A heterogeneous end-edge-cloud collaborative computing method and system based on elastic coupling mechanism

Abstract

This application discloses a heterogeneous end-edge-cloud collaborative computing method based on an elastic coupling mechanism. The method includes: establishing a cross-domain joint elastic coupling model for the distributed nodes of the end-edge-cloud system; conducting a comprehensive analysis of various available resources in the end-edge-cloud system by establishing a heterogeneous fusion resource pool between end devices, edge computing nodes, and cloud centers; and dynamically adjusting the task allocation strategy based on the joint analysis results of the elastic coupling model and various available resources to optimize the load of the end-edge-cloud system and generate the optimal end-edge-cloud system load solution. The present invention introduces a more intelligent and flexible resource scheduling and management mechanism in the field of collaborative computing to maximize resource utilization and optimize system performance.

Description

Technical Field

[0001] The present application relates to a collaborative computing method in a heterogeneous end-edge-cloud system, and in particular to an end-edge-cloud collaborative computing method and system for large-scale terminal devices. Background Art

[0002] Currently, edge-edge cloud collaborative computing systems have been widely used in various scenarios, such as the Internet of Things, smart manufacturing, and smart cities. However, existing edge-edge cloud collaborative computing systems have the following shortcomings:

[0003] 1) When handling large-scale distributed computing tasks, we face numerous challenges, including uneven resource allocation, high data transmission latency, and insufficient system scalability. These issues stem from the fact that traditional systems lack efficient resource scheduling and data management mechanisms. This leads to a significant decrease in computing efficiency in highly concurrent environments and makes it difficult to flexibly respond to changing computing needs.

[0004] 2) Furthermore, traditional edge-cloud collaborative systems lack flexibility in resource allocation and task scheduling, making it difficult to adapt to dynamically changing computing scenarios in real time. This leads to low resource utilization and unstable overall system performance. Existing systems typically rely on pre-set, fixed computation mechanisms, especially when handling complex and changing computing tasks. This rigid design exhibits significant limitations when faced with diverse application scenarios and requirements, failing to fully leverage the respective computing advantages of the edge and cloud, making it difficult to provide flexible and adaptable solutions.

[0005] 3) With the increasing heterogeneity of computing resources and the increasing complexity of tasks, it is becoming increasingly difficult for existing systems to achieve optimal coordination of end, edge, and cloud resources while maintaining low latency and high performance.

[0006] In summary, the above defects not only lead to serious waste of computing resources and reduced task execution efficiency, but also make it difficult for the system to cope with dynamic and changing business needs, limiting its application prospects in highly complex and changeable computing environments.

[0007] Therefore, there is an urgent need to introduce more intelligent and flexible resource scheduling and management mechanisms in the field of end-edge-cloud collaborative computing to maximize resource utilization and optimize system performance, thereby meeting the increasingly complex needs of future computing tasks. Summary of the Invention

[0008] In order to solve the problems of insufficient flexibility, low resource utilization efficiency, task processing delay and other problems in the above-mentioned existing technologies, the present invention proposes a heterogeneous end-edge-cloud collaborative computing method based on an elastic coupling mechanism.

[0009] In a first aspect, an embodiment of the present application provides a heterogeneous device-edge-cloud collaborative computing method based on an elastic coupling mechanism, which is applied to a device-edge-cloud system. The method includes:

[0010] Elastic coupling model construction steps: Establish a cross-domain joint elastic coupling model for the distributed nodes of the end-edge-cloud system. The distributed nodes include: end devices, edge computing servers, and cloud centers;

[0011] Resource pool establishment steps: By establishing a heterogeneous fusion resource pool between end devices, edge computing nodes, and cloud centers, a comprehensive analysis of all available resources in the end-edge-cloud system is conducted. Resources include computing resources, storage resources, and communication resources.

[0012] Load optimization steps: Based on the joint analysis results of the elastic coupling model and various available resources, dynamically adjust the task allocation strategy, optimize the load of the end-edge cloud system, and generate the optimal end-edge cloud system load solution.

