Computing power sharing method, device and equipment based on multiple edge service providers

Abstract

The invention belongs to the technical field of computing power sharing, and relates to a computing power sharing method, device and equipment based on multiple edge service providers. The method comprises the following steps: acquiring an edge computing environment, and constructing a multi-edge service provider computing power sharing framework; according to a multi-edge service provider computing power sharing framework, the computing task amount undertaken by edge service providers, the scheduling decision of the edge service providers, the scheduling matrix of the edge service providers and the computing task amount processed by the edge service providers are used as constraints to improve economic benefits. Establishing an optimization problem of edge computing power service pricing; solving an optimization problem of edge computing power service pricing to obtain service pricing of multiple edge service providers; and according to the service pricing of the multi-edge service providers, computing power sharing of the multi-edge service providers is carried out. According to the invention, the profitability of the computing power service can be improved, and the economic benefit of the ESP is increased.

Description

Technical Field

[0001] The present application relates to the field of computing power sharing technology, and in particular to a computing power sharing method, device and equipment based on multiple edge service providers. Background Art

[0002] Edge computing is a cutting-edge technology that has emerged in recent years and has become a hot topic of great concern in the industrial and academic circles at home and abroad. Its basic idea is to provide cloud computing and IT service capabilities to application developers and content providers at the edge of the network close to the terminal devices, thereby creating a big data processing service environment with high performance, low latency, and high bandwidth. In fact, edge computing and 5G technologies complement each other, jointly providing more localized services to a variety of terminal devices, ensuring lower network latency, and thus improving the experience of end users. To this end, telecommunications companies represented by China Unicom, China Mobile, and China Telecom usually deploy edge computing servers near base stations to provide services to users nearby, becoming de facto edge service providers (Edge Service Provider, ESP).

[0003] However, with the widespread deployment of edge computing power, how to achieve coordination and sharing of these decentralized computing infrastructures among ESPs is the key to unleashing the potential of edge computing performance, reducing ESP costs and maximizing social welfare.

[0004] In the prior art, computing power sharing methods based on edge computing include: a cloud-edge integrated computing power sharing framework to solve the problem of computing power infrastructure providers independently deploying and managing computing power nodes; a mobile-assisted computing power sharing framework to solve the problem of mismatch between supply and demand of fixed edge infrastructure resources; and a public-private hybrid computing power sharing framework to solve the respective needs of public / private computing power services.

[0005] However, although the current methods have studied computing power sharing from different perspectives, they have ignored the impact of the economic benefits of computing power facilities, lacked a profit orientation to control operating expenses, and did not establish cooperative relationships to increase economic income. ESP has a distinct profitability and hopes to provide paid services through extensive integration of resources. How to maintain a low marginal cost and ultimately expand economic benefits has become a challenge. Summary of the invention

[0006] Based on this, it is necessary to provide a computing power sharing method, device and equipment based on multiple edge service providers to address the above-mentioned technical problems. In view of the current deficiencies in the research on the benefits of computing power services, an optimization problem for edge computing power service pricing with the goal of improving economic benefits is designed and solved, which can improve the profitability of computing power services and expand the economic benefits of ESP.

[0007] The computing power sharing method based on multiple edge service providers includes: Obtain edge computing environment and build a computing power sharing framework for multiple edge service providers; Based on the computing power sharing framework of multiple edge service providers, with the goal of improving economic benefits, the optimization problem of edge computing power service pricing is established with the amount of computing tasks undertaken by edge service providers, the scheduling decisions of edge service providers, the scheduling matrix of edge service providers, and the amount of computing tasks processed by edge service providers as constraints; Solve the optimization problem of edge computing service pricing and obtain the service pricing of multiple edge service providers; The computing power of multiple edge service providers is shared according to their service pricing.

[0008] The above-mentioned computing power sharing method, device and equipment based on multiple edge service providers construct a multi-ESP computing power sharing framework (CoEdge), and deploy a core component scheduler for it to realize cross-ESP service users; further, a delay-aware optimized edge computing power scheduling strategy (DSO) is designed to solve the problem of efficient scheduling of edge computing power tasks with the goal of reducing service latency; further, a service-aware optimized edge computing power pricing strategy (SPO) is designed to solve the problem of optimizing edge computing power service pricing with the goal of increasing economic benefits; further, a two-stage solution method for CoEdge optimal service (including DSO and SPO) is designed to reduce the complexity of the solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 This is an application scenario diagram of a computing power sharing method based on multiple edge service providers in one embodiment; Figure 2 It is a flowchart of a computing power sharing method based on multiple edge service providers in one embodiment; Figure 3 is a schematic diagram of an edge computing environment in one embodiment; Figure 4 A schematic diagram of a computing power sharing framework for multiple edge service providers in one embodiment; Figure 5 A schematic diagram of a framework of an edge layer in a computing power sharing framework for multiple edge service providers in one embodiment; Figure 6 is a flow chart of a computing power sharing method based on multiple edge service providers in another embodiment; Figure 7 A flowchart of a two-stage solution method in another embodiment; Figure 8The minimum service delay of CoEdge and UnCoEdge in a specific embodiment is shown with ESP as the research object, where (a) is 35-CoEdge and 35-UnCoEdge, and (b) is 55-CoEdge and 55-UnCoEdge; Fig. 9 The maximum economic benefits of CoEdge and UnCoEdge in a specific embodiment with ESP as the research object, where (a) is 35-CoEdge and 35-UnCoEdge, and (b) is 55-CoEdge and 55-UnCoEdge; Fig.10 The minimum service delay of CoEdge and UnCoEdge in a specific embodiment is studied with a telecom operator as the research object, where (a) is 35-CoEdge and 35-UnCoEdge, and (b) is 55-CoEdge and 55-UnCoEdge; Fig.11 The maximum economic benefits of CoEdge and UnCoEdge in a specific embodiment with telecom operators as the research object, where (a) is 35-CoEdge and 35-UnCoEdge, and (b) is 55-CoEdge and 55-UnCoEdge; Fig.12 It is a structural block diagram of a computing power sharing device based on multiple edge service providers in one embodiment; Fig.13 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0010] In order to make the purpose, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0011] It should be noted that all directional indications in the embodiments of the present application (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0012] In addition, the descriptions of "first", "second", etc. in this application are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "multiple groups" means at least two groups, such as two groups, three groups, etc., unless otherwise clearly and specifically defined.

