WO2022021176A1

WO2022021176A1 - Cloud-edge collaborative network resource smooth migration and restructuring method and system

Info

Publication number: WO2022021176A1
Application number: PCT/CN2020/105691
Authority: WO
Inventors: 陈伯文; 刘玲; 沈纲祥; 高明义; 向练; 陈虹
Original assignee: 苏州大学
Priority date: 2020-07-28
Filing date: 2020-07-30
Publication date: 2022-02-03
Also published as: CN111901424A; CN111901424B

Abstract

The present invention relates to a cloud-edge collaborative network resource smooth migration and reconstruction method and system, comprising: reading topology information of a cloud-edge collaborative network, initializing the parameters of the cloud-edge collaborative network; generating a connection request set according to a user request; processing any one connection request from the connection request set until all connection requests have been processed; and calculating the average path transmission delay and average data processing delay of user requests. The present invention decreases service delay as much as possible, and improves network service quality.

Description

Cloud-edge collaborative network resource smooth migration and reconstruction method and system

technical field

The invention relates to the technical field of cloud computing and edge computing networks, in particular to a method and system for smooth migration and reconstruction of cloud-edge collaborative network resources.

Background technique

In recent years, with the rapid development of the Internet of Things (IoT for short) and the widespread use of a large number of data applications, users' demands for network computing resources have increased dramatically. In addition, technological advancements in smartphones, laptops and tablets have enabled the emergence of new demanding services and applications. Although new mobile devices are getting more powerful in terms of central processing unit (CPU), they may not be able to handle high-traffic applications in a short period of time. Cloud computing has powerful computing capabilities. Devices can offload computing resources and transfer computing tasks to remote cloud servers for execution, which can effectively alleviate the problem of large computing resource requirements. However, the transmission of computing tasks to the cloud server will cause unacceptable delays, increase additional transmission energy consumption, and bring about data leakage and other problems.

In order to solve the problem of high latency in the process of offloading cloud computing, the European Telecommunications Standards Institute (ETSI) first proposed the concept of Mobile Edge Computing (MEC) in 2014: a wireless access network close to users to provide IT and The cloud computing platform is considered to be one of the key technologies of the fifth generation mobile communication. The MEC system allows devices to offload computing tasks to network edge nodes, such as base stations, wireless access points, etc., which not only satisfies the expansion requirements of terminal devices' computing capabilities, but also makes up for the long delay of cloud computing. MEC technology helps to achieve key technical indicators such as ultra-low latency, ultra-high energy efficiency, and ultra-high reliability for 5G services. By deploying cloud computing and cloud storage to the network edge, a telecom service environment with high performance, low latency and high bandwidth is provided to accelerate the distribution and download of various contents, services and applications in the network, so that consumers can enjoy higher Quality web experience.

At present, in the research of cloud computing and edge computing, there are mainly the following two computing resource allocation methods: the computing resource allocation method with delay priority and the computing resource allocation method with resource priority. However, both of these allocation methods are resource allocation methods designed for only one purpose. The delay-first allocation method can effectively reduce the data transmission delay, but it cannot improve the computing capability of the node to process services, and the data processing delay still needs to be optimized; the resource-first allocation method can improve the speed of the node processing services, but the transmission Latency is likely to increase significantly.

SUMMARY OF THE INVENTION

Therefore, the technical problem to be solved by the present invention is to overcome the problems of low data transmission delay and high node processing business computing capability in the prior art, so as to provide a method that can ensure low data transmission delay while ensuring node processing A method and system for smooth migration and reconstruction of cloud-edge collaborative network resources with high business computing capability.

