Task-oriented collaborative distributed node grouping method

Abstract

The embodiment of the application provides a task-oriented distributed node grouping method for grouping a plurality of nodes in a distributed mobile edge network, each node representing an edge device providing edge service, the method being applied to each node and comprising the following steps: S1, constructing an initial node grouping table for a current node, the node grouping table comprising resource states of other discovered nodes; S2, when the current node receives a task, utilizing resources of other nodes in the latest node grouping table to cooperatively execute the task, and obtaining a task execution result; and S3, updating the node grouping table according to the task execution result, the updating mode comprising: a periodic updating mode configured to update the node grouping table according to an updating period, wherein the updating period is dynamically adjusted according to the task execution result; and a task-driven updating mode configured to update the node grouping table in real time when the task execution result shows that the task execution fails.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of mobile edge network, in particular to the field of resource coordination technology, and more particularly to a distributed node grouping method for task coordination. BACKGROUND

[0002] The resource coordination strategy of the mobile edge network supports the task coordination of the nodes to other nodes for execution, which not only can alleviate the resource limitation of the device itself, but also can improve the efficiency of the task processing through the task decomposition and parallel execution, for example, reference [1]. In the resource coordination process, obtaining the network node grouping information maintained by the current node is the basis of the resource coordination. The node grouping information determines which edge nodes can execute the current task, and is the premise of formulating a specific resource coordination scheme. The resource usage, location and function of each node or device in the network determine the computing and data processing tasks that it can participate in. Therefore, the node grouping information is crucial in resource coordination, but in actual operation, it is often challenging to obtain the grouping information.

[0003] Challenge one: the dynamic network environment means that the state and connectivity of the nodes can change frequently, which requires the system to update and maintain the node information in real time. However, most of the current resource coordination schemes assume that the node grouping information and node available information are known, and make resource coordination decisions on this basis. Challenge two: in actual systems, collecting node information consumes computing and communication resources, especially on resource-constrained edge devices, which greatly affects the overall performance and energy efficiency of the device, ultimately limiting the performance of the resource coordination strategy. Edge devices usually rely on limited energy supply, such as batteries. High-frequency data updates and information exchange can quickly deplete the energy of these devices, leading to an energy crisis. This not only affects the continuous running time of the nodes, but also can limit the ability of the device to participate in future tasks, ultimately affecting the performance and reliability of the entire network.

[0004] In order to cope with the above challenges, a variety of schemes have been proposed in traditional research. Typical node grouping research can be roughly divided into two categories according to its network architecture: centralized node grouping and distributed node grouping.

[0005] In centralized node grouping, there is a central controller that integrates the edge node information in the network and makes resource coordination decisions for the system accordingly and issues decision instructions. In theory, in order to realize real-time monitoring of the network state, the way the central controller updates the global information will lead to frequent data interaction in the edge network, thus consuming a large amount of communication resources, and at the same time, certain computing resources need to be provided for the central controller to ensure the normal progress of data collection and strategy formulation.

[0006] Compared with the centralized architecture, the partial distributed node grouping research uses game theory to maintain the grouping information between nodes. By using the analysis and optimization mechanism of game theory, a more efficient node organization strategy is provided in the edge computing environment, which realizes the balance of resource allocation, considers the demand of service quality, and optimizes the overall use efficiency of resources. Under this model, edge nodes only need to make autonomous decisions based on necessary information. This distributed node grouping method reduces the dependence on the central controller and reduces the frequency of data synchronization, thereby showing significant advantages in reducing the operating cost of mobile devices, improving task processing efficiency, and saving communication resources, for example, reference [2]. Although using game theory or on-demand routing method to find suitable resource sharing nodes in the edge network has a certain effect in providing resource coordination information, it is limited by the mechanism of game theory and on-demand routing information interaction. Before making resource coordination decisions, edge nodes need to interact with other nodes at least once. For some time-sensitive tasks, the waiting time for the game will affect the quality of service. In addition, in the face of frequent changes in node resources in the edge network, high mobility, dynamic joining and leaving, etc., multiple rounds of information interaction may be required to make resource coordination decisions, thereby affecting the service quality of the edge network and the effective use of resources.

[0007] Based on the above analysis, in the traditional network environment, the centralized or distributed architecture in the prior art to process network tasks has been widely applied, but in the face of the challenges unique to mobile edge networks, the existing methods will have obvious limitations and shortcomings, as follows:

[0008] (1) High energy consumption: centralized processing and data transmission in edge networks can lead to high energy consumption. Since edge devices are usually powered by batteries, frequent data synchronization and processing consume a large amount of energy, which is a serious challenge for energy-limited edge devices. In addition, single-point processing in centralized architecture can also lead to low energy efficiency.

[0009] (2) Static and lack of flexibility: Traditional centralized and distributed architectures usually rely on static node configuration and pre-set network models. In the rapidly changing edge network environment, these architectures often cannot quickly adapt to real-time changes in network status, such as dynamic joining and exiting of nodes, or immediate fluctuations in network resources.

[0010] (3) Quality of Service (QoS) is difficult to guarantee: In a highly dynamic edge network environment, it becomes more difficult to maintain consistent service quality. Existing architectures often struggle to predict and adapt to environmental changes, leading to increased network latency, decreased data processing efficiency, and an inability to meet growing service quality demands. In particular, there is a lack of effective mechanisms to address instantaneous network fluctuations and node performance changes in terms of resource allocation and task resource collaboration.

[0011] In summary, existing methods typically rely on static node configurations and pre-set network models, which cannot quickly and flexibly adapt to real-time changes in network status, further leading to low quality of service in mobile edge networks.

[0012] It should be noted that: the background art is only used to introduce the related information of the present application, in order to help understand the technical scheme of the present application, but does not mean that the related information must be prior art. In the absence of evidence that the related information has been disclosed before the filing date of the present application, the related information should not be regarded as prior art.

[0013] The references are as follows:

[0014] [1] GY. Multi-dimensional resource collaboration intelligent optimization of mobile edge network [Z]. Beijing University of Posts and Telecommunications, 2023.

[0015] [2] Li T, Qiu Z, Cao L, et al. Privacy-Preserving Participant Grouping for Mobile Social Sensing Over Edge Clouds [J]. IEEE Trans. Netw. Sci. Eng., 2021, 8(2): 865-880. SUMMARY

[0016] Therefore, the purpose of the present application is to overcome the defects of the prior art, and to provide a distributed node grouping method for task collaboration.

[0017] The purpose of the present application is achieved by the following technical scheme:

[0018] According to a first aspect of the present invention, a distributed node grouping method for task collaboration is provided, which is used to group multiple nodes for providing edge services in a distributed mobile edge network, each node representing an edge device, and the method is applied to each node, and the method includes: S1, constructing an initial node grouping table for the current node, the node grouping table including the resource status of other discovered nodes; S2, when the current node receives a task, according to the latest node grouping table, using the resources of other nodes to collaboratively execute the task to obtain the task execution result; S3, updating the node grouping table according to the task execution result, and the update method includes: a periodic update method, which is configured to: update the node grouping table according to the update period, wherein the update period is dynamically adjusted according to the task execution result; an update method based on task-driven, which is configured to: immediately update the node grouping table when the task execution result shows that the task execution failed.

[0019] In some embodiments of the present invention, in step S3, the dynamic adjustment method includes: counting the number of task execution failures based on the task execution results of all tasks of the current node within a predetermined time period; determining a dynamic adjustment parameter for indicating the service quality of the mobile edge network based on the quantitative value of the network status obtained by the current evaluation of the current node and the proportion of unfinished tasks of the node, wherein the proportion is the ratio of the number of task execution failures to the total number of all tasks; dynamically adjusting the update period according to the dynamic adjustment parameter and a preset threshold range, wherein: when the dynamic adjustment parameter is less than the lower limit threshold of the preset threshold range, reducing the update period; when the dynamic adjustment parameter is within the preset threshold range, keeping the update period unchanged; when the dynamic adjustment parameter is greater than the upper limit threshold of the preset threshold range, increasing the update period.