[0013] In this application, unless otherwise clearly specified and limited, the terms "connection", "fixation", etc. should be understood in a broad sense. For example, "fixation" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, an electrical connection, a physical connection, or a wireless communication connection; it can be a direct connection, or an indirect connection through an intermediate medium, or it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0014] In addition, the technical solutions between the various embodiments of the present application can be combined with each other, but it must be based on the fact that ordinary technicians in the field can implement it. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0015] The computing power sharing method based on multiple edge service providers provided in this application can be applied to Figure 1 In the application scenario diagram shown, the terminal 102 communicates with the server 104 through the network, and the terminal 102 may include but is not limited to various personal computers, laptops, smart phones, tablet computers and portable wearable devices, and the server 104 may be various portal websites, servers corresponding to the backend of the work system, etc.

[0016] This application provides a computing power sharing method based on multiple edge service providers, such as Figure 2 The flowchart shown in FIG. 1 shows a method for applying the method to Figure 1 The following is an example of a terminal in the example, including: Step 202, obtain the edge computing environment and build a computing power sharing framework for multiple edge service providers.

[0017] Specifically: Obtain an edge computing environment and build a multi-edge service provider computing power sharing framework including: a user layer, an edge layer, and a cloud layer; the user layer, the edge layer, and the cloud layer are connected in sequence, and the user layer is also connected to the cloud layer.

[0018] The user layer includes multiple terminal devices, which dynamically generate computing tasks and hope to obtain services. Specifically: The user layer includes a large number of terminal devices such as computers, smart phones, and smart IoT terminals. They are the source and end point of data transmission, and they will dynamically generate computing tasks and hope to obtain high-quality services.

[0019] The edge layer includes multiple edge service providers that provide responsive computing power for computing tasks. Specifically: The edge layer includes multiple ESPs (or their deployed edge computing servers), which are responsible for providing computing, storage and other resources for computing tasks, and providing the technology and platform required for computing tasks to run, thereby responding to computing power service needs in real time.

[0020] The edge layer also includes: a service analyzer, a central optimizer and a policy executor; the service analyzer, the central optimizer and the policy executor are connected in sequence, the service analyzer is also connected to the user layer, the service analyzer is also connected to each edge service provider, and the policy executor is also connected to each edge service provider.

[0021] in: The service analyzer continuously analyzes the computing tasks at the user layer, describes the service characteristics and outputs them to the central optimizer. Specifically: The service analyzer is essentially a service characteristic extraction module, which is responsible for continuously analyzing the requests of terminal users. It can obtain and analyze the request information of all terminal users to different ESPs within a time and space range in real time, including geographic location, data traffic, data transmission rate, and service delay tolerance, so as to accurately describe the service characteristics and output them.

[0022] The central optimizer takes the service features output by the service analyzer as input, executes the optimization algorithm for resource sharing and the optimization algorithm for service benefits, generates a scheduling matrix for computing tasks and service pricing for each edge service provider, thereby forming a computing power sharing strategy and outputting it to the strategy executor. Specifically: The central optimizer is the central module of the scheduler, responsible for executing the optimization algorithm for resource sharing and service benefits. It takes the service features output by the service analyzer as basic input, generates a scheduling matrix for computing tasks and a service pricing plan for each ESP by executing the optimization algorithm, thereby forming a computing power scheduling strategy and outputting it.

[0023] The policy executor redirects each computing task to the best edge service provider according to the computing power sharing strategy output by the central optimizer. Specifically: The policy executor is the specific implementation module of the computing power scheduling strategy, responsible for redirecting the service to the best ESP. This redirection can be assisted by the existing Domain Name System (DNS) resolution technology in the edge network. Therefore, after receiving the service request, the local DNS server first resolves to the DNS server of the corresponding ESP, and then the ESP uses the DNS server of the scheduler to modify its CNAME record. Finally, according to the scheduling strategy and CNAME record optimized by the central optimizer for the service request, the policy executor redirects the service request to its best ESP.

[0024] The cloud layer includes multiple computing centers, which provide the user layer with greater response computing power than the edge layer. Specifically: the cloud layer includes multiple remote large computing centers such as intelligent computing centers and data centers, which can provide the user layer with various network services similar to those of the edge layer, but compared with the edge layer, its resource inventory is richer and the server scale is larger.

[0025] In this step, if Figure 3 The general edge computing environment shown in the figure abstracts the edge computing environment into an undirected graph .in, Representation diagram A point set whose elements are ESPs or edge computing servers affiliated with ESPs; Representation diagram The edge set, whose elements are the data transmission links between ESPs or edge computing servers. Specifically, express A specific ESP or edge computing server in Figure 1. There are multiple ESPs in the network, and each ESP has multiple edge computing servers to respond to user service requests. Each edge server can not only directly process computing tasks from users, but also process computing tasks from other edge servers. express Different edge computing servers of the same ESP are connected through a local area network (LAN), and edge computing servers of different ESPs are connected through the Internet.

[0026] In fact, if the user request is processed locally, the request will be distributed to the edge server of the ESP via the LAN for processing, which has a lower latency due to its proximity to the user; if the user is processed remotely, the request will be distributed to the edge server of another ESP via the Internet with the help of the edge server of the ESP, which has a higher latency due to the need for cross-ESP wide area transmission. Therefore, the latency of the former can be ignored in the process of processing the request. Then, in Figure In , all edge computing servers belonging to the same ESP are modeled as a node.

[0027] In addition, to describe the graph Accepted service requests, introduction time period , and use It represents the total computing tasks from users undertaken by the multi-edge service provider computing power sharing framework (CoEdge) during this period, and it is assumed that the overall computing resources of CoEdge can meet all computing requests from users.

[0028] For this edge computing environment, a multi-edge service provider computing power sharing framework is proposed. Figure 4 As shown in the figure, the framework diagram of the edge layer in the multi-edge service provider computing power sharing framework is as follows Figure 5 shown.

[0029] Specifically, the data streams and task streams generated by the user layer are uploaded to the edge layer. Since ESP and its edge computing servers are interconnected and can reasonably decide on the local / remote processing of computing tasks at the edge layer, ESP can collaboratively process user service requests, thereby ensuring that the edge layer processes service requests in a timely and efficient manner. When the computing resources of the edge layer are fully utilized and most tasks are preprocessed, if there are still unfinished computing tasks, these tasks will be further submitted to the cloud layer. The cloud layer has a cloud data center with more powerful computing power and massive data storage space, which will take over the remaining tasks and process them.