In order to solve the above technical problems, a method for smooth migration and reconstruction of cloud-edge collaborative network resources of the present invention includes: reading cloud-edge collaborative network topology information, initializing the parameters of the cloud-edge collaborative network; A set of connection requests; process any connection request in the connection request set until the processing of all connection requests is completed; the method for processing any connection request is: first determine whether the local area requested by the user is There are enough computing resources. If so, the user request is sent to the edge computing server in the local area for processing. If not, continue to judge whether the server in the adjacent area has enough computing resources to process the user request. If so, migrate the user request. To other regional edge computing servers on the same switch as the local area, if not, send the user request to the cloud server through the switch for data processing; calculate the multiple candidate paths from the user request to the edge computing server separately. The sum of the path transmission delay and the data processing delay, the path with the lowest delay is used as the working path; after allocating spectrum resources to the working path, the computing resources of the edge computing server processing user requests are updated in real time; Requested average path propagation delay and average data processing delay.

In an embodiment of the present invention, in the cloud-edge collaboration network, network topology information, network connection status, number of user requests, number of edge computing servers, number of base stations and switches are configured.

In an embodiment of the present invention, the method for calculating the sum of the path transmission delay and data processing delay of multiple candidate paths from the user request to the edge computing server is: calculating the path transmission delay and data processing delay of the multiple paths respectively. Delay, and then calculate the sum of the path transmission delay and data processing delay.

In an embodiment of the present invention, the method for calculating the path transmission delay and data processing delay of multiple paths is: calculating the path transmission delay of multiple paths according to the formula TRD=Weight _k /c, where Weight _k represents the first The path length of k working paths, c represents the optical fiber transmission rate; the data processing delay of multiple paths is calculated according to the formula COD=r _u /TR _i , where r _u represents the computing resources required by the user to request u, and TR _i represents the node The sum of computing resources for i.

In an embodiment of the present invention, the method for allocating spectrum resources to the working path is: assigning spectrum resources to the working path according to the constraints of spectrum consistency and spectrum continuity.

In one embodiment of the present invention, after the computing resources of the edge computing server processing user requests are updated in real time, the number of successfully established connection requests is recorded, and the cloud-edge collaboration network status is updated.

In an embodiment of the present invention, when updating the cloud-edge collaborative network state, the spectrum resources occupied by the working path are released; at the same time, the computing resources of the edge computing server processing the user request are released; finally, the connection request is established the working path to clear the information.

In an embodiment of the present invention, the method for calculating the average path transmission delay and the average data processing delay requested by the user is: according to the formula

Calculate the average path transmission delay aveTRD requested by the user, where sucConReq represents the number of successful connection requests; according to the formula

Calculate the average path transmission delay aveCOD requested by the user, where sucConReq indicates the number of successful connection establishment requests.

In an embodiment of the present invention, after the step S5 is completed, the state of the cloud-edge collaboration network is continued to be monitored.

The present invention also provides a system for smooth migration and reconstruction of cloud-edge collaborative network resources, comprising: a cloud-edge collaborative network initialization module for reading cloud-edge collaborative network topology information, and performing parameters of the cloud-edge collaborative network. initialization; a connection request generation module, used to generate a set of connection request sets according to user requests; a connection request processing module, used to process any connection request in the connection request set until the processing of all connection requests is completed; The method for processing any connection request is as follows: first determine whether the local area requested by the user has sufficient computing resources, if so, the user request is sent to the edge computing server in the local area for processing, if not, continue to determine the relevant Whether the server in the adjacent area has enough computing resources to process the user request, if so, migrate the user request to other regional edge computing servers on the same switch as the local area; if not, send the user request to the cloud server through the switch Perform data processing on the server; calculate the sum of the path transmission delay and data processing delay of multiple candidate paths from the user request to the edge computing server, and use the path with the lowest delay as the working path; perform spectrum resource analysis on the working path. After the allocation, the computing resources of the edge computing server processing user requests are updated in real time; the computing module is used to calculate the average path transmission delay and average data processing delay requested by the user.

The above-mentioned technical scheme of the present invention has the following advantages compared with the prior art:

In the method and system for smooth migration and reconstruction of cloud-edge collaborative network resources of the present invention, the working path is set according to the sensitivity of the service to the delay, and the working path with low delay sensitivity is selected for transmission and processing, which is conducive to reducing the delay of the service. Path transmission delay and data processing delay; in addition, the strategy of hierarchical deployment of mobile edge computing is used to allocate computing resources and bandwidth resources. First, it is considered whether the server in the local area where the service is generated has the network resources required to process the service, and secondly, it is considered through the switch. Whether the connected servers in other areas have sufficient network resources, if the local area and other areas do not have enough network resources, consider migrating the service to the server in the cloud through the switch for processing, so as to further reduce the service path through layered deployment Transmission delay and data processing delay.