[0020] In some embodiments of the present invention, the dynamic adjustment parameter is determined as follows:

[0021] ,

[0022] in, Indicates dynamic adjustment parameters, represents the scale parameter, Indicates the current node at the current time The previous time period The number of task execution failures in internal statistics With this time period The total number of tasks within The ratio, represents the scale parameter, Indicates the quantitative value of the currently evaluated network status.

[0023] In some embodiments of the present application, the node group table further comprises communication cost between the current node and other nodes, and the periodic updating manner in step S3 comprises: receiving the updated node group table sent by other nodes directly connected to the current node, calculating the new communication cost between the current node and other nodes according to the node group table of other nodes and the node group table of the current node; and updating the communication cost between the current node and other nodes when the new communication cost is different from the communication cost between the current node and other nodes before the update.

[0024] In some embodiments of the present application, the resource status comprises computing resource and storage resource, and the network condition quantization value is calculated as follows:

[0025] ,

[0026] wherein, represents the network condition quantization value, represents the probability that the computing resource requirement of one task of the current node can be met within the hop, represents the number of the current node, represents the maximum hop number within the preset communication cost threshold, represents the hop number, represents the probability that the storage resource requirement of one task of the current node can be met within the hop, represents the node connectivity rate of the current node within the hop, , represents the total number of nodes in the network, represents the node set of the current network except the node, represents the i-th node in the network except the current node, represents the number of one node in the network except the current node, represents the connectivity between the current node and the node.

[0027] ​​​​​​​​​​​​​In some embodiments of the present application, in the step S3, the task-driven updating manner comprises: generating a task error report when the task execution result of the current node shows that the task execution fails and the task resource coordination fails, updating the node grouping table of the current node according to the task error report; when there is no other node in the node grouping table of the current node that can participate in resource coordination, initiating a new task resource coordination request in the form of broadcast, and updating the node grouping table according to the task reply sent by another node indicating that it can participate in resource coordination.

[0028] In some embodiments of the present application, in the step S2, the manner of coordinating the execution of the task by using the resources of other nodes comprises: selecting a resource coordination path for the task according to the latest node grouping table, wherein the resource coordination path comprises other nodes that can meet the resource requirements of the task and have the minimum communication cost; and the current node offloads the task to other nodes on the corresponding resource coordination path for coordinated execution, thereby obtaining the task execution result.

[0029] According to a second aspect of the present application, a distributed node grouping system is provided, which is implemented based on the method of the first aspect of the present application and is configured in each node of a plurality of nodes in a distributed mobile edge network for providing edge services, and the system comprises:

[0030] a task coordination execution module configured to, when the current node receives a task, coordinate the execution of the task by using the resources of other nodes in the latest node grouping table, thereby obtaining a task execution result; a dynamic adjuster configured to dynamically adjust an update period according to the situation of the task execution result; a periodic update module configured to construct an initial node grouping table for the current node and update the node grouping table according to the update period; and a task-driven updating module configured to update the node grouping table immediately when the task execution result shows that the task execution fails.

[0031] According to a third aspect of the present application, an electronic device is provided, comprising: one or more processors; and a memory, wherein the memory is configured to store executable instructions; and the one or more processors are configured to implement the steps of the method of any one of the first aspect of the present application via execution of the executable instructions.

[0032] Compared with the prior art, the present application has the following advantages:

[0033] In the method, on one hand, the node group table constructed by the node includes the resource state of the discovered other nodes, and the node group table is dynamically updated, and when the node receives or generates a task, the latest node group table is used to process the task to ensure the cooperation and resource sharing among the nodes and improve the network service quality. On the other hand, the task-driven updating mode is used, that is, when the task execution result shows that the task execution fails, the node group table is updated in real time, so that the node group table can support the task resource cooperation requirement of the node in real time. The periodic updating mode is used, that is, the node group table is updated according to the updating period, and the updating period is dynamically adjusted according to the task execution result, so that the real-time change of the network state can be quickly and flexibly adapted. The method not only ensures the effectiveness and reliability of the node group table, but also improves the success rate of the task cooperation execution, reduces the energy consumption, and further improves the network service quality. BRIEF DESCRIPTION OF DRAWINGS

[0034] The embodiments of the present application will be further described below with reference to the accompanying drawings, in which:

[0035] Figure 1 The flowchart of the task cooperation-oriented distributed node grouping method according to the embodiments of the present application is shown in the figure.

[0036] Figure 2 The structure diagram of the node group table according to the embodiments of the present application is shown in the figure.

[0037] Figure 3 The execution flowchart of the edge node grouping method according to the embodiments of the present application is shown in the figure.

[0038] Figure 4 The principle diagram of the interaction process of the node grouping method according to the embodiments of the present application is shown in the figure.

[0039] Figure 5 The task completion rate comparison result diagram of the existing method and the method of the present application under different dynamic node proportions according to the embodiments of the present application is shown in the figure.

[0040] Figure 6 The task completion rate comparison result diagram of the existing method and the method of the present application under different node numbers and the speed of each dynamic node being 20 m / s according to the embodiments of the present application is shown in the figure.

[0041] Figure 7 The task completion rate comparison result diagram of the existing method and the method of the present application under different node numbers and the speed of each dynamic node being 40 m / s according to the embodiments of the present application is shown in the figure.

[0042] Figure 8 The task completion rate comparison result diagram of the existing method and the method of the present application under different task calculation amount requirements according to the embodiments of the present application is shown in the figure.

[0043] Figure 9 Fig. 4 is a diagram showing the comparison results of the task completion rate of the existing method and the method according to the embodiment of the present application for 15 nodes at different average moving speeds;

[0044] Figure 10 Fig. 5 is a diagram showing the comparison results of the task completion rate of the existing method and the method according to the embodiment of the present application for 30 nodes at different average moving speeds;

[0045] Figure 11 Fig. 6 is a diagram showing the comparison results of the energy consumption of the existing method and the method according to the embodiment of the present application for different numbers of nodes and each dynamic node speed of 20 m / s;

[0046] Figure 12 Fig. 7 is a diagram showing the comparison results of the energy consumption of the existing method and the method according to the embodiment of the present application for different numbers of nodes and each dynamic node speed of 40 m / s. DETAILED DESCRIPTION

[0047] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings. It should be understood that the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application.

[0048] As mentioned in the background section, the existing method usually relies on static node configuration and preset network model, and has the problem of being unable to quickly and flexibly adapt to real-time changes in network state, further leading to low service quality of mobile edge network and other problems.

[0049] Based on the above problems, the inventors propose a task-oriented collaborative distributed node grouping method for grouping a plurality of nodes for providing edge services in a distributed mobile edge network, each node representing an edge device. The method of the present application is applied to each node, that is, on the one hand, the node constructs a node grouping table including the resource states of other discovered nodes, and the node grouping table is dynamically updated, avoiding the dependence on static node configuration and preset network model. When the node receives a task, the latest resource of other nodes in the node grouping table is used to collaboratively execute the task, ensuring the collaborative work and resource sharing between edge nodes, and improving the quality of mobile edge network services. On the other hand, to solve the problem that the network state will change dynamically, the node grouping table is updated by periodic updating method and task-driven updating method. The task-driven updating method is to update the node grouping table in real time when the task execution result shows that the task execution fails, so that the node grouping table can support the task resource collaboration requirements of the node in real time. The periodic updating method updates the node grouping table according to the update period, and takes the network task execution as the core to dynamically adjust the update period. According to the update period, the node grouping table is updated, which can quickly and flexibly adapt to the real-time changes of the network state, not only ensures the effectiveness and reliability of the node grouping table, but also improves the success rate of task collaborative execution. The task failure rate is small, the number of task-driven updates is reduced, the network energy consumption is reduced, the quality of mobile edge network services is improved, and the overall grouping service efficiency of the node grouping method is improved.

