Computing node distribution method and device and electronic equipment

Abstract

The invention provides a computing node distribution method and apparatus, and an electronic device. The method comprises the steps of obtaining target request data and first delay time in a target time period; determining candidate computing nodes from all nodes in the computing space based on the target request data; calculating to obtain processing time, transmission time and waiting time; and determining a first total processing time based on the processing time, the transmission time and the waiting time, and determining a target processing node from the candidate computing nodes based on the first total processing time and the first delay time. Therefore, the optimal target processing node is selected by analyzing the processing time of the candidate computing node on the target request data, the transmission time of the target request data transmitted to the candidate computing node and the waiting time of the candidate computing node for processing the target request data, so that the processing efficiency of the target request data can be ensured; and the time delay of data processing is reduced, so that the use experience of a user can be improved.

Description

Technical Field

[0001] The present disclosure relates to the field of cloud computing, big data and edge computing technology, and in particular to a computing node allocation method, device and electronic device. Background Art

[0002] Multi-access edge computing is an emerging computing model that extends computing power from data centers to the edge of the network, allowing data to be processed and analyzed where it is generated. Common algorithms include machine learning algorithms, which can automatically learn and improve model performance. However, machine learning algorithms require a large amount of training data, and the training process can be very time-consuming. In addition, the results of machine learning algorithms are often difficult to explain, which may affect user trust. Summary of the invention

[0003] The present disclosure aims to solve one of the technical problems in the related art at least to some extent.

[0004] To this end, an objective of the present disclosure is to propose a computing node allocation method.

[0005] The second objective of the present disclosure is to provide a computing node allocation device.

[0006] A third objective of the present disclosure is to provide an electronic device.

[0007] A fourth object of the present disclosure is to provide a non-transitory computer-readable storage medium.

[0008] A fifth object of the present disclosure is to provide a computer program product.

[0009] To achieve the above-mentioned purpose, the first aspect of the present disclosure proposes a computing node allocation method, including: obtaining target request data within a target time period, and obtaining a first delay time of the target request data; determining a candidate computing node from all nodes in a computing space based on the target request data, wherein the candidate computing node is a node capable of processing the target request data; for any candidate computing node, calculating the processing time of the candidate computing node to process the target request data, as well as calculating the transmission time of the target request data to the candidate computing node, and calculating the waiting time of the candidate computing node to process the target request data; determining a first overall processing time based on the processing time, the transmission time and the waiting time, and determining a target processing node from the candidate computing nodes based on the first overall processing time and the first delay time, and allocating the target request data to the target processing node for processing.

[0010] According to one embodiment of the present disclosure, calculating the processing time of the candidate computing node to process the target request data includes: obtaining the data volume of the target request data, and obtaining the computing power value and influence value of the candidate computing node; multiplying the data volume by the influence value, and dividing the product by the computing power value to calculate the processing time.

[0011] According to one embodiment of the present disclosure, obtaining the influence value of the candidate computing node includes: obtaining the remaining storage space, total storage space and weight factor of the candidate computing node; multiplying the remaining storage space by the weight factor, and dividing the product by the total storage space to calculate the influence value.

[0012] According to one embodiment of the present disclosure, calculating the waiting time for the candidate computing node to process the target request data includes: obtaining the total amount of unprocessed data in the processing of the candidate computing node, and obtaining the computing power value and influence value of the candidate computing node; multiplying the total amount of unprocessed data by the influence value, and dividing the product by the computing power value to calculate the waiting time.

[0013] According to one embodiment of the present disclosure, determining the target processing node from the candidate computing nodes based on the first overall processing time and the first delay time includes: selecting a candidate computing node whose first overall processing time is less than the first delay time from the candidate computing nodes as the target candidate computing node; obtaining a distance value between the target candidate computing node and the terminal corresponding to the target request data; and selecting the target candidate computing node with the smallest distance value as the target processing node.