[0013] In a specific embodiment of the present invention, the elastic coupling model construction step includes:

[0014] Initialization step: Initialize the performance parameters, current load and target load level of each node; and initialize the coupling strength matrix between nodes;

[0015] Coupling strength calculation steps: By defining dynamic adjustment parameters, optimizing the performance and load matching values ​​of each node, and calculating the coupling strength values ​​between nodes, an elastic coupling model of the edge-cloud system is established;

[0016] Steps for establishing a dependency model: Analyze the dependency between the end, edge, and cloud. Define the calculation model of the dependency between the end, edge, and cloud based on the proportion of data received by each node and the evaluation strength value of the service dependency between nodes.

[0017] In a specific embodiment of the present invention, the resource pool establishment step includes:

[0018] Initialization step: Based on the elastic coupling model, a heterogeneous fusion resource pool model is established between the terminal devices, edge computing nodes, and cloud centers. The number and capacity of each terminal device, edge server, and cloud server resource are initialized, and the weights of different types of resources are defined.

[0019] Steps for establishing a heterogeneous resource fusion model: Based on the quantity of each resource, the capacity of the resource, and the weights of different types of resources, a computing model for heterogeneous resource fusion for the edge-cloud system architecture is established.

[0020] In a specific embodiment of the present invention, the load optimization step includes:

[0021] Fitness function initialization step: Initialize the calculation formula that defines the fitness function, and define the weight of the elasticity score and load level of the edge-cloud system;

[0022] Meta-heuristic iterative optimization steps: select individuals based on fitness, with high-fitness individuals having a higher probability of being selected; perform crossover operations on the selected high-fitness individuals to generate new offspring, perform mutation operations on the offspring individuals, and evaluate the offspring individuals through the fitness function, selecting the better individuals to enter the next generation, and continuously iterating and optimizing;

[0023] When the preset number of iterations is reached or the stopping condition is met, the algorithm converges and outputs the optimal end-edge-cloud system load solution.

[0024] In a specific embodiment of the present invention, the elastic coupling model is: Where i represents node i and j represents node j, E represents the total resilience score of the system, α, β, and γ are adjustment parameters, and N is the number of devices; Used to optimize the performance and load matching of each node, Represents the coupling strength between node i and node j.

[0025] In a specific embodiment of the present invention, the calculation model of the dependency relationship between the device, edge, and cloud is:

[0026] Where i represents node i and j represents node j, is the number of interactions between node i and node j, V i j represents the amount of data received by nodes i and j, V max is the maximum amount of data transmitted between any two nodes in the system, Used to evaluate the average strength of service dependency between node i and node j, w i represents the weight of node i, w j represents the weight of node j, represents the correlation coefficient between node i and node j, w i , w j , The value range is between 0 and 1.

[0027] In a specific embodiment of the present invention, the heterogeneous resource fusion model for the edge-cloud system architecture is as follows: Among them, X end , X edge , X cloud Represents the number of each terminal device, edge server and cloud server resources, I end , I edge , I cloudRepresents the capabilities of each terminal device, edge server, and cloud server resource; D end , D edge , D cloud They represent the weights of device resources of different types of terminal devices, edge servers, and cloud servers respectively.

[0028] In a second aspect, an embodiment of the present application provides a heterogeneous device-edge-cloud collaborative computing system based on an elastic coupling mechanism, which is applied to a device-edge-cloud system and adopts the heterogeneous device-edge-cloud collaborative computing method based on the elastic coupling mechanism described above. The system includes:

[0029] Elastic coupling model building module: used to establish a cross-domain joint elastic coupling model for distributed nodes in the end-edge-cloud system, including end devices, edge computing servers, and cloud centers;

[0030] Resource pool establishment module: This module is used to establish a heterogeneous fusion resource pool between end devices, edge computing nodes, and cloud centers to comprehensively analyze the various available resources in the end-edge-cloud system, including computing resources, storage resources, and communication resources.

[0031] Load optimization module: Used to dynamically adjust task allocation strategies based on the joint analysis results of the elastic coupling model and various available resources, optimize the load of the end-edge cloud system, and generate the optimal end-edge cloud system load solution.

[0032] In a third aspect, an embodiment of the present application provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the heterogeneous end-edge-cloud collaborative computing method based on the elastic coupling mechanism are implemented.

[0033] In a fourth aspect, an embodiment of the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the program, the steps of the heterogeneous end-edge-cloud collaborative computing method based on the elastic coupling mechanism are implemented.