[0050] According to an embodiment of the present application, the distributed mobile edge network is a mobile edge network system comprising a plurality of edge devices, and each edge device is distributed in different positions. The edge device refers to a computing device or a service node with computing capability located in the edge network, which is used to process and manage data and applications in the edge computing environment. In the network system, each edge device is regarded as a node, the nodes are connected through a wireless network, data transmission can be carried out between the nodes, and the edge network tasks are collaboratively processed by sharing resource and location information.

[0051] According to an embodiment of the present application, if the edge network system comprises nodes, the network system is represented by the set , wherein, are the number of the first node, the number of the second node and the number of the third node respectively. The numbers of nodes are numbered, and each node is randomly distributed in the network system space. In the network system, some nodes are in a stationary state, and some nodes are in a moving state, for example, unmanned aerial vehicles, vehicles and mobile terminal devices in a moving state. The nodes in a moving state change the connection state between nodes in the network, and these changes cause the dynamic change of the network topology, thereby affecting the information exchange and resource sharing between nodes. Therefore, not only the old nodes need to be deleted from the network topology and the new nodes need to be included in the network topology, but also the connection relationship between nodes and the node resource information need to be updated in time, so as to realize the dynamic grouping of nodes in the network.

[0052] According to an embodiment of the present application, referring to Figure 1 , which is a flowchart of a distributed node grouping method for task cooperation. The method is applied to each node and includes the following steps S1, S2 and S3. In order to better understand the present application, each step will be described in detail below in combination with specific embodiments.

[0053] Step S1, constructing an initial node grouping table for the current node, the node grouping table including the resource state of the discovered other nodes.

[0054] According to an embodiment of the present application, the initial node grouping table can be constructed by initializing the node grouping table (the node grouping table is denoted as ) in a periodic updating manner. The resource state of the other nodes recorded in the node grouping table includes computing resources and storage resources, in addition, the node grouping table also includes the communication cost between the current node and the other nodes. Therefore, the node grouping table of each node maintains the grouping information in the network.

[0055] According to an embodiment of the present application, the process of initializing the node grouping table in a periodic updating manner includes: filling the node grouping table to ensure that the basic information of each node and the information of its directly connected neighbor nodes are recorded more accurately. Wherein, the "neighbor node" refers to the directly connected other nodes . For the directly connected other nodes , the current node and the other nodes The physical distance between nodes is set as the communication cost. Among them, a shorter physical distance between nodes usually means low communication cost and fast data transmission speed, thereby ensuring efficient network communication. By linking the communication cost with the distance, it is possible to give priority to paths with lower communication costs for data transmission, thereby improving the communication efficiency and performance of the entire network. For non-directly connected nodes, their communication costs are set to infinity during initialization to indicate that these nodes in the network are unreachable under current conditions. That is, direct communication is not possible without a relay. The node grouping table of the present invention helps to avoid selecting currently inaccessible paths during resource allocation and resource collaborative decision-making, thereby optimizing network performance and improving the efficiency and accuracy of resource management and resource collaborative decision-making.

[0056] According to one embodiment of the present invention, in order to control the resource coordination process in the mobile edge computing scenario and achieve low energy consumption, the transmission power upper limit of the node is set to , and set the maximum communication cost threshold between nodes Among them, since the energy consumption of task transmission is directly related to the distance between nodes, and the nodes include direct connections and indirect connections, when the distance between two nodes exceeds the threshold When the two parties communicate directly, they cannot communicate directly and need multi-hop relay communication.

[0057] According to one embodiment of the present invention, initially, when the node and nodes Direct connection and distance between Exceeding the threshold When , the communication cost will be set to infinity, as shown in the following formula:

[0058] , (1)

[0059] in, Representation node and nodes The distance between Indicates the maximum distance between nodes. and nodes The distance between them exceeds the threshold When , the communication cost will be set to infinity, that is, ,in, Representation node and nodes The communication cost between nodes and nodes The distance between Not exceeding the threshold When , the communication cost between the current node and the directly connected node is: . And the communication cost between a node and itself is 0.

[0060] According to one embodiment of the present invention, during initialization, the communication cost between nodes that are not directly connected to the current node is unknown. When the periodic update method is periodically executed, the node grouping table is periodically updated, and the communication cost between the current node and the indirectly connected nodes is updated to be represented by the multi-hop communication cost. Therefore, for indirectly connected nodes, the communication cost is as follows:

[0061] , (2)

[0062] in, Representation node and nodes The first Hop communication cost, Indicates the maximum number of hops. Needs to be satisfied , Representation node and nodes The first The hop communication cost needs to meet the maximum communication cost threshold between nodes For example, nodes a and c are indirectly connected through relay node b, that is, the first hop communication cost between nodes a and c is the distance between nodes a and b, and the second hop communication cost is the distance between nodes b and c, and the distance between nodes a and b and the distance between nodes b and c are both less than or equal to the maximum communication cost threshold. According to one embodiment of the present invention, in the node grouping table, see Figure 2 , which is a schematic diagram of the structure of the node grouping table. Each row in the figure includes the resource status of another node and the network connection status between the current node and the other node. The first column is the number of each node, and the middle two columns represent the computing resources and storage resources of different nodes. The last column represents the communication cost, in which the value represents the connection status and communication cost between nodes. When the communication cost exceeds a certain threshold, the connection between the nodes will be disconnected. At this time, the communication cost is set to infinity. Schematically, as shown in Figure 2 As shown, the computing resources of nodes 1, 2, ..., n are 0, 5, ..., 6, respectively, in CU. The storage resources of nodes 1, 2, ..., n are 0, 2, ..., 3, respectively, in TB. The communication cost between nodes 1, 2, ..., n and the current node is 21 meters, 220 meters, ..., infinity. Each entry in the node grouping table is dynamic and changes with the dynamic network environment and task requirements.

[0063] Step S2, when the current node receives a task, the task is cooperatively executed by using resources of other nodes according to the latest node grouping table, and a task execution result is obtained.

[0064] According to one embodiment of the present application, the way of cooperatively executing the task by using resources of other nodes comprises: selecting a resource cooperation path for the task according to the latest node grouping table, wherein the resource cooperation path comprises other nodes that can meet resource requirements of the task and have minimum communication cost; and the current node offloads the task to other nodes on the corresponding resource cooperation path for cooperative execution, and obtains a task execution result. The technical solution of this embodiment can at least achieve the following beneficial technical effects: the communication cost mainly considers energy consumption of task transmission, and the node selects the optimal resource cooperation path according to resource states of other nodes in the node grouping table and communication costs between the node and other nodes, that is, selects nodes that can cooperatively process the task and have minimum communication cost to meet current task computing and storage resource requirements, thereby improving task processing efficiency and energy utilization rate.

[0065] Step S3, updating the node grouping table according to the task execution result, and the updating manner comprises: a periodic updating manner configured to update the node grouping table according to an updating period, wherein the updating period is dynamically adjusted according to the task execution result; and a task-driven updating manner configured to update the node grouping table in real time when the task execution result shows that the task execution fails.

[0066] The periodic updating manner and the task-driven updating manner in step S3 will be described below.

[0067] I. Periodic updating manner

[0068] According to one embodiment of the present application, in the process of updating the node grouping table according to the updating period, an adaptive adjustment mechanism is designed to dynamically adjust the updating period, and the adjustment mechanism can dynamically adjust the execution period of the periodic updating manner according to network conditions and task completion conditions. The method of the present application can flexibly cope with changes in network environment and optimize the node grouping process by adjusting the updating period in real time, thereby improving the energy efficiency and service quality of the overall network.

