Calculation network task scheduling method based on improved three-way decision and related device

Abstract

The invention discloses an improved three-way decision-making-based computing network task scheduling method and a related device, and relates to the technical field of computing power resources and computing network task scheduling, and the method comprises the following steps: obtaining computing network task scheduling data; classifying calculation network indexes according to the calculation network task scheduling data, and constructing a calculation network index system; based on the calculation network index system, dividing each platform and each task by adopting a three-way decision theory to obtain a platform division result and a task division result; and according to the platform division result and the task division result, matching a corresponding task for each platform to obtain a preliminary calculation network task scheduling result, and according to the load factor and the matching degree of each platform, optimizing the preliminary calculation network task scheduling result to obtain a final calculation network task scheduling result. The computing network resource utilization rate and the task scheduling efficiency can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of computing power resources and computing network task scheduling, and in particular to a computing network task scheduling method and related devices based on improved three-way decision making. Background Art

[0002] In recent years, the widespread application of digital technologies such as artificial intelligence, blockchain, and the Internet of Things has led to a surge in data in the whole society, rapidly increasing the demand for data storage, computing, transmission, and application. The rise of computing power has become a new productivity, and the computing network, as a new architecture, aims to optimize business paths through the coordinated scheduling of distributed computing nodes, and maximize the user experience, network resources, and computing resource utilization efficiency. However, as a major challenge, the scheduling of computing network tasks requires solving how to intelligently and reasonably allocate tasks to different computing and network resources to achieve the goals of improving service quality, shortening task completion time, and achieving load balancing. How to efficiently process data with complexity, large scale, and spatiotemporal dynamics has become the focus of today's attention. In the design of many computing network task scheduling algorithms, tasks and resources are generally divided based on two-branch decision-making. However, for data with information uncertainty, traditional two-branch decision-making is difficult to provide an effective solution strategy, task scheduling efficiency is low, and computing network resource utilization is not high.

[0003] For example, in life, when information is uncertain, people's choices often include acceptance, rejection, and delayed decision-making. This is the theory of three-way decision. When dealing with such uncertainty, the three-way decision theory can provide an effective solution. The three-way decision is a decision-making model based on human cognition. It means that in the actual decision-making process, people can make quick judgments immediately for things that they are fully confident to accept or reject; for those things that cannot be decided immediately, people tend to postpone their judgments on events, that is, delayed decisions. The three-way decision theory has been successfully applied in the fields of medicine, engineering, management, and information. With the continuous deepening of the theoretical research on computing power networks, the value of the three-way decision theory in computing network applications has gradually emerged. Drawing on the theory and results of the three-way decision, we can better study the scheduling of computing network tasks, improve the utilization of computing network resources, and improve the efficiency of task scheduling, meeting the needs of cross-cloud and cross-region computing network resource scheduling.

[0004] Based on this, how to provide a computing network task scheduling method based on improved three-way decision-making to improve the computing network resource utilization and task scheduling efficiency has become a technical problem that needs to be solved urgently in this field. Summary of the invention

[0005] The purpose of this application is to provide a computing network task scheduling method and related devices based on improved three-way decision-making, which can improve the computing network resource utilization and task scheduling efficiency.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a method for scheduling network computing tasks based on improved three-branch decision making, and the method for scheduling network computing tasks based on improved three-branch decision making includes:

[0008] Obtain computing network task scheduling data; the computing network task scheduling data includes computing power information and network information of each platform and computing power information and network information of each task.

[0009] According to the computing network task scheduling data, the computing network indicators are classified to construct a computing network indicator system; the computing network indicator system includes a number of computing network indicators, and the computing network indicators include computing power management indicators, computing power performance indicators, network capability indicators and network performance indicators. The computing power management indicators refer to indicators used to evaluate the management level of computing power providers, the computing power performance indicators refer to indicators used to evaluate the performance of computing power resources used by services, the network capability indicators refer to indicators used to evaluate the functions and responsiveness of the network, and the network performance indicators refer to indicators used to evaluate the quality and stability of network transmission.

[0010] Based on the computing network indicator system, the three-branch decision theory is used to divide each platform and each task respectively, and the platform division results and task division results are obtained; the platform division results include strong computing platform, strong network platform and balanced platform, the strong computing platform refers to a platform with computing power performance stronger than network performance, the strong network platform refers to a platform with computing power performance weaker than network performance, and the balanced platform refers to a platform with computing power performance equal to network performance; the task division results include strong computing tasks, strong network tasks and balanced tasks, the strong computing tasks refer to tasks with more computing power resource requirements than network resource requirements, the strong network tasks refer to tasks with computing power resource requirements weaker than network resource requirements, and the balanced tasks refer to tasks with computing power resource requirements equal to network resource requirements.