[0034] Compared with the related existing technologies, it has the following outstanding beneficial effects:

[0035] 1) The present method proposes a cross-domain joint elastic coupling model for distributed nodes. This model is based on a design that encompasses three domains: computing nodes, network topology, and service requirements. Each domain contains multiple subdomains that dynamically interact through defined coupling coefficients, taking into account component performance, load, and dependencies between them to adapt to varying computing requirements and network conditions.

[0036] 2) The present invention proposes a multi-level fusion method for heterogeneous resources. By establishing a heterogeneous fusion resource pool between end devices, edge computing nodes, and cloud centers, large-scale computing tasks can be executed distributedly across these three levels. By analyzing task requirements and changes in the real-time computing environment, the resource pool configuration is dynamically adjusted to ensure optimal execution of computing tasks in terms of timeliness and efficiency.

[0037] 3) This method proposes an end-edge-cloud load optimization approach suitable for large-scale tasks. It optimizes the distribution of complex computing tasks across end devices, edge nodes, and cloud servers by mimicking heuristic behaviors found in nature. By defining a fitness function, evaluating factors such as each node's computing resources, task complexity, and network status, and through an iterative search optimization process, it dynamically adjusts the task allocation strategy to optimize the end-edge-cloud system load. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0039] Figure 1 This is a flow chart of the heterogeneous device-edge-cloud collaborative computing method of the present invention;

[0040] Figure 2 This is a flow chart of a heterogeneous device-edge-cloud collaborative computing method according to an embodiment of the present invention;

[0041] Figure 3 This is a schematic diagram of a heterogeneous device-edge-cloud collaborative computing system according to an embodiment of the present invention;

[0042] Figure 4 Schematic diagram of computer hardware of the present invention. DETAILED DESCRIPTION

[0043] It should be noted that the processor described in the present invention is the control center of an electronic device and can be a single processor or a collective term for multiple processing elements. For example, it can be one or more central processing units (CPUs), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs) or one or more field programmable gate arrays (FPGAs).

[0044] Optionally, the processor can perform various functions of the electronic device by running or executing a software program stored in the memory, and calling data stored in the memory.

[0045] In a specific implementation, as an embodiment, the processor may include one or more CPUs. Each of these processors may be a single-core processor or a multi-core processor. The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions). Electronic devices may include: servers, desktop computers, laptop computers, smartphones, tablet computers, embedded computers, etc., wherein the embedded computers include vehicles and robots, etc.

[0046] The memory is used to store the software program for executing the solution of the present invention, and the execution is controlled by the processor. The specific implementation method can refer to the above method embodiment and will not be repeated here.

[0047] It should be noted that the structure of the electronic device shown in the drawings of the present invention does not constitute a limitation thereto, and the actual knowledge structure recognition device may include more or fewer components than shown in the drawings, or a combination of certain components, or a different arrangement of components.

[0048] The above embodiments can be implemented in whole or in part through software, hardware (such as circuits), firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired method (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, or magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0049] It should also be understood that the term "and / or" in this document simply describes an association between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " in this document generally indicates an "or" relationship between the related objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0050] In this disclosure, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, "at least one of a, b, or c" can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0051] It should also be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0052] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of the device or unit, which can be electrical, mechanical or other forms.

[0053] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0054] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0055] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0056] To illustrate the above-mentioned features and effects of the present invention more clearly and easily, the following embodiments are specifically described below with reference to the accompanying drawings. This specification discloses one or more embodiments incorporating the features of the present invention. The disclosed embodiments are for illustrative purposes only. The scope of protection of the present invention is not limited to the disclosed embodiments; the present invention is defined by the appended claims.

[0057] The following is a system embodiment corresponding to the above method embodiment. This embodiment can be implemented in conjunction with the above embodiment. The relevant technical details mentioned in the above embodiment are still valid in this embodiment and will not be repeated here to reduce repetition. Accordingly, the relevant technical details mentioned in this embodiment can also be applied to the above embodiment.