[0069] According to one embodiment of the present application, in the adaptive adjustment mechanism, the dynamic adjustment manner of the updating period comprises steps a1, a2 and a3:

[0070] Step a1, according to the task execution result of each task of the current node in a predetermined time period, the number of task execution failures is counted.

[0071] According to one embodiment of the present application, it is determined whether the task is successfully executed according to the case of the task execution result. For example, if the task execution result shows the failure information of the resource cooperation failure, it is determined that the task execution fails. Since each task has a deadline, if the task is not completed before the deadline, the task execution result shows the failure information of the timeout, and it is determined that the task execution fails.

[0072] In step a2, the dynamic adjustment parameter for indicating the quality of service of the mobile edge network is determined according to the network condition quantitative value obtained by the current node in the current evaluation and the proportion of the tasks not completed by the node, wherein the proportion is the ratio of the number of the task execution failures to the total number of all tasks.

[0073] According to one embodiment of the present application, the network condition quantitative value is calculated as follows:

[0074] , (3)

[0075] wherein, represents the network condition quantitative value, represents the probability that the computing resource requirement of one task of the current node can be met within the hop, represents the number of the current node, represents the maximum hop number within the preset communication cost threshold, represents the hop number, represents the probability that the storage resource requirement of one task of the current node can be met within the hop, represents the node connectivity rate of the current node to the nodes in the network except the current node within the hop, represents the total number of the nodes in the network, represents the node set of the current network except the node, represents the i-th node in the network except the current node, represents the number of a node in the network except the current node, represents the node connectivity rate of the current node to the nodes in the network except the current node within the hop, , represents the total number of the nodes in the network, represents the node set of the current network except the node, represents the i-th node in the network except the current node, represents the number of a node in the network except the current node, represents the node connectivity rate of the current node to the nodes in the network except the current node within the hop, , represents the total number of the nodes in the network, represents the node set of the current network except the node, represents the i-th node in the network except the current node, represents the number of a node in the network except the current node, represents the node connectivity rate of the current node to the nodes in the network except the current node within the hop, , The embodiment can at least achieve the following beneficial technical effects: in the edge network, the main consideration is the connectivity of the task, the connectivity rate also includes whether the current network topology can meet the task offloading demand, and whether the current network connection can meet the resource demand of the task needs to be considered. Therefore, the network condition of the present application is embodied in the probability index of meeting the task offloading demand, thereby reflecting the overall service quality of the network, and providing accurate data support for adjusting the grouping method execution period.

[0076] The calculation principle process of formula (3) in the above embodiment is described below:

[0077] Firstly, the connectivity of the edge network can be measured by using the connectivity rate. Therefore, the connectivity rate of the network is represented as follows:

[0078] , (4)

[0079] wherein, represents the total number of nodes in the network, represents the connectivity between the node and the node . In actual application, when initializing the node grouping table, the node includes the unique identifier of the node itself, i.e. the mac address, when broadcasting the announcement or reply, and the total number of nodes in the edge network can be known by counting according to the unique identifier . The connectivity means whether two nodes are directly connected, wherein the connectivity between nodes can be represented as follows:

[0080] , (5)

[0081] Secondly, the connectivity of the task is considered, i.e. whether the current network topology can meet the task offloading demand. Therefore, whether the node grouping table of the current node can meet the resource coordination demand of the task needs to be considered to determine whether the current network connection state can solve the current task. The probability that the computing resource demand of a task of the current node can be met within hops is represented as follows:

[0082] , (6)

[0083] wherein, represents the set of other nodes directly connected with the node when a task of the current node is transmitted to the th hop, represents the computing resource of the other node , and represents the computing resource of the current node the computing resource requirement of one task. For example The set includes 4 nodes, and There are 2 nodes in the set satisfying the current node the computing resource requirement of one task, the first hop is calculated as one half. The probability values of each hop in the first hop are summed up to obtain .

[0084] The probability that the storage resource requirement of one task of the current node can be satisfied within the first hop is expressed as follows:

[0085] , (7)

[0086] wherein represents the storage resource of other nodes , and represents the storage resource requirement of one task of the current node . The probability value is calculated in the same way as the principle of formula (7).

[0087] Then, according to the above formula (5), the connectivity rate of the current node is calculated, and the calculation method is as follows:

[0088] , (8)

[0089] According to formula (8), the node connectivity rate of the node in the first hop can be expressed as follows:

[0090] , (9)

[0091] wherein represents the number of nodes contained in the network within the first hop, and represents all the nodes contained in the network except the nodes in the first hop. For example, the network within the first 3 hops contains 3 nodes, which are node 1 in the first hop, node 2 in the second hop, and node 3 in the third hop. Therefore, the value is 3 at this time, if the value is 7 and includes node 1, node 2, node 3, node 4, node 5, node 6 and node 7, then the set includes node 4, node 5, node 6 and node 7 in addition to the 3 nodes contained in the network within the first 3 hops.

[0092] ​Finally, the network condition quantitative value can be represented as formula (3) shown above according to formula (6), (7) and (8) in the above embodiments.

[0093] According to an embodiment of the present application, the dynamic adjustment parameter determination method is as follows:

[0094] , (10)

[0095] wherein, represents the dynamic adjustment parameter, represents the proportion parameter, represents the number of task execution failures of the current node in the current time period compared with the total number of tasks in the time period , , represents the proportion parameter, represents the network condition quantitative value obtained by the current evaluation. Wherein, 0≤α≤1, 0≤β≤1. The technical scheme of the embodiment can achieve at least the following beneficial technical effects: the task completion condition can reflect the network service quality directly, and the dynamic adjustment parameter is determined in combination with the network condition quantitative value, so that the update period is intelligently adjusted, the network environment changes can be flexibly coped with, the node grouping process is optimized, and the overall network energy efficiency and service quality are improved.

[0096] Step a3, dynamically adjusting the update period according to the dynamic adjustment parameter and the preset threshold range, wherein: when the dynamic adjustment parameter is less than the lower limit threshold of the preset threshold range, the update period is reduced; when the dynamic adjustment parameter is within the preset threshold range, the update period is kept unchanged; when the dynamic adjustment parameter is greater than the upper limit threshold of the preset threshold range, the update period is increased.

[0097] According to an embodiment of the present application, the preset threshold range is set to , the lower limit threshold is , and the upper limit threshold is . The double-threshold determination method is used to dynamically adjust the update period as follows:

[0098] Dynamic adjustment method one: when the dynamic adjustment parameter is less than the lower limit threshold , that is, ​​, which indicates that the current task failure number is high or the task completion rate corresponding to the current node grouping table is low, that is, the current node grouping table cannot support the edge network to efficiently process the current task, and the low task completion rate will also lead to an increase in the execution frequency of the task-driven update mode. Therefore, the update period of the periodic update mode needs to be reduced to support the edge network to efficiently process the current task and reduce the node energy consumption.

[0099] Dynamic adjustment mode two: when the dynamic adjustment parameter is greater than the upper threshold, that is, , which indicates that the current task failure number is small or the task network completion rate corresponding to the current node grouping table is high, and the current grouping information is sufficient to support the edge network to efficiently process the current task, but the current periodic update mode exceeds the expected energy consumption. Therefore, the update period needs to be increased to reduce the energy consumption of the node to improve the energy efficiency of the edge network.

[0100] Dynamic adjustment mode three: when the dynamic adjustment parameter is within the preset threshold range, that is, , which indicates that the current node grouping table can support the execution of the edge network task, and the current periodic update mode meets the expected energy consumption. Therefore, the update period remains unchanged. The present application not only guarantees the task execution efficiency and improves the service quality, but also improves the energy efficiency of the edge network and reduces the energy consumption of the edge network node due to the execution of the task-driven update mode.