[0011] According to the platform division results and the task division results, the corresponding tasks are matched for each platform to obtain a preliminary network computing task scheduling result, and according to the full load rate and matching degree of each platform, the preliminary network computing task scheduling result is optimized to obtain the final network computing task scheduling result; the final network computing task scheduling result is used to represent the best scheduling result corresponding to allocating each task to the most suitable platform for execution.

[0012] In a second aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any one of the above-mentioned methods for scheduling computing network tasks based on improved three-way decision making.

[0013] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-mentioned methods for scheduling computing network tasks based on improved three-way decision making.

[0014] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned methods for scheduling computing network tasks based on improved three-way decision-making.

[0015] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0016] The present application provides a method and related device for scheduling computing network tasks based on improved three-branch decision-making. By dividing the computing network indicators into computing power management indicators, computing power performance indicators, network capability indicators and network performance indicators, a computing network indicator system is constructed. Then, based on the computing network indicator system, the three-branch decision theory is adopted to divide the platform into three categories: computing power platform, network power platform and balanced platform, and at the same time, the tasks are divided into three categories: computing power task, network power task and balanced task, so as to help assign different types of tasks to different types of platforms, realize targeted matching between different types of platforms and tasks, and thus complete the scheduling of computing network tasks more quickly and conveniently, thereby improving the efficiency of computing network task scheduling. In addition, after the present application classifies and preliminarily schedules the platforms and tasks based on the three-branch decision theory, it starts from the two dimensions of the full load rate and matching degree of the platform, and optimizes the preliminary computing network task scheduling results according to the full load rate and matching degree of each platform after the preliminary scheduling, so as to ensure that each task is assigned to the most suitable platform for execution, and obtain the best computing network task scheduling results, thereby effectively improving the utilization rate of computing network resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 An application environment diagram of a computing network task scheduling method based on improved three-way decision-making provided in an embodiment of the present application.

[0019] Figure 2 A flowchart of a method for scheduling computing network tasks based on improved three-way decision-making is provided for one embodiment of the present application.

[0020] Figure 3 A schematic diagram of the content of network computing task scheduling based on improved three-branch decision-making provided in an embodiment of the present application.

[0021] Figure 4 A schematic diagram of a computing network indicator system provided in accordance with an embodiment of the present application.

[0022] Figure 5 A flowchart of the classification of platforms and task types provided in one embodiment of the present application.

[0023] Figure 6 A flowchart of a task scheduling process provided in accordance with an embodiment of the present application.

[0024] Figure 7 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0026] For the computing resource scheduling and computing network task scheduling, the accurate implementation of computing network task scheduling can not only better adapt to the dynamic environment and task changes, but also achieve more refined task allocation and have better resource optimization potential. However, the existing computing network task scheduling methods are mainly based on binary decision classification, ignoring the complexity of task scheduling caused by information uncertainty, resulting in resource waste and insufficient decision-making flexibility. In addition, the existing task scheduling methods still have problems such as the lack of standardized indicator system construction, fuzzy task classification, and lack of scheduling flexibility, which are mainly concentrated in the following three aspects:

[0027] (1) In terms of computing network indicator construction: The existing computing network task scheduling method ignores the importance of unified indicator construction, and the measurement scheme has the problem of being simplistic and one-sided, that is, it does not fully cover the current mainstream computing power and network performance indicators, resulting in the lack of key data and unreasonable indicator construction.

[0028] (2) Network computing task classification: Existing network computing task scheduling methods are mostly based on the traditional binary decision method to achieve the classification of network computing tasks and complete the task scheduling on this basis. However, in actual applications, the classification of network computing tasks is usually not completely either-or, and there is a phenomenon of intermediate fuzzy task sets, which leads to the limitations of traditional binary decision methods in terms of the fine-grainedness of classification selection and resource utilization.

[0029] (3) Network computing task scheduling decision-making: The existing network computing task scheduling methods still have problems such as low matching accuracy and poor flexibility, which leads to uneven resource utilization and lack of scheduling flexibility, ultimately reducing the accuracy of network computing task scheduling.

[0030] Based on this, the technical purpose of this application is to propose a computing network task scheduling method based on improved three-way decision-making, which is applied to the technical fields of computing power resource scheduling and computing network task scheduling, aiming to solve the above-mentioned technical problems existing in the existing task scheduling methods and improve the computing network resource utilization and task scheduling efficiency.