[0030] Therefore, with the support of the scheduler (the scheduler includes: service analyzer, central optimizer and policy executor), CoEdge adopts a hierarchical mechanism to process computing tasks, which not only optimizes the allocation of computing resources, but also reduces network latency and improves service response speed, especially by reasonably allocating computing tasks at the edge.

[0031] Step 204, based on the computing power sharing framework of multiple edge service providers, with the goal of improving economic benefits, and with the amount of computing tasks undertaken by the edge service providers, the scheduling decisions of the edge service providers, the scheduling matrix of the edge service providers, and the amount of computing tasks processed by the edge service providers as constraints, establish an optimization problem for edge computing power service pricing.

[0032] The specific process of this step is described in detail in step 308.

[0033] Step 206, solve the optimization problem of edge computing service pricing and obtain service pricing of multiple edge service providers.

[0034] The specific process of this step is described in detail in step 310.

[0035] Step 208, sharing computing power among multiple edge service providers based on their service pricing.

[0036] This application provides a computing power sharing method based on multiple edge service providers, such as Figure 3 The flowchart shown in another embodiment includes: Step 302, obtain the edge computing environment and build a computing power sharing framework for multiple edge service providers.

[0037] The specific process of this step is exactly the same as step 202 and will not be repeated here.

[0038] Step 304, based on the computing power sharing framework of multiple edge service providers, with the goal of minimizing service delay, and with the amount of computing tasks undertaken by the edge service providers, the scheduling decisions of the edge service providers, the scheduling matrix of the edge service providers, and the amount of computing tasks processed by the edge service providers as constraints, establish an optimization problem for edge computing power task scheduling.

[0039] Specifically:

[0040] st

[0041]

[0042]

[0043]

[0044]

[0045]

[0046] in,

[0047] In the formula, For ESP In the period The scheduling strategy (scheduling strategy is a 0-1 variable indicating whether ESP is scheduled in the time period), For ESP In the period The scheduling matrix, For an ESP, It is a collection of edge computing servers belonging to ESP or ESP. For ESP In the period The service delay, For ESP In the period The total amount of computing tasks undertaken The proportion of For ESP In the period The amount of computing tasks undertaken within For ESP The amount of local computing resources, For another ESP, For ESP In the period The scheduling strategy, For the period ESP Dispatch to ESP The computational tasks For ESP In the period The amount of computing tasks that need to be scheduled, For ESP In the period The amount of computational tasks processed internally, For ESP The unit calculation delay is For ESP The unit calculation delay is For ESP The unit upload / download transmission delay, For ESP To ESP The transmission delay between For ESP The unit upload / download transmission delay, for a continuous period of time.

[0048] More specifically: First, ESP undertakes the computing tasks of users. Considering the regional differences of service requests, that is, due to the different service request volumes in different regions, the computing tasks undertaken by ESPs deployed in different regions are different. Indicates time period Internal ESP Total computing tasks undertaken Then, for the ratio, there are constraints that the ratio value is non-negative, cannot exceed 1, and the sum of the ratios is 1: (4-1) (4-2) Therefore, ESP In the period The amount of computing tasks undertaken for: (4-3) Then, ESP decides whether to schedule computing tasks. According to the connection mode of data transmission links in CoEdge, compared with remote processing, that is, scheduling tasks to other ESPs for processing, local processing of computing tasks does not rely on external networks and can respond to service requests faster. Therefore, in order to reduce service delays, ESP prioritizes local processing. If local resources are insufficient, it considers processing some computing tasks remotely.

[0049] Based on the above considerations, each ESP has only two options: scheduling and not scheduling. A 0-1 variable is introduced. Then, if , then ESP In the period The computing tasks need to be scheduled; the reverse is not true. ESP The amount of local computing resources. Therefore, the mathematical expression of this 0-1 variable is as follows: (4-4) Next, we discuss the different The computing task processing status of ESP.

[0050] like ESP No need to schedule computing tasks, then the time period The computing tasks of users can be completely undertaken locally. At this time, if , then ESP The computing resources available are just enough to handle the computing tasks from users, and there is no need to share resources with other ESPs, and there are no surplus resources to share with other ESPs; if , then ESP Complete the user's computing tasks locally After that, there are still remaining computing resources , which can be used to assist ESPs with insufficient resources in processing computing tasks and reduce local resource waste.

[0051] like ESP If you need to schedule computing tasks, then they should be scheduled in the time period The ESP cannot completely undertake the computing tasks from users locally, and needs to combine remote processing, that is, dispatching tasks that exceed its computing capacity to ESP with excess resources for processing. ESP In the period The amount of computing tasks that need to be scheduled within the ESP needs to share the resources of other ESPs. From the perspective of reducing service delay, ESP should process as many computing tasks as possible locally and schedule as few computing tasks as possible to other ESPs, that is, make full use of local resources. Then: (4-5) At this time, use ESP In the period The scheduling matrix in Indicates time period From ESP Dispatch to ESP (ESP There is no need to schedule computing tasks, i.e. ) computing tasks. To avoid idle resources, it is assumed that computing tasks can be quickly scheduled to the appropriate ESP through the least number of network hops (usually 1 hop) so that computing resources can be quickly allocated to the tasks. In addition, the non-negative amount of computing tasks is the basis for ensuring the effectiveness of resource allocation and the rationality of scheduling decisions. Then there is .therefore, It can be expressed as: (4-6) At the same time, to ensure that all tasks can be completed on the allocated resources, it is assumed that the computational tasks are conserved during the scheduling process, that is, during the scheduling process, the total computational tasks remain unchanged before and after the scheduling. Therefore, the scheduling matrix The sum of the elements of is equal to the initial computing task amount before scheduling: (4-7) Then, the resource utilization of ESP in sharing is analyzed. In order to ensure the normal operation of computing tasks and the continuous service of ESP to users, the computing tasks processed by ESP should be within the scope of its computing resources. For ESPs that need to schedule computing tasks, they make full use of local computing resources and process some computing tasks remotely, so the computing tasks they process are equal to the amount of local computing resources; for ESPs that do not need to schedule computing tasks, they have local resources that can be used for sharing, so the computing tasks they process may come from other ESPs in addition to users. ESP In the period The amount of computational tasks to be processed internally is: (4-8) Finally, we discuss the service latency in resource sharing. Service latency, which is the time interval from user request to response, is a key indicator for measuring service quality and user experience in high-real-time CoEdge. This application focuses on computing latency and content transmission latency, and does not involve the latency of the process from request to server reception and server response back to the user.