[0058] While conducting research on device-edge-cloud collaborative computing, the inventors discovered that existing technologies overly rely on pre-set, fixed computing rules, resulting in significant limitations in diverse application scenarios. This invention proposes a heterogeneous device-edge-cloud collaborative computing method based on an elastic coupling mechanism. The development of this invention began with an in-depth analysis of existing device-edge-cloud collaborative systems, identifying shortcomings in resource scheduling, task allocation, and flexibility. To address these issues, the inventors introduced an elastic coupling mechanism that dynamically adjusts the collaborative relationship between the device, edge, and cloud based on real-time task requirements and resource status. To address the issue of integrating heterogeneous resources, the inventors designed a multi-level fusion method that efficiently integrates multiple heterogeneous resources. This allows for flexible adjustment of resource allocation strategies, ensuring optimal resource utilization and task execution efficiency in different scenarios. To optimize the processing of large-scale tasks, the inventors also proposed a novel load optimization method that rationally distributes task loads across the device, edge, and cloud, significantly improving the overall efficiency of the system. This invention, ultimately resulting in the invention, can effectively address the problems of low resource utilization efficiency and poor task execution flexibility found in traditional device-edge-cloud systems.

[0059] This paper proposes a heterogeneous end-edge-cloud collaborative computing method based on an elastic coupling mechanism. First, a cross-domain joint elastic coupling model is established to address the resource coordination problem between distributed nodes. This model can dynamically adjust the coupling strategy to achieve flexible scheduling and efficient utilization of cross-domain resources, thereby enhancing the system's adaptability and stability in dynamic environments. Second, a multi-level fusion approach based on heterogeneous resources is used to address the inefficient management and integration of heterogeneous resources. This method enables refined management and fusion optimization of heterogeneous resources at multiple levels. Starting from multiple dimensions such as computing power, storage capacity, and network bandwidth, it fully explores and utilizes the advantages of different resource types, improving resource utilization and overall task processing efficiency. Finally, a large-scale task-based end-edge-cloud load optimization method is used to address the load distribution and optimization problems in large-scale task processing. This method introduces a dynamic load adjustment mechanism to achieve flexible task allocation and resource scheduling between the end, edge, and cloud based on task complexity and current resource status.

[0060] The following is a detailed description with reference to specific embodiments:

[0061] Example 1

[0062] like Figure 1 As shown, the embodiment of the present application provides a heterogeneous device-edge-cloud collaborative computing method based on an elastic coupling mechanism, which is applied to a device-edge-cloud system. The method includes:

[0063] Elastic coupling model construction step 101: establishing a cross-domain joint elastic coupling model for distributed nodes of the end-edge-cloud system, including: end devices, edge computing servers, and cloud centers;

[0064] Resource pool establishment step 102: By establishing a heterogeneous fusion resource pool between the end device, edge computing node and cloud center, a comprehensive analysis of various available resources in the end-edge-cloud system is performed. The resources include computing resources, storage resources and communication resources.

[0065] Load optimization step 103: Based on the joint analysis results of the elastic coupling model and various available resources, the task allocation strategy is dynamically adjusted to optimize the load of the end-edge-cloud system and generate the optimal end-edge-cloud system load solution.

[0066] like Figure 2 As shown, in a specific embodiment of the present invention, the elastic coupling model construction step 101 includes:

[0067] Initialization step: Initialize the performance parameters, current load and target load level of each node; and initialize the coupling strength matrix between nodes;

[0068] First, assume that there is a system consisting of multiple computing nodes, each of which can be a cloud server, edge server, or IoT device. Each node i has a performance parameter P i 、Current load L i and a target load level T i , which is the load value under ideal conditions. The coupling strength between nodes is represented by the matrix C = [c ij ] means, where c ij Indicates the dependency strength between nodes i and j.

[0069] Coupling strength calculation steps: By defining dynamic adjustment parameters, optimizing the performance and load matching values ​​of each node, and calculating the coupling strength values ​​between nodes, an elastic coupling model of the edge-cloud system is established;