[0014] According to one embodiment of the present disclosure, after the target request data is assigned to the target processing node for processing, it also includes: obtaining the overlap value of the target processing node; in response to the overlap value of the target processing node being 1, obtaining the second overall processing time and the second delay time of the other processing data, wherein the other processing data is the data to be processed by the target processing node other than the target request data within the target time period; based on the second overall processing time and the second delay time of the other processing data and the first overall processing time and the first delay time of the target request data, determining the processing order of the target processing node for the target request data.

[0015] According to one embodiment of the present disclosure, the processing order of the target request data by the target processing node is determined based on the second overall processing time and the second delay time of the other processing data and the first overall processing time and the first delay time of the target request data, including: calculating the difference between the first delay time and the first overall processing time of the target request data as the first remaining time of the target request data, and calculating the difference between the second delay time and the second overall processing time of the other processing data as the second remaining time of the other processing data; based on the first remaining time, the second remaining time, the processing time of the target request data and the processing time of the other processing data, determining the processing order of the target request data by the target processing node.

[0016] According to one embodiment of the present disclosure, obtaining the overlap value of the target processing node includes: obtaining the data to be processed of the target processing node within the target time period; in response to the data to be processed only containing the target request data, determining that the overlap value is 0; in response to the data to be processed containing the target request data and other data, determining that the overlap value is 1.

[0017] To achieve the above-mentioned purpose, the second aspect embodiment of the present disclosure proposes a computing node allocation device, including: an acquisition module, used to acquire target request data within a target time period, and acquire a first delay time of the target request data; a determination module, used to determine a candidate computing node from all nodes in a computing space based on the target request data, wherein the candidate computing node is a node capable of processing the target request data; a calculation module, used to calculate, for any candidate computing node, the processing time of the candidate computing node for processing the target request data, as well as the transmission time of the target request data to the candidate computing node, and the waiting time of the candidate computing node for processing the target request data; an allocation module, used to determine a first overall processing time based on the processing time, the transmission time, and the waiting time, and determine a target processing node from the candidate computing nodes based on the first overall processing time and the first delay time.

[0018] To achieve the above-mentioned purpose, the third aspect embodiment of the present disclosure proposes an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to implement the computing node allocation method as described in the first aspect embodiment of the present disclosure.

[0019] To achieve the above-mentioned purpose, the fourth aspect embodiment of the present disclosure proposes a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to implement the computing node allocation method as described in the first aspect embodiment of the present disclosure.

[0020] To achieve the above-mentioned purpose, the fifth aspect of the present disclosure proposes a computer program product, including a computer program, which, when executed by a processor, is used to implement the computing node allocation method as described in the first aspect of the present disclosure.

[0021] Therefore, by analyzing the processing time of the candidate computing node for the target request data, the transmission time of the target request data to the candidate computing node, and the waiting time for the candidate computing node to process the target request data, the optimal target processing node is selected to ensure the processing efficiency of the target request data and reduce the delay of data processing, thereby improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is a schematic diagram of a computing node allocation method according to an embodiment of the present disclosure;

[0023] Figure 2 is a schematic diagram of another computing node allocation method according to an embodiment of the present disclosure;

[0024] Figure 3 is a schematic diagram of another computing node allocation method according to an embodiment of the present disclosure;

[0025] Figure 4 is a schematic diagram of another computing node allocation method according to an embodiment of the present disclosure;

[0026] Figure 5 is a schematic diagram of another computing node allocation method according to an embodiment of the present disclosure;

[0027] Figure 6 A schematic diagram of the structure of a computing node allocation system disclosed in the present invention;

[0028] Figure 7 is a schematic diagram of a computing node allocation device according to one embodiment of the present disclosure;

[0029] Figure 8 is a schematic diagram of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0030] Embodiments of the present disclosure are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, and should not be construed as limiting the present disclosure.

[0031] The acquisition, storage, use, and processing of data in the technical solution disclosed in this disclosure are in compliance with the relevant provisions of relevant laws and regulations.

