Data processing based on end-edge-cloud scheduling system

Abstract

Disclosed in embodiments of the present description are a data processing method, apparatus and device based on an end-edge-cloud scheduling system. The method comprises: acquiring a first service processing request in the process of executing a target service; then, determining a computing power allocation strategy for a terminal side and an edge server side and corresponding to the first service processing request, wherein the computing power allocation strategy is determined on the basis of the number of service processing requests processed by the terminal side, the number of service processing requests processed by the edge server side, and the trusted release rates of service processing requests corresponding to the terminal side and the edge server side, under the computing power constraint conditions and / or service time consumption conditions of the edge server side; and finally, on the basis of the computing power allocation strategy, determining a device side for processing the first service processing request, and processing the first service processing request on the basis of the determined device side.

Description

Data processing based on edge-cloud scheduling system Technical Field

[0001] This document relates to the field of computer technology, and in particular to methods, devices, and equipment for data processing based on an edge-cloud scheduling system. Background Art

[0002] To address challenges such as poor performance on low-end devices, limited model size on the terminal side, high cloud computing costs, and privacy compliance, we need to build an intelligent engine for device-edge-cloud collaboration. Edge server computing power is limited, so intelligent scheduling is needed to address the dynamic deployment of computing power on both the terminal and edge server sides.

[0003] While user privacy, data security, and other financial payment scenarios are achieving significant business results on the smart side, resource constraints on mobile devices and other terminals are insufficient to sustainably meet expanding business demands. Therefore, leveraging the advantages of the terminal side, near-end edge server nodes are being introduced to form an end-to-end solution. However, how to better couple the end and edge requires more than just link establishment, but also the allocation of computing power. Both the terminal side and the edge server side have their own advantages. Local processing on the terminal side offers greater convenience and advantages, without the overhead of data transmission. Edge servers possess greater real-time computing power, enabling them to handle more complex tasks. How to leverage the advantages of the end-to-end based on different tasks, real-time computing power, devices, and even businesses requires computing power allocation. Therefore, a more optimal computing power allocation solution between the terminal side and the edge server side is needed to maintain a balance between computing power consumption, business value, and technology, thereby improving the stability of the end-to-edge cloud scheduling system. Summary of the Invention

[0004] The purpose of the embodiments of this specification is to provide a better computing power allocation solution between the terminal side and the edge server side to maintain a balance between computing power consumption, business value and technology, and improve the stability of the end-edge cloud scheduling system.

[0005] In order to implement the above technical solution, the embodiments of this specification are implemented as follows.

[0006] An embodiment of the present specification provides a data processing method based on an end-edge cloud scheduling system, the method comprising: obtaining a first business processing request in the process of executing a target business; determining a computing power allocation strategy on the terminal side and the edge server side corresponding to the first business processing request, the computing power allocation strategy being determined based on the computing power constraint conditions and / or business time consumption conditions on the edge server side, the number of business processing requests processed on the terminal side, the number of business processing requests processed on the edge server side, and the trusted release rate of the business processing requests corresponding to the terminal side and the edge server side; based on the computing power allocation strategy, determining a device side for processing the first business processing request, and processing the first business processing request based on the determined device side.

[0007] An embodiment of the present specification provides a data processing device based on an end-edge cloud scheduling system, the device comprising: a request acquisition module, which acquires a first business processing request in the process of executing a target business; a computing power allocation module, which determines a computing power allocation strategy on the terminal side and the edge server side corresponding to the first business processing request, wherein the computing power allocation strategy is determined based on the computing power constraint condition and / or business time consumption condition on the edge server side, the number of business processing requests processed on the terminal side, the number of business processing requests processed on the edge server side, and the trusted release rate of the business processing requests corresponding to the terminal side and the edge server side; the data processing module, based on the computing power allocation strategy, determines the device side for processing the first business processing request, and processes the first business processing request based on the determined device side.

[0008] An embodiment of the present specification provides a data processing device based on an end-edge-cloud scheduling system, the data processing device based on the end-edge-cloud scheduling system including a processor and a memory arranged to store computer-executable instructions, wherein the executable instructions, when executed, cause the processor to: obtain a first business processing request in the process of executing a target business; determine a computing power allocation strategy for the terminal side and the edge server side corresponding to the first business processing request, the computing power allocation strategy being determined based on the number of business processing requests processed on the terminal side, the number of business processing requests processed on the edge server side, and the trusted release rate of the business processing requests corresponding to the terminal side and the edge server side, under the computing power constraint conditions and / or business time consumption conditions on the edge server side; based on the computing power allocation strategy, determine the device side that processes the first business processing request, and process the first business processing request based on the determined device side.

[0009] An embodiment of this specification also provides a storage medium, which is used to store computer-executable instructions. When the executable instructions are executed by a processor, they implement the following process: obtaining a first business processing request in the process of executing a target business; determining a computing power allocation strategy on the terminal side and the edge server side corresponding to the first business processing request, wherein the computing power allocation strategy is determined based on the computing power constraint conditions and / or business time consumption conditions on the edge server side, the number of business processing requests processed on the terminal side, the number of business processing requests processed on the edge server side, and the trusted release rate of the business processing requests corresponding to the terminal side and the edge server side; based on the computing power allocation strategy, determining the device side that processes the first business processing request, and processing the first business processing request based on the determined device side. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the embodiments of this specification or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0011] FIG1A and FIG1B are schematic diagrams showing the change in the number of 24-hour business processing requests on different days in this specification.

[0012] FIG2 is a schematic diagram showing the time variation required for an edge server to process a business processing request in this specification.

[0013] FIG3 is a schematic diagram showing the time variation required for a terminal side to process a service processing request in this specification.

[0014] FIG4 is a schematic diagram of the present specification showing the time variation required for a device to process a business processing request divided into 30-60 periods.

[0015] FIG5 is a schematic diagram showing the time variation of a device with a device score of 70 or more in this specification that is required to process a service processing request.

[0016] FIG6 is an embodiment of a data processing method based on an edge-cloud scheduling system in this specification.

[0017] FIG7 is another embodiment of a data processing method based on an edge-cloud scheduling system in this specification.

[0018] FIG8 is a schematic diagram of data distribution of a received business processing request in this specification.

[0019] FIG9 is another embodiment of a data processing method based on an edge-cloud scheduling system in this specification.

[0020] Figure 10 is a schematic diagram of the DAU fluctuations of a financial transaction program during special periods such as promotions and entry traffic control in this manual.

[0021] FIG11 is another embodiment of a data processing method based on an edge-cloud scheduling system in this specification.

[0022] FIG12 is another embodiment of a data processing device based on an edge-cloud scheduling system in this specification.

[0023] FIG13 is an embodiment of a data processing device based on an edge-cloud scheduling system in this specification. DETAILED DESCRIPTION

[0024] The embodiments of this specification provide a data processing method, device, and equipment based on an edge-cloud scheduling system.

[0025] To help those skilled in the art better understand the technical solutions in this specification, the following will provide a clear and complete description of the technical solutions in the embodiments of this specification, in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of them. All other embodiments derived by those skilled in the art based on the embodiments in this specification without creative effort shall fall within the scope of protection of this specification.