[0052] Computational latency refers to the time it takes for ESP to process a computing task. ESP The unit computing delay is the time consumed to process a unit computing task. When the ESP that needs to be scheduled collaborates with local and remote computing resources to complete computing tasks, in order to respond to user requests faster, it prefers to choose low latency, that is, it hopes that the computing delay scheduled to other ESPs is as small as possible from the computing delay of its own processing. This article calls it the latency-aware constraint, which is described as: (4-9) Content transmission delay refers to the time it takes to transmit a computing task in the network. Content transmission delay mainly considers the transmission delay of computing tasks between ESPs and the upload / download transmission delay of computing tasks in ESPs.

[0053] Transmission delay of computing tasks between ESPs: In CoEdge, the transmission delay between ESPs is quantified as the ratio of the computing task scheduling amount to the link transmission rate. Indicates that from ESP Dispatch to ESP The amount of computational tasks, among which, and . Unified use represents the link transmission rate between any two ESPs. Then, ESP To ESP The transmission delay between It can be expressed as: (4-10) The upload / download transmission delay of computing tasks in ESP: The upload / download transmission delay corresponds to the process of uploading computing tasks from the ESP receiving end to the ESP computing module and downloading from the ESP computing module to the ESP sending end. Assume that for the same ESP, the upload and download only differ in the transmission direction of computing tasks, while the data transmission method, transmission protocol, etc. are the same, and the amount of data in the transmission process will not change. ESP The unit upload / download transmission delay.

[0054] For ESPs that need to schedule computing tasks, since they share resources from other ESPs, their service latency needs to consider the latency of local processing of computing tasks and the latency caused by the scheduling process. The latter includes the transmission latency between ESPs and the computing latency of other ESPs. Scheduling is a parallel process, and its latency depends on the longest subtask execution time. For ESPs that do not need to schedule computing tasks, their service latency only needs to consider the latency of local processing of computing tasks. Then ESP In the period Service delay It can be expressed as: (4-11)

[0055] For ESP, it is hoped that computing tasks can be completed faster by sharing resources; for CoEdge, it is hoped that the total service delay is minimized. Therefore, the DSO problem can be obtained, that is, the optimization problem of realizing efficient edge computing task scheduling with the goal of reducing service delay:

[0056] st

[0057] In this step, the optimization problem of edge computing task scheduling is established, and the specific solution is given below.

[0058] Step 306, solve the optimization problem of edge computing task scheduling and obtain the scheduling matrix of multiple edge service providers.

[0059] Specifically: Initialize the scheduling matrix, calculate the objective function value of the scheduling matrix, and obtain the objective function change value; Construct a Lagrangian function based on the objective function and constraints of the optimization problem of edge computing task scheduling; According to the Lagrangian function and the change value of the objective function, the minimum quadratic programming problem is constructed; Adjust the Lagrange multiplier to meet the KKT condition and obtain the updated value of the Lagrange multiplier; According to the updated value of Lagrange multiplier, the least quadratic programming problem is solved to obtain the optimal solution of the change value of the objective function; Perform line search based on the optimal solution of the objective function change value to obtain the calculation step length; According to the calculation step size, the scheduling matrix is ​​updated along the search direction, and it is iterated to recalculate the objective function value of the scheduling matrix until the preset conditions are met, and the current scheduling matrix is ​​output as the scheduling matrix of the multi-edge service provider.

[0060] More specifically: To solve the DSO problem, we first analyze its complexity. It is known that scheduling jobs on parallel machines (Scheduling Jobs on Identical Parallel Machines, SIPM) is NP-hard, and its specific definition is: Assign jobs to Each job runs in parallel on multiple machines. Must be on a machine with The processing time is uninterrupted, and each machine can only process one job at a time. The purpose is to minimize the processing time of all jobs. In DSO, there are Computational tasks and ESP. The size and completion time of each computing task are recorded as and , each computing task is divided into several jobs , then the corresponding completion time is Then, we perform a polynomial reduction of the SIPM problem to the DSO problem. Assume that the SIPM instance is to be assigned Homework The machines process the time to run on one machine. This means that each job can be scheduled to any machine. In this way, SIPM minimizes the processing time of all jobs, which is equivalent to DSO minimizing the completion time of all computing tasks. Moreover, this reduction process can be completed in polynomial time. Therefore, SIPM can be obtained DSO, DSO problem is also NP-hard. Moreover, DSO problem is NP-hard, so there is no polynomially solvable algorithm for DSO problem.

[0061] When solving the DSO problem, the problem involves a large number of constraints (number of ), designed the idea of ​​designing a solution algorithm starting from processing constraints, and handled a large number of constraints by constructing Lagrangian functions. Its flexibility and convergence help to realize computing power scheduling in complex edge computing environments, thereby achieving numerically stable convergence. The core idea of ​​the solution is to construct a Lagrangian function (line 8) and a least squares problem (line 9) in each iteration to find the optimal and judgement Whether the convergence threshold is met to update the current solution (line 13); in order to further ensure the optimality of the Lagrangian operator in the Lagrangian function, the KKT (Karush Kuhn Tucker) condition judgment (line 10) is set to improve the robustness of the required computing task scheduling matrix. The detailed process is shown in Table 1.

[0062] Table 1: Pseudocode for solving the DSO problem

[0063] In this step, Algorithm 1 enables the DSO problem to be solved in time complexity The solution is completed under the condition of . This is because: the time complexity of Initialize in Algorithm 1 is , its time complexity is mainly based on Iterate. ① Number of iterations: According to the iteration counter, set The algorithm ends after the update, and the time complexity it brings is ;②Construct the minimum quadratic programming problem: Algorithm 1 needs to transform each constraint when constructing the minimum quadratic programming problem. constraints, the time complexity is ; ③Problem scale: The number of variables involved in solving the DSO problem is , then the time complexity it brings is Therefore, the time complexity of Algorithm 1 is .

