Edge service calling method and system

Abstract

The invention is suitable for the technical field of edge service calling, and particularly relates to an edge service calling method and system, and the method comprises the steps: dividing an access range of edge services, marking edge nodes, calculating the distance between the edge nodes, sorting the edge nodes according to the sequence of the distance from small to large, and obtaining the edge service. Obtaining distance lists, wherein each edge node corresponds to one distance list; and selecting a preset number of edge nodes from the front of the distance list to obtain cooperative nodes, searching the edge node closest to the user terminal after receiving a calling request uploaded by the user terminal, defining the edge node as a target node, and calculating a load value of the target node. According to the invention, by establishing the data channel, the call request can be directly processed, the network overhead and resource consumption are reduced, the use requirements of low delay and resource saving are considered, and the call efficiency of the edge service and the user experience are greatly improved.

Description

Technical Field

[0001] The present invention relates to the field of edge service calling technology, and in particular to an edge service calling method and system. Background Art

[0002] In edge computing environments, edge nodes are often geographically dispersed, have highly heterogeneous network connections, and are resource-constrained. This leads to frequent changes in edge services, such as online and offline operations, migrations, and load fluctuations. However, traditional edge service processing often relies on a centralized service architecture, where access requests are written to and distributed from the same node. While this approach provides excellent load balancing, it is prone to instability in edge environments. High concurrent requests can easily lead to data congestion, significantly reducing access request processing efficiency and impacting overall service quality and responsiveness.

[0003] Therefore, “how to preferentially write user requests to the nearest edge node” is the technical problem that the present invention needs to solve. Summary of the Invention

[0004] The purpose of the present invention is to provide an edge service calling method and system to solve the problem of "how to preferentially write user requests to the nearest edge node" raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for invoking an edge service, the method comprising:

[0007] Determine the access range of the edge service, mark the edge nodes, calculate the distances between the edge nodes, and sort the edge nodes in ascending order of distance to obtain a distance list, where each edge node corresponds to a distance list;

[0008] A preset number of edge nodes are selected from the front column of the distance list to obtain a collaborative node. When a call request uploaded by a user terminal is received, the edge node closest to the user terminal is found and defined as the target node. The load value of the target node is calculated and it is determined whether the load value is greater than a threshold. If so, a pre-stored probe request in the target node is triggered and distributed to the collaborative node in parallel.

[0009] receiving feedback information sent by the collaborative node, defining the collaborative node corresponding to the feedback information received first as the optimal node, forwarding the call request to the optimal node via the target node, and establishing a communication link between the optimal node and the user terminal;

[0010] The unique identifier of the optimal node is collected and cached in the target node. When the target node receives the call request again, it traces back to the request source and sends the unique identifier to the request source to establish a data channel between the request source and the optimal node.

[0011] Furthermore, the step of sorting the edge nodes to obtain a distance list includes:

[0012] Select several indicators of edge nodes and set weights;

[0013] The real-time values of the indicators in the edge nodes are collected, and the load value of each edge node is calculated based on the weights and the real-time values.

[0014] Furthermore, the method further comprises:

[0015] Constructing a time window, and updating the load value after the time window arrives;

[0016] The adjustment strategy is edited to adjust the order in the distance list using the load value.

[0017] Furthermore, the step of calculating the load value of the target node includes:

[0018] Use time as the horizontal axis and load value as the vertical axis to draw a load fluctuation trend graph;

[0019] Attribute information of edge nodes is collected, a label is generated, and the label is inserted into a load fluctuation trend graph, wherein the attribute information at least includes: a deployment location and a unique identifier.

[0020] Furthermore, the step of forwarding the call request to the optimal node via the target node includes:

[0021] Find the two closest edge nodes, form a mutual assistance pair, and select a set value from the load value;

[0022] When the load value of the edge node is greater than the set value, the mutual assistance pair is used to reconnect the call request.

[0023] Furthermore, the method further comprises:

[0024] Creating a layered architecture consisting of a top layer, a parent layer, and a child layer, and dividing the access range into a number of blocks;

[0025] A correspondence between the layered architecture and the blocks is established, and based on the correspondence, the edge nodes in the blocks are mounted into the layered architecture to generate an edge service graph.