[0026] The embodiments of this specification provide a mechanism for stabilizing the end-edge-cloud scheduling system. This mechanism addresses challenges such as poor performance on low-end devices, model size limitations on the terminal side, high cloud computing costs, and privacy compliance by building an intelligent engine for end-edge-cloud collaboration. Edge server computing power is limited, so intelligent scheduling is needed to address the dynamic deployment of computing power on both the terminal and edge server sides.

[0027] Security and other financial payment scenarios are achieving significant business results on the smart side. However, resource constraints on mobile devices and other terminals are insufficient to sustainably meet expanding business demands. Therefore, while leveraging the advantages of the terminal side, "near-end" edge server nodes are being introduced to form an end-to-end solution. However, how to better couple the end and edge requires more than just link establishment, but also the allocation of computing power. Both the terminal side and the edge server side have their own advantages. The local nature of the terminal side allows for more convenient and advantageous processing, without the overhead of data transmission. The edge server side possesses greater real-time computing power, enabling it to handle more complex tasks. How to leverage the advantages of the end and edge based on different tasks, real-time computing power, devices, and even businesses requires computing power allocation.

[0028] In order to pursue a balance between computing power consumption, business value and technology, and taking into account the different business values ​​of different devices, different users, different execution links, etc.; different execution links have different computing overheads; different execution links have different technical indicators (including consumed time, success rate and other technical indicators), therefore, intelligent computing power allocation is required.

[0029] Taking secure and trusted services as an example, to maximize the trusted release rate, let's say the service focuses on the trusted release rate within a certain period of time (e.g., one day). Assuming there are n payment requests in a day and the trusted release rate of model A is 50%, then each service processing request contributes 0.5 / n to the trusted release rate. In practical applications, the trusted release rate of the small model A on the terminal side (small model, poor model performance, but low cost) is α, and the trusted release rate of the large model on the edge server side (large model, good model performance, but high cost) is α. Given the total traffic (i.e., the total number of service processing requests) in a day is n, the service processing requests handled by the small model A on the terminal side account for 30% of the total traffic, while the service processing requests handled by the large model on the edge server account for 70%. Accordingly, the total service value is: 0.3*α+0.7*β.

[0030] The magnitude of business processing requests at different times is different. As shown in Figures 1A and 1B, the changes in the number of business processing requests (i.e., traffic) for 24 hours on different days can be seen. It can be seen that the daily change trend is almost the same, with two traffic peaks. In addition, different links have different time requirements for processing business processing requests. Figure 2 shows the time required for the edge server side to process business processing requests, and Figure 3 shows the time required for the terminal side to process business processing requests. It can be seen that the time required for the terminal side to process business processing requests is generally shorter. In addition, the time required for different devices to process requests is different. Figure 4 shows the time required for devices with a score of 30-60 to process business processing requests, and Figure 5 shows the time required for devices with a score of 70 or more to process business processing requests. It can be seen that the higher the device score, the shorter the time required for the device to process business processing requests and the lower the probability of timeout.

[0031] To ensure the stability of the edge-cloud scheduling system, it is necessary to maximize business value based on existing variable constraints. These variable constraints may include: Computing power constraints on the edge server: The edge server is a public resource node. If the edge server node is blocked or down due to a business processing request, serious network problems will occur, even affecting other businesses. Therefore, the amount of data sent to the edge server must be less than the amount of data the edge server can process per unit time, and the amount of data the edge server can process varies. Business time constraints: Whether processing on the edge server or on the terminal side, there is a processing time, which cannot be infinite and must meet business requirements. Each business processing request has a quantifiable value. The value of business processing requests sent to the terminal and to the edge server is inconsistent. After the required traffic is offloaded, the sum of the business values ​​must be maximized.

[0032] In the above scenario, there are the following difficulties: the variable uncertainty factors corresponding to a single business processing request are relatively large. For example, the time taken to process each business processing request is somewhat random. Specifically, the time taken to execute a business processing request locally is related to the busyness of the terminal side. If the terminal side is currently slow and there are many processes started, resource preemption will occur, and the time taken will naturally be long. For another example, there is network time taken to send it to the edge server side for execution, and the network time taken is also random. The above uncertain variables bring great difficulty to solving the problem; and when a priori distribution is introduced, the distribution will also be inaccurate, and it will not be able to cope with emergencies, such as traffic peaks in special circumstances such as promotions. The scheduling algorithm needs to be manually set. In addition, there will be error deviations in the end-side scheduling, which will cause error accumulation. To this end, the embodiment of this specification provides an implementable solution, which schedules and processes the business processing requests of the target business through the end-side scheduling strategy in the end-side cloud scheduling system, so as to perform business processing more efficiently and quickly. For specific processing, please refer to the specific content in the following embodiments.

[0033] As shown in Figure 6, an embodiment of this specification provides a data processing method based on an end-edge cloud scheduling system. The execution subject of the method can be a terminal device or a server, etc., wherein the terminal device can be a mobile terminal device such as a mobile phone, a tablet computer, or a computer device such as a laptop or a desktop computer, or an IoT device (specifically such as a smart watch, a car-mounted device, etc.), etc., wherein the server can be an independent server, or a server cluster composed of multiple servers, etc. The server can be a background server for a financial business or an online shopping business, etc., or a background server for an application, etc. In this embodiment, the execution subject is a server as an example for detailed description. For the case where the execution subject is a terminal device, please refer to the following server case processing, which will not be repeated here. The method can specifically include the following steps.

[0034] In step S602, a first business processing request in the process of executing the target business is obtained.

[0035] The target business can be any business. In this embodiment, the target business can be a secure and trusted business, such as a payment business, a transfer business, a shopping business, etc., which can be set according to actual circumstances and is not limited in this embodiment. The first business processing request can be a business processing request for the target business, such as a request to obtain designated business data, an identity identification request, a risk prevention and control request, etc. The first business processing request can include an identifier of the target business, the content of the request, etc., which can be set according to actual circumstances and is not limited in this embodiment.

[0036] During implementation, when a certain business (i.e., target business) needs to be processed, corresponding data can be obtained, such as the identification of the target business and the content of the request, and a corresponding business processing request (i.e., the first business processing request) can be generated based on the obtained data, thereby obtaining the first business processing request in the process of executing the target business.

[0037] The above is the first business processing request generated when processing the target business. The above first business processing request may also be generated at a certain moment in the execution of the target business, etc. The specific setting may be based on actual conditions.

[0038] In step S604, the computing power allocation strategy of the terminal side and the edge server side corresponding to the first business processing request is determined. The computing power allocation strategy is determined based on the computing power constraint condition and / or business time consumption condition on the edge server side, the number of business processing requests processed on the terminal side, the number of business processing requests processed on the edge server side, and the trusted release rate of the business processing requests corresponding to the terminal side and the edge server side.

[0039] Among them, the computing power allocation strategy on the terminal side and the edge server side can be a strategy for scheduling the terminal side and the edge server side to execute respectively for the data to be processed, such as, within a certain time period, the terminal side processes each data to be processed with a specified first probability and the edge server side processes each data to be processed with a specified second probability, or, for the data to be processed, when the current terminal side and the edge server side meet the specified conditions, the processing equipment is determined, etc., which can be set according to actual conditions, and the embodiments of this specification do not limit this. The computing power constraint condition on the edge server side can be a condition that the amount of data sent to the edge server side is less than the amount of data that the edge server side can process per unit time (that is, the total computing power of the edge server side). The service time consumption condition can be a condition that needs to meet the preset requirements of the service.