[0064] Step 308, based on the scheduling matrix of multiple edge service providers and the computing power sharing framework of multiple edge service providers, with the goal of improving economic benefits, and with the amount of computing tasks undertaken by edge service providers, the scheduling decisions of edge service providers, the scheduling matrix of edge service providers, and the amount of computing tasks processed by edge service providers as constraints, establish an optimization problem for edge computing power service pricing.

[0065] Specifically:

[0066] st

[0067]

[0068]

[0069]

[0070]

[0071]

[0072] in,

[0073] In the formula, For ESP In the period The scheduling strategy, For ESP In the period The scheduling matrix, For ESP Unit service pricing for processing computing tasks from other ESPs, for The ratio of the number of ESPs, for The ratio of the number of ESPs, For an ESP, It is a collection of edge computing servers belonging to ESP or ESP. For ESP In the period Economic benefits based on service pricing, For ESP In the period The total amount of computing tasks undertaken The proportion of For ESP In the period The amount of computing tasks undertaken within For ESP The amount of local computing resources, For another ESP, For ESP In the period The scheduling strategy, For the period ESP Dispatch to ESP The computational tasks For ESP In the period The amount of computing tasks that need to be scheduled, For ESP In the period The amount of computational tasks processed internally, For ESP The unit calculation delay is For ESP The unit calculation delay is For ESP In the period of economic income, For ESP In the period economic expenditure.

[0074] More specifically: First, we discuss the source of ESP's economic income. ESPs in the independent service user model can only meet economic demands by providing edge computing services to end users. However, user requests change dynamically over time and space, and there are situations where the request volume is extremely high or extremely low, so the economic income of ESPs is difficult to control. However, in CoEdge, ESPs establish cooperative relationships with each other and can coordinate resources in the face of dynamically changing requests. Therefore, their economic income not only comes from users, but also benefits from providing resources to other ESPs.

[0075] For ESPs that need to schedule computing tasks, they are limited by local resources and cannot provide surplus resources for other ESPs to use. Therefore, their economic income comes only from end users. For ESPs that do not need to schedule computing tasks, they have surplus resources locally and can share them with ESPs that have resource needs. Therefore, their economic income comes from end users and other ESPs. Considering that CoEdge service users should have unified industry standards, represents the user fee of all ESP units; considering the operation of computing task scheduling within CoEdge, ESP The unit service pricing for processing computing tasks from other ESPs. In the period Economic income It can be expressed as:

[0076] Then, we discuss where the economic expenditure of ESP goes. In the independent service user model, the economic expenditure of ESP is mainly used for construction and maintenance, that is, ESP consumes funds for the initial construction and subsequent maintenance of infrastructure such as server deployment, network connectivity, and energy use. In CoEdge, there are also construction and maintenance costs. At the same time, some ESPs also need to spend money on scheduling computing tasks. Because construction expenditure is a one-time cost, that is, once the construction is completed, it will not change in the long term, and it will remain unchanged in the short period of time studied in this article, so the construction cost is not included in the study.

[0077] For ESPs that need to schedule computing tasks, their expenditures include infrastructure maintenance and task scheduling; for ESPs that do not need to schedule computing tasks, their expenditures are only used to maintain infrastructure. Assuming that the ESPs in CoEdge only have differences in resource volume, service latency, and service pricing, they are unified with represents the unit maintenance cost of all ESPs. In the period Economic expenditure It can be expressed as:

[0078] Finally, the economic benefits of ESP are explained. Based on the above analysis of economic income and economic expenditure, we can get ESP In the period Economic benefits of service-based pricing :

[0079] For ESP, they hope to increase economic benefits by sharing resources; for CoEdge, the relatively balanced reciprocal state between ESPs is not only conducive to motivating more ESPs to join, but also conducive to the stable operation of the entire framework. Therefore, the SPO problem can be obtained, that is, the optimization problem of achieving reasonable edge computing service pricing with the goal of increasing economic benefits, where and Respectively and The number of ESPs is proportional to:

[0080] st

[0081] In this step, based on step 204, the scheduling matrix of multiple edge service providers is added as input to establish the optimization problem of edge computing service pricing, which is specifically solved as follows.

[0082] Step 310, solve the optimization problem of edge computing service pricing and obtain service pricing of multiple edge service providers.

[0083] Specifically: Initialize service pricing and give an initial value to the objective function of the edge computing service pricing optimization problem; For a certain direction of the search direction set, perform a one-dimensional search of the service pricing to obtain the change value of the objective function; calculate the objective function value according to the change value of the objective function; when it is determined that the objective function value is greater than the initial value, update the objective function of the optimization problem of edge computing service pricing; update the service pricing according to the change value of the objective function, and obtain the change value of the service pricing; According to the service pricing change value, the search direction set is updated and iterated, and a one-dimensional search for service pricing is re-performed in a certain direction of the search direction set until the preset conditions are met, and the current service pricing is output as the service pricing of the multi-edge service provider.

[0084] More specifically: To solve the SPO problem, we first analyze its complexity. It is known that the Knapsack Problem (KP) is NP-hard, and its specific definition is: items and items The weight and price are and , with a maximum bearing weight of The goal of a backpack is to select items to put into the backpack so that the total weight of the items does not exceed the maximum weight of the backpack and the total price is the largest. In SPO, there are ESPs that need to schedule computing tasks, denoted as ESP and , the amount of tasks to be scheduled is ;have An ESP that does not need to schedule computing tasks is denoted as ESP and , the amount of resources it has is The goal is to find the optimal price for each task to maximize the weighted economic benefits of ESP. Then, the polynomial reduction of the knapsack problem to SPO is performed. Assume that the knapsack problem instance has The weight is Items ,have The maximum bearing weight is Backpack . In this way, the optimal packing strategy for each backpack is equivalent to each price optimization process in the SPO problem. Moreover, this reduction process can be completed in polynomial time. Therefore, KP SPO, SPO problem is also NP-hard. In addition, SPO problem is NP-hard, so there is no polynomially solvable algorithm for SPO problem.

[0085] When solving the SPO problem, the problem involves a large number of optimization variables (the number is ), designed the idea of ​​designing the solution algorithm from the perspective of promoting the search of optimization variables, and accelerated the speed of finding the optimal solution by "updating" the search direction. Its direct search and high computational efficiency are helpful in dealing with the large-scale ESP computing power pricing problem in the edge computing environment and achieving rapid convergence. The core idea of ​​the solution is to find the optimal optimization direction by constructing a search direction set and performing a one-dimensional search (line7~line13); to maintain the diversity of search directions by removing search directions with poor results and introducing new directions (line14~line16); to further ensure the optimality and extensiveness of the search, a search direction set update mechanism is set, that is, to assign weights to search directions (line 2) and to reduce the weights of directions with poor search results (line 15) to improve the adaptability of the required service pricing. The detailed process is shown in Table 2.