[0026] Furthermore, the step of establishing a data channel between the request source and the optimal node includes:

[0027] Receiving a processing task sent by a user terminal via the data channel and dividing the task into a plurality of task segments;

[0028] The task fragments are distributed to mutual assistance pairs and target nodes based on the edge nodes corresponding to the unique identifiers.

[0029] Furthermore, the system includes:

[0030] A module is provided for dividing the access range of edge services, marking edge nodes, and calculating the distances between edge nodes. The edge nodes are sorted in ascending order of distance to obtain a distance list, wherein each edge node corresponds to a distance list.

[0031] The parallel module is used to select a preset number of edge nodes from the front column of the distance list to obtain the collaborative node. When receiving the call request uploaded by the user terminal, it finds the edge node closest to the user terminal and defines it as the target node. It calculates the load value of the target node and determines whether the load value is greater than a threshold. If so, it triggers the detection request pre-stored in the target node and distributes it to the collaborative node in parallel.

[0032] a building module, configured to receive feedback information sent by the collaborative node, define the collaborative node corresponding to the feedback information received first as the optimal node, forward the call request to the optimal node via the target node, and build a communication link between the optimal node and the user terminal;

[0033] A module is established to collect the unique identifier of the optimal node and cache it in the target node. When the target node receives a call request again, it traces back to the request source and sends the unique identifier to the request source, establishing a data channel between the request source and the optimal node.

[0034] Furthermore, the obtaining module includes:

[0035] The setting unit is used to select several indicators of edge nodes and set weights;

[0036] The calculation unit is used to collect the real-time values of the indicators in the edge nodes, and calculate the load value of each edge node based on the weight and the real-time value.

[0037] Furthermore, the parallel module includes:

[0038] Drawing unit, used to draw a load fluctuation trend graph with time as the horizontal axis and load value as the vertical axis;

[0039] The insertion unit is used to collect attribute information of the edge node, generate a label, and insert the label into the load fluctuation trend graph, wherein the attribute information at least includes: a deployment location and a unique identifier.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] By connecting the call request to the edge node closest to the user terminal, the transmission delay can be significantly reduced, the response speed of user requests can be improved, and the user experience can be optimized. By determining the optimal node, a method similar to "first responder takes over" can be used to quickly find nodes with available resources, avoiding request queuing, delays, or even loss due to node overload. At the same time, while minimizing the forwarding path, resource utilization is improved and resource idleness is avoided. By establishing a data channel, direct processing of call requests can be achieved, reducing network overhead and resource consumption, taking into account the use requirements of low latency and resource conservation, and greatly improving the call efficiency and user experience of edge services. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A flowchart of the edge service caller provided by an embodiment of the present invention;

[0043] Figure 2 A first sub-flow diagram of the edge service calling method provided by an embodiment of the present invention;

[0044] Figure 3 A second sub-flow diagram of the edge service calling method provided in an embodiment of the present invention;

[0045] Figure 4 A third sub-flow diagram of the edge service calling method provided in an embodiment of the present invention;

[0046] Figure 5 A fourth sub-flow diagram of the edge service calling method provided in an embodiment of the present invention;

[0047] Figure 6 A block diagram of the edge service invocation system provided by an embodiment of the present invention;

[0048] Figure 7 A block diagram of the components of the edge service invocation system provided by an embodiment of the present invention;

[0049] Figure 8 A block diagram of the composition of parallel modules in the edge service calling system provided by an embodiment of the present invention;

[0050] Figure 9 A block diagram of the components of the edge service calling system provided in an embodiment of the present invention;

[0051] Figure 10 A block diagram of the components of the establishment module in the edge service calling system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0053] In Example 1, Figure 1 The implementation process of the edge service calling method provided by the embodiment of the present invention is shown and is described in detail below:

[0054] S100: Divide the access range of the edge service, mark the edge nodes, and calculate the distance between the edge nodes. Sorting the edge nodes in ascending order of the distance to obtain a distance list, wherein each edge node corresponds to a distance list.

[0055] Based on geographic location, user distribution density, and network topology information, the access scope of edge services is divided. The access scope is also the coverage range of edge services. In the access scope, devices with computing, storage, and network capabilities are selected to obtain edge nodes. The specific position of the edge nodes in the access scope is located, the longitude and latitude coordinates of each node are determined, and the distance between the edge nodes is calculated. The nodes are sorted in this distance order to obtain a distance list.