[0070] Establish an elasticity scoring model for the edge-cloud system through a flexible modular design: Here, i represents node i and j represents node j. E represents the system's overall resilience score; a higher value indicates a more optimized system. E can be used to adjust the load on each node to optimize the system's overall performance and resilience. For example, resource allocation can be adjusted based on each node's load and the system's resilience score, E. If a node is overloaded, its resilience score may be affected, and thus the overall score, E. By adjusting the task or data distribution among nodes, the load on each node can be more balanced, reducing system bottlenecks. α, β, and γ are tuning parameters used to balance the effects of performance, load variation, and inter-node coupling. α primarily controls the impact of overall system performance on the overall score. A larger α value indicates a higher importance for performance, thus focusing more on optimizing performance in areas such as computing power and resource utilization. α is adjusted within the range [0, 1]; closer to 1 indicates a greater impact of performance on the score, while closer to 0 indicates a greater impact of load variation. β is used to balance the differences in load across nodes. A larger β value increases the impact of load variation on the overall score, prompting the system to prioritize load balancing and reduce load imbalances across nodes. β is also adjusted within the range of [0, 1]. A value closer to 1 indicates a greater impact of load differences on the score, while a value closer to 0 negligible load differences. γ primarily controls the impact of inter-node coupling on the overall score. Larger values ​​of γ mean the system needs to pay more attention to inter-node dependencies or communication costs. γ is adjusted within the range of [0, 1]. A value closer to 1 indicates a greater impact of coupling on the system score, while a value closer to 0 indicates that inter-node coupling can be ignored. N is the number of devices. It is mainly used to optimize the performance and load matching of each node, and emphasizes the importance of performance and load differences through the exponential function. It mainly considers the coupling strength between node i and node j, aiming to reduce the load imbalance caused by dependency.

[0071] Steps for establishing a dependency model: Analyze the dependency between the end, edge, and cloud. Define the calculation model of the dependency between the end, edge, and cloud based on the proportion of data received by each node and the evaluation strength value of the service dependency between nodes.

[0072] Finally, we need to analyze the dependencies between devices, edges, and clouds. The calculation model for these dependencies is:

[0073] Where i represents node i and j represents node j, is the number of interactions between node i and node j, and uses a logarithmic function to slow down the linear growth effect of high-frequency interactions. i j represents the amount of data received by nodes i and j, V max It is the maximum amount of data transmitted between any two nodes in the system, which is used to normalize the data dependency. Used to evaluate the average strength of service dependency between node i and node j, w i Represents the weight of node i, ranging from 0 to 1, w j Represents the weight of node j, ranging from 0 to 1, Represents the correlation coefficient between node i and node j, with a value range of 0 to 1. Then, according to the real-time status of task execution and environmental changes, the connection strength and resource allocation between modules are dynamically adjusted. By dynamically adjusting the T i , E can be optimized at runtime to achieve load balancing and coupling optimization. Based on real-time performance monitoring and load forecasting, the system can automatically adjust the α, β, and γ parameters to adapt to environmental changes and demand fluctuations. For systems with higher performance requirements, the value of α may need to be increased; for systems with large load differences between nodes, the value of β can be increased; and for systems with complex communications or inter-node dependencies, it is necessary to consider increasing the value of γ. By comprehensively considering the coupling relationship between the end-edge and cloud, node performance, load balancing, and inter-node dependencies, the overall efficiency of large-model task processing and elastic computing can be improved.

[0074] In a specific embodiment of the present invention, the resource pool establishment step 102 includes:

[0075] Initialization step: Based on the elastic coupling model, a heterogeneous fusion resource pool model is established between the terminal devices, edge computing nodes, and cloud centers. The number and capacity of each terminal device, edge server, and cloud server resource are initialized, and the weights of different types of resources are defined.

[0076] Based on the elastic coupling model, a heterogeneous fusion resource pool model is established between end devices, edge computing nodes, and cloud centers, enabling large-scale computing tasks to be executed in a distributed manner across these three levels. End devices are responsible for collecting data and performing some preliminary data processing tasks, while edge computing nodes undertake simple data processing and analysis tasks. The cloud platform is responsible for executing complex and computationally intensive tasks. Suppose we have three types of computing resources: end devices, edge servers, and cloud servers. The number of each type of end device, edge server, and cloud server resource is X. end , X edge , X cloud The capabilities of each terminal device, edge server, and cloud server resource are represented by I end , I edge , I cloud These resource capabilities are not limited to the single dimensions of computing power, storage capacity, and communication capacity, but rather reflect the comprehensive nature of these three. Specifically, resource capabilities are calculated based on the product of a device or server's computing resources, storage resources, and communication resources.

