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WO2022120959A1 - Procédé et système de migration multiservice à équilibrage de charges basés sur un flux maximal à coût minimal - Google Patents

Procédé et système de migration multiservice à équilibrage de charges basés sur un flux maximal à coût minimal Download PDF

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Publication number
WO2022120959A1
WO2022120959A1 PCT/CN2020/138815 CN2020138815W WO2022120959A1 WO 2022120959 A1 WO2022120959 A1 WO 2022120959A1 CN 2020138815 W CN2020138815 W CN 2020138815W WO 2022120959 A1 WO2022120959 A1 WO 2022120959A1
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Prior art keywords
service
node
edge server
edge
nodes
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Ceased
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PCT/CN2020/138815
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English (en)
Chinese (zh)
Inventor
唐欢
王洋
张锦霞
须成忠
叶可江
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Shenzhen Institute of Advanced Technology of CAS
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Shenzhen Institute of Advanced Technology of CAS
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Publication of WO2022120959A1 publication Critical patent/WO2022120959A1/fr
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/18—Network planning tools
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/16—Central resource management; Negotiation of resources or communication parameters, e.g. negotiating bandwidth or QoS [Quality of Service]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W48/00—Access restriction; Network selection; Access point selection
    • H04W48/02—Access restriction performed under specific conditions
    • H04W48/04—Access restriction performed under specific conditions based on user or terminal location or mobility data, e.g. moving direction, speed
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W48/00—Access restriction; Network selection; Access point selection
    • H04W48/08—Access restriction or access information delivery, e.g. discovery data delivery
    • H04W48/10—Access restriction or access information delivery, e.g. discovery data delivery using broadcasted information
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W76/00—Connection management
    • H04W76/10—Connection setup

Definitions

  • the present invention relates to the field of information technology, and more particularly, to a multi-service migration method and system based on load balancing with minimum cost and maximum flow.
  • MEC Mobile edge computing
  • QoS quality of service
  • computing resources can be pushed from the cloud center to the edge of the network, which enables data services and other related processing tasks to run close to mobile users. Therefore, it not only reduces service latency, but also minimizes network traffic, both benefits of which are quite important for those time-bound services (typical applications in mobile computing).
  • MEC is quickly becoming the focus of the next wave of research, especially in 5G networks with smarter and more powerful computing resources, which can be installed at the edge of wireless networks, enabling many time-sensitive services deployment is closer to the user.
  • 5G networks with smarter and more powerful computing resources which can be installed at the edge of wireless networks, enabling many time-sensitive services deployment is closer to the user.
  • the time difference of access to services, and the limited coverage of a single edge server if multiple services are fixed on one edge server at the same time, there will be resource contention and delay in accessing services. Increase. Therefore, if these factors are not taken into account, the service provided may significantly increase the access delay, and worse, cause a large amount of network traffic, cause network congestion, lead to service performance degradation, or even service interruption.
  • the purpose of the present invention is to provide a multi-service migration method and system based on load balancing with minimum cost and maximum flow in view of the technical problems existing in the prior art, so that the placement of virtual services on edge nodes is more balanced and flexible and adjustable .
  • the present invention provides a multi-service migration method based on load balancing with minimum cost and maximum flow.
  • the specific steps of the method include the following steps:
  • a minimum cost maximum flow model is established, and the model includes source nodes, service nodes, edge server nodes and endpoints;
  • Update the access information of each service adjust the weight of the edge connecting the edge server node to the endpoint, and update the position of the service node on the edge server node to achieve load balancing;
  • Real-time monitoring and statistics are performed on the access information of each service.
  • the change rate of the access information exceeds the set threshold, the corresponding service nodes and edge server nodes are adjusted.
  • Ni represents the ith service node
  • Ej represents the jth edge server node
  • the source node S is only connected to the service node Ni
  • each Each service node Ni is connected to all edge server nodes Ej
  • each edge server node Ej is connected to the termination node T; each edge in the model is associated with capacity and communication cost.
  • each edge server node Ej is connected to the terminal T through at least two edges according to the number of services it can bear, and the cost weights of each edge are different and in an increasing relationship.
  • the specific process of updating the position of the service node on the edge server node includes:
  • the incremental minimum cost and maximum flow algorithm is used to solve the problem, and the position of the service node on the edge server node is updated according to the obtained minimum cost and maximum flow.
  • the incremental minimum cost maximum flow algorithm specifically includes the following:
  • the access volume of the service node changes, and when the variable of access times is greater than the pre-learned threshold, the location of the service node is updated;
  • the location of the service node is updated.
  • the present invention also provides a load balancing multi-service migration system based on minimum cost and maximum flow, the system includes the following:
  • Topology map building module used to build a network topology map according to the geographical location and connection relationship of edge servers;
  • Matrix generation module used to calculate the shortest path distance between each pair of edge servers in the network topology diagram, and generate a shortest path distance matrix
  • Model building module used to establish a minimum-cost maximum flow model according to the distance matrix, combined with the access information of each service, the load capacity of each edge server and its relationship with the service delay, and the model includes the source node , service nodes, edge server nodes and edge nodes;
  • Service node migration module used to solve the model to obtain the minimum cost and maximum flow, migrate the service nodes, and place them on the corresponding edge server nodes;
  • Service node update module used to update the access information for each service, adjust the weight of the edge connecting the edge server node to the endpoint, and update the position of the service node on the edge server node;
  • Monitoring and adjustment module It is used to monitor and count the access information of each service in real time, and adjust the corresponding service nodes and edge server nodes when the change rate of the access information exceeds the set threshold.