Description of drawings

In order to make the content of the present invention easier to understand clearly, the present invention will be described in further detail below according to specific embodiments of the present invention and in conjunction with the accompanying drawings, wherein

Fig. 1 is the flow chart of the method for smooth migration and reconstruction of cloud-edge collaborative network resources according to the present invention;

Fig. 2 is the network diagram of cloud-side collaborative network resource migration and reconstruction of the present invention;

FIG. 3 is a schematic diagram of the cloud-edge collaborative network resource smooth migration and reconstruction system of the present invention.

detailed description

Example 1

As shown in FIG. 1 , this embodiment provides a method for smooth migration and reconstruction of cloud-edge collaborative network resources, including: step S1: reading cloud-edge collaborative network topology information, and initializing the parameters of the cloud-edge collaborative network; Step S2: generate a group of connection request sets according to user requests; Step S3: process any connection request in the connection request set until the processing of all connection requests is completed; wherein the method for processing any connection request It is: first judge whether the local area requested by the user has sufficient computing resources, if so, the user request is sent to the edge computing server in the local area for processing, if not, continue to judge whether the server in the adjacent area has sufficient computing resources for processing If there is a user request, migrate the user request to other regional edge computing servers on the same switch as the local area; if not, send the user request to the cloud server through the switch for data processing; calculate the data from the user request separately The sum of the path transmission delay and data processing delay of multiple candidate paths to the edge computing server, and the path with the lowest delay is used as the working path; The computing resources of the server are updated in real time; Step S4: Calculate the average path transmission delay and average data processing delay requested by the user.

In the method for smooth migration and reconstruction of cloud-edge collaborative network resources in this embodiment, in step S1, the cloud-edge collaborative network topology information is read, and the parameters of the cloud-edge collaborative network are initialized, which is beneficial to the operation of services; In the step S2, a set of connection requests is generated according to the user request, which is conducive to processing the connection request; in the step S3, for each connection request, first determine whether the local area requested by the user has sufficient computing resources, If yes, the user request is sent to the edge computing server in the local area for processing. At this time, the network transmission delay can be ignored, and the end-to-end processing delay only needs to consider the calculation delay; if not, continue to judge the adjacent area Whether the internal server has enough computing resources to process user requests, and if so, migrate user requests to other regional edge computing servers on the same switch in the local area. At this time, the end-to-end delay includes network transmission delay and computing Resource delay, if not, the user request is sent to the cloud server through the switch for data processing. At this time, the end-to-end delay of the user request includes the network transmission delay and computing resource delay in the edge computing area and cloud computing area. , through layered deployment, it is beneficial to reduce the path transmission delay and data processing delay of the service; calculate the sum of the path transmission delay and data processing delay of multiple candidate paths from the user request to the edge computing server respectively, and calculate the delay The lowest path is used as the working path. Since the working path with low delay sensitivity is selected for transmission and processing, it is beneficial to reduce the path transmission delay and data processing delay of the service; The requested edge computing server updates the computing resources in real time, which is conducive to recording the number of connection requests successfully established; in the step S4, the average path transmission delay and average data processing delay requested by the user are calculated, and the present invention is conducive to reasonable planning. Resource scheduling, migration, and reconstruction in edge computing and cloud computing areas, effectively balancing the relationship between network resources and service latency, and reducing latency as much as possible while reasonably allocating resources to minimize service latency. At the same time, it is ensured that the nodes have high computing capacity for processing business, so as to improve the service quality of the network.

In the step S1, in the cloud-edge collaboration network, the topology information of the network, the network connection state, the number of user requests, the number of edge computing servers, the number of base stations and switches are configured. Specifically, in the cloud-edge collaboration network G (U, B, J, S), the topology information of the network, the network connection status, the number of user requests, the number of edge computing servers, the number of base stations and switches are configured.