[0077] Steps for establishing a heterogeneous resource fusion model: Based on the quantity of each resource, the capacity of the resource, and the weights of different types of resources, a computing model for heterogeneous resource fusion for the edge-cloud system architecture is established.

[0078] Then, a calculation formula for heterogeneous resource integration for the edge-cloud system architecture is established: Among them, D end The weight of the terminal device's device resources, D edge The weight of the device resources of the edge server and D cloud Indicates the weight of the cloud server's device resources, including computing resources, storage resources, and communication resources. end 、D edge and D cloud The value range of is 0 to 1. For example, in the actual task scheduling process, D end 、D edge and D cloud The value of weight determines the resource allocation strategy for each device or server for a specific task. A higher weight indicates a greater contribution to the total resource capacity, and the system prioritizes its availability. In scenarios requiring high computing power, the weight of computing resources can be set to a higher value, while in storage-intensive or bandwidth-intensive applications, the weight of storage or communication resources is increased accordingly. Virtualization technology abstracts physical resources into virtual resources, enabling logically unified management and scheduling of resources of different types and sources. A resource abstraction layer is then designed to shield heterogeneous hardware and software differences, providing a unified resource interface for easy invocation by upper-layer applications. This enables global resource optimization across physical locations and network boundaries.

[0079] In a specific embodiment of the present invention, the load optimization step 103 includes:

[0080] Fitness function initialization step: Initialize the calculation formula that defines the fitness function, and define the weight of the elasticity score and load level of the edge-cloud system;

[0081] In edge-cloud load optimization, the first step is to determine the calculation formula for the fitness function. The fitness function generally reflects the efficiency and effectiveness of task allocation. The purpose of fitness calculation is to provide an evaluation criterion for the algorithm to select the best solution during the iterative process. To better evaluate the quality of solutions through metaheuristic algorithms, the fitness value can be calculated using the following formula: F = ω1·E + ω2·L. Here, E represents the system's overall resilience score, L is the system's total load, ω1 is the weight of the resilience score, and ω2 is the weight of the load level. Both ω1 and ω2 range from 0 to 1.

[0082] Meta-heuristic iterative optimization steps: select individuals based on fitness, with high-fitness individuals having a higher probability of being selected; perform crossover operations on the selected high-fitness individuals to generate new offspring, perform mutation operations on the offspring individuals, and evaluate the offspring individuals through the fitness function, selecting the better individuals to enter the next generation, and continuously iterating and optimizing;

[0083] When the preset number of iterations is reached or the stopping condition is met, the algorithm converges and outputs the optimal solution for the end-edge-cloud system load. Typically, convergence conditions can include reaching the maximum number of iterations or the fitness function value no longer changing significantly. Ultimately, by combining the global search capabilities of the metaheuristic algorithm with the elastic coupling mechanism of the end-edge-cloud system, it can effectively address load optimization problems in dynamic and heterogeneous resource environments.

[0084] Then, the current solution is improved through continuous iteration. Iterative optimization is the core process of the metaheuristic algorithm. In each iteration, the algorithm generates new solutions and evaluates them through the fitness function, and then selects better individuals to enter the next generation. The updated formula is as follows: t+1 =Selection(Y t )∪Crossover(Y t )∪Mutation(Y t ). Among them, Y t is the population of generation t, Y t+1 is the population of generation t+1. This process involves three steps: selection, crossover, and mutation. First, individuals are selected based on their fitness; individuals with higher fitness have a higher probability of being selected. Then, a crossover operation is performed on these selected individuals to produce new offspring. Finally, mutation is performed on these offspring to increase the diversity of the population.

[0085] Example 2

[0086] like Figure 3 As shown, the embodiment of the present application provides a heterogeneous device-edge-cloud collaborative computing system based on an elastic coupling mechanism, which is applied to the device-edge-cloud system and adopts the heterogeneous device-edge-cloud collaborative computing method based on the elastic coupling mechanism as described above. The system includes:

[0087] Elastic coupling model building module 201: used to establish a cross-domain joint elastic coupling model for distributed nodes of the end-edge-cloud system, the distributed nodes including: end devices, edge computing servers and cloud centers;

[0088] Resource pool establishment module 202: used to comprehensively analyze various available resources in the end-edge-cloud system by establishing a heterogeneous fusion resource pool between the end device, edge computing node and cloud center. The resources include computing resources, storage resources and communication resources.

