Computing node allocation method, apparatus, and electronic device

Abstract

The present disclosure provides a computing node allocation method and device and electronic equipment, comprising: obtaining target request data and a first delay time in a target time period; determining candidate computing nodes from all nodes in a computing space based on the target request data; calculating a processing time, a transmission time and a waiting time; 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. Thus, by analyzing the processing time of the candidate computing nodes on the target request data, the transmission time of the target request data to the candidate computing nodes, and the waiting time of the candidate computing nodes processing the target request data, the optimal target processing node is selected, which can ensure the processing efficiency of the target request data and reduce the delay of data processing, thereby improving the user experience.

Description

Technical Field

[0001] This disclosure relates to the fields of cloud computing, big data, and edge computing technologies, and in particular to a computing node allocation method, apparatus, and electronic device. Background Technology

[0002] Multi-access edge computing is an emerging computing paradigm that extends computing power from the data center to the network edge, enabling data to be processed and analyzed where it is generated. Common algorithms include machine learning algorithms, which can automatically learn and improve the performance of models. 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 interpret, which may affect users' trust. Summary of the Invention

[0003] This disclosure aims to at least partially address one of the technical problems in the related art.

[0004] Therefore, one objective of this disclosure is to propose a method for allocating computing nodes.

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

[0006] The third objective of this disclosure is to propose an electronic device.

[0007] The fourth objective of this disclosure is to provide a non-transitory computer-readable storage medium.

[0008] The fifth objective of this disclosure is to provide a computer program product.

[0009] To achieve the above objectives, a first aspect of this disclosure provides a computing node allocation method, comprising: acquiring target request data within a target time period and acquiring a first delay time of the target request data; determining candidate computing nodes from all nodes in a computing space based on the target request data, wherein the candidate computing nodes are nodes capable of processing the target request data; for any candidate computing node, calculating the processing time of the candidate computing node in 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 in processing the target request data; 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.

[0010] According to one embodiment of this disclosure, calculating the processing time of the candidate computing node for processing 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 this 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 this 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 this 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 candidate computing nodes from the candidate computing nodes whose first overall processing time is less than the first delay time as target candidate computing nodes; obtaining the 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 this disclosure, after allocating the target request data to the target processing node for processing, the method further includes: obtaining an overlap value of the target processing node; in response to the overlap value of the target processing node being 1, obtaining a second overall processing time and a second delay time for the other processed data, wherein the other processed data is the data to be processed by the target processing node within the target time period, excluding the target request data; and determining the 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 the other processed data, as well as the first overall processing time and the first delay time of the target request data.

[0015] According to one embodiment of this disclosure, determining the processing order of the target processing node for the target request data based on the second overall processing time and second delay time of the other processed data, and the first overall processing time and first delay time of the target request data, includes: 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 processed data as the second remaining time of the other processed data; and determining the processing order of the target processing node for the target request 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 processed data.

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

[0017] To achieve the above objectives, a second aspect of this disclosure provides a computing node allocation apparatus, comprising: an acquisition module, configured 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 candidate computing nodes from all nodes in a computing space based on the target request data, wherein the candidate computing nodes are nodes capable of processing the target request data; a calculation module, configured to, for any candidate computing node, calculate the processing time of the candidate computing node in processing 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 in processing the target request data; and an allocation module, configured 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 objectives, a third aspect of this disclosure provides 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 to implement the computing node allocation method as described in the first aspect of this disclosure.

[0019] To achieve the above objectives, a fourth aspect of this disclosure provides 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 of this disclosure.

[0020] To achieve the above objectives, a fifth aspect of this disclosure provides a computer program product, including a computer program that, when executed by a processor, implements the computing node allocation method as described in the first aspect of this disclosure.