[0031] In order to make the purpose, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0032] The present application embodiment provides a method for scheduling computing network tasks based on improved three-branch decision making, which can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the computing network task scheduling data to the server 104. After the server 104 receives the computing network task scheduling data, for the computing network task scheduling data, the server 104 classifies the computing network indicators according to the computing network task scheduling data, and constructs a computing network indicator system; based on the computing network indicator system, the three-branch decision theory is used to divide each platform and each task respectively, and the platform division result and the task division result are obtained; according to the platform division result and the task division result, the corresponding tasks are matched for each platform, and the preliminary computing network task scheduling result is obtained, and the preliminary computing network task scheduling result is optimized according to the full load rate and matching degree of each platform, and the final computing network task scheduling result is obtained. The server 104 can feedback the final computing network task scheduling result obtained to the terminal 102. In addition, in some embodiments, the network computing task scheduling method based on the improved three-branch decision can also be implemented independently by the server 104 or the terminal 102. For example, the terminal 102 can directly perform calculations and scheduling processing on the network computing task scheduling data, and output the network computing task scheduling results. The server 104 can also obtain the network computing task scheduling data from the data storage system, and perform calculations and scheduling processing on the network computing task scheduling data, and output the network computing task scheduling results.

[0033] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0034] In an exemplary embodiment, Figure 2 As shown, a method for scheduling network tasks based on improved three-branch decision making is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 Taking the server 104 in the example as an example, the following steps are included:

[0035] Step S1, obtaining computing network task scheduling data.

[0036] In this embodiment, the computing network task scheduling data includes computing power information and network information of each platform, as well as computing power information and network information of each task.

[0037] Step S2: classify the network computing indicators according to the network computing task scheduling data, and construct a network computing indicator system.

[0038] In this embodiment, the computing network indicator system includes several computing network indicators, which include computing power management indicators, computing power performance indicators, network capability indicators and network performance indicators. The computing power management indicators refer to indicators used to evaluate the management level of computing power providers, and the computing power performance indicators refer to indicators used to evaluate the performance of computing power resources used by the service. The network capability indicators refer to indicators used to evaluate the functions and responsiveness of the network, and the network performance indicators refer to indicators used to evaluate the quality and stability of network transmission.

[0039] In this embodiment, the computing power management indicators include service status, deployment location, cloud business type and cloud business area. The computing power performance indicators include turbo frequency, cache, video memory, CPU (Central Processing Unit) main frequency, number of CPU cores, number of CPU threads, average CPU load, CPU utilization, memory capacity, memory utilization, storage capacity, storage utilization, storage IOPS (Input / Output Operations Per Second, the number of read and write operations per second), GPU (Graphics Processing Unit) utilization, FPGA (Field Programmable Gate Array, Field Programmable Logic Gate Array) utilization, ASCI (Standard Universal Coding) utilization and disk IO (Input / Output, Input / Output) utilization. Network capability indicators include average confirmation time, average recovery time and reliability. Network performance indicators include bandwidth, packet loss rate, end-to-end delay, switching delay, switching success rate, jitter and bit rate.

[0040] Step S3: Based on the computing network indicator system, the three-branch decision theory is used to divide each platform and each task respectively to obtain the platform division results and the task division results.

[0041] In this embodiment, the platform division results include three categories: strong computing platform, strong network platform and balanced platform, among which, strong computing platform refers to a platform with computing power performance stronger than network performance, strong network platform refers to a platform with computing power performance weaker than network performance, and balanced platform refers to a platform with computing power performance equal to network performance. The task division results include three categories: strong computing tasks, strong network tasks and balanced tasks, among which, strong computing tasks refer to tasks with computing power resource requirements greater than network resource requirements, strong network tasks refer to tasks with computing power resource requirements weaker than network resource requirements, and balanced tasks refer to tasks with computing power resource requirements equal to network resource requirements.

[0042] In this embodiment, step S3 specifically includes the following steps:

[0043] Step S31: According to the computing network indicator system, the Euclidean distance calculation method is used to quantify each computing network indicator, and the computing power performance quantification value and network performance quantification value of each platform, as well as the computing power performance quantification value and network performance quantification value of each task are calculated.

[0044] Step S32: Determine the size relationship between the computing power performance and network performance corresponding to each platform based on the computing power performance quantification value and the network performance quantification value of each platform.

[0045] Step S33: Determine the size relationship between the computing power resource demand and the network resource demand corresponding to each task based on the computing power performance quantification value and the network performance quantification value of each task.

[0046] Step S34: According to the relationship between the computing power performance and network performance corresponding to each platform, and the relationship between the computing power resource requirements and network resource requirements corresponding to each task, the three-branch decision theory is used to divide each platform and each task respectively to obtain the platform division results and the task division results.

[0047] Step S4: According to the platform division result and the task division result, the corresponding tasks are matched for each platform to obtain a preliminary network computing task scheduling result, and according to the full load rate and matching degree of each platform, the preliminary network computing task scheduling result is optimized to obtain the final network computing task scheduling result. The final network computing task scheduling result is used to indicate the best scheduling result corresponding to allocating each task to the most suitable platform for execution.