[0032] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned, and they should be considered as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0033] Figure 1 is a schematic diagram of a computing node allocation method according to an embodiment of the present disclosure, such as Figure 1 As shown, the computing node allocation method includes the following steps:

[0034] S101, obtaining target request data within a target time period, and obtaining a first delay time of the target request data.

[0035] The computing node allocation method of the embodiment of the present application can be applied to the scenario of computing devices of a multi-access edge computing system of cloud computing. The executor of the computing node allocation of the embodiment of the present application can be the computing node allocation device of the embodiment of the present application, and the computing node allocation device can be set on an electronic device.

[0036] It should be noted that the target time period in the embodiment of the present disclosure may be a certain data acquisition cycle, which is designed in advance and can be changed according to actual design requirements, and is not limited here.

[0037] It should be noted that the first delay time is the maximum time required for the target request data to be processed. Under normal circumstances, the target request data needs to be processed within the first delay time. The first delay time includes but is not limited to the time it takes for the target request data to be transmitted to the processing node and the time it takes for the data node to process the target request data.

[0038] S102, determining a candidate computing node from all nodes in the computing space based on the target request data, wherein the candidate computing node is a node capable of processing the target request data.

[0039] It should be noted that the computing space in the embodiments of the present disclosure may include multiple types, which are not limited here. For example, the computing space may be an edge computing space, a cloud computing space, and a local computing space. The computing space includes multiple computing nodes, which are devices with independent data processing capabilities.

[0040] In the embodiments of the present disclosure, there may be a variety of methods for determining whether a candidate computing node is a node capable of processing target request data, which are not limited here. For example, the candidate computing node may be determined by the positional relationship between the computing node and the terminal that generates the target request data. For example, a computing node within a certain distance from the terminal may be used as a candidate computing node. In another possible implementation, it is also possible to determine whether a computing node is a candidate computing node by combining the data size of the target request data and the processing capability of the computing node. For example, the computing nodes in the computing space may be screened according to a set multiple of the data size to screen out qualified candidate computing nodes.

[0041] S103, for any candidate computing node, calculating the processing time of the candidate computing node for processing the target request data, calculating the transmission time of the target request data to the candidate computing node, and calculating the waiting time of the candidate computing node for processing the target request data.

[0042] It should be noted that since a computing node may need to process multiple data, it may be necessary to process the existing data before processing the current target request data. The waiting time can be calculated based on the computing power of the candidate computing node and the amount of existing data.

[0043] It should be noted that, due to factors such as different processing capabilities of different candidate processing nodes and different distances from the terminal corresponding to the target request data, the processing time, transmission time and waiting time corresponding to different candidate processing nodes may be different.

[0044] S104, determining a first overall processing time based on the processing time, the transmission time and the waiting time, determining a target processing node from the candidate computing nodes based on the first overall processing time and the first delay time, and allocating the target request data to the target processing node for processing.

[0045] In the embodiment of the present disclosure, there may be multiple methods for determining the target processing node from the candidate computing nodes based on the first overall processing time and the first delay time, which are not limited here.

[0046] In a possible implementation, the target processing node may be determined by setting a screening condition, which may be that the first overall processing time is the shortest, or the first overall time is less than the first delay time, and the processing capacity of the candidate computing node is the largest.

[0047] In the disclosed embodiment, firstly, the target request data within the target time period is obtained, and the first delay time of the target request data is obtained, and then the candidate computing node is determined from all nodes in the computing space based on the target request data, wherein the candidate computing node is a node capable of processing the target request data, and then for any candidate computing node, the processing time of the candidate computing node processing the target request data, and the transmission time of the target request data transmitted to the candidate computing node, and the waiting time of the candidate computing node processing the target request data are calculated, and finally the first overall processing time is determined based on the processing time, the transmission time and the waiting time, and the target processing node is determined from the candidate computing node based on the first overall processing time and the first delay time. Thus, by analyzing the processing time of the candidate computing node for the target request data, the transmission time of the target request data transmitted to the candidate computing node, and the waiting time of the candidate computing node processing the target request data, the optimal target processing node is selected, the processing efficiency of the target request data can be guaranteed, and the delay of data processing can be reduced, so as to improve the user experience.