[0086] Table 2: Pseudocode for solving the SPO problem

[0087] In this step, Algorithm 2 enables the SPO problem to be solved in time complexity The solution is completed under the condition of , because: the time complexity of Initialize in Algorithm 2 is , its time complexity is mainly based on Iterate. ① Number of iterations: According to the idea of ​​global search of this algorithm, its search direction set have search directions, the time complexity is ; ② Update the search direction set: After the algorithm traverses all search directions, it will judge each direction to remove the old direction or introduce a new direction, and the time complexity it brings is ; ③Problem scale: The number of variables involved in solving the SPO problem is , then the time complexity it brings is Therefore, the time complexity of Algorithm 2 is .

[0088] Step 312, sharing computing power among multiple edge service providers based on the scheduling matrix of the multiple edge service providers and the service pricing of the multiple edge service providers.

[0089] In this step, DSO and SPO are collectively referred to as the Optimal Service of CoEdge (OS-CoEdge). Accordingly, the optimization algorithms of DSO and SPO together constitute the two-stage solver for the optimal service of CoEdge (Solver for OS-CoEdge, OSer-CoEdge).

[0090] In fact, there is a close internal connection between DSO and SPO in OS-CoEdge. In resource sharing, due to the sensitivity of computing tasks to low latency, ESP first needs to obtain a task scheduling strategy based on latency, which determines the scheduling direction of computing tasks. The service pricing strategy is optimized based on the determination of the task scheduling strategy. It is necessary to consider the scheduling volume of computing tasks and the economic income and expenditure of ESP to ensure that a suitable service pricing strategy is obtained, thereby expanding the service revenue of ESP.

[0091] like Figure 7 The two-stage solution method (OSer-CoEdge) shown in the figure is: the first stage is algorithm 1 for service latency optimization to solve the ESP computing task scheduling matrix; the second stage is algorithm 2 for service pricing optimization to solve the ESP service pricing scheme with the help of the ESP computing task scheduling matrix obtained in the first stage. In this way, OSer-CoEdge maps the intrinsic relationship between DSO and SPO in OS-CoEdge to a specific solution method, that is, by calculating the task scheduling matrix Relate Algorithm 1 with Algorithm 2 so that It not only indicates the scheduling direction for edge computing scheduling, but also provides the required cost information for service revenue. The specific process is shown in Table 3.

[0092] Table 3: Pseudocode of the two-stage solution method

[0093] The above-mentioned computing power sharing method based on multiple edge service providers has the following beneficial effects: 1) A multi-edge service provider computing power sharing framework (CoEdge) is designed to form a cooperative relationship between multiple ESPs, thereby forming a cross-ESP service user model, so that CoEdge supports the scheduling and adaptation of computing tasks, realizes the interconnection of multiple ESPs, avoids the imbalance of resource utilization due to excess or shortage of resources, and thus improves the user service experience. For ESP, by sharing computing power resources, on the one hand, it can flexibly schedule and expand resources according to actual needs, and on the other hand, it can collaboratively serve widely distributed terminals and process massive requests without having to deploy servers on a large scale to achieve comprehensive service coverage; for end users, thanks to the cross-ESP service model, on the one hand, they can obtain service support that is difficult to achieve in the traditional independent service model, expanding the scope of accessible resources, and on the other hand, they can enjoy responsive and high-quality services, and can focus more on their core business. The CoEdge proposed in this application can provide a reference model for building a new generation of computing power sharing, and is expected to become a win-win solution for ESPs and end users.

[0094] 2) In response to the challenge of limited resources of a single ESP in CoEdge, an edge computing task scheduling optimization problem (DSO) was established and solved with the goal of minimizing service latency to avoid performance degradation, response delays, and even service interruptions that are very likely to occur during peak service hours, achieve efficient computing resource sharing and efficient edge computing task scheduling, and provide users with responsive services.

[0095] 3) In response to the challenge of the distinct profitability of ESP in CoEdge, with the goal of improving economic benefits, an optimization problem (SPO) for edge computing service pricing was established and solved to formulate a reasonable service pricing plan, which not only helps to ensure that computing service providers expand their economic benefits, but also helps to attract more computing service providers to participate in sharing, thereby promoting the healthy operation of the computing sharing framework, maintaining low marginal costs, and improving the profitability of computing services, thereby expanding the economic benefits of ESP.

[0096] 4) In response to the challenge that sharing problems are usually very complex, the intrinsic connection between computing resource sharing and computing service benefits is considered, and a two-stage solution method for CoEdge optimal services (including DSO and SPO) is designed to reduce the complexity of the solution.

[0097] It should be understood that although Figure 3 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 3 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0098] In a specific embodiment, after DSO and SPO are collectively referred to as CoEdge optimal services, this application conducts comparative experiments based on real data sets.

[0099] Taking service latency and economic benefits as indicators, 35 ESPs and 55 ESPs were selected to form CoEdge, recorded as 35-CoEdge and 55-CoEdge respectively, and the performance differences between the multi-edge service provider computing power sharing framework (CoEdge) of this application and the conventional computing power sharing framework (UnCoEdge) were compared.

[0100] Firstly, ESP is taken as the research object.

[0101] In terms of service latency, Figure 8 The minimum service delay of each ESP group in CoEdge and UnCoEdge is shown. It can be found that compared with UnCoEdge, the minimum service delay of each ESP group is reduced in CoEdge, which shows that in terms of service delay, the ESP cooperative service method is more advantageous than the independent operation method, which proves the sharing of CoEdge. In addition, in terms of the degree of reduction in service delay, 35-CoEdge is reduced by about 1.61~2.97 times; 55-CoEdge is reduced by about 2.87~4.15 times. And as Figure 8 As shown in (a), the maximum minimum service delay in 35-CoEdge is 0.650 milliseconds. Figure 8 As shown in (b), the maximum minimum service delay in 55-CoEdge is 0.423 milliseconds, which means that overall, 55-CoEdge has a lower service delay than 35-CoEdge, and can therefore respond to user requests more quickly.