[0048] In this embodiment, step S4 specifically includes the following steps:

[0049] Step S41: According to the platform division results and the task division results, the matching degree between each computing power platform and each computing power task, the matching degree between each balancing platform and each balancing task, and the matching degree between each network strength platform and each network strength task are calculated respectively to obtain the matching degree calculation results.

[0050] Step S42: According to the matching degree calculation result, the task with the highest matching degree is assigned to each platform to obtain a preliminary network computing task scheduling result.

[0051] Step S43: Calculate the full load rate of each platform based on the preliminary network computing task scheduling results.

[0052] Step S44: adopt a greedy strategy to determine whether the current full load rate and matching degree of each platform are the optimal solution. If it is the optimal solution, the preliminary network computing task scheduling result is output as the final network computing task scheduling result; otherwise, return to step S3 "based on the network computing indicator system, use the three-branch decision theory to divide each platform and each task respectively, and obtain the platform division result and the task division result", reset the threshold used by the three-branch decision theory, and re-divide each platform and each task until the current full load rate and matching degree of each platform are the optimal solution.

[0053] In order to make the technical solution of this embodiment clearer, the specific implementation process of the technical solution is described in detail below in the form of examples.

[0054] like Figure 3 As shown, the computing network task scheduling method based on improved three-branch decision-making in this embodiment mainly consists of three parts: computing network indicator construction, platform and task division, and task scheduling, and specifically includes the following steps.

[0055] Step 1: Construction of computing network indicators. Using step-by-step and modular construction technology, classify computing network indicators and build a standardized computing network indicator system, including computing power management indicators, computing power performance indicators, network capability indicators, and network performance indicators.

[0056] In this embodiment, the construction of computing network indicators adopts a step-by-step, modular construction technology, starting from the two perspectives of computing power information and network information, and on this basis, it is subdivided into computing power management indicators, computing power performance indicators, network capability indicators, and network performance indicators, with the characteristics of flexible combination, dynamic adjustment, and hierarchical systematization. Among them, the constructed computing network indicator system is as follows: Figure 4 The different types of network computing indicators are as follows:

[0057] Computing power management indicators: used to evaluate the relevant information of computing power providers to ensure the authenticity of computing power resources. They can be subdivided into service status, deployment location, cloud provider type, cloud provider region, etc.

[0058] Computing performance indicators: used to evaluate the computing resources used by the service, which can be subdivided into turbo frequency, cache, video memory, CPU information (main frequency, number of cores, number of threads, average load, utilization rate), memory information (capacity, utilization rate), storage information (capacity, utilization rate, IOPS), utilization rate (GPU, FPGA, ASCI, disk IO), etc.

[0059] Network capability indicators: used to evaluate the functionality and responsiveness of the network to ensure that the computing network can provide stable services under various circumstances. They can be subdivided into average confirmation time, average recovery time, reliability, etc.

[0060] Network performance indicators: used to evaluate the quality and stability of network transmission to ensure that data can be transmitted accurately and timely. They can be subdivided into bandwidth, packet loss rate, end-to-end delay, switching delay, switching success rate, jitter, rate / bit rate, etc.

[0061] Step 2: Platform and task division. Use three decisions to divide the existing platforms and tasks. Among them, the platform is divided into platforms with computing power performance stronger than network performance (computing power platform), platforms with computing power performance weaker than network performance (network power platform), and platforms with computing power performance equal to network performance (balanced platform); tasks are divided into tasks with computing power resource requirements greater than network resource requirements (computing power tasks), tasks with computing power resource requirements weaker than network resource requirements (network power tasks), and tasks with computing power resource requirements equal to network resource requirements (balanced tasks).

[0062] It should be noted that, in actual applications, balancing platforms include not only platforms whose computing power performance is equal to network performance, but also platforms whose computing power performance is close to network performance. Similarly, balancing tasks include not only tasks whose computing power resource requirements are equal to network resource requirements, but also tasks whose computing power resource requirements are close to network resource requirements. The "closeness" here means that the difference between computing power performance and network performance, and between computing power resource requirements and network resource requirements is less than a preset threshold, where the preset threshold can be set according to actual conditions.

[0063] In this embodiment, when dividing platforms and tasks, on the one hand, it is taken into account that the computing power and network performance of different platforms have different characteristics; on the other hand, it is taken into account that different tasks have different computing power and network performance, and there are fuzzy characteristics in classification. Therefore, the platforms and tasks are subdivided based on the constructed computing network indicator system. First, according to the Euclidean distance, the computing power and network performance of the platforms and tasks are quantified respectively; secondly, the platforms and tasks are classified through three-way decision and threshold design, that is, the platforms are subdivided into strong computing platforms, strong network platforms and balanced platforms, and the tasks are subdivided into strong computing tasks, strong network tasks and balanced tasks. Finally, the division of tasks and platforms is completed. The specific steps of platform and task division are as follows: Figure 5 As shown, the following steps are included.