[0048] In actual operation, network delay, data transmission loss and other issues will also affect the overall data processing time and extend the overall data processing time. Therefore, in actual operation, it is also necessary to analyze this part of the time loss. Since network delay, data transmission loss and other issues can be periodic problems in the same computing node, for any computing node, the historical transmission data of the computing node can be analyzed to determine the jamming time, that is, the delay caused by network delay, data transmission loss and other issues.

[0049] In the embodiment of the present disclosure, the jamming information of the distribution area within the cycle time can be first obtained, and the jamming duration of the jamming information can be accumulated to obtain the cycle delay, which can be expressed by the following formula in the embodiment of the present disclosure:

[0050]

[0051] Among them, F is the cycle score, AL is the number of jamming information within the cycle time, ZS is the cycle delay, P is the average delay of the jamming information, that is, P = ZS divided by AL, β1, β2 and β3 are weight coefficients respectively.

[0052] In one embodiment of the present disclosure, the cycle score F can be compared with the threshold value Fy. When the cycle score is less than or equal to Fy, it indicates that there is a low level of delay frequency in the distribution area of ​​cloud computing. Conversely, when the cycle score is greater than Fy, a delay alarm signal will be generated, and an audio-visual reminder information will be generated based on the delay alarm signal and transmitted to the corresponding management personnel.

[0053] In the above embodiment, the processing time of the candidate computing node to process the target request data can also be calculated by Figure 2 Explaining further, the method includes:

[0054] S201, obtain the data volume of the target requested data, and obtain the computing power value and influence value of the candidate computing node.

[0055] It should be noted that the computing power value of the candidate computing node may represent the computing capability of the candidate computing node, and the higher the computing power value, the stronger the computing capability of the candidate computing node. Different candidate computing nodes may correspond to different computing power values.

[0056] The impact value refers to the degree of influence of the candidate computing node on the overall system performance, efficiency, service quality, etc., as well as the efficiency and effect of data processing in the computing space. In the embodiment of the present disclosure, the impact value of the candidate computing node can be determined by the space utilization rate of the candidate computing node.

[0057] In the disclosed embodiment, the remaining storage space, total storage space and weight factor of the candidate computing node may be first obtained, and then the remaining storage space may be multiplied by the weight factor and the product may be divided by the total storage space to calculate the influence value.

[0058] In a possible implementation, the influence value of the candidate computing node may be calculated using the following formula.

[0059]

[0060] Among them, αj is the influence value of candidate computing node j, j = 1, 2, ..., J, Csj is the remaining storage space of candidate computing node j, Czj is the total storage space of candidate computing node j, and λ1 is the weight factor. It should be noted that λ1 is designed in advance and can be changed according to actual design needs, and is not limited here.

[0061] S202, multiplying the data volume by the impact value, and dividing the product by the computing power value to calculate the acquisition processing time.

[0062] In the embodiment of the present disclosure, the formula for calculating the acquisition processing time may be as follows:

[0063]

[0064] Among them, Ci represents the data volume of target request data i, S represents the computing power of candidate computing node, and αj is the influence value of candidate computing node j.

[0065] In the disclosed embodiment, the data volume of the target request data is first obtained, and the computing power value and influence value of the candidate computing node are obtained, and then the data volume is multiplied by the influence value, and the product is divided by the computing power value to calculate the acquisition processing time. Thus, by determining the processing time through the influence value and computing power value of the candidate computing node, the processing speed of the target data can be estimated from the two aspects of the storage space and data processing speed of the candidate computing node, which is more accurate.

[0066] In the above embodiment, the waiting time for the candidate computing node to process the target request data can also be calculated by Figure 3 Explaining further, the method includes:

[0067] S301, obtaining the total amount of unprocessed data in the candidate computing node processing, and obtaining the computing power value and influence value of the candidate computing node.