[0056] For example, there are five edge nodes A, B, C, D, and E within the access range. With node A as the center, the distances from B, C, D, and E to A are calculated, and they are sorted in ascending order. The sorted results are defined as a distance list. Then, with node B as the center, the distances from A, C, D, and E to B are calculated to obtain the distance list corresponding to B. Similarly, each edge node corresponds to a distance list.

[0057] The distance list shows the distance between edge nodes. The smaller the distance, the lower the communication delay, which means that higher frequency and larger bandwidth links can be used, reducing congestion and transmission bottlenecks.

[0058] S200: Select a preset number of edge nodes from the front column of the distance list to obtain a collaborative node. After receiving the call request uploaded by the user terminal, find the edge node closest to the user terminal and define it as the target node. Calculate the load value of the target node and determine whether the load value is greater than the threshold. If so, trigger the detection request pre-stored in the target node and distribute it to the collaborative node in parallel.

[0059] From the front column of the distance list, a preset number of edge nodes are selected and defined as collaborative nodes, where the preset number is pre-determined by professionals; after receiving the call request uploaded by the user terminal, the call request is a service request sent by the user terminal to the edge node, requiring the execution of a specific calculation, processing or data interaction task; according to the call request, the user's location information is traced through GPS, base station positioning or Wi-Fi positioning, and the edge node closest to the user terminal is found and defined as the target node; a number of indicators that can reflect the operating status of the edge node are selected, such as CPU utilization, memory usage, bandwidth Occupancy rate, storage space utilization rate and current task queue length, etc., set a weight value for each indicator, adopt a weighted calculation method, multiply the real-time value of each indicator by the corresponding weight, and then summarize the results to obtain the load value of each edge node; compare the load value of the target node with the preset load threshold. If the load value is greater than the threshold, it means that the target node is at risk of overload, triggering the detection request pre-stored in the target node. The detection request usually contains information such as the current task status, resource requirements and data scale. The detection request is mainly used to query the available resources and processing capabilities of other nodes, and the detection request is distributed in parallel to the above-mentioned collaborative nodes.

[0060] S300: Receive feedback information sent by the collaborative node, define the collaborative node corresponding to the feedback information received first as the optimal node, forward the call request to the optimal node via the target node, and establish a communication link between the optimal node and the user terminal.

[0061] Receive feedback information from collaborative nodes. The feedback information usually includes the node's current available resource status, processing capacity, and expected response time. The collaborative node that first receives the feedback information is regarded as the node with the fastest response speed and the best network connectivity, and is defined as the optimal node. The target node acts as a relay to forward the call request received by the target node to the optimal node, directly establish a communication link between the optimal node and the user terminal, and upload subsequent tasks directly to the optimal node for processing.

[0062] S400: Collect the unique identifier of the optimal node and cache it in the target node. When the target node receives the call request again, it traces back to the request source and sends the unique identifier to the request source to establish a data channel between the request source and the optimal node.

[0063] Collect the unique identifier of the optimal node, which can be the node's IP address, node number, MAC address or other tag information that can uniquely identify the identity of the optimal node, and cache the unique identifier of the optimal node in the target node; when the target node receives a call request from the same or other user terminal again in the future, trace back the user terminal corresponding to the call request and define it as the request source, send the unique identifier of the optimal node to the request source, establish a data channel between the request source and the optimal node, and directly transfer the call request to the optimal node through this data channel.

[0064] In Example 2, Figure 2 The implementation process of the edge service calling method provided by an embodiment of the present invention is shown. The steps of sorting the edge nodes and obtaining the distance list are described in detail below:

[0065] S101: Select several indicators of edge nodes and set weights.

[0066] Determine several indicators of edge nodes, such as CPU utilization, memory utilization, bandwidth occupancy, storage space utilization, and current task queue length, and set a corresponding weight for each indicator.

[0067] S102: Collecting real-time values of indicators in edge nodes, and calculating a load value of each edge node based on the weights and real-time values.

[0068] The real-time value of each indicator is multiplied by the weight, and the calculation results of all indicators are superimposed to obtain the load value of each edge node.