[0064] (1) In the indicator quantification, it is known that the computing power platform P = {p1, p2, ..., p m}, where the computing power and network performance of the platform are represented by p i ={p comi ,p neti}, the computing power performance of the platform (p comi ) by computing power management indicators (Mp comi ) and computing power performance indicators (Pp comi ) composition, the platform's network performance (p neti) is composed of network capability indicators (Mp neti ) and network performance indicators (Pp neti ). The task set is represented by T = {t1, t2, ..., t n}, where the computing power and network resource requirements of the task are t i ={t comi ,t neti}, t comi Indicates the computing resources requested by the task, t neti Indicates the network resources requested by the task and the computing performance of the task (t comi ) by computing power management indicators (Mt comi ) and computing power performance indicators (Pt comi ) composition, the network performance of the task (t neti ) is composed of network capability indicators (Mt neti ) and network performance indicators (Pt neti ). All computing power and network indicators are normalized and calculated using the Euclidean distance method, with the calculation formulas shown in (1) and (2).

[0065] In this embodiment, the platform computing power and network index quantification formula are as follows:

[0066]

[0067] in, Indicates the quantified value of the computing power performance of the platform. Represents the quantitative value of the platform's network performance, ω p1 Represents the weight of platform computing power management indicators, ω p2 Indicates the weight of the platform computing power performance indicators, ω p3 Represents the weight of platform network capability indicators, ω p4 Represents the weight of platform network performance indicators, ω p1 +ω p2 =1,ω p3 +ω p4 =1, It represents the quantitative value of the jth computing power management indicator under the i-th platform. It represents the quantitative value of the jth computing power performance indicator under the i-th platform. represents the quantitative value of the jth network management indicator under the i-th platform, It represents the quantitative value of the j-th network performance indicator under the i-th platform.

[0068] Similarly, the quantitative formulas for computing power and network indicators of different tasks can be obtained as follows:

[0069]

[0070] in, Indicates the quantified value of the computing power performance of the task, Represents the quantitative value of the network performance of the task, ω t1 Represents the weight of task computing power management indicators, ω t2 Represents the weight of the task computing performance indicator, ω t3 represents the weight of the task network capability indicator, ω t4 represents the weight of the task network performance indicator, ω t1 +ω t2 =1,ω t3 +ω t4 =1, It represents the quantitative value of the jth computing power management indicator under the i-th task. It represents the quantitative value of the jth computing power performance indicator under the i-th task. represents the quantitative value of the jth network management indicator under the i-th task, It represents the quantitative value of the j-th network performance indicator under the i-th task.

[0071] (2) In classification, the three-branch decision-making idea is used to classify platforms and tasks by setting thresholds. The expression for platform classification is designed as P = POS(P) ∪ BND(P) ∪ NEG(P), which satisfies the following properties:

[0072] 1) There is no intersection between the three platforms:

[0073] 2) Three platforms cover all platforms: POS(P)∪BND(P)∪NEG(P)=P.

[0074] The three decisions of each platform in this embodiment are expressed as follows:

[0075]

[0076] Among them, α1 and β1 are the thresholds for platform type classification, and 0≤α1<0.5<β1≤1, P represents the platform set, POS(P), BND(P), and NEG(P) correspond to the above three situations, namely, the platform with computing power performance stronger than network performance (computing power platform), the platform with computing power performance equal to network performance (balanced platform), and the platform with computing power performance weaker than network performance (network power platform). The union of the three is the number of all platforms. For each domain, assign appropriate tasks for execution.

[0077] Similarly, the expression for design task partitioning is T = POS(T) ∪ BND(T) ∪ NEG(T), which satisfies the following properties:

[0078] 1) The three tasks have no intersection:

[0079] 2) Three tasks cover all tasks: POS(T)∪BND(T)∪NEG(T)=T.

[0080] The three decision-making methods for each task in this embodiment are as follows:

[0081]

[0082] Among them, α2 and β2 are the thresholds for task type classification, which are the same as the thresholds for platform type classification and will be continuously adjusted and updated in the later stage, and 0≤α2<0.5<β2≤1, T represents the task set, POS(T), BND(T), and NEG(T) correspond to the above three situations, namely, tasks with more computing power resources than network resources (computing strong tasks), tasks with computing power resources equal to network resources (balanced tasks), and tasks with computing power resources weaker than network resources (network strong tasks). For each domain, tasks are assigned to the appropriate platform for execution to achieve task scheduling.

[0083] Step 3: Task scheduling. Match the tasks to the platform, and adjust the threshold setting by the optimal platform full load rate and matching degree to achieve the optimal match between the task and the platform and realize accurate task scheduling.