In the step S2, when a set of connection requests is generated according to the user request, information such as the number of connection requests, the number of spectrum gaps required by different connection requests, and computing resources are configured.

In the step S3, for any connection request, it is determined whether the local server requested by the user has the computing resources required by the user request, and if the computing resources of the local server are sufficient, the user request is directly processed locally. If the computing resources of the local server are insufficient, consider whether the edge computing servers in other regions outside the local region have the computing resources required by the user request. If the servers in other regions have sufficient computing resources, the user request will be migrated to other regions through the switch for processing. ; If the computing resources of the local and other regions do not meet the computing resources requested by the user, the user request will be migrated to the cloud server through the switch for processing. The above-mentioned hierarchical deployment will help reduce the path transmission delay and data processing time of the service. extension.

The method for calculating the sum of the path transmission delay and data processing delay of multiple candidate paths from the user request to the edge computing server is as follows: calculate the path transmission delay and data processing delay of the multiple paths respectively, and then calculate the path transmission delay. The sum of the data processing delay and the path transmission delay of the service is beneficial to reduce the service path transmission delay and data processing delay.

Specifically, according to the user request of the connection request CR(u, f, r) and the edge computing server processing the request, the K shortest path algorithm is used to calculate the K candidate paths from the user request to the server, so as to find the optimal path path as the working path.

The K shortest path algorithm is used to calculate the working path from the user request to the edge computing server. The K shortest path algorithm calculates K candidate paths and arranges them in ascending order of distance, that is, the smaller the path distance, the higher the priority. When a path with a high priority is blocked on a certain link, paths with lower priorities are selected in turn to allocate spectrum resources until the resources are allocated successfully or all paths are blocked.

The method for calculating the path transmission delay and data processing delay of multiple paths is: Calculate the path transmission delay of multiple paths according to the formula TRD=Weight _k /c, where Weight _k represents the path length of the kth working path, c Represents the optical fiber transmission rate; calculates the data processing delay of multiple paths according to the formula COD=r _u /TR _i , where r _u represents the computing resources required by the user to request u, and TR _i represents the sum of the computing resources of node i.

The method for allocating spectrum resources to the working path is: assigning spectrum resources to the working path according to the constraints of spectrum consistency and spectrum continuity.

Specifically, according to the number of spectrum gaps f required by the connection request CR(u, f, r), the selected working path is searched for the bandwidth resources required to satisfy the connection request. If both spectrum continuity and spectrum consistency are satisfied at the same time If the constraint conditions are met, the connection request is successfully established; if the dual constraints of spectrum continuity and spectrum consistency cannot be satisfied at the same time, the connection request establishment fails.

After the connection request CR(u, f, r) successfully establishes the working path, when assigning spectrum resources to the working path according to the constraints of spectrum consistency and spectrum continuity, the spectrum allocation algorithm that hits the first time is used, according to all links on the path. A spectrum resource table is generated for numbering the spectrum resource status, and the available spectrum gaps are searched from the end with the smaller number. If an available spectrum gap is found, spectrum resource allocation is performed and spectrum status update is performed; if no spectrum is found, spectrum allocation fails and services are blocked.

After the spectrum resources are successfully allocated, real-time update of computing resources is performed on the edge computing server processing user requests, and after real-time update of computing resources on the edge computing server processing user requests, the number of successfully established connection requests is recorded, and the cloud-edge collaborative network is updated. state.

When updating the cloud-edge collaboration network status, the spectrum resources occupied by the working path are released; at the same time, the computing resources of the edge computing server processing user requests are released; finally, the information of the working path established by the connection request is cleared.

For the remaining user requests, the same method as above is used until all connection requests are processed.

In the step S4, the method for calculating the average path transmission delay and the average data processing delay requested by the user is: according to the formula

Specifically, after each connection request is successfully established, record the shortest path length of each connection request transmission, calculate the path transmission delay of each connection request, and use the average path transmission delay aveTRD to calculate the average path of a group of connection requests. Transmission delay: After all connection requests are processed, record the cumulative computing resources of each connection request and the initial computing resources of the edge computing server, and use the average path transmission delay aveCOD to calculate the average data processing delay of a group of connection requests .