[0040] In implementation, in order to construct a computing power allocation strategy for the edge, corresponding constraints need to be met. The constraints in this embodiment may include computing power constraints and / or service time consumption conditions on the edge server side. Based on the above content, the computing power constraints and / or service time consumption conditions on the edge server side can be expressed by the following expression: x ij ∈(0,1)

[0041] Where i represents the i-th business processing request, j represents execution on the terminal side or edge server side, j=0 represents execution on the terminal side, j=1 represents execution on the edge server side, n represents the total number of business processing requests in a period of time, cost i,j＝1 represents the computing power cost executed on the edge server side, C represents the total computing power on the edge server side, time i,j Indicates the script execution time, L indicates the total time. ij＝0 Indicates the ratio of the number of business processing requests processed on the terminal side to the total computing power on the terminal side, x ij＝1 Indicates the ratio of the number of business processing requests processed by the edge server to the total computing power of the edge server.

[0042] Considering that each business processing request has a quantifiable value, the value of the business processing request sent to the terminal side and the business processing request sent to the edge server side is inconsistent. After the required traffic is unloaded, the sum of the business values ​​needs to be maximized. Based on this, the computing power allocation strategy expression of the terminal side and the edge server side can be constructed as follows: max∑ i,j value ij *x ij (1)

[0043] Among them, value ijRepresents business value. In order to solve the above expression to obtain the computing power allocation strategy on the terminal side and the edge server side, historical data can be obtained, and the specific numerical values ​​or data distribution in the historical data can be used to solve the above expression to obtain the computing power allocation strategy on the terminal side and the edge server side, thereby obtaining the computing power allocation strategy on the terminal side and the edge server side corresponding to the first business processing request. Alternatively, the numerical values ​​of the various parameters in the above expression can be determined through expert experience, and then the above expression can be solved to obtain the computing power allocation strategy on the terminal side and the edge server side, thereby obtaining the computing power allocation strategy on the terminal side and the edge server side corresponding to the first business processing request, etc. In addition, the above expression can also be solved in other ways to obtain the computing power allocation strategy on the terminal side and the edge server side corresponding to the first business processing request. The specific method can be set according to actual conditions and will not be repeated here.

[0044] In step S606, based on the computing power allocation strategy, the device side that processes the first business processing request is determined, and the first business processing request is processed based on the determined device side.

[0045] During implementation, after determining the computing power allocation strategy corresponding to the first business processing request in the above manner, the device side for processing the first business processing request can be determined based on the computing power allocation strategy. Then, the determined device side can be used to process the first business processing request. Specifically, for example, the determined device side can be used to obtain corresponding business data, or the determined device side can be used to perform risk prevention and control processing on a certain user or certain data, or the determined device side can be used to initiate preset identity recognition rules to perform identity recognition processing on the current user, etc. The specific setting can be based on actual conditions, and the embodiments of this specification do not limit this.

[0046] An embodiment of the present specification provides a data processing method based on an end-edge cloud scheduling system. By obtaining a first business processing request during the execution of a target business, a computing power allocation strategy for the terminal side and the edge server side corresponding to the first business processing request can be determined. The computing power allocation strategy is determined based on the number of business processing requests processed by the terminal side, the number of business processing requests processed by the edge server side, and the trusted release rate of the business processing requests corresponding to the terminal side and the edge server side, under the computing power constraint condition and / or business time consumption condition on the edge server side. Finally, based on the computing power allocation strategy, a device side for processing the first business processing request can be determined, and the first business processing request is processed based on the determined device side. In this way, by determining the computing power allocation strategy for the terminal side and the edge server side corresponding to the first business processing request based on the number of business processing requests processed by the terminal side, the number of business processing requests processed by the edge server side, and the trusted release rate of the business processing requests corresponding to the terminal side and the edge server side, under the computing power constraint condition and / or business time consumption condition on the edge server side, the computing power allocation strategy for the terminal side and the edge server side corresponding to the first business processing request can be intelligently allocated to the execution device (i.e., computing power) for the first business processing request, thereby maximizing business value and improving the stability of the end-edge cloud scheduling system.

[0047] In practical applications, there are various specific processing methods for the above step S604. An optional processing method is provided below, as shown in FIG7 , which may specifically include the processing of the following steps S60402 to S60406.

[0048] In step S60402, prediction information of business processing requests received within a preset time period is generated based on a preset traffic prediction model. The preset time period is the time period after the generation time of the above prediction information, and the preset time period includes the acquisition time of the first business processing request.

[0049] Among them, the traffic prediction model can be a model used to predict the business processing requests (i.e., traffic) received or generated at a certain moment or time period (i.e., time series data prediction). The traffic prediction model can be constructed through a neural network, or through a Transformer module, or through a specified algorithm (such as naive prediction method, simple average method, moving average method, weighted moving average algorithm, simple exponential smoothing method, Hall characteristic line trend method, Holt-Winters method, etc.) or a specified model (such as autoregressive model, moving average model, autoregressive moving average model, etc.), etc. It can be set specifically according to actual conditions, and the embodiments of this specification do not limit this.

[0050] In practice, sufficient sampling samples can be used to obtain better results. When linear programming problems are applied on a large scale, the algorithm can be optimized by variable estimation to make the results more robust and the effect better. In actual applications, the reception of business processing requests is random, which is consistent with the situation of the random arrival model. In addition to the random arrival model, it can also be implemented through another model, namely the adversarial arrival model. The adversarial arrival model assumes that there is such an adversary: ​​he knows the strategy of the algorithm and generates a poor data sequence based on it. The algorithm corresponding to the adversarial arrival model needs to try to obtain more benefits in the adversarial environment. As shown in Figure 8, in actual business scenarios, the data distribution of received business processing requests will neither be as ideal as the random arrival model nor as bad as the adversarial arrival model. Instead, it is a subtle combination with a certain degree of randomness. However, there may be drift in the data distribution at different times. The randomness is reflected in the two variables in the above expression (1): the total number of business processing requests in a certain time period in the future, including the number of business processing requests for each device model and scenario; and the timeout rate of each scenario in a certain time period in the future.

[0051] Based on the above content, in order to solve the above expression (1), it is necessary to know how many business processing requests will be received at each moment in the future, and it is necessary to know the data distribution of the above business processing requests received at each moment. To this end, a traffic prediction model can be set to perform traffic prediction. A corresponding traffic prediction model can be constructed based on a preset algorithm, model, network or Transformer module. Historical data can be obtained as a training sample. The training sample and the preset loss function can be used to train the traffic prediction model to obtain a trained traffic prediction model. Then, the trained traffic prediction model can be used to generate prediction information of the business processing requests received within a preset time period, that is, to generate time series data of the business processing requests within the preset time period.

[0052] In step S60404, based on the generated prediction information of the business processing requests received within the preset time period, the number of business processing requests processed by the terminal side and the number of business processing requests processed by the edge server side are determined.

[0053] In implementation, the time series data of the business processing requests in the prediction information of the business processing requests received within the preset time period can be statistically generated to determine the number of business processing requests processed on the terminal side and the number of business processing requests processed on the edge server side.