[0069] In Example 3, Figure 3 The implementation process of the edge service calling method provided by an embodiment of the present invention is shown. The steps of calculating the load value of the target node are described in detail below:

[0070] S201: Draw a load fluctuation trend graph with time as the horizontal axis and load value as the vertical axis.

[0071] With time as the horizontal axis and the load value at the corresponding time as the vertical axis, a load fluctuation trend graph is drawn; since each edge node corresponds to a load value, each edge node also corresponds to a load fluctuation trend graph.

[0072] S202: Collect attribute information of edge nodes, generate labels, and insert the labels into a load fluctuation trend graph, wherein the attribute information at least includes: a deployment location and a unique identifier.

[0073] Collect the attribute information of the edge node, including the deployment location of the edge node, the unique identifier of the edge node, etc., and write the attribute information into the corresponding load fluctuation trend chart in the form of a label; for example, write "Node A (Shanghai-ID: 1003)" in the blank space of the load fluctuation trend chart corresponding to edge node A, where "Shanghai" is the deployment location. In actual operation, it should be accurate to the specific longitude and latitude, and ID is the unique identifier.

[0074] In Example 4, Figure 4 The implementation process of the edge service calling method provided by an embodiment of the present invention is shown. The steps of forwarding the calling request to the optimal node via the target node are described in detail as follows:

[0075] S301: Find the two closest edge nodes, form a mutual assistance pair, and select a set value from the load value.

[0076] Determine the two closest edge nodes and form a mutual assistance pair. For example, if B is closest to A among all edge nodes, then use A and B to form a mutual assistance pair. Then, form mutual assistance pairs for all edge nodes in a certain order (such as numbering order).

[0077] S302: When the load value of the edge node is greater than the set value, the calling request is reconnected by using the mutual assistance pair.

[0078] A set value is selected from the load value. When the load value of the edge node is greater than the set value, after the edge node receives the call request, the call request is transferred to another edge node of the mutual assistance pair.

[0079] In Example 5, Figure 5 The implementation process of the edge service calling method provided by an embodiment of the present invention is shown. The steps of establishing a data channel between the request source and the optimal node are described in detail below:

[0080] S401: Receive a processing task sent by a user terminal via the data channel, and divide the task into several task segments.

[0081] Analyze the processing task, extract its internal structure, resource requirements and processing flow, and divide the processing task into several relatively independent task fragments that can be executed in parallel based on the task scale and logical dependencies. Each task fragment can be processed by a specific data block or computing unit.

[0082] S402: Distribute the task fragment to the mutual assistance pair and the target node based on the edge node corresponding to the unique identifier.

[0083] The task fragments are distributed to the edge nodes in the mutual assistance pairs and the target node for parallel processing.

[0084] In Example 6, different from Example 1, in this embodiment of the present invention, the method further includes:

[0085] Constructing a time window, and updating the load value after the time window arrives;

[0086] The adjustment strategy is edited to adjust the order in the distance list using the load value.

[0087] Select two adjacent time points and construct a time window. In each time window, update the load value. In the distance list, use the load value to adjust the sorting. Assume that the interval is 5 seconds, select several time points and construct a time window based on them. In other words, update the load value every 5 seconds.

[0088] For example, if the distances from edge nodes B and C to A are roughly the same (within 5 meters), the load values of B and C are compared. If B has a smaller load value, B is ranked higher. It should be noted that a larger load value indicates a greater load on the corresponding edge node, and data congestion is more likely to occur.

[0089] In Example 7, different from Example 1, in this embodiment of the present invention, the method further includes:

[0090] Creating a layered architecture consisting of a top layer, a parent layer, and a child layer, and dividing the access range into a number of blocks;

[0091] A correspondence between the layered architecture and the blocks is established, and based on the correspondence, the edge nodes in the blocks are mounted into the layered architecture to generate an edge service graph.

[0092] Create a hierarchical architecture similar to the binary tree in the prior art, with the top layer as the uppermost layer, the parent layer as the middle layer, and the child layer as the lowermost layer; further, according to the attribute information and indicators of each edge node, it is divided into three levels according to performance, namely, optimal level, ordinary level, and poor level; the edge nodes corresponding to the optimal level are transferred to the top layer, the edge nodes corresponding to the ordinary level are transferred to the parent layer, and the edge nodes corresponding to the poor level are transferred to the child layer, and an edge service graph is generated; the edge service graph can intuitively display the performance of each edge node; when the user terminal initiates a call request, the edge service graph can be queried, and the call request can be uploaded to the edge node with better performance first; according to the number of call requests, administrative divisions, etc., the access range is divided into multiple blocks, and each block corresponds to an edge service graph.