[0101] According to an embodiment of the present application, the pseudo code flow of the dynamic adjustment mode is as follows:

[0102] Input: task failure number , total number of tasks, node grouping table, lower threshold of adjustment , upper threshold

[0103] 1: Calculate the network condition quantitative value ;

[0104] 2: Calculate the proportion of tasks not completed by the node

[0105] 3: Calculate the dynamic adjustment parameter

[0106] 4: if the dynamic adjustment parameter is less than the lower threshold then

[0107] 5: Output the dynamic adjustment parameter and reduce the update period

[0108] 6: else if the dynamic adjustment factor is greater than the upper threshold then

[0109] 7: Output the dynamic adjustment parameter and increase the update period

[0110] 8: else

[0111] 9: Output dynamic adjustment parameters and maintain the current update period unchanged;

[0112] 10: end if

[0113] 11: Output: dynamic adjustment parameters .

[0114] According to one embodiment of the present application, the periodic update method includes four parts: 1) initializing the node grouping table in the manner of the above-mentioned embodiment, 2) periodically updating the grouping table, 3) responding to the connection update of neighbors, and 4) triggering the grouping table update.

[0115] 2) Periodic grouping table update method: periodically update the grouping table By sending the updated node grouping table to each neighbor node and checking the response of the neighbor node, the reachability changes of the nodes can be discovered and handled in a timely manner. If a neighbor node does not respond within a specified time, the neighbor node will be considered unreachable, and the grouping table will be updated accordingly to ensure the accuracy and timeliness of the grouping information. This method can cope with network dynamic changes such as node movement, new node joining or old node leaving, etc.

[0116] 3) Responding to the connection update of neighbor nodes: when the current node receives a connection update from a neighbor node, receives the updated grouping table of the neighbor node, and updates the current node grouping table based on the current node grouping table and the updated grouping table of the neighbor node , the updated grouping table is broadcast to all neighbor nodes. This ensures that each node in the network can obtain the latest grouping table, which is crucial for maintaining the overall communication efficiency and resource allocation efficiency of the network. This mechanism supports self-maintenance and self-optimization of the network, which is particularly important for dynamic changing edge network environment.

[0117] 4) Triggering grouping table update method: when the node grouping table of the current node changes, send the updated node grouping table to all neighbor nodes to ensure that each node in the network has the latest grouping table.

[0118] According to one embodiment of the present application, the current node receives the updated node group table sent by other nodes directly connected to the current node, and the current node traverses each entry in the updated node group table of other nodes and the node group table of the current node to calculate a new communication cost, and updates the node group table of the current node according to the new communication cost. Specifically, according to the node group table of other nodes and the node group table of the current node, a new communication cost between the current node and other nodes is calculated; when the new communication cost is different from the communication cost between the current node and other nodes before the update, the communication cost between the current node and other nodes in the node group table of the current node is updated according to the size of the new communication cost. Wherein, the process of updating the communication cost between the current node and other nodes in the node group table of the current node follows the following principles:

[0119] Principle 1: If the new communication cost is the distance between two directly connected nodes and is greater than the maximum communication cost threshold, it means that the current nodes cannot be directly connected for communication, therefore, the group table needs to be updated, i.e. the relevant information in the group table is deleted.

[0120] Principle 2: If the new communication cost is less than the communication cost between the current node and other nodes, it means that a smaller data transmission path is found, therefore, the group table needs to be updated, i.e. the new communication cost is taken as the communication cost between the current node and other nodes.

[0121] Principle 3: There is no such entry in the node group table of the node, which means that the two nodes cannot be connected originally, but can be connected now. That is, the node group table of the current node shows that there is no communication cost information between the current node and the other node. Therefore, the group table needs to be updated, i.e. the relevant communication cost information of the current node and the other node needs to be added to the group table.

[0122] According to one embodiment of the present application, the pseudo code flow of the periodic update method is as follows:

[0123] Input: current node group table , neighbor node updated group table

[0124] 1: Initialize the node group table ;

[0125] 2: Loop every fixed time interval (according to the update period)

[0126] 3: Send the updated node group table to each neighbor node ;

[0127] 4: Check the neighbor node response

[0128] 5: if the neighbor node does not respond do

[0129] 6: Consider neighbor node unreachable, update group table;

[0130] 7: end if

[0131] 8: while receiving connection update from neighbor node do

[0132] 9: for each group table updated by neighbor node do

[0133] 10: Calculate new communication cost according to the above embodiment;

[0134] 11: if new communication cost is less than the communication cost between current node and other node or there is no such item in group table then

[0135] 12: update group table ;

[0136] 13: end if

[0137] 14: if new communication cost is the distance between two nodes directly connected and new communication cost ≥ then

[0138] 15: delete this item from ;

[0139] 16: end if

[0140] 17: end for

[0141] 18: end while

[0142] 19: if node group table changes then

[0143] 20: send update to all neighbor nodes;

[0144] 21: end if

[0145] 22: output updated node group table .

[0146] In general, the periodic update method in the present application can periodically and actively update the network node group table, establish a basic network node group table, thereby providing the basis for resource coordination for task execution, and maintain the basic node group information, which has a significant effect on static nodes and resource level stable node information maintenance. The periodic update method not only improves the efficiency of resource allocation, but also reduces the execution probability of the task-driven update method.

[0147] 2. Task-driven update method

[0148] According to one embodiment of the present invention, a task-driven update method includes: when the task execution result of the current node shows that the task execution has failed and the task resource collaboration has failed, a task error report is generated, and the node grouping table of the current node is updated according to the task error report; when there are no other nodes that can participate in resource collaboration in the node grouping table of the current node, a new task resource collaboration request is initiated in the form of a broadcast, and the node grouping table is updated according to the task reply sent by another node indicating that it can participate in resource collaboration. The present invention updates the node grouping table on demand according to the execution status of the specific task based on the task-driven update method. This ensures that resource allocation is closely matched with actual needs, achieves flexible adaptation to changes in the network environment, and optimizes the efficiency of resource collaboration.

[0149] According to one embodiment of the present invention, the task-driven update method includes the following five parts:

[0150] 1) Handling resource collaboration failure information:

[0151] In actual applications, resource coordination may fail for a variety of reasons, such as disconnection, target node overload, or other network problems. When resource coordination fails, a task error report is generated immediately. , record the reasons for failure and related information in detail, and use Information update group table. For example, task error report If the failure is caused by resource changes, the resource information in the grouping table should be updated; if the communication cost is too high, the node information should be deleted to avoid future attempts to use the known failed path. The message will be broadcast to the affected neighbor nodes so that they can also update their grouping information, ensuring that nodes in the network can adjust their resource coordination paths in a timely manner to avoid repeated failures.

[0152] 2) Initiate a new task request:

[0153] When there is no node in the group table that can perform resource coordination, active measures will be taken to create and broadcast task requests. , exploring new available resource collaboration paths to dynamically adapt to network changes. This approach allows the network to find new resource collaboration opportunities when current connections fail or resources are insufficient. If certain nodes are already known to be available for resource collaboration, they will be prioritized to improve the efficiency of the resource collaboration process.

[0154] 3) Respond to task requests:

[0155] When a node receives a task request from another node When a task request is received, the node first assesses its ability to execute the task request. If it can execute, the node creates and sends a task reply directly to the task request node, indicating that the resource coordination task can be executed at this node. If the current node cannot execute, it forwards the task request outward to expand the search range and increase the likelihood of finding a resource coordination target.