[0054] In step S60406, based on the number of business processing requests processed on the terminal side, the number of business processing requests processed on the edge server side, and the trusted release rate of the business processing requests corresponding to the terminal side and the edge server side, the computing power constraint conditions and / or business time consumption conditions on the edge server side are used to determine the computing power allocation strategy on the terminal side and the edge server side corresponding to the first business processing request.

[0055] In implementation, the computing power constraint condition and / or the business time consumption condition on the edge server side can be referred to the expression in the aforementioned step S606, which will not be repeated here. After obtaining the number of business processing requests processed by the terminal side and the number of business processing requests processed by the edge server side in the above manner, the number of business processing requests processed by the terminal side, the number of business processing requests processed by the edge server side, and the trusted release rate of the business processing requests corresponding to the terminal side and the edge server side can be input into the above expression (1), and the above expression (1) is solved under the computing power constraint condition and / or the business time consumption condition on the edge server side. The final result is the computing power allocation strategy on the terminal side and the edge server side corresponding to the first business processing request.

[0056] In actual applications, the above-mentioned traffic prediction model is also an autoregressive differential moving average ARIMA model. Through the ARIMA model, prediction information of business processing requests received within a preset time period can be generated. The preset time period is the time period after the generation time of the above-mentioned prediction information, and the preset time period includes the acquisition time of the first business processing request.

[0057] In actual applications, there are various specific processing methods for the above-mentioned step S60406. The following is another optional processing method, which may specifically include the following: based on the number of business processing requests processed on the terminal side, the number of business processing requests processed on the edge server side, and the trusted release rate of the business processing requests corresponding to the terminal side and the edge server side, through a preset dual parameter, under the computing power constraint condition and / or business time consumption condition of the edge server side, determine the computing power allocation strategy of the terminal side and the edge server side corresponding to the first business processing request. The dual parameter is used to characterize the perceived speed of the data distribution of the business processing requests received within a preset time period in the prediction information.

[0058] In practice, the data distribution of received business processing requests can also be quickly perceived by reducing the prediction cycle. This rapid perception is reflected in the dual parameter of the above expression. If the hourly time series data is slow, it can be set to tend to the minute level, or even updated in real time. At the same time, the requirements for resources and system link robustness will increase dramatically. The most extreme prediction cycle is to update the dual parameter in real time, which corresponds to solving online optimization problems in engineering. The above dual parameter can be seen in the following expression: λ l+1 =max{0,λ l -η l d l}

[0059] Wherein, λ represents the dual parameter, l represents time, and ηd represents the direction of the dual parameter update (or update step size). After obtaining the number of business processing requests processed by the terminal side and the number of business processing requests processed by the edge server side in the above manner, the number of business processing requests processed by the terminal side, the number of business processing requests processed by the edge server side, and the trusted release rate of the business processing requests corresponding to the terminal side and the edge server side can be input into the above expression (1). Under the computing power constraint condition and / or business time consumption condition on the edge server side, the above expression (1) is solved by the dual parameter. The final result is the computing power allocation strategy on the terminal side and the edge server side corresponding to the first business processing request.

[0060] In practical applications, there are various specific processing methods for the above step S604. An optional processing method is provided below, as shown in FIG9 , which may specifically include the processing of the following steps S60408 to S60412.

[0061] In step S60408, the data distribution of the business processing requests received within a preset time period is obtained.

[0062] In practice, as shown in Figure 10, during special periods such as promotions and during entrance traffic control, the daily DAU (Daily Active User) of some financial transaction programs fluctuates greatly. From the perspective of the algorithm's business performance, once the DAU fluctuates, the algorithm's estimation deviation will become larger. In essence, the online decision-making adopted is based on the assumption that business processing requests arrive randomly, that is, it is assumed that the data distribution between days is consistent. However, the above situation obviously does not meet the premise of the assumption that business processing requests arrive randomly, that is, it cannot be determined that the business processing requests arriving in the future are consistent with the data population distribution in the training data. To this end, the data distribution of a specified time period (such as August 1st to October 31st, November 1st to November 11th, November 12th to December 31st) can be pre-set based on historical data or expert experience. Based on the above-set data distribution, the data distribution of the preset time period (such as November 1st to November 11th) can be obtained, and the obtained data distribution can be used as the data distribution of the business processing requests received in the preset time period.

[0063] In step S60410, based on the above data distribution, the number of business processing requests processed on the terminal side and the number of business processing requests processed on the edge server side are determined.

[0064] In implementation, the data distribution of the business processing requests received within a preset time period may be analyzed and counted to determine the number of business processing requests processed on the terminal side and the number of business processing requests processed on the edge server side.

[0065] In step S60412, based on the number of business processing requests processed on the terminal side, the number of business processing requests processed on the edge server side, and the trusted release rate of the business processing requests corresponding to the terminal side and the edge server side, under the computing power constraint conditions and / or business time consumption conditions on the edge server side, the computing power allocation strategy of the terminal side and the edge server side corresponding to the first business processing request is determined.

[0066] In implementation, the computing power constraint condition and / or the business time consumption condition on the edge server side can be referred to the expression in the aforementioned step S604, which will not be repeated here. After obtaining the number of business processing requests processed by the terminal side and the number of business processing requests processed by the edge server side in the above manner, the number of business processing requests processed by the terminal side, the number of business processing requests processed by the edge server side, and the trusted release rate of the business processing requests corresponding to the terminal side and the edge server side can be input into the above expression (1), and the above expression (1) is solved under the computing power constraint condition and / or the business time consumption condition on the edge server side. The final result obtained is the computing power allocation strategy on the terminal side and the edge server side corresponding to the first business processing request.

[0067] In actual applications, there are various specific processing methods for the above-mentioned step S60408. An optional processing method is provided below, which may specifically include the following: obtaining the data distribution of business processing requests received within a preset time period based on an uncertainty optimization algorithm, the uncertainty optimization algorithm includes a random programming algorithm and / or a robust optimization algorithm, and the random programming algorithm includes an optimization algorithm based on a chance constraint programming model or an optimization algorithm based on a related chance constraint programming model.

[0068] In practice, based on the above, since it is uncertain whether the business processing requests arriving in the future will be consistent with the data group distribution in the training data, including the above-mentioned DAU fluctuations, such uncertainty is reflected in two points: the fluctuation of DAU leads to a deviation in the overall traffic estimation; and the use of data from the previous time period to estimate the time consumption rate also has deviations. To this end, an uncertainty optimization algorithm can be used to ensure the robustness of the solution of the above expression (1). Specifically, the data distribution of business processing requests received within a preset time period is obtained through the uncertainty optimization algorithm. This can be performed according to the calculation process of the uncertainty optimization algorithm, which will not be repeated here.

[0069] In practical applications, there are various specific processing methods for the above step S604. An optional processing method is provided below, as shown in FIG11 , which may specifically include the processing of the following steps S60414 and S60416.

[0070] In step S60414, information on changes in the number of business processing requests processed by the terminal side and the number of business processing requests processed by the edge server side over time are obtained.