[0021] Therefore, by analyzing the processing time of the target request data by the candidate computing nodes, the transmission time of the target request data to the candidate computing nodes, and the waiting time of the candidate computing nodes in processing the target request data, the optimal target processing node can be selected to ensure the processing efficiency of the target request data and reduce the data processing latency, thereby improving the user experience. Attached Figure Description

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

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

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

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

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

[0027] Figure 6 This is a schematic diagram of the structure of a computing node allocation system disclosed herein;

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

[0029] Figure 8 This is a schematic diagram of an electronic device according to one embodiment of the present disclosure. Detailed Implementation

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

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

[0032] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

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

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

[0035] The computing node allocation method of this application embodiment can be applied to the scenario of computing devices in a multi-access edge computing system of cloud computing. The execution subject of the computing node allocation in this application embodiment can be the computing node allocation device of this application embodiment, which can be installed on an electronic device.

[0036] It should be noted that the target time period in this embodiment of the disclosure may be a certain data acquisition cycle. This data acquisition cycle is pre-designed and can be changed according to actual design needs. No limitation is made 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. This first delay time includes, but is not limited to, the time for the target request data to be transmitted to the processing node and the time for the data node to process the target request data.

[0038] S102, candidate computing nodes are determined from all nodes in the computing space based on the target request data, wherein the candidate computing nodes are nodes that have the ability to process the target request data.

[0039] It should be noted that the computing space in this embodiment may include various types, and no limitation is made here. For example, the computing space may be an edge computing space, a cloud computing space, or a local computing space. The computing space contains multiple computing nodes, which are devices with independent data processing capabilities.

[0040] In this embodiment, there are various methods for determining whether a candidate computing node is capable of processing the target request data, and no limitation is made here. For example, candidate computing nodes can be determined by the positional relationship between the computing node and the terminal that generates the target request data; for instance, computing nodes within a certain distance from the terminal can be considered as candidate computing nodes. In another possible implementation, the determination of whether a computing node is a candidate computing node can also be made by considering the data size of the target request data and the processing capability of the computing node; for example, computing nodes in the computing space can be filtered according to a set multiple of the data size to select candidate computing nodes that meet the criteria.

[0041] S103, for any candidate computing node, calculate the processing time of the candidate computing node in processing 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 in processing the target request data.

[0042] It should be noted that since computing nodes may need to process multiple data sets, there may be situations where existing data needs to be processed before the current target data request is processed. The waiting time can be calculated based on the computing power of the candidate computing nodes and the size of the existing data set.

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

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

[0045] In this embodiment of the disclosure, there are various methods for determining the target processing node from the candidate computing nodes based on the first overall processing time and the first delay time, and no limitation is made here.

[0046] In one possible implementation, the target processing node can be determined by setting filtering criteria. These criteria could be, for example, the minimum overall processing time, or the first overall time being less than a first delay time, and the candidate computing node having the maximum processing capacity.

[0047] In this embodiment, target request data within a target time period is first acquired, along with a first delay time for the target request data. Then, candidate computing nodes are determined from all nodes in the computing space based on the target request data. These candidate computing nodes are nodes capable of processing the target request data. Next, for any candidate computing node, the processing time for processing the target request data, the transmission time for transmitting the target request data to the candidate computing node, and the waiting time for the candidate computing node to process the target request data are calculated. Finally, a first overall processing time is determined based on the processing time, transmission time, and waiting time. Based on the first overall processing time and the first delay time, the target processing node is determined from the candidate computing nodes. Therefore, by analyzing the processing time of the candidate computing nodes for the target request data, the transmission time for transmitting the target request data to the candidate computing nodes, and the waiting time for the candidate computing nodes to process the target request data, the optimal target processing node is selected. This ensures efficient processing of the target request data and reduces data processing latency, thereby improving the user experience.

[0048] In practice, network latency and data transmission losses can also impact the overall data processing time, extending it. Therefore, it's necessary to analyze this time loss in actual operation. Since network latency and data transmission losses can be periodic issues within the same computing node, for any given computing node, historical transmission data can be analyzed to determine the stuttering time, i.e., the delay caused by network latency and data transmission losses.

[0049] In this embodiment of the disclosure, the stuttering information of the distributed area within the periodic time can be obtained first, and the stuttering duration of the stuttering information can be accumulated to obtain the periodic delay. In this embodiment of the disclosure, it can be expressed by the following formula;

[0050]

[0051] Where F is the period score, AL is the number of stuttering messages within the period, ZS is the period delay, P is the average delay of the stuttering messages, i.e., P = ZS divided by AL, and β1, β2 and β3 are the weight coefficients.