  • the service node update module includes:
  • Cost update sub-module used to update the access cost and migration cost of each service node placed on the edge server node in the residual network of the model solving algorithm
  • Node deletion and addition sub-module It is used to delete and add service nodes and edge server nodes in the residual network according to the updated access information
  • Weight adjustment sub-module adjust the weight of the edge connecting the edge server node to the endpoint according to the current edge network index changes and the load of each edge server node;
  • Node update sub-module use the incremental minimum cost and maximum flow algorithm to solve, and update the position of the service node on the edge server node according to the minimum cost and maximum flow obtained by the solution.
  • the present invention also provides an electronic device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of any one of the methods when the processor executes the program.
  • the present invention also provides a non-transitory computer-readable storage medium on which a computer program is stored, characterized in that, when the computer program is executed by a processor, the steps of any one of the methods are implemented.
  • the multi-service migration method and system provided by the present invention transform the communication cost problem and load balancing problem of accessing services in the edge network into the problem of minimum cost and maximum flow, and can realize that only a small part of the access cost is increased.
  • the placement on the edge nodes is more balanced and flexible, and it can also well cope with the sudden increase in the number of service accesses and abnormal situations. It is simple, reliable and easy to implement, ensuring fast response to users. Improved service quality.
  • FIG. 1 is a flowchart of a multi-service migration method for load balancing according to the present invention.
  • FIG. 2 is a network topology diagram of virtual service migration according to the present invention.
  • FIG. 3 is a schematic diagram of the minimum cost maximum flow model of the present invention.
  • FIG. 4 is a flow chart of the present invention for updating the location of a service node.
  • FIG. 5 is a schematic diagram of the last residual network of the present invention.
  • FIG. 6 is a schematic diagram of the modified residual network of the present invention.
  • FIG. 7 is a schematic diagram of a multi-service migration system for load balancing according to the present invention.
  • FIG. 8 is a schematic diagram of a service node update module in the multi-service migration system of the present invention.
  • FIG. 9 is a schematic diagram of the principle of the electronic device of the present invention.
  • the present invention provides a load balancing multi-service migration method based on minimum cost and maximum flow, and the specific steps of the method include the following:
  • Step S1 Build a network topology map according to the geographic location and connection relationship of the edge server. Specifically, the weight of the edge in the network topology graph is determined by the distance between the connected edge nodes and the network transmission speed.
  • the application scenario of the present invention can be abstracted into a network topology diagram as shown in Figure 2.
  • the virtual service runs on the server of the edge node, so the edge node is abstracted into the edge server node, that is, Figure 2
  • the edge servers 1 to 5 connected to the decision center users can access the corresponding edge servers for the services they need through the nearest access points 1 to 5.
  • Step S2 Calculate the shortest path distance between each pair of edge servers in the network topology diagram, and generate a shortest path distance matrix. Specifically, using the shortest path algorithm to calculate the shortest path distance, the method is simple and reliable.
  • Step S3 Count the access information of each service in the most recent time period, and establish a minimum cost maximum flow model according to the shortest path distance matrix and in combination with the load capacity of each edge server and its relationship with the service delay.
  • the model includes source nodes, service nodes, edge server nodes, and endpoints.
  • the access cost and migration cost of each service placed on each edge node are calculated, and the relationship between the load capacity of each edge server and the service delay is calculated, and a minimum cost maximum flow model is established. .
  • the minimum cost maximum flow model is specifically:
  • Ni represents the ith service node
  • Ej represents the jth edge server node, where i is 1 to n, and j is 1 to m, that is, there are a total of n service nodes and m edge server nodes.
  • the source node S is only connected to the service node Ni
  • each service node Ni is connected to all edge server nodes Ej
  • each edge server node Ej is connected to the termination node T.
  • Each edge in the model is associated with two values, capacity and communication cost.
  • the capacity of each edge connecting the source node S and the service node Ni is 1, so that all services can be placed at the edge server or cloud center, and the cost is 0.
  • the capacity of the edge connecting each service node Ni with all edge server nodes Ej is 1, and the cost is the sum of the delay cost and the migration cost of placing the service node on the edge server node Ej.
  • each edge server node Ej is connected to the terminal T through several edges (that is, at least two edges) according to the number of services that the edge server node can carry, and each edge has a different cost weight. and an increasing relationship. Specifically, the weight of each edge can be determined according to the delay and cost of placing a corresponding number of services in the past, or it can be determined according to the user's needs or the real-time load of the entire edge network.
  • the result is the theoretical minimum total access delay, but it often occurs that the number of services that need to be placed on some edge nodes is particularly large, while other edge nodes are The number of services that need to be placed is relatively small, or even zero, which is likely to cause the edge nodes with heavy load to face resource contention, which will increase the delay of the service, or even cause downtime, which will degrade the quality of service.