[0068] It should be noted that the influence value of the candidate computing node can be obtained by referring to Figure 2 The contents in the embodiments will not be repeated here.

[0069] S302, multiplying the total amount of unprocessed data by the impact value, and dividing the product by the computing power value to calculate the acquisition waiting time.

[0070] It should be noted that the formula for calculating the waiting time can be referred to as shown in the following formula:

[0071]

[0072] Among them, Qdj represents the total amount of unprocessed data, S represents the computing power of the candidate computing node, and αj is the influence value of the candidate computing node j.

[0073] In the disclosed embodiment, the total amount of unprocessed data in the candidate computing node processing is first obtained, and the computing power value and influence value of the candidate computing node are obtained, and then the total amount of unprocessed data is multiplied by the influence value, and the product is divided by the computing power value to calculate the acquisition waiting time. Thus, by determining the waiting time through the influence value and computing power value of the candidate computing node, the processing speed of the unprocessed data can be estimated from the two aspects of the storage space and data processing speed of the candidate computing node, which is more accurate and provides a data basis for subsequent node allocation.

[0074] In the above embodiment, the target processing node is determined from the candidate computing nodes based on the first overall processing time and the first delay time, and the target processing node can also be determined by Figure 4 Explaining further, the method includes:

[0075] S401 , selecting a candidate computing node whose first overall processing time is less than a first delay time from the candidate computing nodes as a target candidate computing node.

[0076] It should be noted that the first overall processing time is less than the first delay time, which indicates that the candidate computing node has the ability to process the target request data.

[0077] S402, obtaining a distance value between a target candidate computing node and a terminal corresponding to target request data.

[0078] It should be noted that the distance value is the value of the physical distance between the target candidate computing node and the terminal corresponding to the target request data. The smaller the distance value, the closer the terminal is to the target candidate computing node.

[0079] S403: Use the target candidate computing node with the smallest distance value as the target processing node.

[0080] In the disclosed embodiment, first, a candidate computing node whose first overall processing time is less than the first delay time is selected from the candidate computing nodes as the target candidate computing node, then the distance value between the target candidate computing node and the terminal corresponding to the target request data is obtained, and finally the target candidate computing node with the smallest distance value is selected as the target processing node. Thus, based on the distance value, the target candidate computing node with the smallest distance value is determined from the target candidate computing nodes as the target processing node, which can shorten the transmission distance between the terminal and the target processing node as much as possible, and improve the transmission efficiency and data processing speed.

[0081] In the above embodiment, after the target request data is assigned to the target processing node for processing, since the target processing node may contain unprocessed data or data assigned by other terminals, it is necessary to determine the processing priority of the target request data when processing the data. The specific steps are as follows: Figure 5 As shown:

[0082] S501, obtaining the coincidence value of the target processing node.

[0083] In the embodiment of the present disclosure, the coincidence value indicates whether the current target processing node has other data that needs to be processed in addition to the target request data. In the embodiment of the present disclosure, the coincidence value includes 0 and 1. Among them, 0 indicates that there is no other data that needs to be processed at present, and 1 indicates that there is other data that needs to be processed.

[0084] In an embodiment of the present disclosure, the data to be processed of the target processing node within the target time period can be obtained. In response to the data to be processed only containing the target request data, the overlap value is determined to be 0. In response to the data to be processed containing the target request data and other data, the overlap value is determined to be 1.

[0085] S502, in response to the coincidence value of the target processing node being 1, obtaining a second overall processing time and a second delay time of other processing data, wherein the other processing data is data to be processed by the target processing node except the target request data within the target time period.

[0086] S503: Determine a processing order of the target processing node for the target request data based on the second overall processing time and the second delay time of other processing data and the first overall processing time and the first delay time of the target request data.

[0087] In an embodiment of the present disclosure, the difference between the first delay time and the first overall processing time of the target request data can be first calculated as the first remaining time of the target request data, and the difference between the second delay time and the second overall processing time of other processing data can be calculated as the second remaining time of other processing data. Then, based on the first remaining time, the second remaining time, the processing time of the target request data and the processing time of the other processing data, the processing order of the target request data by the target processing node is determined.