[0156] 4) Forwarding task replies:

[0157] Upon receiving a task reply , the node needs to update its node grouping table to reflect the new connection status and resource coordination path information. If the node receiving the task reply is the original request node, the task search for executable nodes is complete, and resource coordination operations can begin. Otherwise, the node will be responsible for further forwarding to the original request node to ensure the successful resolution of the resource coordination request.

[0158] 5) Node grouping table maintenance:

[0159] Continuously monitor the information in the node grouping table to confirm whether the information in the grouping table has expired. Expired or no longer valid information will be removed to ensure that the grouping table reflects the latest network status, thereby maintaining the efficiency and accuracy of the network.

[0160] According to one embodiment of the present application, the pseudo code flow based on the task-driven update method is as follows:

[0161] Input: resource coordination failure information, task request , task reply , task error , node grouping table ;

[0162] 1: while there is resource coordination failure information do

[0163] 2: generate a task error report;

[0164] 3: update the grouping table according to the task error report;

[0165] 4: broadcast the task error report to the affected neighbor nodes;

[0166] 5: if there is no node in the node grouping table that can perform resource coordination then

[0167] 6: create and broadcast a task request;

[0168] 7: else

[0169] ​8: Use existing nodes that can perform resource coordination

[0170] 9: end if

[0171] 10: end while

[0172] 11: while receiving task requests do

[0173] 12: if can perform current task request then

[0174] 13: create and send task reply

[0175] 14: else

[0176] 15: forward task request

[0177] 16: end if

[0178] 17: end while

[0179] 18: while receiving task replies do

[0180] 19: update grouping table according to task replies

[0181] 20: if not original task request node then

[0182] 21: forward task reply to original task request node

[0183] 22: end if

[0184] 23: end while

[0185] 24: remove expired information from grouping table

[0186] 25: output updated grouping table .

[0187] According to one embodiment of the present application, the overall flow of the node grouping method will now be described. Referring to FIG. 8, which is a schematic diagram of the execution flow of the edge node grouping method. The execution flow is as follows: Figure 3

[0188] First, the user generates tasks: including generated task 1, task 2 and task 3, wherein task 1 and task 3 are two tasks that can be executed in parallel, task 2 cannot be executed in parallel with task 1 and 3, task 2 is divided into two subtasks that can be executed in parallel, obtaining task 21 and task 22. The node obtains two tasks that can be executed in parallel, such as task 1 and task 3, or task 21 and task 22.

[0189] ​Secondly, selecting nodes: using resource coordination strategy to coordinate processing of two tasks, including selecting nodes for each task according to the latest node grouping table, determining resource coordination path, and selecting nodes which can meet resource requirements of the tasks and have minimum communication cost.

[0190] Then, attempting transmission processing: unloading tasks to selected nodes, including attempting transmission processing of the tasks according to the resource coordination path, so as to realize coordinated processing of the tasks.

[0191] Thirdly, judging whether the task is completed: when the task is executed, the selected nodes need to return task execution results to the source node which requests resource coordination, so as to judge whether the task is completed. In this process, the source node needs to wait for response of the selected nodes, so as to ensure correct execution of the task. When the source node cannot receive response of the selected nodes, the source node needs to reselect nodes according to the latest grouping table, unload the task to new nodes for processing, until the task is completely processed and the processing is ended. However, due to dynamic change of network topology structure, the selected nodes can be changed, which can cause task execution result to show failure, indicating that the task is not completed.

[0192] Finally, when the task is not completed, triggering an update mode based on task driving to update the grouping table, so that the source node can update the grouping table in time according to dynamic change of the network topology structure. At the same time, the dynamic adjustment mode in the above embodiment is used to dynamically adjust the update period, and the grouping table is updated periodically according to the adjusted update period.

[0193] According to one embodiment of the present application, based on the above single node grouping method execution flow, the interaction mode between multiple nodes is described when the multiple nodes respectively execute the grouping method according to the above embodiment. Figure 4It is a schematic diagram of the principle of the node grouping method interaction process. The figure includes node A, node B and node C. Each node selects a node for the task generated or received by itself according to its own node grouping table, transmits the task to the selected node for processing, and judges whether the task is completed according to the response of the selected node. If not, the node needs to be reselected and the node grouping table is updated by using the grouping method of the application. Among them, since node A and node B are in effective connection, node B and node C are in effective connection, and node A and node C are in invalid connection, “effective connection” means that two nodes can exchange updated grouping table information with each other to complete the information maintenance of the grouping table, and can also transmit resource collaborative data. “Effective connection” means that the connectivity between two directly connected nodes is 1, that is, the connectivity state. “Invalid connection” refers to a connection with too high communication cost or too few remaining resources of the corresponding node, that is, the two nodes of “invalid connection” do not transmit resource collaborative data. The application supports dynamic adjustment of the update period to ensure the update frequency of the information in the node grouping table to maintain the quality of resource collaboration.

[0194] According to an embodiment of the application, a distributed node grouping system based on the grouping method described in the above embodiment is provided. The system is configured in each node of a plurality of nodes for providing edge services in a distributed mobile edge network. The system comprises: a task collaborative execution module, configured to, when a task is received or generated by a current node, use the resources of other nodes in the latest node grouping table to collaboratively execute the task to obtain a task execution result; a dynamic adjuster, configured to dynamically adjust an update period according to the situation of the task execution result; a periodic update module, configured to build an initial node grouping table for the current node and update the node grouping table according to the update period; and a task-driven update module, configured to update the node grouping table immediately when the task execution result shows that the task execution fails.

[0195] According to an embodiment of the application, the periodic update module is configured to perform the periodic update mode in step S1 and step S3 of the above embodiment, the task collaborative execution module is configured to perform step S2 of the above embodiment, and the task-driven update module is configured to perform the task-driven update mode in step S3 of the above embodiment. The dynamic adjuster is configured to dynamically adjust the update period in the periodic update mode in step S3.

[0196] According to one embodiment of the present application, one of the main functions of the dynamic adjuster is to monitor the number of task failures in real time and calculate the task completion rate of the network. This includes evaluating the completion of various tasks in the current network, thereby providing accurate data support for adjusting the grouping method. By continuously tracking the progress of task completion, the dynamic adjuster can accurately judge the overall service quality of the network, especially when dealing with complex or variable tasks, it can improve the task completion rate. Based on the monitoring of the task network completion rate, another main function of the dynamic adjuster is to dynamically adjust the update cycle of the periodic update method. When the task completion rate is lower than expected, the dynamic adjuster can guide the method to run with a more frequent cycle to quickly respond to changes in network status and increases in task demand, thereby ensuring that tasks can be completed on time and improving the overall network service quality. Conversely, when the task completion rate is high, the dynamic adjuster can reduce the execution frequency of the periodic update method, especially in mobile edge network environments where node energy is limited, to reduce unnecessary resource consumption, thereby reducing energy consumption and optimizing resource utilization. This not only helps to improve energy efficiency, but also helps to extend the service life of the device and reduce the operation and maintenance cost of the entire network.

[0197] To verify the beneficial effects of the present application, the following simulation experiments of the present application are carried out.

[0198] First, an evaluation model is set for the simulation experiment of the method of the present application, including a resource collaborative transmission energy consumption model and a time delay model. The time delay model is used to measure the completion time of the task, and then the flag of whether the task fails is obtained. The energy consumption model is used to measure the energy consumption of the collaborative execution of the task resources. The two models are only used to evaluate the effect in the simulation experiment, and in the actual system, the time delay and energy consumption are obtained by actual test records.

[0199] According to one embodiment of the present application, the energy consumption model not only considers the energy consumption of the node grouping method, but also considers the energy consumption of the task in the process of unloading transmission and result return. Therefore, the energy consumption model can be expressed as follows:

[0200] , (11)

[0201] wherein, represents the total energy consumption of the entire edge network task in the process of resource collaborative execution, represents the total grouping energy consumption of the entire node grouping method.