[0102] In terms of economic benefits, Fig. 9 The maximum economic benefits of each ESP group in CoEdge and UnCoEdge are shown. It can be found that regardless of whether the number of ESPs deployed is 35 or 55, CoEdge can always bring positive economic benefits, while UnCoEdge is the opposite. This shows that in terms of economic benefits, compared with the independent operation mode, the ESP cooperative service user mode can usually bring about qualitative changes, that is, to a greater extent guarantee the positive income of ESP. In addition, by comparing 35-CoEdge and 55-CoEdge, it can be found that 55-CoEdge has the phenomenon of negative economic benefits, and the degree of improvement is about 0.30~2.46 times, such as Fig. 9 (b) shows that 35-CoEdge has positive economic benefits, with the improvement being about 1.07 to 3.87 times. Fig. 9 (a) This shows that the optimization of service delay and economic benefits is mutually exclusive.

[0103] Then telecom operators are taken as the research objects.

[0104] In terms of service latency, Fig.10 The distribution of the minimum service delay of each telecom operator in CoEdge and UnCoEdge is shown. It can be found that compared with UnCoEdge, the minimum service delay of each telecom operator is reduced in CoEdge, and the minimum service delay of the ESP belonging to each telecom operator is not much different from each other. This shows that in terms of service delay, CoEdge is beneficial to all telecom operators and can better balance the service delay of different ESPs in each telecom operator. In addition, in terms of the degree of reduction in service delay, 35-CoEdge is reduced by about 3.71~5.96 times, such as Fig.10 (a) shows that 55-CoEdge is reduced by about 6.23 to 8.87 times, as shown in Fig.10 (b). This shows that the number of deployed ESPs has a positive optimization effect on telecom operators in reducing service delay. In addition, it can be clearly found that the minimum service delay of Rogers in 35-CoEdge is reduced less than that in 55-CoEdge, which is 4.05 times and 6.23 times respectively. The reason is that the average local resource volume of Rogers in 35-CoEdge is less than that in 55-CoEdge, which is 36.18GB and 43.11GB respectively. This shows that the telecom operators with more local resources have a greater degree of optimization in service delay in CoEdge.

[0105] In terms of economic benefits, Fig.11 The distribution of the maximum economic benefits of each telecom operator in CoEdge and UnCoEdge is shown. It can be found that compared with UnCoEdge, the maximum economic benefits of each telecom operator have increased in CoEdge, about 2.42~3.24 times and 1.12~1.93 times in 35-CoEdge and 55-CoEdge, respectively, and the difference in the maximum economic benefits of ESPs belonging to each telecom operator has decreased. This shows that in terms of economic benefits, CoEdge is beneficial to all telecom operators and can alleviate the differences between different ESPs within them to a certain extent. In addition, it is known that the average local resource volume of Rogers in 35-CoEdge and 55-CoEdge is different. By comparing Fig.11 (a) and Fig.11 (b) It can be found that Rogers in 55-CoEdge can reduce the difference in economic benefits of ESPs belonging to Rogers to a greater extent. This shows that the more local resources a telecom operator has, the more obvious the convergence effect on the differences in its internal ESPs. It can also be found that compared with 35-CoEdge, telecom operators in 55-CoEdge perform better in service delay, but their performance in economic benefits is poor, which further shows that the optimization of service delay and economic benefits is mutually exclusive.

[0106] The experimental results show that: Thanks to the sharing of multiple ESPs, CoEdge performs outstandingly in service latency and economic benefits (compared with UnCoEdge, the best can be improved by about 2.87~4.15 times and 1.07~3.87 times respectively) and can balance the differences between ESPs.

[0107] The present application also provides a computing power sharing device based on multiple edge service providers, such as Fig.12As shown, in one embodiment, it includes: an acquisition module 1202, a modeling module 1204, a solution module 1206 and an output module 1208, wherein: An acquisition module 1202 is used to acquire an edge computing environment and build a computing power sharing framework for multiple edge service providers; A modeling module 1204 is used to establish an optimization problem for edge computing service pricing based on a multi-edge service provider computing power sharing framework, with the goal of improving economic benefits, and with the amount of computing tasks undertaken by the edge service provider, the scheduling decision of the edge service provider, the scheduling matrix of the edge service provider, and the amount of computing tasks processed by the edge service provider as constraints; A solution module 1206 is used to solve the optimization problem of edge computing service pricing and obtain service pricing of multiple edge service providers; The output module 1208 is used to share computing power among multiple edge service providers based on the service pricing of the multiple edge service providers.

[0108] For the specific definition of the computing power sharing device based on multiple edge service providers, please refer to the definition of the computing power sharing method based on multiple edge service providers mentioned above, which will not be repeated here. Each module in the above device can be implemented in whole or in part by software, hardware and a combination thereof. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0109] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Fig.13 As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. 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 and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network 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 power sharing method based on multiple edge service providers is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a button, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0110] Those skilled in the art will understand that Fig.13The 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.

[0111] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method in the above embodiment when executing the computer program.

[0112] 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.

[0113] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A computing power sharing method based on multiple edge service providers, characterized in that: include: Obtain edge computing environment and build a computing power sharing framework for multiple edge service providers; Based on the computing power sharing framework of multiple edge service providers, with the goal of improving economic benefits, the optimization problem of edge computing power service pricing is established with the amount of computing tasks undertaken by edge service providers, the scheduling decisions of edge service providers, the scheduling matrix of edge service providers, and the amount of computing tasks processed by edge service providers as constraints; Solve the optimization problem of edge computing service pricing and obtain the service pricing of multiple edge service providers; The computing power of multiple edge service providers is shared according to their service pricing.

2. The computing power sharing method based on multiple edge service providers according to claim 1 is characterized in that: Before establishing the optimization problem of edge computing service pricing, based on the computing power sharing framework of multiple edge service providers, with the goal of minimizing service latency, the optimization problem of edge computing task scheduling is established with the amount of computing tasks undertaken by edge service providers, the scheduling decisions of edge service providers, the scheduling matrix of edge service providers, and the amount of computing tasks processed by edge service providers as constraints; Solve the optimization problem of edge computing task scheduling and obtain the scheduling matrix of multiple edge service providers; According to the scheduling matrix of multiple edge service providers and the computing power sharing framework of multiple edge service providers, an optimization problem of edge computing service pricing is established to obtain the service pricing of multiple edge service providers; The computing power of multiple edge service providers is shared according to their scheduling matrices and service pricing.