[0088] In the embodiment of the present disclosure, taking the case where there is only one other processing data as an example, if the processing time of the target request data is greater than the remaining time of the other processing data, and the processing time of the other processing data is greater than the remaining time of the target request data, the processing time of the target request data is subtracted from the second remaining time of the other processing data to obtain difference time 1, and the processing time of the other processing data is subtracted from the remaining time of the target request data to obtain difference time 2. Difference time 1 is compared with difference time 2. When difference time 1 is less than difference time 2, the target request data is taken as the first priority. Otherwise, when difference time 1 is greater than difference time 2, the other processing data is taken as the first priority. If the difference 1 is equal to difference time 2, the waiting processing request with a short processing time is taken as the first priority.

[0089] When the processing time of target request data is less than or equal to the remaining time of other processing data, the target request data is marked as the first priority, and the other processing data is marked as the second priority, and the waiting processing requests are prioritized in turn.

[0090] Figure 6 FIG. 1 is a schematic diagram of a computing node allocation system disclosed in the present invention. Figure 6As shown, the system includes a user request sending end, an access information storage end, a signal identification end, a dynamic resource allocation end, an edge information collection end, a dynamic resource allocation end, a computing power equipment detection end, a time detection end and a signal output end, wherein the user request sending end is used for the user to send target request data or receive target request data sent by the user, the access information storage end user stores the target request data sent by the user, the signal identification end user identifies the target request data to determine the size of the target request data, the request type and the physical location of the user end, etc., the edge information collection end is used to collect information data of each processing node of the computing node allocation system, including The computing power equipment detection end is used to detect each processing node to determine whether each processing node has the ability to process data. The dynamic resource allocation end is used to determine the optimal target processing node from the processing nodes for the target request data. The timeliness detection end is used to detect the duration of the target access information within the cycle time, and then timely detect the capacity of the computing power equipment, so that the management personnel can timely understand the computing power of the distribution area, and maintain the computing power equipment in the distribution area in real time according to the cycle value. The signal output end is used to output the data after the target processing node processes the target request data.

[0091] Corresponding to the computing node allocation methods provided in the above-mentioned embodiments, an embodiment of the present disclosure also provides a computing node allocation device. Since the computing node allocation device provided in the embodiment of the present disclosure corresponds to the computing node allocation methods provided in the above-mentioned embodiments, the implementation methods of the above-mentioned computing node allocation methods are also applicable to the computing node allocation device provided in the embodiment of the present disclosure, and will not be described in detail in the following embodiments.

[0092] Figure 7 is a schematic diagram of a computing node allocation device according to an embodiment of the present disclosure. Figure 7 As shown, the computing node allocation device 700 includes: an acquisition module 710, a determination module 720, a calculation module 730 and an allocation module 740.

[0093] The acquisition module 710 is used to acquire target request data within a target time period and acquire a first delay time of the target request data.

[0094] The determination module 720 is used to determine a candidate computing node from all nodes in the computing space based on the target request data, wherein the candidate computing node is a node capable of processing the target request data.

[0095] The calculation module 730 is used to calculate, for any candidate computing node, the processing time of the candidate computing node to process the target request data, the transmission time of the target request data to the candidate computing node, and the waiting time of the candidate computing node to process the target request data.

[0096] The allocation module 740 is configured to determine a first overall processing time based on the processing time, the transmission time, and the waiting time, and to determine a target processing node from the candidate computing nodes based on the first overall processing time and the first delay time.

[0097] In one possible implementation, the computing module 730 is also used to: obtain the data volume of the target requested data, and obtain the computing power value and influence value of the candidate computing node; multiply the data volume by the influence value, and divide the product by the computing power value to calculate the acquisition processing time.

[0098] In one possible implementation, the computing module 730 is further used to: obtain the remaining storage space, total storage space and weight factor of the candidate computing node; multiply the remaining storage space by the weight factor, and divide the product by the total storage space to calculate the influence value.