[0093] Figure 6The following is a structural block diagram of the edge service invocation system provided by an embodiment of the present invention. The edge service invocation system 1 includes:

[0094] Obtaining module 11, for dividing the access range of edge services, marking edge nodes, and calculating the distances between edge nodes, sorting the edge nodes in ascending order of the distances to obtain a distance list, wherein each edge node corresponds to a distance list;

[0095] The parallel module 12 is configured to select a preset number of edge nodes from the front column of the distance list to obtain collaborative nodes. Upon receiving a call request uploaded by a user terminal, the parallel module 12 finds the edge node closest to the user terminal, defines it as the target node, calculates the load value of the target node, and determines whether the load value is greater than a threshold. If so, the parallel module 12 triggers a pre-stored probe request in the target node and distributes it to the collaborative nodes in parallel.

[0096] A building module 13 is configured to receive feedback information sent by the collaborative node, define the collaborative node corresponding to the feedback information received first as the optimal node, forward the call request to the optimal node via the target node, and build a communication link between the optimal node and the user terminal;

[0097] Establish module 14, which is used to collect the unique identifier of the optimal node and cache it in the target node. When the target node receives the call request again, it traces back to the request source and sends the unique identifier to the request source to establish a data channel between the request source and the optimal node.

[0098] Figure 7 The following is a structural block diagram of the edge service invocation system provided by an embodiment of the present invention. The obtaining module 11 includes:

[0099] A setting unit 111 is used to select several indicators of edge nodes and set weights;

[0100] The calculation unit 112 is configured to collect real-time values of indicators in edge nodes, and calculate a load value of each edge node based on the weights and the real-time values.

[0101] Figure 8 The following is a structural block diagram of the edge service invocation system provided by an embodiment of the present invention. The parallel module 12 includes:

[0102] A drawing unit 121 is used to draw a load fluctuation trend graph with time as the horizontal axis and load value as the vertical axis;

[0103] Insertion unit 122 is used to collect attribute information of edge nodes, generate labels, and insert the labels into the load fluctuation trend graph, wherein the attribute information includes at least: deployment location and unique identifier

[0104] Figure 9 The following is a structural block diagram of the edge service calling system provided by an embodiment of the present invention. The building module 13 includes:

[0105] A selection unit 131 is configured to find two edge nodes that are closest to each other, form a mutual assistance pair, and select a set value from the load value;

[0106] The access unit 132 is configured to re-access the call request by using the mutual assistance pair when the load value of the edge node is greater than a set value.

[0107] Figure 10 The following is a structural block diagram of the edge service invocation system provided by an embodiment of the present invention. The establishment module 14 includes:

[0108] The splitting unit 141 is configured to receive a processing task sent by a user terminal via the data channel and split the task into a plurality of task segments;

[0109] The distribution unit 142 is configured to distribute the task fragment to the mutual assistance pair and the target node according to the edge node corresponding to the unique identifier.

[0110] The obtaining module 11 is mainly used to complete step S100, the parallel module 12 is mainly used to complete step S200, the building module 13 is mainly used to complete step S300, and the establishing module 14 is mainly used to complete step S400;

[0111] The setting unit 111 is mainly used to complete step S101, and the calculation unit 112 is mainly used to complete step S102;

[0112] The drawing unit 121 is mainly used to complete step S201, and the inserting unit 122 is mainly used to complete step S202;

[0113] The selection unit 131 is mainly used to complete step S301, and the access unit 132 is mainly used to complete step S302;

[0114] The segmentation unit 141 is mainly used to complete step S401, and the distribution unit 142 is mainly used to complete step S402.

[0115] 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.