[0202] According to one embodiment of the present invention, since task computational energy consumption has no impact on the performance evaluation of the node grouping method, computational energy consumption is not taken into consideration. That is, energy consumption consists of two parts: one is the energy consumption during the offloading process, and the other is the energy consumption during the result transmission process. At the same time, considering the task offloading and execution for each hop, the model calculates its energy consumption separately, and then accumulates it to obtain the total task energy consumption. The total task energy consumption is expressed as follows:

[0203] , (12)

[0204] in, Indicates that the task is Energy consumption during the jump offloading process (i.e., the task is transferred to the first Transmission energy consumption of the node corresponding to the hop), Indicates that the task is Energy consumption during the jump result transmission process, and Respectively represent the tasks in The transmit power and receive power during the offloading and execution of the jump, Indicates the amount of data for the task, Indicates the amount of result data after task execution. Indicates the The transmission rate of the hop. Indicates the maximum number of hops.

[0205] According to one embodiment of the present invention, when maintaining and updating the node grouping table, the amount of data transmitted between nodes is often much smaller than the task's data volume. Therefore, the energy consumption of the node grouping method is primarily related to the number of information exchanges between nodes during the maintenance and update of the node grouping table. In the method of the present invention, the energy consumption of the node grouping method is primarily affected by the frequency of node grouping table maintenance and updates. Therefore, the energy consumption of the node grouping method can be expressed as follows:

[0206] , (13)

[0207] in, Represents the total energy consumption of the entire node grouping method. represents the energy consumption coefficient of the node grouping method, Indicates the number of node grouping method information exchanges. Maintenance frequency of node grouping information There is a relationship between: ,in, represents the information interaction coefficient of the node grouping method, represents the execution time of the node grouping method, The update period of the periodic update method, The number of times of on-demand detection in the task-driven update method in the update method, The number of nodes in the edge network, and each node in the edge network can process tasks, The number of all nodes in the edge network. The number of reference nodes, which is a standard value. Since the types of edge devices in the edge network are different, they have different functions, such as gateway nodes or security nodes that implement security protocols, encrypt data, and access control. These nodes with special functions can be called reference nodes. The present application links the energy consumption of the node grouping method with the maintenance frequency of the node grouping information, and further adjusts the energy consumption of the node grouping method through the maintenance frequency of the node grouping information.

[0208] According to an embodiment of the present application, in the edge computing environment, the delay model for resource coordination is as follows:

[0209] , (14)

[0210] wherein, Total delay of the entire resource coordination process, Offloading delay, Computing delay, Result return delay. The transmission delay The time for the task data to be calculated to be transmitted from the MD or source node to the selected node (i.e., the target node). The transmission delay of each hop depends on the amount of task data and the transmission rate of the hop. Therefore, the total offloading delay is the sum of the offloading delays in all hops. The computing delay The time required for the target node to process the transmitted task, and the result return delay is the time for the task execution result to be returned from the target node to the source node. The result return delay depends on the amount of result data and the transmission rate of each hop. Therefore, the total result return delay is the sum of the return delays in all hops. The entire delay model of the present application integrates the transmission, processing, and return processes of the task data in the edge network. Through the model, the key factors affecting the resource coordination delay can be analyzed in the simulation experiment, and the design and operation of the edge network can be optimized accordingly to reduce the task processing time and improve the overall efficiency. For example, by improving the processing capacity of the edge nodes or optimizing the efficiency of the computing resources , the computing delay can be reduced. Similarly, by increasing the transmission rate Or reduce the amount of task data And the amount of result data Both of which can effectively reduce the latency of offloading and result returning. This latency model is crucial for designing an efficient edge computing strategy, especially in application scenarios with high real-time requirements.

[0211] Secondly, the simulation parameter settings in the simulation experiment are shown in Table 1:

[0212] Table 1: Simulation parameter settings

[0213] Simulation parameters Value Unit Task number 1000 Dynamic node proportion Dynamic node speed 50 % Node total number 20-40 Figure 5-12 Figure 5-10 30 Figure 11

[0214] Finally, simulation verification comparison is carried out:

[0215] In the simulation verification process, the performance of the existing method and the method of the present application for processing mobile edge network resource coordination is compared. The method of the present application (referred to as TADNG) is compared with two existing methods. The first existing method is a Scheduled State Update (SSU) grouping method, referred to as the SSU aggregation algorithm. The second existing method is a Task-driven Resource Discovery (TRD) grouping method, referred to as the TRD aggregation algorithm. The simulation process and the verification comparison results are described as follows:

[0216] 1. Task generation and preparation: a group of tasks are randomly generated, which can be split into multiple subtasks that can be executed in parallel, and the subtasks are independent of each other. Each task requires a certain amount of computation and data transmission.

[0217] 2. Resource coordination path according to node grouping table: when a node receives or generates a task, it selects the optimal resource coordination path according to the information in the grouping table. When the node completes resource coordination, it updates the information in the grouping table to reflect the results of resource coordination. The information in the grouping table is also used to update the topology of the network to ensure the connectivity and stability of the network. When the information in the grouping table changes, the node sends an update to the neighbor nodes directly connected to it to ensure that each node in the network has the latest grouping table. Illustratively, after the user equipment determines the resource coordination decision scheme in combination with the task situation and the grouping table, the resource coordination scheme can be obtained Wherein, , , Respectively represent the first task, the second task, the Task respectively corresponding to the resource coordination node.

[0218] 3. Execute resource coordination strategy: according to the resource coordination strategy, the task is transmitted to the designated resource coordination node for cooperative execution, and the node grouping information is updated according to the execution of the task.

[0219] The verification comparison results are shown in Figure 12 , wherein Figure 5-10 is a task completion rate comparison result graph, the task completion rate is the ratio of the number of successfully completed tasks to the total number of tasks, and whether the task is successfully completed is determined according to whether the delay calculated by the delay model formula (14) provided in the above embodiment exceeds the preset delay threshold. If the calculated delay is within the preset threshold, it means that the task has been successfully completed. Figure 5 and Figure 6 is an energy consumption comparison result graph, the energy consumption is calculated by using the energy consumption model formula (11) provided in the above embodiment, and the unit of energy consumption is KJ.

[0220] The following describes each comparison result graph in Figure 7 :

[0221] Figure 8 is a task completion rate comparison result graph of the existing method and the method of the present application under different dynamic node proportions. In the graph, the vertical coordinate is the task completion rate, and the horizontal coordinate is the dynamic node proportion. It can be seen that, with the increase of dynamic nodes, the task completion rate of the method of the present application remains at the highest state, which shows that the present application can still maintain a relatively high task completion rate level under the condition of high dynamic node proportion.

[0222] Figure 9 is a task completion rate comparison result graph of the existing method and the method of the present application under different node quantities and with each dynamic node speed being 20 m / s. In the graph, the vertical coordinate is the task completion rate, and the horizontal coordinate is the node quantity, which is in units of pieces. It can be seen that, with the increase of the total number of nodes in the network, the task completion rate of the method of the present application is the highest, which shows that the present application can still maintain a relatively high task completion rate level under the condition of different node quantities.

[0223] Figure 10 is a task completion rate comparison result graph of the existing method and the method of the present application under different node quantities and with each dynamic node speed being 40 m / s. In the graph, the vertical coordinate is the task completion rate, and the horizontal coordinate is the node quantity, which is in units of pieces. It can be seen that, under the condition of high-speed movement of nodes, the task completion rate of the method of the present application is the highest under the condition of different node quantities, which shows that the present application can always maintain a relatively high task completion rate level under the condition of high-speed movement of nodes.