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

[0053] In the above embodiments, the processing time of candidate computing nodes for processing target request data can also be calculated by... Figure 2 To further explain, 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 nodes.

[0055] It should be noted that the computing power value of a candidate computing node represents its computing capability; the higher the computing power value, the stronger the computing capability of the candidate computing node. Different candidate computing nodes may have different computing power values.

[0056] The impact value refers to the degree to which a candidate computing node affects the overall system performance, efficiency, and service quality within the computing space, as well as its impact on the efficiency and effectiveness of data processing. In this embodiment, the impact value of a candidate computing node can be determined by its space utilization rate.

[0057] In this embodiment of the disclosure, the remaining storage space, total storage space and weight factor of the candidate computing node can be obtained first. Then, the remaining storage space is multiplied by the weight factor and the product is divided by the total storage space to calculate the influence value.

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

[0059]

[0060] Where 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 weighting factor. It should be noted that λ1 is pre-designed and can be changed according to actual design needs; no restrictions are imposed here.

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

[0062] In this embodiment of the disclosure, the formula for calculating the acquisition processing time is as follows:

[0063]

[0064] Where Ci represents the amount of data requested by target data i, S represents the computing power of candidate computing nodes, and αj is the influence value of candidate computing node j.

[0065] In this embodiment, the data volume of the target request data is first obtained, along with the computing power and influence values ​​of the candidate computing nodes. Then, the data volume and influence values ​​are multiplied, and the product is divided by the computing power value to calculate the processing time. Therefore, by determining the processing time using the influence and computing power values ​​of the candidate computing nodes, the processing speed for the target data can be estimated more accurately from both the storage space and data processing speed of the candidate computing nodes.

[0066] In the above embodiments, the waiting time for candidate computing nodes to process target request data can also be calculated by... Figure 3 To further explain, the method includes:

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

[0068] It should be noted that the influence values ​​of candidate computing nodes can be obtained by referring to [reference needed]. Figure 2 The details in the embodiments will not be repeated here.

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

[0070] It should be noted that the formula for calculating the waiting time is as follows:

[0071]

[0072] Where 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 candidate computing node j.

[0073] In this embodiment, the total amount of unprocessed data processed by the candidate computing nodes is first obtained, along with the computing power and influence value of each candidate computing node. 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 waiting time. Therefore, by determining the waiting time using the influence and computing power values ​​of the candidate computing nodes, the processing speed of unprocessed data can be estimated more accurately from both the perspectives of the candidate computing node's storage space and data processing speed, providing a data foundation for subsequent node allocation.

[0074] In the above embodiments, the target processing node is determined from the candidate computing nodes based on the first overall processing time and the first delay time. This can also be achieved through... Figure 4 To further explain, the method includes:

[0075] S401, Select the 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.

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

[0077] S402, obtain the distance value between the target candidate computing node and the terminal corresponding to the target requested data.

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

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

[0080] In this embodiment, firstly, candidate computing nodes with a first overall processing time less than a first delay time are selected as target candidate computing nodes. Then, the distance value between the target candidate computing node and the terminal corresponding to the target request data is obtained. Finally, the target candidate computing node with the smallest distance value is selected as the target processing node. Therefore, by determining the target candidate computing node with the smallest distance value from the target candidate computing nodes based on the distance value, the transmission distance between the terminal and the target processing node can be shortened as much as possible, improving transmission efficiency and data processing speed.

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

[0082] S501, obtain the overlap value of the target processing node.

[0083] In this embodiment of the disclosure, the overlap value indicates whether the current target processing node has any other data that needs to be processed besides the target request data. In this embodiment of the disclosure, the overlap value includes 0 and 1. Wherein, 0 indicates that there is no other data that needs to be processed, and 1 indicates that there is other data that needs to be processed.