[0071] In implementation, the above-mentioned algorithms are based on the perspective of operational optimization. The simplest idea is to maximize the business value under certain conditions. In addition, the above expression (1) can also be solved from the perspective of random network optimization. Random network optimization is essentially to bring additional gains when the network system is running stably (at time t). This gain in random network optimization is often the overall data transmission efficiency, etc. Based on the above content, the above expression (1) and the corresponding computing power constraints and / or business time-consuming conditions on the edge server side can be adjusted as follows: x ij (t)∈[0,1]

[0072] Among them, t is a time slot, T represents the deadline, value ij (t) represents the service value of time slot t. ij＝0(t) represents the ratio of the number of business processing requests processed by the terminal side to the total computing power of the terminal side over time, x ij＝1 (t) represents the ratio of the number of business processing requests processed by the edge server over time to the total computing power of the edge server over time. Based on the above, the computing power constraint and business time constraint on the edge server are respectively: on average, the number of business processing requests offloaded to edge server j does not exceed its capacity, that is, congestion does not occur; on average, the time it takes to offload business processing requests to edge server j does not exceed T.

[0073] In order to solve the above expression to obtain the computing power allocation strategy on the terminal side and the edge server side, historical data can be obtained, and the time series data in the historical data can be used to obtain the change information of the number of business processing requests processed by the terminal side and the change information of the number of business processing requests processed by the edge server side over time. Alternatively, the change information of the number of business processing requests processed by the terminal side and the change information of the number of business processing requests processed by the edge server side over time can be determined through expert experience. The specific settings can be made according to actual conditions and will not be repeated here.

[0074] In step S60416, based on the information on changes in the number of business processing requests processed on the terminal side over time, the information on changes in the number of business processing requests processed on the edge server side over time, and the information on changes in the trusted release rate of the business processing requests corresponding to the terminal side and the edge server side over time, under the computing power constraints and / or business time consumption conditions on the edge server side, the computing power allocation strategy on the terminal side and the edge server side corresponding to the first business processing request is determined.

[0075] In implementation, the above expression can be solved based on the information on the change in the number of business processing requests processed on the terminal side and the change in the number of business processing requests processed on the edge server side over time to obtain the computing power allocation strategy on the terminal side and the edge server side, thereby obtaining the computing power allocation strategy on the terminal side and the edge server side corresponding to the first business processing request.

[0076] In practical applications, there are various specific processing methods for the above step S60416. An optional processing method is provided below, which may specifically include the processing of the following steps A2 and A4.

[0077] In step A2, for any edge server side, a first virtual queue and a second virtual queue are constructed, the first virtual queue is a queue constructed based on the computing power information of processing business processing requests on the edge server side, the change information of the number of business processing requests processed by the terminal side over time, the change information of the number of business processing requests processed by the edge server side over time, and the corresponding total computing power information of the edge server side, and the second virtual queue is a queue constructed based on the change information of the trusted release rate of the business processing requests corresponding to the terminal side and the edge server side over time, the change information of the number of business processing requests processed by the terminal side over time, the change information of the number of business processing requests processed by the edge server side over time, and the trusted release rate of the business processing requests corresponding to the terminal side and the edge server side.

[0078] In practice, two sets of virtual queues can be constructed, namely the first virtual queue V l (t) and the second virtual queue Q i (t), where V l (t) and Q i (t) are greater than 0, if V l (t) and / or Q i If (t) is negative, set it equal to 0. The two sets of virtual queues can be constructed as follows.

[0079] For any edge server side j:

[0080] In step A4, based on the first virtual queue and the second virtual queue, a computing power allocation strategy on the terminal side and the edge server side corresponding to the first service processing request is determined.

[0081] In implementation, the above expression can be solved based on the time-varying information of the number of business processing requests processed on the terminal side and the time-varying information of the number of business processing requests processed on the edge server side, as well as the first virtual queue and the second virtual queue to obtain the computing power allocation strategy on the terminal side and the edge server side, thereby obtaining the computing power allocation strategy on the terminal side and the edge server side corresponding to the first business processing request.

[0082] In actual applications, there are various specific processing methods for the above-mentioned step A4. The following is an optional processing method, which may specifically include the following: based on the first virtual queue and the second virtual queue, the computing power allocation strategy on the terminal side and the edge server side corresponding to the first business processing request is determined through a preset minimization drift plus penalty optimization algorithm.

[0083] In practice, solving the above optimization problem is essentially equivalent to solving the following min-drift-plus-penlaty optimization problem:

[0084] Among them, P is a hyperparameter, and its value can be set according to the actual situation.

[0085] in,

[0086] Using gradient descent, we have

[0087] Based on the first virtual queue and the second virtual queue, as well as the above expressions, the above expressions can be solved by a preset drift minimization plus penalty optimization algorithm to obtain the computing power allocation strategy on the terminal side and the edge server side, thereby obtaining the computing power allocation strategy on the terminal side and the edge server side corresponding to the first business processing request.

[0088] An embodiment of the present specification provides a data processing method based on an end-edge cloud scheduling system. By obtaining a first business processing request during the execution of a target business, a computing power allocation strategy for the terminal side and the edge server side corresponding to the first business processing request can be determined. The computing power allocation strategy is determined based on the number of business processing requests processed by the terminal side, the number of business processing requests processed by the edge server side, and the trusted release rate of the business processing requests corresponding to the terminal side and the edge server side, under the computing power constraint condition and / or business time consumption condition on the edge server side. Finally, based on the computing power allocation strategy, a device side for processing the first business processing request can be determined, and the first business processing request is processed based on the determined device side. In this way, by determining the computing power allocation strategy for the terminal side and the edge server side corresponding to the first business processing request based on the number of business processing requests processed by the terminal side, the number of business processing requests processed by the edge server side, and the trusted release rate of the business processing requests corresponding to the terminal side and the edge server side, under the computing power constraint condition and / or business time consumption condition on the edge server side, the computing power allocation strategy for the terminal side and the edge server side corresponding to the first business processing request can be intelligently allocated to the execution device (i.e., computing power) for the first business processing request, thereby maximizing business value and improving the stability of the end-edge cloud scheduling system.

[0089] In addition, three solutions are proposed for the variable uncertainty in the edge-cloud scheduling system: for situations where more accurate estimation can be solved, an accurate traffic prediction model is adopted to reduce the solution cycle of expression (1), making the traffic estimation more accurate; for situations where the data distribution can be mastered, uncertain optimization is directly introduced to solve expression (1); for situations where data distribution cannot be predicted, from the perspective of network stability, it is converted into stability in the time dimension, and the Ryaplov optimization algorithm is used to solve it.

[0090] The above is a data processing method based on the end-edge cloud scheduling system provided in an embodiment of this specification. Based on the same idea, an embodiment of this specification also provides a data processing device based on the end-edge cloud scheduling system, as shown in Figure 12.

[0091] The data processing device based on the end-edge cloud scheduling system includes: a request acquisition module 1201, a computing power allocation module 1202 and a data processing module 1203, wherein: the request acquisition module 1201 obtains the first business processing request in the process of executing the target business; the computing power allocation module 1202 determines the computing power allocation strategy of the terminal side and the edge server side corresponding to the first business processing request, and the computing power allocation strategy is determined based on the computing power constraint condition and / or business time consumption condition of the edge server side, the number of business processing requests processed by the terminal side, the number of business processing requests processed by the edge server side, and the trusted release rate of the business processing requests corresponding to the terminal side and the edge server side; the data processing module 1203 determines the device side for processing the first business processing request based on the computing power allocation strategy, and processes the first business processing request based on the determined device side.