[0089] Load optimization module 203: used to dynamically adjust the task allocation strategy based on the joint analysis results of the elastic coupling model and various available resources, optimize the load of the end-edge cloud system, and generate the optimal end-edge cloud system load solution.

[0090] Example 3

[0091] An embodiment of the present application provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the heterogeneous end-edge-cloud collaborative computing method based on the elastic coupling mechanism are implemented.

[0092] Example 4

[0093] An embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the program, the steps of the heterogeneous end-edge-cloud collaborative computing method based on the elastic coupling mechanism are implemented.

[0094] In addition, combined Figure 1 The heterogeneous end-edge-cloud collaborative computing method based on the elastic coupling mechanism described in the embodiment of the present application can be implemented by electronic devices, such as computer devices. Figure 4 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present application.

[0095] In some embodiments, the computer device may further include a communication interface 83 and a bus 80. Figure 4 As shown, the processor 81, the memory 82, and the communication interface 83 are connected via a bus 80 and communicate with each other.

[0096] Specifically, the processor 81 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0097] The memory 82 may be used to store or cache various data files required for processing and / or communication, as well as possible computer program instructions executed by the processor 81 .

[0098] The processor 81 reads and executes computer program instructions stored in the memory 82 to implement any one of the heterogeneous end-edge-cloud collaborative computing methods based on the elastic coupling mechanism in the above embodiments.

[0099] In summary, the present invention introduces a cross-domain joint elastic coupling model that dynamically schedules and integrates heterogeneous resources distributed across the end, edge, and cloud based on real-time needs. This mechanism not only improves resource utilization efficiency but also effectively reduces computing bottlenecks caused by uneven resource allocation. For example, in smart transportation scenarios, the system can flexibly allocate computing resources between edge nodes and cloud servers based on real-time traffic conditions, ensuring real-time and accurate data processing. Through a multi-level integration approach, the system can deeply integrate heterogeneous resources to meet the needs of different computing tasks. This integration approach optimizes resource allocation and scheduling based on task complexity and priority, thereby achieving efficient computing. For example, in smart healthcare, the system can flexibly allocate local, edge, and cloud resources based on patient urgency and data complexity, ensuring priority processing of critical tasks while improving overall medical service efficiency. The system also features a real-time adaptive load optimization mechanism that dynamically adjusts the computing load distribution between the end, edge, and cloud based on the current system status and task requirements. This mechanism not only improves system responsiveness but also reduces latency, ensuring efficient execution of computing tasks. For example, in video surveillance, the system can dynamically adjust the distribution of computing tasks between the edge and cloud based on fluctuations in data traffic at monitoring points, reducing data transmission latency and improving real-time monitoring. Overall, this invention differs from traditional computing architectures in that the proposed system utilizes a flexible coupling mechanism, enabling it to rapidly adjust resource and task configurations in response to changing computing environments and complex business needs, demonstrating high scalability and adaptability.

[0100] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned 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.

[0101] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A heterogeneous device-edge-cloud collaborative computing method based on an elastic coupling mechanism, applied to a device-edge-cloud system, characterized in that: The method comprises: Elastic coupling model construction steps: Establish a cross-domain joint elastic coupling model for the distributed nodes of the end-edge-cloud system, the distributed nodes including: end devices, edge computing servers and cloud centers; Resource pool establishment step: by establishing a heterogeneous fusion resource pool between the terminal device, edge computing node and cloud center, a comprehensive analysis of various available resources in the terminal-edge-cloud system is performed, and the resources include computing resources, storage resources and communication resources; Load optimization step: based on the joint analysis results of the elastic coupling model and the various available resources, dynamically adjust the task allocation strategy, optimize the load of the end-edge cloud system, and generate an optimal end-edge cloud system load solution; The elastic coupling model construction step includes: Initialization step: initializing the performance parameters, current load and target load level of each node; and initializing the coupling strength matrix between nodes; Coupling strength calculation steps: By defining dynamic adjustment parameters, optimizing the performance and load matching values ​​of each node, and calculating the coupling strength values ​​between nodes, an elastic coupling model of the edge-cloud system is established; Dependency model establishment steps: Analyze the dependency between the end, edge, and cloud, and define the calculation model of the dependency between the end, edge, and cloud based on the proportion of data received by each node and the evaluation strength value of the service dependency between the nodes.