[0084] In task scheduling, this embodiment implements the classification of platforms and tasks according to the initially set thresholds, and assigns different types of tasks to corresponding platforms. Considering the dynamic nature of tasks and the requirements of fine-grained scheduling strategies, the threshold settings are dynamically adjusted according to the optimal platform full load rate, system resources are optimized, and accurate task scheduling is achieved. Specifically, the following steps are included.

[0085] (1) Calculate the matching degree of the classified platforms and tasks respectively, see formula (5). Assign the task with the highest platform matching degree to the corresponding platform, and deduct the resources consumed by the task from the platform, and wait for the next matching.

[0086]

[0087] in, Represents task t i With platform p i In the matching degree calculation, since the previous step has realized the classification of tasks and platforms, it is not necessary to calculate the matching degree of all tasks and all platforms, that is, the similarity calculation of strong computing tasks and strong computing platforms, the similarity calculation of strong network tasks and strong network platforms, and the similarity calculation of balanced tasks and balanced platforms, which reduces the calculation complexity and improves the matching performance.

[0088] (2) After completing the initial task matching, calculate the full load rate of each type of platform, see formula (6).

[0089]

[0090] in, They represent the overall full load rates of the strong computing platform, balanced platform, and strong network platform, respectively. g, k, and q are the numbers of strong computing platforms, balanced platforms, and strong network platforms, respectively. Indicates the computing resources used in the platform. Indicates the network resources used in the platform. The remaining amount is determined by the tasks that have been assigned to it.

[0091] (3) According to the distribution of full load rate and matching degree of computing power platform, balanced platform and network power platform, combined with greedy strategy, the threshold is adjusted dynamically. Among them, the platform full load rate and matching degree calculation method based on greedy strategy is iterated according to the full load rate of different types of platforms as greedy objects, and the task is mapped to the appropriate platform. After each task is assigned, the task list will be reduced, and each iteration will generate the current best full load rate and matching degree calculation results, and update the threshold. When all iterations are completed, the current global allocation method is an approximate optimal solution. Finally, the reasonable scheduling of tasks is achieved, ensuring the full utilization of platform resources.

[0092] like Figure 6 As shown, the execution steps of the task scheduling method in this embodiment are as follows:

[0093] 1) Matching degree calculation: According to the classified platforms and tasks, the platform matching degree is calculated, and the tasks are scheduled to the platform with the highest matching degree.

[0094] 2) Full load rate calculation: After the task allocation is completed, the full load rates of different platforms are calculated.

[0095] 3) Determine the optimal solution. Based on the greedy strategy, determine whether the current full load rate and matching degree are optimal. If they are optimal, jump to step 5), otherwise jump to step 4).

[0096] 4) Threshold update: Update the thresholds (i.e., α1, β1, α2, and β2) set based on the three-branch decision-making idea, and jump to step 1).

[0097] 5) Output the network computing task scheduling strategy and complete the network computing task scheduling.

[0098] This embodiment adopts a step-by-step, modular method for constructing a computing network indicator system. Based on the step-by-step, modular construction technology, the computing network indicators are constructed from the two perspectives of computing power information and network information, and the indicators are subdivided into computing power management indicators, computing power performance indicators, network capability indicators, and network performance indicators. All aspects of the computing network indicators are considered comprehensively to ensure the comprehensiveness of the indicators. In addition, according to the Euclidean distance, this embodiment quantifies the computing power and network performance of the platform and tasks by using the Euclidean distance, combines the idea of ​​three-branch decision-making and threshold design, and divides the tasks and platform capabilities into three categories, subdividing the platform into a strong computing platform, a strong network platform, and a balanced platform, and subdividing the tasks into strong computing tasks, strong network tasks, and balanced tasks. In addition, this embodiment also uses matching degree calculation to schedule tasks to the platform with the highest matching degree, and solves the optimal task matching degree and platform full load rate through a greedy strategy; dynamically updates the threshold to obtain the optimal task scheduling strategy and complete task scheduling. By subdividing computing power and network indicators, a multi-dimensional and comprehensive indicator system is built; the classification module is used to achieve accurate classification of platforms and tasks; the task scheduling module is used to optimize the matching degree and complete accurate and dynamic scheduling of tasks, thus solving the shortcomings of existing computing network task scheduling methods in terms of unified indicator construction, ambiguity of binary decision-making, and scheduling dynamics, and overcoming problems such as single measurement scheme, coarse-grained task classification, and fixed task scheduling methods, providing a more flexible, comprehensive, and accurate solution for computing network task scheduling.