After the step S4 is completed, continue to monitor the state of the cloud-edge collaboration network. Specifically, it mainly completes the initialization of cloud-edge collaborative network, connection request generation, service priority selection, edge computing server selection, working path establishment, spectrum resource allocation, computing resource update, resource release, network transmission delay calculation and data processing time. The state monitoring function of delayed computing is implemented to achieve the goal of reducing service delay as much as possible when computing resources are allocated.

In addition, the method also includes the steps of judging and warning. Specifically, the coordination function between each module is performed, and the judgment and early warning function of each module is successfully established, so as to achieve the goal of reducing service delay in mobile edge computing.

In the present invention, for each connection request, the business with higher delay sensitivity is preferentially processed according to the business delay sensitivity, and the edge computing server processing the business is selected according to the mobile edge computing hierarchical deployment strategy. The K shortest path algorithm is used to calculate the working path between the service and the edge computing server. After the working path is successfully selected, the spectrum allocation algorithm with the first hit is used to allocate spectrum resources to the path, which needs to satisfy both spectrum consistency and spectrum continuity. Constraints; then update the status of network computing resources and spectrum resources in real time; after each connection request is successfully established, the path transmission delay is calculated according to the path length and path transmission rate, and the resource capacity and Data processing delay. The present invention effectively balances the relationship between network resources and service delay through cloud-side coordination network resource migration and reconstruction sensitive to service delay, and reduces the delay as much as possible under the condition of rational resource allocation.

In addition, the present invention adopts the classification processing method of service delay sensitivity according to the difference of service transmission sensitivity to time delay, and preferentially transmits and processes services with high time delay sensitivity. The present invention only needs to consider two different delays, including network transmission delay and computing resource delay. The network transmission delay refers to the shortest path length between the user's service area and the edge computing server, which is calculated by the cumulative delay of the link delay; the computing resource delay is related to the computing resource requirements of each user and the edge computing server. computing power.

The following describes how to allocate computing resources through hierarchical deployment of mobile edge computing, how to process user requests according to service delay sensitivity, how to calculate path transmission delay through link accumulation delay, and how to use edge computing server resources and user request resources. Calculate the data processing delay to minimize the total business delay.

As shown in Figure 2, layer 1 has three local areas, each area has base stations and corresponding edge computing servers, and layer 2 is a cloud network composed of switches and corresponding edge computing servers. Assume that the computing resources of the edge computing server in the local area of layer 1 are 20, the computing resources of the edge computing server of the cloud network of layer 2 are 1000, and the number of service computing resources and the number of spectrum slots are randomly generated.

First, initialize the cloud-edge collaboration network G (U, B, J, S), including user requests, base stations, switches, and locations that can be selected by the edge computing server, and initialize the computing resources of the edge computing server. The connection request is represented by CR(u, f, r), where u represents the user request number, f represents the number of spectrum slots required to establish a working path, and r represents the computing resources required by the user request. Three user request sets CR(1,3,15), CR(2,8,10) and CR(3,5,30) are generated in the base station area of node 3 and node 2 in Fig. 2 .

Second, for the user request CR(1, 3, 15) generated by node 2, first determine whether the user request can be processed locally. Since the number of computing resources requested by the user is 15, the computing resources of the edge computing server node 2 in the local area are 20. At this time, the user request is sent to the local edge computing server for processing, and the data transmission route is ①.

Third, for the user request CR(2, 8, 10) generated by node 2, since the dynamic computing resource of node 2 is 5 at this time, it cannot meet the computing resources required by the user request. The computing resources are migrated to the edge computing server node 8 in other regions, the computing resources of the node 8 are updated to 10, and the transmission path is ②.

Fourth, for the user request CR (3,5,30) generated by node 3, the computing resources on the local and adjacent edge computing area servers cannot meet the computing resources required by the user request, and the user request needs to pass through the switch node. 4 is transmitted to the cloud computing area server node 5 connected to the switch, and the data transmission route is ③.