[0099] In one possible implementation, the computing module 730 is also used to: obtain the total amount of unprocessed data in the processing of the candidate computing node, and obtain the computing power value and influence value of the candidate computing node; multiply the total amount of unprocessed data by the influence value, and divide the product by the computing power value to calculate the acquisition waiting time.

[0100] In one possible implementation, the allocation module 740 is also used to: select a candidate computing node whose first overall processing time is less than the first delay time from the candidate computing nodes as a target candidate computing node; obtain a distance value between the target candidate computing node and the terminal corresponding to the target request data; and select the target candidate computing node with the smallest distance value as the target processing node.

[0101] In one possible implementation, the allocation module 740 is also used to: obtain the overlap value of the target processing node; in response to the overlap value of the target processing node being 1, obtain the second overall processing time and the second delay time of other processing data, wherein the other processing data is the data to be processed by the target processing node other than the target request data within the target time period; based on the second overall processing time and the second delay time of the other processing data and the first overall processing time and the first delay time of the target request data, determine the processing order of the target processing node for the target request data.

[0102] In one possible implementation, the allocation module 740 is also used to: calculate the difference between the first delay time and the first overall processing time of the target request data as the first remaining time of the target request data, and calculate the difference between the second delay time and the second overall processing time of other processing data as the second remaining time of other processing data; based on the first remaining time, the second remaining time, the processing time of the target request data and the processing time of the other processing data, determine the processing order of the target request data by the target processing node.

[0103] In one possible implementation, the allocation module 740 is also used to: obtain the data to be processed of the target processing node within the target time period; in response to the data to be processed only containing the target request data, determine that the overlap value is 0; in response to the data to be processed containing the target request data and other data, determine that the overlap value is 1.

[0104] Therefore, by analyzing the processing time of the candidate computing node for the target request data, the transmission time of the target request data to the candidate computing node, and the waiting time for the candidate computing node to process the target request data, the optimal target processing node is selected to ensure the processing efficiency of the target request data and reduce the delay of data processing, thereby improving the user experience.

[0105] In order to implement the above embodiment, the present disclosure further provides an electronic device 800, Figure 8 is a schematic diagram of an electronic device according to an embodiment of the present disclosure, such as Figure 8 As shown, the electronic device 800 includes: a processor 801 and a memory 802 that is communicatively connected to the processor, the memory 802 stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor 801 to implement the present disclosure. Figure 1-Figure 6 A computing node allocation method according to an embodiment.

[0106] In order to implement the above embodiments, the present disclosure also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable the computer to implement the above embodiments. Figure 1-Figure 6 A computing node allocation method according to an embodiment.

[0107] In order to implement the above embodiments, the present disclosure also provides a computer program product, including a computer program. When the computer program is executed by a processor, the computer program implements the above embodiments. Figure 1-Figure 6 A computing node allocation method according to an embodiment.

[0108] It should be noted that personal information from users should be collected for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. In addition, such collection / sharing should be carried out after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign the agreement / authorization including authorization of relevant user information before the user uses the function. In addition, any necessary steps should be taken to protect and safeguard access to such personal information data and ensure that others who have access to personal information data comply with its privacy policy and procedures.

[0109] The present application is expected to provide an implementation scheme for users to selectively block the use or access of personal information data. That is, the present disclosure is expected to provide hardware and / or software to prevent or block access to such personal information data. Once the personal information data is no longer needed, the risk can be minimized by limiting data collection and deleting the data. In addition, when applicable, such personal information is de-identified to protect the privacy of the user.