[0099] This embodiment provides a computing network task scheduling method based on improved three-branch decision-making. First, a step-by-step, modular construction technology is adopted to flexibly combine, dynamically adjust, and hierarchically systematize computing network indicators from the two perspectives of computing power information and network information, and the indicators are divided into computing power management indicators, computing power performance indicators, network capability indicators, and network performance indicators, thereby realizing a more detailed computing network indicator system. Secondly, combined with the idea of ​​three-branch decision-making, the traditional binary decision-making method is improved, and tasks and platforms are subdivided into three categories, namely, tasks with computing power resource requirements greater than network resource requirements, tasks with computing power resource requirements less than network resource requirements, tasks with computing power resource requirements equal to network resource requirements, platforms with computing power resource requirements greater than network resource requirements, platforms with computing power resource requirements less than network resource requirements, and platforms with computing power resource requirements equal to network resource requirements. Finally, according to the full load rate of the platform and the task matching degree, the classification threshold is dynamically adjusted to enhance the flexibility of the scheduling strategy and realize a more fine-grained overall strategy selection. The comprehensive improvements in the above three aspects will effectively improve the adaptability and flexibility of computing network task scheduling in complex and dynamic environments, optimize system resources, and enhance the stability of system performance.

[0100] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 7As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store relevant data for computing network task scheduling. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a computing network task scheduling method based on improved three-branch decision-making is implemented.

[0101] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0102] In an exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the steps of the above-mentioned method for scheduling computing network tasks based on improved three-way decision making are implemented.

[0103] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for scheduling computing network tasks based on improved three-way decision making.

[0104] In an exemplary embodiment, a computer program product is provided, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for scheduling computing network tasks based on improved three-way decision making.

[0105] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0106] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0107] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0108] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for scheduling computing network tasks based on improved three-branch decision making, characterized in that: The computing network task scheduling method based on improved three-branch decision-making includes: Obtaining computing network task scheduling data; the computing network task scheduling data includes computing power information and network information of each platform and computing power information and network information of each task; According to the computing network task scheduling data, the computing network indicators are classified to construct a computing network indicator system; the computing network indicator system includes several computing network indicators, and the computing network indicators include computing power management indicators, computing power performance indicators, network capability indicators and network performance indicators. The computing power management indicators refer to indicators used to evaluate the management level of computing power providers, the computing power performance indicators refer to indicators used to evaluate the performance of computing power resources used by services, the network capability indicators refer to indicators used to evaluate the functions and responsiveness of the network, and the network performance indicators refer to indicators used to evaluate the quality and stability of network transmission; Based on the computing network indicator system, the three-branch decision theory is used to divide each platform and each task respectively, and the platform division results and task division results are obtained; the platform division results include a strong computing platform, a strong network platform and a balanced platform, the strong computing platform refers to a platform with computing power performance stronger than network performance, the strong network platform refers to a platform with computing power performance weaker than network performance, and the balanced platform refers to a platform with computing power performance equal to network performance; the task division results include strong computing tasks, strong network tasks and balanced tasks, the strong computing tasks refer to tasks with computing power resource requirements greater than network resource requirements, the strong network tasks refer to tasks with computing power resource requirements weaker than network resource requirements, and the balanced tasks refer to tasks with computing power resource requirements equal to network resource requirements; According to the platform division results and the task division results, the corresponding tasks are matched for each platform to obtain a preliminary network computing task scheduling result, and according to the full load rate and matching degree of each platform, the preliminary network computing task scheduling result is optimized to obtain the final network computing task scheduling result; the final network computing task scheduling result is used to represent the best scheduling result corresponding to allocating each task to the most suitable platform for execution.

2. The method for scheduling network computing tasks based on improved three-branch decision making according to claim 1 is characterized in that: Based on the computing network indicator system, the three-branch decision theory is used to divide each platform and each task respectively, and the platform division results and task division results are obtained, which specifically include: According to the computing network indicator system, the Euclidean distance calculation method is used to quantify each of the computing network indicators, and the computing power performance quantification value and network performance quantification value of each platform, as well as the computing power performance quantification value and network performance quantification value of each task are calculated; According to the quantified values ​​of computing power performance and network performance of each platform, determine the size relationship between the computing power performance and network performance of each platform; According to the quantified values ​​of computing power performance and network performance of each task, determine the size relationship between the computing power resource requirements and network resource requirements corresponding to each task; According to the relationship between the computing power performance and network performance corresponding to each platform, as well as the relationship between the computing power resource requirements and network resource requirements corresponding to each task, the three-branch decision theory is used to divide each platform and each task respectively, and the platform division results and task division results are obtained.