2. The heterogeneous device-edge-cloud collaborative computing method based on the elastic coupling mechanism according to claim 1 is characterized in that: The resource pool establishment step includes: Initialization step: Based on the elastic coupling model, a heterogeneous fusion resource pool model is established between the terminal device, edge computing node and cloud center, and the number and capacity of each terminal device, edge server and cloud server resources, as well as the weights of different types of resources are initialized; Steps for establishing a heterogeneous resource fusion model: Based on the quantity of each resource, the capacity of the resource and the weights of different types of resources, a computing model for heterogeneous resource fusion for the end-edge cloud system architecture is established.

3. The heterogeneous device-edge-cloud collaborative computing method based on the elastic coupling mechanism according to claim 1 is characterized in that: The load optimization step includes: Fitness function initialization step: Initialize the calculation formula that defines the fitness function, and define the weight of the elasticity score and the weight of the load level of the edge-cloud system; Meta-heuristic iterative optimization steps: select individuals based on fitness, with individuals with high fitness having a higher probability of being selected; perform crossover operations on the selected individuals with high fitness to generate new offspring, perform mutation operations on the offspring individuals, and evaluate the offspring individuals using the fitness function, selecting better individuals to enter the next generation, and continuously iterating and optimizing; When the preset number of iterations is reached or the stopping condition is met, the algorithm converges and outputs the optimal end-edge-cloud system load solution.

4. The heterogeneous device-edge-cloud collaborative computing method based on the elastic coupling mechanism according to claim 1 is characterized in that: The elastic coupling model is: , where i represents node and j represents node , represents the overall resilience score of the system, , , is the adjustment parameter, is the number of devices; Used to optimize the performance and load matching of each node, Representation node and nodes The coupling strength between them.

5. The heterogeneous device-edge-cloud collaborative computing method based on the elastic coupling mechanism according to claim 2 is characterized in that: The calculation model of the dependency relationship between the device, edge, and cloud is: , where i represents the node and j represents the node , For nodes and nodes Number of interactions, Representation node and nodes The amount of data received, is the maximum amount of data transmitted between any two nodes in the system, Used to evaluate nodes and nodes The average strength of service dependencies, represents the weight of node i, N is the number of devices, represents the weight of node j, represents the correlation coefficient between node i and node j, , , The value range is between 0 and 1.

6. The heterogeneous device-edge-cloud collaborative computing method based on the elastic coupling mechanism according to claim 3 is characterized in that: The heterogeneous resource fusion model for the edge-cloud system architecture is: ,in, , , Represents the number of each type of terminal device, edge server, and cloud server resources respectively; , , Represents the capabilities of each type of terminal device, edge server, and cloud server resource; , , They represent the weights of device resources of different types of terminal devices, edge servers, and cloud servers respectively.

7. A heterogeneous device-edge-cloud collaborative computing system based on an elastic coupling mechanism, applied to a device-edge-cloud system, adopting a heterogeneous device-edge-cloud collaborative computing method based on an elastic coupling mechanism as described in any one of claims 1-6, characterized in that: The system comprises: Elastic coupling model building module: used to establish a cross-domain joint elastic coupling model for distributed nodes in the end-edge-cloud system, including end devices, edge computing servers, and cloud centers; Resource pool establishment module: used to establish a heterogeneous fusion resource pool between the terminal device, edge computing node and cloud center, and conduct a comprehensive analysis of various available resources in the terminal-edge-cloud system, including computing resources, storage resources and communication resources; Load optimization module: used to dynamically adjust the task allocation strategy based on the joint analysis results of the elastic coupling model and the various available resources, optimize the load of the end-edge cloud system, and generate the optimal end-edge cloud system load solution.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the heterogeneous end-edge-cloud collaborative computing method based on the elastic coupling mechanism described in any one of claims 1-6 are implemented.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the heterogeneous end-edge-cloud collaborative computing method based on the elastic coupling mechanism are implemented as described in any one of claims 1 to 6.

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