Fifth, for user requests CR(1,3,15), CR(2,8,10), CR(3,5,30), the routing algorithm of K shortest paths needs to be used to calculate node 2 to node 1, node 1 K paths from 2 to node 8 and from node 3 to node 5. Calculate the sum of the path transmission delay and data processing delay of the K paths respectively according to the formula TRD=Weight _k /c and the formula COD=r _u /TR _i , and select the path with the lowest delay among the K paths as the working path.

Sixth, after the working path is selected, the first hit spectrum allocation algorithm is used, and the user requests CR(1,3,15), CR(2,8,10), CR according to the constraints of spectrum consistency and spectrum continuity. The working paths of (3, 5, 30) perform spectrum resource allocation. After the spectrum resource is allocated, the connection request is successfully established. At this time, the status of computing resources and spectrum resources is updated in real time and the number of successful connections is recorded.

Finally, after each connection request is successfully established, record the link length of the working path, the computing resources of the connection request, and use the formula

and

Calculate the average path transmission delay and the average data processing delay, respectively.

Embodiment 2

As shown in FIG. 3 , based on the same inventive concept, this embodiment provides a cloud-edge collaborative network resource smooth migration and reconstruction system. The principle of solving the problem is similar to the cloud-edge collaborative network resource smooth migration and reconstruction method. Repeat will not be repeated here.

This embodiment provides a cloud-edge collaborative network resource smooth migration and reconstruction system, including:

The cloud-edge collaborative network initialization module is used to read the cloud-edge collaborative network topology information, and initialize the parameters of the cloud-edge collaborative network;

The connection request generation module is used to generate a set of connection request sets according to user requests;

The connection request processing module is used to process any connection request in the connection request set until the processing of all connection requests is completed; the method for processing any connection request is: first determine the local area requested by the user Whether there are enough computing resources, if so, the user request is sent to the edge computing server in the local area for processing; Migrate to other regional edge computing servers on the same switch in the local area, if not, send the user request to the cloud server through the switch for data processing; calculate multiple candidate paths from the user request to the edge computing server separately The sum of the path transmission delay and the data processing delay is the sum of the path transmission delay and the data processing delay, and the path with the lowest delay is used as the working path; after the spectrum resources are allocated to the working path, the computing resources of the edge computing server processing user requests are updated in real time;

The calculation module is used to calculate the average path transmission delay and average data processing delay requested by the user.

As will be appreciated by those skilled in the art, the embodiments of the present application may be provided as a method, a system, or a computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It will be understood that each process and/or block in the flowchart illustrations and/or block diagrams, and combinations of processes and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to the processor of a general purpose computer, special purpose computer, embedded processor or other programmable data processing device to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing device produce Means for implementing the functions specified in a flow or flow of a flowchart and/or a block or blocks of a block diagram.

These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory result in an article of manufacture comprising instruction means, the instructions The apparatus implements the functions specified in the flow or flow of the flowcharts and/or the block or blocks of the block diagrams.

These computer program instructions can also be loaded on a computer or other programmable data processing device to cause a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process such that The instructions provide steps for implementing the functions specified in the flow or blocks of the flowcharts and/or the block or blocks of the block diagrams.

Obviously, the above-mentioned embodiments are only examples for clear description, and are not intended to limit the implementation manner. For those of ordinary skill in the art, other different forms of changes or modifications can also be made on the basis of the above description. There is no need and cannot be exhaustive of all implementations here. And the obvious changes or changes derived from this are still within the protection scope of the present invention.

