple components in electronic device 300 are connected to I / O interface 305, including: input unit 306, such as keyboard, mouse, etc.; output unit 307, such as various types of monitors, speakers, etc.; storage unit 308, such as disk, optical disk, etc.; and communication unit 309, such as network card, modem, wireless transceiver, etc. Communication unit 309 allows electronic device 300 to exchange information / data with other electronic devices through computer networks such as the Internet and / or various telecommunications networks.

[0113] The computing unit 301 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 301 performs the various methods and processes described above, such as data offloading methods for edge computing nodes. For example, in some embodiments, the data offloading method for edge computing nodes can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 308. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 300 via ROM 302 and / or communication unit 309. When the computer program is loaded into RAM 303 and executed by the computing unit 301, one or more steps of the data offloading method for edge computing nodes described above can be performed. Alternatively, in other embodiments, computing unit 301 may be configured by any other suitable means (e.g., by means of firmware) to perform a data offloading method for edge computing nodes.

[0114] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic electronic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0115] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0116] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or electronic device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or electronic devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage electronics, magnetic storage electronics, or any suitable combination of the foregoing.

[0117] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0118] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), the Internet, and blockchain networks.

[0119] Computer systems can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. A server can be a cloud server, also known as a cloud computing server or cloud host, a hosting product within the cloud computing service system that addresses the shortcomings of traditional physical hosts and VPS (Virtual Private Server) services, such as high management difficulty and weak business scalability. Servers can also be servers for distributed systems or servers incorporating blockchain technology.

[0120] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0121] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A data offloading system for edge computing nodes, characterized in that, The system includes a cloud computing center, which is communicatively connected to a data acquisition module, a data storage module, a data processing module, a data analysis module, and an edge scheduling module. The data acquisition module is connected to the cloud computing center in a distributed manner through IoT nodes to acquire data collected by edge computing nodes. The data storage module is communicatively connected to the data acquisition module and is used to receive the acquired data transmitted by the data acquisition module and store the acquired data separately according to the edge computing nodes. The data processing module is used to obtain real-time data and historical data of all edge computing nodes from the data storage module, and determine the data processing priority of the edge computing nodes based on the real-time data and historical data. The data analysis module is used to request a data transmission path queue from the cloud computing center and determine the path priority of each data transmission path in the data transmission path queue. The edge scheduling module is used to determine the data transmission slice length corresponding to the data transmission path, and allocate the data to be transmitted to the corresponding data transmission path and transmit it to the cloud computing center according to the data processing priority and the path priority. Determining the path priority corresponding to each data transmission path in the data transmission path queue includes: Send probe data to each data transmission path in the data transmission path queue, and obtain the start time and end time of the probe data transmission; Obtain a graph showing the transmission rate of the probed data as a function of time; The real-time transmission rate in the graph is compared with the rate threshold, and the corresponding positive and negative absolute values ​​of deviation are determined. Based on the absolute values ​​of the positive and negative deviations, the transmission coefficient of the corresponding data transmission path is calculated using the fourth formula; wherein, the fourth formula is: in, Let j be the transmission coefficient corresponding to the data transmission path. Let j be the absolute value of the positive deviation corresponding to data transmission path j. This represents the absolute value of the negative deviation corresponding to data transmission path j. The weighting coefficient corresponds to the absolute value of the positive deviation. The weighting coefficient corresponding to the absolute value of the negative deviation; The path priority of each data transmission path is determined based on the transmission coefficient of each data transmission path.

2. The system as described in claim 1, characterized in that, Determining the data processing priority of the edge computing node based on the real-time data and historical data includes: The data activity of the edge computing node is determined based on the number of data collections within a preset time period; Based on the real-time and historical data, determine the type attraction value of the edge computing node; The data processing priority of the edge computing node is determined based on the data activity, the type attraction value, and the data size in the real-time data. The data processing priority of the edge computing node is determined based on the data processing priority value.

3. The system as described in claim 1, characterized in that, Determining the data transmission slice length corresponding to the data transmission path includes: matching the transmission coefficient corresponding to the data transmission path with a data transmission slice length lookup table to determine the data transmission slice length corresponding to the data transmission path.

4. The system as described in claim 1, characterized in that, The step of allocating the data to be transmitted to the cloud computing center according to the data processing priority and the path priority includes: Based on the descending order of data processing priorities of each edge computing node, the data to be transmitted is allocated to data transmission paths arranged in descending order of path priority; If high-priority data is not transmitted within the data transmission slice length of the current data transmission path, the untransmitted data will be allocated to the next higher priority data transmission path for transmission until the data allocation is completed.

5. The system as described in claim 1, characterized in that, The edge scheduling module is also used to: determine the data round-trip delay time of the data transmission path, and send the data round-trip delay time to the cloud computing center.

6. The system as described in claim 5, characterized in that, The cloud computing center is used to: determine whether to adjust the data transmission slice length of the data transmission path based on the data round-trip delay time.

7. The system as described in claim 6, characterized in that, The step of determining whether to adjust the data transmission slice length of the data transmission path based on the data round-trip delay time includes: The data round-trip delay time is compared with a preset delay threshold. If the data round-trip delay time is greater than or equal to the preset delay threshold, then it is determined that the data transmission slice length of the data transmission path will not be adjusted, and the data transmission slice length of the data transmission path is marked as the data transmission slice quota of the data transmission path. If the data round-trip delay time is less than the preset delay threshold, then the data transmission slice length of the data transmission path is increased until the data transmission slice length of the data transmission path reaches the data transmission slice quota.

8. A data offloading method for edge computing nodes, characterized in that, The method includes: Acquire real-time and historical data from edge computing nodes; The data processing priority of the edge computing node is determined based on the real-time data and historical data. In response to the edge computing node transmitting data to the cloud computing center, it requests a data transmission path queue from the cloud computing center and determines the path priority corresponding to each data transmission path in the data transmission path queue. The data transmission slice length corresponding to the data transmission path is determined, and the data to be transmitted is allocated to the corresponding data transmission path and transmitted to the cloud computing center according to the data processing priority and the path priority. Determining the path priority corresponding to each data transmission path in the data transmission path queue includes: Send probe data to each data transmission path in the data transmission path queue, and obtain the start time and end time of the probe data transmission; Obtain a graph showing the transmission rate of the probed data as a function of time; The real-time transmission rate in the graph is compared with the rate threshold, and the corresponding positive and negative absolute values ​​of deviation are determined. Based on the absolute values ​​of the positive and negative deviations, the transmission coefficient of the corresponding data transmission path is calculated using the fourth formula; wherein, the fourth formula is: in, Let j be the transmission coefficient corresponding to the data transmission path. Let j be the absolute value of the positive deviation corresponding to data transmission path j. This represents the absolute value of the negative deviation corresponding to data transmission path j. The weighting coefficient corresponds to the absolute value of the positive deviation. The weighting coefficient corresponding to the absolute value of the negative deviation; The path priority of each data transmission path is determined based on the transmission coefficient of each data transmission path.

9. A non-transitory computer-readable storage medium storing computer-executable instructions; wherein the computer-executable instructions, when executed by a processor, are capable of implementing the method of claim 8.

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