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

[0085] S502, in response to the overlap value of the target processing node being 1, the second overall processing time and the second delay time of other processing data are obtained, wherein the other processing data are the data to be processed by the target processing node within the target time period, excluding the target request data.

[0086] S503, based on the second overall processing time and second delay time of other processed data and the first overall processing time and first delay time of the target requested data, determine the processing order of the target processing node for the target requested data.

[0087] In this embodiment of the disclosure, the difference between the first delay time and the first overall processing time of the target request data can be calculated first 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 processed data can be calculated as the second remaining time of the other processed 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 processed data, the processing order of the target processing node for the target request data can be determined.

[0088] In this embodiment of the disclosure, taking the existence of 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, then the processing time of the target request data is subtracted from the second remaining time of the other processing data to obtain the 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 the difference time 2. The difference time 1 and the difference time 2 are compared. When the difference time 1 is less than the difference time 2, the target request data is given the first priority. Conversely, when the difference time 1 is greater than the difference time 2, the other processing data is given the first priority. If the difference time 1 is equal to the difference time 2, the waiting request with the shorter processing time is given the first priority.

[0089] When the processing time for the target request data is less than or equal to the remaining time for other processing data, the target request data is marked as the first priority, and the other processing data is marked as the second priority. The requests waiting to be processed are then processed according to their priorities.

[0090] Figure 6 This is a schematic diagram of the structure of a computing node allocation system disclosed herein, as shown below. Figure 6As shown, the system includes a user request sending end, an access information storage end, a signal recognition end, a dynamic resource allocation end, an edge information collection end, a computing power device detection end, a timeliness detection end, and a signal output end. The user request sending end is used for users to send or receive target request data. The access information storage end stores the target request data sent by users. The signal recognition end identifies the target request data to determine its size, request type, and the physical location of the user. The edge information collection end collects information data from each processing node of the computing node allocation system. This includes the computing power and storage space of processing nodes. The computing power device 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 lag time of the target access information within a periodic period, thereby timely detecting the capability of the computing power device, enabling managers to understand the computing power of the distributed area in a timely manner, and to perform periodic and real-time maintenance of the computing power device in the distributed area. The signal output end is used to output the data after the target processing node has processed the target request data.

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

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

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

[0094] The determination module 720 is used to determine candidate computing nodes from all nodes in the computing space based on the target request data, wherein the candidate computing nodes are nodes that have the ability to process the target request data.

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

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

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

[0098] In one possible implementation, the calculation module 730 is further configured to: obtain the remaining storage space, total storage space, and weight factor of the candidate calculation 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 further configured to: obtain the total amount of unprocessed data in the candidate computing node processing, 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 waiting time.

[0100] In one possible implementation, the allocation module 740 is further configured to: select candidate computing nodes from the candidate computing nodes whose first overall processing time is less than a first delay time as target candidate computing nodes; obtain the 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 further configured 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 are the data to be processed by the target processing node within the target time period, excluding the target request data; and determine the 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 the other processing data and the first overall processing time and the first delay time of the target request data.

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

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

[0104] Therefore, by analyzing the processing time of the target request data by the candidate computing nodes, the transmission time of the target request data to the candidate computing nodes, and the waiting time of the candidate computing nodes in processing the target request data, the optimal target processing node can be selected to ensure the processing efficiency of the target request data and reduce the data processing latency, thereby improving the user experience.

[0105] To implement the above embodiments, this disclosure also proposes an electronic device 800. Figure 8 This is a schematic diagram of an electronic device according to one embodiment of the present disclosure, such as... Figure 8 As shown, the electronic device 800 includes: a processor 801 and a memory 802 communicatively connected to the processor. The memory 802 stores instructions executable by at least one processor. The instructions are executed by at least one processor 801 to achieve the functions described in this disclosure. Figures 1-6 The computing node allocation method of the embodiment.

[0106] To implement the above embodiments, this disclosure also proposes a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to implement the present disclosure. Figures 1-6 The computing node allocation method of the embodiment.

[0107] To implement the above embodiments, this disclosure also proposes a computer program product, including a computer program, which, when executed by a processor, implements the features of this disclosure. Figures 1-6 The computing node allocation method of the embodiment.