[0116] The above-described embodiments merely illustrate several implementations of the present invention, and while their 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 would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

[0117] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for calling an edge service, characterized in that: The method comprises: Determine the access range of the edge service, mark the edge nodes, calculate the distances between the edge nodes, and sort the edge nodes in ascending order of distance to obtain a distance list, where each edge node corresponds to a distance list; A preset number of edge nodes are selected from the front column of the distance list to obtain a collaborative node. When a call request uploaded by a user terminal is received, the edge node closest to the user terminal is found and defined as the target node. The load value of the target node is calculated and it is determined whether the load value is greater than a threshold. If so, a pre-stored probe request in the target node is triggered and distributed to the collaborative node in parallel. receiving feedback information sent by the collaborative node, defining the collaborative node corresponding to the feedback information received first as the optimal node, forwarding the call request to the optimal node via the target node, and establishing a communication link between the optimal node and the user terminal; The unique identifier of the optimal node is collected and cached in the target node. When the target node receives the call request again, it traces back to the request source and sends the unique identifier to the request source to establish a data channel between the request source and the optimal node.

2. The edge service calling method according to claim 1, characterized in that: The step of sorting the edge nodes to obtain a distance list includes: Select several indicators of edge nodes and set weights; The real-time values of the indicators in the edge nodes are collected, and the load value of each edge node is calculated based on the weights and the real-time values.

3. The edge service calling method according to claim 2, characterized in that: The method further comprises: Constructing a time window, and updating the load value after the time window arrives; The adjustment strategy is edited to adjust the order in the distance list using the load value.

4. The edge service calling method according to claim 3, characterized in that: The step of calculating the load value of the target node includes: Use time as the horizontal axis and load value as the vertical axis to draw a load fluctuation trend graph; Attribute information of edge nodes is collected, a label is generated, and the label is inserted into a load fluctuation trend graph, wherein the attribute information at least includes: a deployment location and a unique identifier.

5. The edge service calling method according to claim 4, characterized in that: The step of forwarding the call request to the optimal node via the target node includes: Find the two closest edge nodes, form a mutual assistance pair, and select a set value from the load value; When the load value of the edge node is greater than the set value, the mutual assistance pair is used to reconnect the call request.

6. The edge service calling method according to claim 1, characterized in that: The method further comprises: Creating a layered architecture consisting of a top layer, a parent layer, and a child layer, and dividing the access range into a number of blocks; A correspondence between the layered architecture and the blocks is established, and based on the correspondence, the edge nodes in the blocks are mounted into the layered architecture to generate an edge service graph.

7. The edge service calling method according to claim 1, characterized in that: The step of establishing a data channel between the request source and the optimal node includes: Receiving a processing task sent by a user terminal via the data channel and dividing the task into a plurality of task segments; The task fragments are distributed to mutual assistance pairs and target nodes based on the edge nodes corresponding to the unique identifiers.

8. An edge service calling system, characterized in that: The system comprises: A module is provided for dividing the access range of edge services, marking edge nodes, and calculating the distances between edge nodes. The edge nodes are sorted in ascending order of distance to obtain a distance list, wherein each edge node corresponds to a distance list. The parallel module is used to select a preset number of edge nodes from the front column of the distance list to obtain the collaborative node. When receiving the call request uploaded by the user terminal, it finds the edge node closest to the user terminal and defines it as the target node. It calculates the load value of the target node and determines whether the load value is greater than a threshold. If so, it triggers the detection request pre-stored in the target node and distributes it to the collaborative node in parallel. a building module, configured to receive feedback information sent by the collaborative node, define the collaborative node corresponding to the feedback information received first as the optimal node, forward the call request to the optimal node via the target node, and build a communication link between the optimal node and the user terminal; A module is established to collect the unique identifier of the optimal node and cache it in the target node. When the target node receives a call request again, it traces back to the source of the request and sends the unique identifier to the source of the request, establishing a data channel between the request source and the optimal node.

9. The edge service calling system according to claim 8, characterized in that: The obtaining module includes: The setting unit is used to select several indicators of edge nodes and set weights; The calculation unit is used to collect the real-time values of the indicators in the edge nodes, and calculate the load value of each edge node based on the weight and the real-time value.

10. The edge service calling system according to claim 9, characterized in that: The parallel module includes: Drawing unit, used to draw a load fluctuation trend graph with time as the horizontal axis and load value as the vertical axis; The insertion unit is used to collect attribute information of the edge node, generate a label, and insert the label into the load fluctuation trend graph, wherein the attribute information at least includes: a deployment location and a unique identifier.

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