Claims

A method for smooth migration and reconstruction of cloud-edge collaborative network resources, characterized in that it includes the following steps:

Step S1: read the cloud-edge collaborative network topology information, and initialize the parameters of the cloud-edge collaborative network;

Step S2: generating a set of connection request sets according to user requests;

Step S3: process any connection request in the connection request set until the processing of all connection requests is completed; wherein the method for processing any connection request is: first determine whether the local area requested by the user has sufficient If there are computing resources, the user request will be sent to the edge computing server in the local area for processing. If not, continue to judge whether the server in the adjacent area has enough computing resources to process the user request. On other regional edge computing servers on the same switch in the region, if not, the user request will be sent to the cloud server through the switch for data processing; the path transmission of multiple candidate paths from the user request to the edge computing server will be calculated separately. The sum of the delay and the data processing delay, and the path with the lowest delay is used as the working path; after the spectrum resources are allocated to the working path, the computing resources of the edge computing server processing the user request are updated in real time;

Step S4: Calculate the average path transmission delay and average data processing delay requested by the user.
The cloud-side collaborative network resource smooth migration and reconstruction method according to claim 1, wherein: in the cloud-side collaborative network, configuration network topology information, network connection status, number of user requests, edge computing server number of base stations and switches.
The method for smooth migration and reconstruction of cloud-edge collaborative network resources according to claim 1, wherein the method for calculating the sum of path transmission delay and data processing delay of multiple candidate paths from a user request to an edge computing server is as follows: : Calculate the path transmission delay and data processing delay of multiple paths respectively, and then calculate the sum of the path transmission delay and data processing delay.
The method for smooth migration and reconstruction of cloud-edge collaborative network resources according to claim 3, wherein the method for calculating the path transmission delay and data processing delay of multiple paths is: calculating according to the formula TRD=Weight k /c The path transmission delay of multiple paths, where Weight k represents the path length of the kth working path, and c represents the optical fiber transmission rate; calculate the data processing delay of multiple paths according to the formula COD=r u /TR i , where r u Represents the computing resources required by the user to request u, and TR i represents the sum of the computing resources of node i.
The method for smooth migration and reconstruction of cloud-edge collaborative network resources according to claim 1, wherein the method for allocating spectrum resources to the working path is: according to the constraints of spectrum consistency and spectrum continuity The working path performs spectrum resource allocation.
The method for smooth migration and reconstruction of cloud-edge collaboration network resources according to claim 1, characterized in that: after the computing resources of the edge computing server processing user requests are updated in real time, the number of successfully established connection requests is recorded, and the cloud-edge collaboration is updated. network status.
The method for smooth migration and reconstruction of cloud-edge collaborative network resources according to claim 6, characterized in that: when updating the cloud-edge collaborative network state, the spectrum resources occupied by the working path are released; The computing resources of the computing server are released; finally, the information of the working path established by the connection request is cleared.
The method for smooth migration and reconstruction of cloud-edge collaborative network resources according to claim 1, wherein the method for calculating the average path transmission delay and average data processing delay requested by the user is: according to the formula
Calculate the average path transmission delay aveTRD requested by the user, where sucConReq represents the number of successful connection requests; according to the formula
Calculate the average path transmission delay aveCOD requested by the user, where sucConReq indicates the number of successful connection establishment requests.
The method for smooth migration and reconstruction of cloud-edge collaborative network resources according to claim 1, characterized in that: after the step S5 is completed, the state of the cloud-edge collaborative network is continued to be monitored.
A cloud-edge collaborative network resource smooth migration and reconstruction system, characterized by comprising:

The cloud-edge collaborative network initialization module is used to read the cloud-edge collaborative network topology information, and initialize the parameters of the cloud-edge collaborative network;

The connection request generation module is used to generate a set of connection request sets according to user requests;

The connection request processing module is used to process any connection request in the connection request set until the processing of all connection requests is completed; the method for processing any connection request is: first determine the local area requested by the user Whether there are enough computing resources, if so, the user request is sent to the edge computing server in the local area for processing; Migrate to other regional edge computing servers on the same switch in the local area, if not, send the user request to the cloud server through the switch for data processing; calculate multiple candidate paths from the user request to the edge computing server separately The sum of the path transmission delay and the data processing delay is the sum of the path transmission delay and the data processing delay, and the path with the lowest delay is used as the working path; after the spectrum resources are allocated to the working path, the computing resources of the edge computing server processing user requests are updated in real time;

The calculation module is used to calculate the average path transmission delay and average data processing delay requested by the user.