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

[0109] This application is intended to provide an implementation scheme for users to selectively prevent the use or access to their personal information data. Specifically, this disclosure is intended to provide hardware and / or software to prevent or block access to such personal information data. Once personal information data is no longer needed, risks can be minimized by restricting data collection and deleting data. Furthermore, where applicable, such personal information is de-identified to protect user privacy.

[0110] In the foregoing descriptions of the embodiments, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0111] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0112] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

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

[0114] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0115] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0116] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0117] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for allocating computing nodes, characterized in that, include: Obtain target request data within the target time period, and obtain the first delay time of the target request data, wherein the first delay time is the maximum time required for the target request data to be processed. Candidate computing nodes are determined from all nodes in the computing space based on the target request data, wherein the candidate computing nodes are nodes capable of processing the target request data; For any candidate computing node, calculate the processing time of the candidate computing node in processing 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 in processing the target request data. A first overall processing time is determined based on the processing time, the transmission time, and the waiting time. A target processing node is determined from the candidate computing nodes based on the first overall processing time and the first delay time. The target request data is then allocated to the target processing node for processing. Obtain the data to be processed by the target processing node within the target time period; In response to the fact that the data to be processed includes the target request data and other data, the overlap value is determined to be 1, and the second overall processing time and the second delay time of the other processing data are obtained, wherein the other processing data is the data to be processed by the target processing node within the target time period other than the target request data; Calculate the difference between the first delay time and the first overall processing time as the first remaining time for the target requested data, and calculate the difference between the second delay time and the second overall processing time as the second remaining time for the other processed 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 processed data, the processing order of the target processing node for the target request data is determined.

2. The method according to claim 1, characterized in that, The calculation of the processing time for the candidate computing node to process the target request data includes: The data volume of the target request data is obtained, and the computing power value and influence value of the candidate computing node are obtained. The influence value is the degree of influence of the candidate computing node on the overall system performance, efficiency and service quality in the computing space. The processing time is calculated by multiplying the data volume by the impact value and dividing the product by the computing power value.

3. The method according to claim 2, characterized in that, Obtaining the influence value of the candidate computing node includes: Obtain the remaining storage space, total storage space, and weighting 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 the influence value.

4. The method according to claim 1, characterized in that, The calculation of the waiting time for the candidate computing node to process the target request data includes: The total amount of unprocessed data in the candidate computing node is obtained, and the computing power value and impact value of the candidate computing node are obtained. The impact value is the degree of influence of the candidate computing node on the overall system performance, efficiency and service quality in the computing space. The waiting time is calculated by multiplying the total amount of unprocessed data by the impact value and dividing the product by the computing power value.

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

6. The method according to claim 1, characterized in that, The method further includes: In response to the fact that the data to be processed contains only the target request data, the overlap value is determined to be 0.

7. A computing node allocation device, characterized in that, include: The acquisition module is used to acquire target request data within a target time period and acquire the first delay time of the target request data, wherein the first delay time is the maximum time required for the target request data to be processed. The determination module is used to determine candidate computing nodes from all nodes in the computing space based on the target request data, wherein the candidate computing nodes are nodes capable of processing the target request data; The computing module is used to calculate, for any candidate computing node, the processing time of the candidate computing node in processing 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 in processing the target request data. The 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; The allocation module is further configured to: Obtain the data to be processed by the target processing node within the target time period; In response to the fact that the data to be processed includes the target request data and other data, the overlap value is determined to be 1, and the second overall processing time and the second delay time of the other processing data are obtained, wherein the other processing data is the data to be processed by the target processing node within the target time period other than the target request data; Calculate the difference between the first delay time and the first overall processing time as the first remaining time for the target requested data, and calculate the difference between the second delay time and the second overall processing time as the second remaining time for the other processed 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 processed data, the processing order of the target processing node for the target request data is determined.

8. An electronic device, characterized in that, Including memory and processor; The processor reads executable program code stored in the memory to run a program corresponding to the executable program code, so as to implement the method as described in any one of claims 1-6.

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