3. The computing power sharing method based on multiple edge service providers according to claim 1 or 2, characterized in that: According to the computing power sharing framework of multiple edge service providers, with the goal of improving economic benefits, the amount of computing tasks undertaken by edge service providers, the scheduling decisions of edge service providers, the scheduling matrix of edge service providers, and the amount of computing tasks processed by edge service providers are used as constraints, and the optimization problem of edge computing power service pricing is established, including: st in, In the formula, For ESP In the period The scheduling strategy, For ESP In the period The scheduling matrix, For ESP Unit service pricing for processing computing tasks from other ESPs, for The ratio of the number of ESPs, for The ratio of the number of ESPs, For an ESP, It is a collection of edge computing servers belonging to ESP or ESP. For ESP In the period Economic benefits based on service pricing, For ESP In the period The total amount of computing tasks undertaken The proportion of For ESP In the period The amount of computing tasks undertaken within For ESP The amount of local computing resources, For another ESP, For ESP In the period The scheduling strategy, For the period ESP Dispatch to ESP The computational tasks For ESP In the period The amount of computing tasks that need to be scheduled, For ESP In the period The amount of computational tasks processed internally, For ESP The unit calculation delay is For ESP The unit calculation delay is For ESP In the period of economic income, For ESP In the period economic expenditure.

4. The computing power sharing method based on multiple edge service providers according to claim 1 or 2, characterized in that: Solve the optimization problem of edge computing service pricing and obtain the service pricing of multiple edge service providers, including: Initialize service pricing and give an initial value to the objective function of the edge computing service pricing optimization problem; For a certain direction of the search direction set, perform a one-dimensional search of the service pricing to obtain the change value of the objective function; calculate the objective function value according to the change value of the objective function; when it is determined that the objective function value is greater than the initial value, update the objective function of the optimization problem of edge computing service pricing; update the service pricing according to the change value of the objective function, and obtain the change value of the service pricing; According to the service pricing change value, the search direction set is updated and iterated, and a one-dimensional search for service pricing is re-performed in a certain direction of the search direction set until the preset conditions are met, and the current service pricing is output as the service pricing of the multi-edge service provider.

5. The computing power sharing method based on multiple edge service providers according to claim 2 is characterized in that: According to the multi-edge service provider computing power sharing framework, with the goal of minimizing service latency, the amount of computing tasks undertaken by edge service providers, the scheduling decisions of edge service providers, the scheduling matrix of edge service providers, and the amount of computing tasks processed by edge service providers as constraints, the optimization problem of edge computing power task scheduling is established, including: st in, In the formula, For ESP In the period The scheduling strategy, For ESP In the period The scheduling matrix, For an ESP, It is a collection of edge computing servers belonging to ESP or ESP. For ESP In the period The service delay, For ESP In the period The total amount of computing tasks undertaken The proportion of For ESP In the period The amount of computing tasks undertaken within For ESP The amount of local computing resources, For another ESP, For ESP In the period The scheduling strategy, For the period ESP Dispatch to ESP The computational tasks For ESP In the period The amount of computing tasks that need to be scheduled, For ESP In the period The amount of computational tasks processed internally, For ESP The unit calculation delay is For ESP The unit calculation delay is For ESP The unit upload / download transmission delay, For ESP To ESP The transmission delay between For ESP The unit upload / download transmission delay.

6. The computing power sharing method based on multiple edge service providers according to claim 2 is characterized in that: Solve the optimization problem of edge computing task scheduling and obtain the scheduling matrix of multiple edge service providers, including: Initialize the scheduling matrix, calculate the objective function value of the scheduling matrix, and obtain the objective function change value; Construct a Lagrangian function based on the objective function and constraints of the optimization problem of edge computing task scheduling; According to the Lagrangian function and the change value of the objective function, the minimum quadratic programming problem is constructed; Adjust the Lagrange multiplier to meet the KKT condition and obtain the updated value of the Lagrange multiplier; According to the updated value of Lagrange multiplier, the least quadratic programming problem is solved to obtain the optimal solution of the change value of the objective function; Perform line search based on the optimal solution of the objective function change value to obtain the calculation step length; According to the calculation step size, the scheduling matrix is ​​updated along the search direction, and it is iterated to recalculate the objective function value of the scheduling matrix until the preset conditions are met, and the current scheduling matrix is ​​output as the scheduling matrix of the multi-edge service provider.

7. The computing power sharing method based on multiple edge service providers according to claim 1 or 2, characterized in that: A computing power sharing framework for multiple edge service providers, including: a user layer, an edge layer, and a cloud layer connected in sequence, wherein the user layer is also connected to the cloud layer; The user layer includes multiple terminal devices that dynamically generate computing tasks and hope to obtain services; The edge layer includes multiple edge service providers that provide responsive computing power for computing tasks; The cloud layer includes multiple computing centers, providing the user layer with greater responsive computing power than the edge layer.

8. The computing power sharing method based on multiple edge service providers according to claim 7 is characterized in that: The edge layer also includes: a service analyzer, a central optimizer, and a policy executor connected in sequence; the service analyzer is also connected to the user layer, the service analyzer is also connected to each edge service provider, and the policy executor is also connected to each edge service provider; The service analyzer continuously analyzes the computing tasks at the user level, describes the service characteristics and outputs them to the central optimizer; The central optimizer uses the service features output by the service analyzer as input, executes the optimization algorithm for resource sharing and service revenue, generates a scheduling matrix for computing tasks and service pricing for each edge service provider, and thus forms a computing power sharing strategy and outputs it to the strategy executor; The policy executor redirects each computing task to the best edge service provider based on the computing power sharing strategy output by the central optimizer.

9. A computing power sharing device based on multiple edge service providers, characterized in that: include: The acquisition module is used to obtain the edge computing environment and build a computing power sharing framework for multiple edge service providers; A modeling module is used to establish an optimization problem for edge computing service pricing based on a computing power sharing framework of multiple edge service providers, with the goal of improving economic benefits, and with the amount of computing tasks undertaken by edge service providers, the scheduling decisions of edge service providers, the scheduling matrix of edge service providers, and the amount of computing tasks processed by edge service providers as constraints; A solution module is used to solve the optimization problem of edge computing service pricing and obtain the service pricing of multiple edge service providers; The output module is used to share computing power among multiple edge service providers based on their service pricing.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

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