[0092] In an embodiment of the present specification, the computing power allocation module 1202 includes: a prediction unit, which generates prediction information of business processing requests received within a preset time period based on a preset traffic prediction model, wherein the preset time period is a time period after the generation time of the prediction information, and the preset time period includes the acquisition time of the first business processing request; a first quantity determination unit, which determines the number of business processing requests processed on the terminal side and the number of business processing requests processed on the edge server side based on the generated prediction information of the business processing requests received within the preset time period; the first computing power allocation unit, which determines the computing power allocation strategy on the terminal side and the edge server side corresponding to the first business processing request under the computing power constraint condition and / or business time consumption condition of the edge server side based on the number of business processing requests processed on the terminal side, the number of business processing requests processed on the edge server side, and the trusted release rate of the business processing requests corresponding to the terminal side and the edge server side.

[0093] In an embodiment of the present specification, the traffic prediction model is an ARIMA model. The first computing power allocation unit determines the computing power allocation strategy of the terminal side and the edge server side corresponding to the first business processing request based on the number of business processing requests processed on the terminal side, the number of business processing requests processed on the edge server side, and the trusted release rate of the business processing requests corresponding to the terminal side and the edge server side, through preset dual parameters, under the computing power constraint conditions and / or business time consumption conditions on the edge server side. The dual parameters are used to characterize the perceived speed of the data distribution of the business processing requests received within a preset time period in the prediction information.

[0094] In an embodiment of the present specification, the computing power allocation module 1202 includes: a data distribution acquisition unit, which acquires the data distribution of business processing requests received within a preset time period; a second quantity determination unit, which determines the number of business processing requests processed on the terminal side and the number of business processing requests processed on the edge server side based on the data distribution; the second computing power allocation unit, which determines the computing power allocation strategy on the terminal side and the edge server side corresponding to the first business processing request based on the number of business processing requests processed on the terminal side, the number of business processing requests processed on the edge server side, and the trusted release rate of the business processing requests corresponding to the terminal side and the edge server side, under the computing power constraint condition and / or business time consumption condition on the edge server side.

[0095] In an embodiment of the present specification, the data distribution acquisition unit acquires the data distribution of the business processing requests received within a preset time period based on an uncertainty optimization algorithm, the uncertainty optimization algorithm includes a random programming algorithm and / or a robust optimization algorithm, and the random programming algorithm includes an optimization algorithm based on a chance-constrained programming model or an optimization algorithm based on a related chance-constrained programming model.

[0096] In an embodiment of the present specification, the computing power allocation module 1202 includes: an information acquisition unit, which acquires information on changes in the number of business processing requests processed on the terminal side and the number of business processing requests processed on the edge server side over time; a third computing power allocation unit, which determines the computing power allocation strategy on the terminal side and the edge server side corresponding to the first business processing request based on the information on changes in the number of business processing requests processed on the terminal side and the number of business processing requests processed on the edge server side over time, and the information on changes in the trusted release rate of the business processing requests corresponding to the terminal side and the edge server side over time, under the computing power constraint conditions and / or business time consumption conditions on the edge server side.

[0097] In an embodiment of the present specification, the third computing power allocation unit constructs a first virtual queue and a second virtual queue for any edge server side, wherein the first virtual queue is a queue constructed based on computing power information for processing business processing requests on the edge server side, information on changes in the number of business processing requests processed by the terminal side over time, information on changes in the number of business processing requests processed by the edge server side over time, and total computing power information corresponding to the edge server side; the second virtual queue is a queue constructed based on information on changes in the trusted release rate of business processing requests corresponding to the terminal side and the edge server side over time, information on changes in the number of business processing requests processed by the terminal side over time, information on changes in the number of business processing requests processed by the edge server side over time, and trusted release rate of business processing requests corresponding to the terminal side and the edge server side; based on the first virtual queue and the second virtual queue, a computing power allocation strategy for the terminal side and the edge server side corresponding to the first business processing request is determined.

[0098] In an embodiment of this specification, the third computing power allocation unit determines the computing power allocation strategy on the terminal side and the edge server side corresponding to the first business processing request based on the first virtual queue and the second virtual queue through a preset drift minimization plus penalty optimization algorithm.

[0099] An embodiment of the present specification provides a data processing device based on an end-edge cloud scheduling system. By obtaining a first business processing request during the execution of a target business, a computing power allocation strategy for the terminal side and the edge server side corresponding to the first business processing request can be determined. The computing power allocation strategy is determined based on the number of business processing requests processed by the terminal side, the number of business processing requests processed by the edge server side, and the trusted release rate of the business processing requests corresponding to the terminal side and the edge server side, under the computing power constraint condition and / or business time condition on the edge server side. Finally, based on the computing power allocation strategy, a device side for processing the first business processing request can be determined, and the first business processing request can be processed based on the determined device side. In this way, by determining the computing power allocation strategy for the terminal side and the edge server side corresponding to the first business processing request based on the number of business processing requests processed by the terminal side, the number of business processing requests processed by the edge server side, and the trusted release rate of the business processing requests corresponding to the terminal side and the edge server side, under the computing power constraint condition and / or business time condition on the edge server side, the computing power allocation strategy for the terminal side and the edge server side corresponding to the first business processing request can be intelligently allocated to the execution device (i.e., computing power) for the first business processing request, thereby maximizing business value and improving the stability of the end-edge cloud scheduling system.

[0100] In addition, three solutions are proposed for the variable uncertainty in the edge-cloud scheduling system: for situations where more accurate estimation can be solved, an accurate traffic prediction model is adopted to reduce the solution cycle of expression (1), making the traffic estimation more accurate; for situations where the data distribution can be mastered, uncertain optimization is directly introduced to solve expression (1); for situations where data distribution cannot be predicted, from the perspective of network stability, it is converted into stability in the time dimension, and the Ryaplov optimization algorithm is used to solve it.

[0101] The above is a data processing device based on the end-edge cloud scheduling system provided in the embodiment of this specification. Based on the same idea, the embodiment of this specification also provides a data processing device based on the end-edge cloud scheduling system, as shown in Figure 13.

[0102] The data processing device based on the end-edge-cloud scheduling system can provide a terminal device or server, etc. for the above-mentioned embodiment.

[0103] The data processing device based on the edge-cloud scheduling system may vary greatly due to different configurations or performances, and may include one or more processors 1301 and memory 1302. The memory 1302 may store one or more applications or data. The memory 1302 may be a temporary storage or a persistent storage. The application stored in the memory 1302 may include one or more modules (not shown in the figure), each of which may include a series of computer-executable instructions for the data processing device based on the edge-cloud scheduling system. Furthermore, the processor 1301 may be configured to communicate with the memory 1302 to execute a series of computer-executable instructions in the memory 1302 on the data processing device based on the edge-cloud scheduling system. The data processing device based on the edge-cloud scheduling system may also include one or more power supplies 1303, one or more wired or wireless network interfaces 1304, one or more input and output interfaces 1305, and one or more keyboards 1306.