[0110] In the description of the aforementioned embodiments, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0111] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0112] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0113] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that contains, stores, communicates, propagates or transmits a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0114] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0115] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0116] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0117] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A computing node allocation method, characterized in that: include: Obtain target request data within a target time period, and obtain a first delay time of the target request data; Determining a candidate computing node from all nodes in the computing space based on the target request data, wherein the candidate computing node is a node capable of processing the target request data; For any candidate computing node, calculate the processing time of the candidate computing node to process the target request data, calculate the transmission time of the target request data to the candidate computing node, and calculate the waiting time of the candidate computing node to process the target request data; A first overall processing time is determined based on the processing time, the transmission time, and the waiting time, and a target processing node is determined from the candidate computing nodes based on the first overall processing time and the first delay time, and the target request data is allocated to the target processing node for processing.

2. The method according to claim 1, characterized in that The calculating the processing time of the candidate computing node to process the target request data includes: Obtaining the data volume of the target request data, and obtaining the computing power value and influence value of the candidate computing node; The data volume is multiplied by the impact value, and the product is divided by the computing power value to calculate the processing time.

3. The method according to claim 2, characterized in that Obtaining the influence value of the candidate computing node includes: Obtaining the remaining storage space, total storage space and weight factor of the candidate computing node; The remaining storage space is multiplied by the weight factor, and the product is divided by the total storage space to calculate and obtain the influence value.

4. The method according to claim 1, characterized in that: The calculating the waiting time for the candidate computing node to process the target request data includes: Obtaining the total amount of unprocessed data being processed by the candidate computing node, and obtaining the computing power value and influence value of the candidate computing node; The total amount of unprocessed data is multiplied by the impact value, and the product is divided by the computing power value to calculate the waiting time.

5. The method according to any one of claims 1 to 4, characterized in that The determining a target processing node from the candidate computing nodes based on the first overall processing time and the first delay time includes: Selecting a candidate computing node whose first overall processing time is less than the first delay time from the candidate computing nodes as a target candidate computing node; Obtaining a distance value between the target candidate computing node and a terminal corresponding to the target request data; The target candidate computing node with the smallest distance value is used as the target processing node.

6. The method according to claim 5, characterized in that After allocating the target request data to the target processing node for processing, the method further includes: Obtaining a coincidence value of the target processing node; In response to the coincidence value of the target processing node being 1, obtaining a second overall processing time and a second delay time of the other processing data, wherein the other processing data is data to be processed by the target processing node in the target time period except the target request data; Based on the second overall processing time and the second delay time of the other processed data and the first overall processing time and the first delay time of the target request data, the processing order of the target processing node on the target request data is determined.

7. The method according to claim 6, characterized in that The determining, based on the second overall processing time and the second delay time of the other processing data and the first overall processing time and the first delay time of the target request data, the processing order of the target processing node on the target request data comprises: Calculate the difference between the first delay time and the first overall processing time of the target request data as the first remaining time of the target request data, and calculate the difference between the second delay time and the second overall processing time of the other processed data as the second remaining time of the other processed data; A processing order of the target request data by the target processing node is determined based on the first remaining time, the second remaining time, the processing time of the target request data, and the processing time of the other processing data.

8. The method according to claim 7, characterized in that The obtaining the coincidence value of the target processing node includes: Acquire the data to be processed by the target processing node within the target time period; In response to the data to be processed only including the target request data, determining that the coincidence value is 0; In response to the data to be processed including the target request data and other data, the coincidence value is determined to be 1.

9. A computing node allocation device, characterized in that: include: An acquisition module, used to acquire target request data within a target time period, and acquire a first delay time of the target request data; A determination module, configured to determine a candidate computing node from all nodes in the computing space based on the target request data, wherein the candidate computing node is a node capable of processing the target request data; A calculation module, used for calculating, for any candidate computing node, a processing time of the candidate computing node for processing the target request data, a transmission time of the target request data to the candidate computing node, and a waiting time of the candidate computing node for processing the target request data; An allocation module is used to determine a first overall processing time based on the processing time, the transmission time and the waiting time, and to determine a target processing node from the candidate computing nodes based on the first overall processing time and the first delay time, and to allocate the target request data to the target processing node for processing.

10. An electronic device, characterized in that: Including memory and processor; The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to implement the method according to any one of claims 1 to 8.

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