3. The method for scheduling network computing tasks based on improved three-branch decision making according to claim 2 is characterized in that: Use the following formula to calculate the quantified value of computing power performance and network performance of each platform: in, Indicates the quantified value of the computing power performance of the platform, p neti Represents the quantitative value of the platform's network performance, ω p1 Represents the weight of platform computing power management indicators, ω p2 Indicates the weight of the platform computing power performance indicators, ω p3 Represents the weight of platform network capability indicators, ω p4 Indicates the weight of platform network performance indicators. It represents the quantitative value of the jth computing power management indicator under the i-th platform. It represents the quantitative value of the jth computing power performance indicator under the i-th platform. represents the quantitative value of the jth network management indicator under the i-th platform, It represents the quantitative value of the jth network performance indicator under the i-th platform; Use the following formula to calculate the computing power performance quantification value and network performance quantification value of each task: in, Indicates the quantified value of the computing power performance of the task, Represents the quantitative value of the network performance of the task, ω t1 Represents the weight of task computing power management indicators, ω t2 Represents the weight of the task computing performance indicator, ω t3 represents the weight of the task network capability indicator, ω t4 represents the weight of the task network performance indicator, It represents the quantitative value of the jth computing power management indicator under the i-th task. It represents the quantitative value of the jth computing power performance indicator under the i-th task. represents the quantitative value of the jth network management indicator under the i-th task, It represents the quantitative value of the j-th network performance indicator under the i-th task.

4. The method for scheduling network computing tasks based on improved three-branch decision making according to claim 2 is characterized in that: The following formula is used to express the three-branch decision theory to divide each platform and obtain the platform division result: Among them, α1 and β1 are the thresholds for platform type classification, POS(P), BND(P) and NEG(P) represent computing-strong platform, balanced platform and network-strong platform respectively, and P represents the platform set. Indicates the quantified value of the computing power performance of the platform. Indicates the quantitative value of the platform's network performance; The following formula is used to express the division of each task using the three-branch decision theory to obtain the task division result: Among them, α2 and β2 are the thresholds for task type classification, POS(T), BND(T) and NEG(T) represent computing-intensive tasks, balancing tasks and network-intensive tasks respectively, and T represents the task set. Indicates the quantified value of the computing power performance of the task, A quantitative value representing the network performance of a task.

5. The method for scheduling network computing tasks based on improved three-branch decision making according to claim 1 is characterized in that: According to the platform division results and the task division results, the corresponding tasks are matched for each platform to obtain a preliminary network computing task scheduling result, and according to the full load rate and matching degree of each platform, the preliminary network computing task scheduling result is optimized to obtain the final network computing task scheduling result, which specifically includes: According to the platform division results and the task division results, respectively calculate the matching degree between each computing power platform and each computing power task, the matching degree between each balancing platform and each balancing task, and the matching degree between each network strength platform and each network strength task to obtain a matching degree calculation result; According to the matching degree calculation result, the task with the highest matching degree is assigned to each platform to obtain a preliminary network computing task scheduling result; Calculate the full load rate of each platform according to the preliminary network computing task scheduling result; A greedy strategy is adopted to determine whether the current full load rate and matching degree of each platform are the optimal solution. If it is the optimal solution, the preliminary computing network task scheduling result is used as the final computing network task scheduling result. Otherwise, the process returns to the step of "based on the computing network indicator system, using the three-branch decision theory to divide each platform and each task respectively, and obtain the platform division result and the task division result", reset the threshold of the three-branch decision theory, and re-divide each platform and each task until the current full load rate and matching degree of each platform are the optimal solution.

6. The method for scheduling network computing tasks based on improved three-branch decision making according to claim 5 is characterized in that: Use the following formula to calculate the full load factor of each platform: in, and They represent the overall full load rates of the strong computing platform, balanced platform, and strong network platform, respectively. g, k, and q are the numbers of strong computing platforms, balanced platforms, and strong network platforms, respectively. Indicates the computing resources used in the platform. Indicates the network resources used in the platform. Indicates the quantified value of the computing power performance of the platform. Indicates the quantitative value of the platform's network performance.

7. The method for scheduling network computing tasks based on improved three-branch decision making according to claim 1 is characterized in that: The computing power management indicators include service status, deployment location, cloud provider type and cloud provider region; The computing power performance indicators include turbo frequency, cache, video memory, CPU main frequency, number of CPU cores, number of CPU threads, CPU average load, CPU utilization, memory capacity, memory utilization, storage capacity, storage utilization, storage IOPS, GPU utilization, FPGA utilization, ASCI utilization and disk IO utilization; The network capability indicators include average confirmation time, average recovery time and reliability; The network performance indicators include bandwidth, packet loss rate, end-to-end delay, switching delay, switching success rate, jitter and bit rate.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the computing network task scheduling method based on improved three-way decision-making as described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the computing network task scheduling method based on improved three-way decision-making described in any one of claims 1 to 7 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the computing network task scheduling method based on improved three-way decision-making described in any one of claims 1 to 7 is implemented.

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