[0104] Specifically in this embodiment, a data processing device based on an end-edge cloud scheduling system includes a memory, and one or more programs, wherein one or more programs are stored in the memory, and one or more programs may include one or more modules, and each module may include a series of computer-executable instructions for the data processing device based on the end-edge cloud scheduling system, and is configured to be executed by one or more processors to include the following computer-executable instructions: obtaining a first business processing request in the process of executing a target business; determining a computing power allocation strategy for the terminal side and the edge server side corresponding to the first business processing request, the computing power allocation strategy being determined based on the computing power constraint condition and / or business time condition of the edge server side, the number of business processing requests processed by the terminal side, the number of business processing requests processed by the edge server side, and the trusted release rate of the business processing requests corresponding to the terminal side and the edge server side; determining the device side for processing the first business processing request based on the computing power allocation strategy, and processing the first business processing request based on the determined device side.

[0105] Each embodiment in this specification is described in a progressive manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences from other embodiments. In particular, the data processing device embodiment based on the edge-cloud scheduling system is basically similar to the method embodiment, so the description is relatively simple. For relevant parts, refer to the partial description of the method embodiment.

[0106] An embodiment of the present specification provides a data processing device based on an end-edge cloud scheduling system. By obtaining a first business processing request during the execution of a target business, a computing power allocation strategy for the terminal side and the edge server side corresponding to the first business processing request can be determined. The computing power allocation strategy is determined based on the number of business processing requests processed by the terminal side, the number of business processing requests processed by the edge server side, and the trusted release rate of the business processing requests corresponding to the terminal side and the edge server side, under the computing power constraint condition and / or business time condition on the edge server side. Finally, based on the computing power allocation strategy, a device side for processing the first business processing request can be determined, and the first business processing request can be processed based on the determined device side. In this way, by determining the computing power allocation strategy for the terminal side and the edge server side corresponding to the first business processing request based on the number of business processing requests processed by the terminal side, the number of business processing requests processed by the edge server side, and the trusted release rate of the business processing requests corresponding to the terminal side and the edge server side, under the computing power constraint condition and / or business time condition on the edge server side, the computing power allocation strategy for the terminal side and the edge server side corresponding to the first business processing request can be intelligently allocated to the execution device (i.e., computing power) for the first business processing request, thereby maximizing business value and improving the stability of the end-edge cloud scheduling system.

[0107] Furthermore, based on the methods shown in Figures 6 to 11 above, one or more embodiments of the present specification also provide a storage medium for storing computer-executable instruction information. In a specific embodiment, the storage medium may be a USB flash drive, an optical disk, a hard disk, etc. When the computer-executable instruction information stored in the storage medium is executed by the processor, it can implement the following process: obtaining a first business processing request in the process of executing the target business; determining the computing power allocation strategy of the terminal side and the edge server side corresponding to the first business processing request, the computing power allocation strategy is determined based on the computing power constraint conditions and / or business time consumption conditions of the edge server side, the number of business processing requests processed by the terminal side, the number of business processing requests processed by the edge server side, and the trusted release rate of the business processing requests corresponding to the terminal side and the edge server side; based on the computing power allocation strategy, determining the device side for processing the first business processing request, and processing the first business processing request based on the determined device side.

[0108] The various embodiments in this specification are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other. Each embodiment focuses on the differences from the other embodiments. In particular, the aforementioned storage medium embodiment is generally similar to the method embodiment, so its description is relatively simple. For relevant portions, refer to the description of the method embodiment.

[0109] An embodiment of the present specification provides a storage medium that obtains a first business processing request during the execution of a target business, and then determines a computing power allocation strategy for a terminal side and an edge server side corresponding to the first business processing request. The computing power allocation strategy is determined based on the number of business processing requests processed by the terminal side, the number of business processing requests processed by the edge server side, and the trusted release rate of the business processing requests corresponding to the terminal side and the edge server side, under the computing power constraint condition and / or business time condition on the edge server side. Finally, based on the computing power allocation strategy, a device side for processing the first business processing request can be determined, and the first business processing request can be processed based on the determined device side. In this way, by determining the computing power allocation strategy for the terminal side and the edge server side corresponding to the first business processing request based on the number of business processing requests processed by the terminal side, the number of business processing requests processed by the edge server side, and the trusted release rate of the business processing requests corresponding to the terminal side and the edge server side, under the computing power constraint condition and / or business time condition on the edge server side, the computing power allocation strategy for the terminal side and the edge server side corresponding to the first business processing request can be intelligently allocated to the execution device (i.e., computing power) for the first business processing request, thereby maximizing business value and improving the stability of the end-edge cloud scheduling system.

[0110] The foregoing description of this specification describes specific embodiments. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0111] In the 1990s, technological improvements could be clearly distinguished as either hardware improvements (for example, improvements to circuit structures such as diodes, transistors, and switches) or software improvements (improvements to process flows). However, with the advancement of technology, many process flow improvements today can now be considered direct improvements to hardware circuit structures. Designers almost always create the corresponding hardware circuit structure by programming the improved process flow into the hardware circuit. Therefore, it cannot be said that a process flow improvement cannot be implemented using hardware modules. For example, a programmable logic device (PLD), such as a field programmable gate array (FPGA), is an integrated circuit whose logical function is determined by user programming. Designers can "integrate" a digital system on a PLD by programming it themselves, without having to hire a chip manufacturer to design and manufacture a dedicated integrated circuit chip. Moreover, nowadays, instead of manually fabricating integrated circuit chips, this programming is mostly done using "logic compiler" software. This is similar to the software compiler used when developing programs. Before compilation, the original code must also be written in a specific programming language, called a hardware description language (HDL). There is not just one HDL, but many, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art will also understand that by simply programming the method flow in one of these hardware description languages ​​and then programming it into an integrated circuit, a hardware circuit that implements the logic method flow can be easily obtained.

[0112] The controller can be implemented in any suitable manner. For example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also know that in addition to implementing the controller in a purely computer-readable program code format, the controller can be implemented in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, such a controller can be considered a hardware component, and the devices included therein for implementing various functions can also be considered as structures within the hardware component. Or even, the devices for implementing various functions can be considered as both software modules that implement the method and structures within the hardware component.

[0113] The systems, devices, modules, or units described in the above embodiments may be implemented by computer chips or entities, or by products having certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0114] For the convenience of description, the above devices are described as being divided into various units according to their functions. Of course, when implementing one or more embodiments of this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0115] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, one or more embodiments of this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, one or more embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0116] The embodiments of this specification are described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable serial and parallel device to produce a machine, so that the instructions executed by the processor of the computer or other programmable serial and parallel device produce a device for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0117] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable serial and parallel device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction device that implements the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0118] These computer program instructions may also be loaded onto a computer or other programmable fraud case serial and parallel device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0119] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0120] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0121] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0122] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0123] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Thus, one or more embodiments of this specification may take the form of a fully hardware embodiment, a fully software embodiment, or an embodiment combining software and hardware. Furthermore, one or more embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0124] One or more embodiments of this specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. One or more embodiments of this specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communications network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0125] The various embodiments in this specification are described in a progressive manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiments are generally similar to the method embodiments, so the description is relatively simple. For relevant parts, refer to the description of the method embodiments.