[0224] Figure 5-10The task completion rate comparison results between the existing method and the method of the present application under different task computation amount requirements are shown in the diagram. The vertical coordinate is the task completion rate, and the horizontal coordinate is the task computation amount requirement. It can be seen that the method of the present application can maintain a relatively high task completion rate level as the task computation amount requirement in the network increases.

[0225] Figure 11 The task completion rate comparison results between the existing method and the method of the present application under different average moving speeds of 15 nodes are shown in the diagram. The vertical coordinate is the task completion rate, and the horizontal coordinate is the average moving speed of the nodes, in meters per second. When the number of nodes is 15, it can be seen that the method of the present application can maintain a relatively high task completion rate level as the task computation amount requirement in the network increases.

[0226] Figure 12 The task completion rate comparison results between the existing method and the method of the present application under different average moving speeds of 30 nodes are shown in the diagram. The vertical coordinate is the task completion rate, and the horizontal coordinate is the average moving speed of the nodes, in meters per second. It can be seen that the task completion rate of the existing method decreases significantly as the average moving speed of the nodes in the network increases, while the method of the present application can maintain a relatively high task completion rate level.

[0227] In summary Figure 11 The comparison results show that compared with the traditional grouping method, the task completion rate of the method of the present application increases by 12.7% to 36.7% in the scenarios of mobility and task demand growth, showing strong adaptability and robustness.

[0228] The following Figure 12 and Figure 11-12 are described below:

[0229] ​ The energy consumption comparison results between the existing method and the method of the present application under different numbers of nodes and a speed of 20 meters per second for each dynamic node are shown in the diagram. The vertical coordinate is the task completion rate, and the horizontal coordinate is the number of nodes. It can be seen that in the case of nodes moving at 20 meters per second, the energy consumption of the method of the present application is lower than that of the SSU aggregation algorithm, and the energy consumption is significantly lower than that of the existing two methods when the number of nodes is large.

[0230] ​ The energy consumption comparison results between the existing method and the method of the present application under different numbers of nodes and a speed of 40 meters per second for each dynamic node are shown in the diagram. The vertical coordinate is the task completion rate, and the horizontal coordinate is the number of nodes. It can be seen that in the case of nodes moving at a high speed of 40 meters per second, the energy consumption of the method of the present application is lower than that of the SSU aggregation algorithm, and the energy consumption of the method of the present application is significantly lower than that of the existing two methods as the number of nodes increases.

[0231] In summary​ The comparison results show that, compared with the traditional grouping method, the energy consumption of the method is reduced by 10.15% to 33.38%, and in the case of high-speed mobile scene and large number of nodes, the energy consumption of resource cooperation can be obviously reduced, so that the method has more competitiveness in performance.

[0232] It should be noted that although the above describes the steps in a specific order, it does not mean that the steps must be performed in the above specific order, in fact, some of the steps can be performed concurrently, or even the order is changed, as long as the required function can be realized.

[0233] The present application can be a system, a method, and / or a computer program product. The computer program product can include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present application.

[0234] The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium can be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium include the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or punched tape, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se.

[0235] The embodiments of the application have been described above, the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes are obvious to those skilled in the art without departing from the scope and spirit of the described embodiments. The choice of terms used herein is intended to best explain the principles, practical applications or technical improvements in the art, or to enable other ordinary skilled persons in the art to understand the embodiments disclosed herein.

Claims

1. A distributed node grouping method for task collaboration, for grouping multiple nodes for providing edge services in a distributed mobile edge network, each node representing an edge device, the method being applied to each node, the method comprising: S1. Build an initial node grouping table for the current node. The node grouping table includes the resource status of other nodes discovered. S2. When the current node receives a task, it uses the resources of other nodes to collaboratively execute the task according to the latest node grouping table to obtain the task execution result; S3. Update the node grouping table based on the task execution results. The update methods include: The periodic update mode is configured to update the node grouping table according to an update period, wherein the update period is dynamically adjusted according to the task execution results, and the dynamic adjustment mode includes: According to the task execution results of all tasks of the current node within the predetermined time period, the number of task execution failures is counted; Determining a dynamic adjustment parameter for indicating the quality of service of the mobile edge network based on a quantified value of the network status obtained by the current node in a current evaluation and a ratio of uncompleted tasks of the node, wherein the ratio is a ratio of the number of task execution failures to the total number of all tasks; The update period is dynamically adjusted according to a dynamic adjustment parameter and a preset threshold range, wherein: when the dynamic adjustment parameter is less than a lower limit threshold of the preset threshold range, the update period is reduced; when the dynamic adjustment parameter is within the preset threshold range, the update period is kept unchanged; when the dynamic adjustment parameter is greater than an upper limit threshold of the preset threshold range, the update period is increased. The dynamic adjustment parameter is determined as follows: ， in, Indicates dynamic adjustment parameters, represents the weight parameter, Indicates the current node at the current time The previous time period The number of task execution failures in internal statistics With this time period The total number of tasks within The ratio, represents the weight parameter, Indicates the quantitative value of the current assessed network status; The task-driven update method is configured to update the node grouping table immediately when the task execution result shows that the task execution has failed.

2. The method according to claim 1, characterized in that The node grouping table also includes the communication cost between the current node and other nodes. In step S3, the periodic update method includes: Receive updated node grouping tables from other nodes directly connected to the current node, and calculate the new communication cost between the current node and the other nodes based on the node grouping tables of the other nodes and the node grouping table of the current node; When the new communication cost is different from the communication cost between the current node and the other nodes before the update, the communication cost between the current node and the other nodes is updated.

3. The method according to claim 2, characterized in that The resource status includes computing resources and storage resources, and the network status quantization value is calculated as follows: ， in, Indicates the quantitative value of network status, Indicates The current node can be satisfied within the hop The probability of computing resource requirements of a task, Indicates the number of the current node, Indicates the maximum number of hops that meets the preset communication cost threshold. Indicates the number of hops, Indicates The current node can be satisfied within the hop The probability of a task's storage resource requirement, Indicates the current node exist Hop within and except the current node The node connectivity rate of nodes in the network other than .

4. The method according to claim 1, wherein In step S3, the task-driven updating method includes: When the task execution result of the current node shows that the task execution failed and the task resource coordination failed, a task error report is generated, and the node grouping table of the current node is updated according to the task error report; When there are no other nodes that can participate in resource collaboration in the node grouping table of the current node, a new task resource collaboration request is initiated by broadcasting, and the node grouping table is updated based on the task reply received from another node indicating that it can participate in resource collaboration.

5. The method according to claim 2, characterized in that In step S2, the method of utilizing resources of other nodes to collaboratively execute the task includes: Select a resource coordination path for the task based on the latest node grouping table, where the resource coordination path includes other nodes that can meet the resource requirements of the task and have the lowest communication cost; The current node offloads the task to other nodes on the corresponding resource collaborative path for collaborative execution to obtain the task execution result.

6. A distributed node grouping system implemented based on the method according to any one of claims 1 to 5, the system being configured in each of a plurality of nodes for providing edge services in a distributed mobile edge network, the system comprising: The task collaborative execution module is used to use the resources of other nodes in the latest node grouping table to collaboratively execute the task when the current node receives the task and obtain the task execution result; Dynamic adjuster, used to dynamically adjust the update cycle according to the task execution results; The periodic update module is used to build an initial node grouping table for the current node and update the node grouping table according to the update period; The task-driven update module is used to update the node grouping table immediately when the task execution result shows that the task execution has failed.

7. A computer-readable storage medium, characterized in that A computer program is stored thereon, and the computer program can be executed by a processor to implement the steps of the method according to any one of claims 1 to 5.

8. An electronic device, characterized in that: include: one or more processors; as well as a memory, wherein the memory is used to store executable instructions; The one or more processors are configured to implement the steps of the method of any one of claims 1 to 5 by executing the executable instructions.

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