[0126] The foregoing description is merely an example of the present invention and is not intended to limit this document. Various modifications and variations are possible within the scope of this specification. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of this specification are intended to be within the scope of the claims of this specification.

Claims

1. A data processing method based on a device-edge-cloud scheduling system, the method comprising: Obtaining a first business processing request in the process of executing a target business; Determining a computing power allocation strategy on the terminal side and the edge server side corresponding to the first business processing request, where the computing power allocation strategy is determined based on a number of business processing requests processed on the terminal side, a number of business processing requests processed on the edge server side, and trusted release rates of the business processing requests corresponding to the terminal side and the edge server side, under a computing power constraint condition and / or a business time consumption condition on the edge server side; Based on the computing power allocation strategy, a device side for processing the first business processing request is determined, and the first business processing request is processed based on the determined device side.

2. The method according to claim 1, wherein determining a computing power allocation strategy between the terminal side and the edge server side corresponding to the first service processing request comprises: Generate prediction information of business processing requests received within a preset time period based on a preset traffic prediction model, wherein the preset time period is a time period after the moment when the prediction information is generated, and the preset time period includes the moment when the first business processing request is obtained; Determining the number of business processing requests processed by the terminal side and the number of business processing requests processed by the edge server side based on the generated prediction information of the business processing requests received within the preset time period; Based on the number of business processing requests processed on the terminal side, the number of business processing requests processed on the edge server side, and the trusted release rate of the business processing requests corresponding to the terminal side and the edge server side, under the computing power constraint conditions and / or business time consumption conditions on the edge server side, determine the computing power allocation strategy on the terminal side and the edge server side corresponding to the first business processing request.

3. The method according to claim 2, wherein the traffic prediction model is an ARIMA model, and the determining of the computing power allocation strategy between the terminal side and the edge server side corresponding to the first business processing request based on the number of business processing requests processed on the terminal side, the number of business processing requests processed on the edge server side, and the trusted release rate of the business processing requests corresponding to the terminal side and the edge server side, under the computing power constraint condition and / or business time consumption condition on the edge server side, comprises: Based on the number of business processing requests processed on the terminal side, the number of business processing requests processed on the edge server side, and the trusted release rate of the business processing requests corresponding to the terminal side and the edge server side, the computing power allocation strategy on the terminal side and the edge server side corresponding to the first business processing request is determined under the computing power constraint condition and / or business time consumption condition on the edge server side through a preset dual parameter. The dual parameter is used to characterize the perceived speed of the data distribution of the business processing requests received within a preset time period in the prediction information.

4. The method according to claim 1, wherein determining a computing power allocation strategy on the terminal side and the edge server side corresponding to the service processing request comprises: Obtaining data distribution of business processing requests received within a preset time period; Determining, based on the data distribution, the number of service processing requests processed on the terminal side and the number of service processing requests processed on the edge server side; Based on the number of business processing requests processed on the terminal side, the number of business processing requests processed on the edge server side, and the trusted release rate of the business processing requests corresponding to the terminal side and the edge server side, under the computing power constraint conditions and / or business time consumption conditions on the edge server side, determine the computing power allocation strategy on the terminal side and the edge server side corresponding to the first business processing request.

5. The method according to claim 4, wherein obtaining the data distribution of the service processing requests received within a preset time period comprises: The data distribution of business processing requests received within a preset time period is obtained based on an uncertainty optimization algorithm, wherein the uncertainty optimization algorithm includes a random programming algorithm and / or a robust optimization algorithm, and the random programming algorithm includes an optimization algorithm based on a chance-constrained programming model or an optimization algorithm based on a related chance-constrained programming model.

6. The method according to claim 1, wherein determining a computing power allocation strategy on the terminal side and the edge server side corresponding to the service processing request comprises: Obtaining information on changes in the number of business processing requests processed by the terminal side and the number of business processing requests processed by the edge server side over time; Based on the time-varying information of the number of business processing requests processed on the terminal side, the time-varying information of the number of business processing requests processed on the edge server side, and the time-varying information of the trusted release rate of the business processing requests corresponding to the terminal side and the edge server side, under the computing power constraint conditions and / or business time consumption conditions on the edge server side, the computing power allocation strategy of the terminal side and the edge server side corresponding to the first business processing request is determined.

7. The method according to claim 6, wherein the determining, based on information about changes over time in the number of business processing requests processed on the terminal side, information about changes over time in the number of business processing requests processed on the edge server side, and information about changes over time in the trusted release rates of business processing requests corresponding to the terminal side and the edge server side, and under computing power constraints and / or business time consumption conditions on the edge server side, a computing power allocation strategy for the terminal side and the edge server side corresponding to the first business processing request comprises: For any edge server side, a first virtual queue and a second virtual queue are constructed. The first virtual queue is a queue constructed based on computing power information for processing business processing requests on the edge server side, information on changes in the number of business processing requests processed by the terminal side over time, information on changes in the number of business processing requests processed by the edge server side over time, and information on the total computing power corresponding to the edge server side. The second virtual queue is a queue constructed based on information on changes in the trusted release rate of business processing requests corresponding to the terminal side and the edge server side over time, information on changes in the number of business processing requests processed by the terminal side over time, information on changes in the number of business processing requests processed by the edge server side over time, and trusted release rates of business processing requests corresponding to the terminal side and the edge server side. Based on the first virtual queue and the second virtual queue, a computing power allocation strategy on the terminal side and the edge server side corresponding to the first service processing request is determined.

8. The method according to claim 7, wherein determining, based on the first virtual queue and the second virtual queue, a computing power allocation strategy on the terminal side and the edge server side corresponding to the first service processing request comprises: Based on the first virtual queue and the second virtual queue, a computing power allocation strategy on the terminal side and the edge server side corresponding to the first service processing request is determined by a preset drift minimization plus penalty optimization algorithm.

9. A data processing device based on a device-edge-cloud scheduling system, the device comprising: A request acquisition module, which acquires the first business processing request in the process of executing the target business; a computing power allocation module, determining a computing power allocation strategy for the terminal side and the edge server side corresponding to the first business processing request, wherein the computing power allocation strategy is determined based on the number of business processing requests processed by the terminal side, the number of business processing requests processed by the edge server side, and the trusted release rate of the business processing requests corresponding to the terminal side and the edge server side, under the computing power constraint condition and / or business time consumption condition on the edge server side; The data processing module determines a device side for processing the first business processing request based on the computing power allocation strategy, and processes the first business processing request based on the determined device side.

10. A data processing device based on an edge-cloud scheduling system, the data processing device based on the edge-cloud scheduling system comprising: processor; as well as a memory arranged to store computer-executable instructions which, when executed, cause the processor to: Obtaining a first business processing request in the process of executing a target business; Determining a computing power allocation strategy on the terminal side and the edge server side corresponding to the first business processing request, where the computing power allocation strategy is determined based on a number of business processing requests processed on the terminal side, a number of business processing requests processed on the edge server side, and trusted release rates of the business processing requests corresponding to the terminal side and the edge server side, under a computing power constraint condition and / or a business time consumption condition on the edge server side; Based on the computing power allocation strategy, a device side for processing the first business processing request is determined, and the first business processing request is processed based on the determined device side.

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