  • the services on the edge server nodes with heavy load are placed on the relatively idle edge server nodes, and the total service access delay will not increase too much.
  • adding multiple edges on the edge connecting the edge server node to the endpoint that is, splitting one edge from each edge server node to the endpoint into multiple edges, and the number of edges is the number that can be placed by the corresponding edge server node.
  • the number of services, and a certain cost weight is given to each edge, so that services are more inclined to be placed on those edge server nodes with low load, rather than on some edge nodes with low load, but no It will increase too much theoretical access cost, so as to achieve load balancing of edge server nodes, and the degree of balance can be ensured by adjusting the cost weight on the edge from the edge server node to the endpoint, which can flexibly meet the The needs of different users, and can deal with many different scenarios.
  • Step S4 Solve the model to obtain the minimum cost and maximum flow, and place the service node on the corresponding edge server node to complete the migration and placement of the service node.
  • the classical minimum cost and maximum flow solution algorithm is used to solve the problem.
  • the solved minimum cost and maximum flow if there is a flow from the service node Ni through the edge server node Ej, it means that the service node Ni is placed on the edge.
  • the service node is migrated and placed, that is, the position of the edge server node corresponding to the service node can be obtained according to the solution result, and then the service node is migrated and placed.
  • the classical minimum cost maximum flow solution algorithm adopts continuous shortest path method, Cost Scaling, scaling method or elimination circle method, all of which can reliably and effectively obtain the minimum cost maximum flow of the model.
  • Step S5 Update the access information for each service, that is, re-statistic the access information for each service every time period, adjust the weight of the edge connecting the edge server node to the endpoint, and update the service node at the edge.
  • the location on the server node to achieve load balancing as shown in Figure 4, specifically includes:
  • Step S51 in the residual network of the model solving algorithm, update the access cost and migration cost of each service node placed on the edge server node;
  • Step S52 according to the updated access information, perform corresponding deletions and additions to the service nodes and edge server nodes in the residual network;
  • Step S53 Adjust the weight of the edge connecting the edge server node to the end node according to the current edge network index change and the load of each edge server node, so that the calculated placement scheme has a certain tendency, but Doesn't add much to the total access cost.
  • Step S54 Use the incremental minimum cost and maximum flow algorithm to quickly solve the problem, migrate and place the service node according to the obtained minimum cost and maximum flow, and update its position on the edge server node.
  • the access information of the service may be re-statistical at intervals.
  • the process of steps S3 and S4 will be chosen to be re-executed, but the solution time is often too long.
  • the embodiment of the present invention adopts the incremental minimum cost maximum flow algorithm, and directly modifies the corresponding service node and the edge connected to the edge server node in the last residual network, instead of constructing the minimum cost maximum flow model from scratch and Solving, performing the deloop method on the modified residual network reduces the solving time by a factor of 8-15.
  • Step S6 Perform real-time monitoring and statistics on the access information of each service, and adjust the corresponding service node and edge server node when the change rate of the access information exceeds the set threshold.
  • the number of user's access to the service node will have a sudden increase in volume and abnormal situation, and the access information will be monitored and counted in real time through the monitor.
  • An optimal threshold is trained by the reinforcement learning method. When the change rate of the monitored or statistical access information exceeds the set threshold, that is, the optimal threshold, the residual network is immediately updated, and the edge server nodes are adjusted in time to connect to The weight of the edge of the termination point can be solved by the incremental minimum cost maximum flow algorithm proposed in step S5, and the service node and the edge server node are adjusted accordingly.
  • each service node is updated by using the incremental minimum cost maximum flow algorithm, which specifically includes the following:
  • Step S541 The user's access to the service node changes, and when the set conditions are met, the location of the service node is updated, specifically including:
  • Step S5411 When the variable of the number of visits is greater than the pre-learned threshold, in the last residual network of the solution algorithm, modify the weights of the edges connected to the service node; otherwise, do not modify the weights that need to be modified. the number of sides.
  • the amount of visits to most service nodes will change every period of time, and since the number of visits by users to some service nodes will not change too much, the number of visits can be Compare with the optimal threshold learned by machine learning method in advance to determine whether to modify the weight of the edge. Further, the machine learning method is used to determine the threshold value that needs to be modified for the associated edge weights of the service nodes. Different from the traditional method, the optimal threshold value is obtained through the machine learning method and the previous data set training. The threshold obtained at this time can adapt to various access modes of users, large-scale and high-frequency changing network structure, and various abnormal situations, so that the decision center will not frequently change the residual network structure, thereby reducing the number of service adjustments and improving service quality.
  • Step S5412 Add a migration cost on the edge connecting the service node to the edge server node, and start from the service node whose cost has changed, and re-find the flow with the minimum cost and the maximum cost. Specifically, using the circle elimination method to find the minimum cost and maximum flow again, because it is based on the minimum cost and maximum flow in the previous stage, the circle elimination method can be used to find the optimal solution faster than other schemes.
  • Step S513 Update the position of the service node on the edge server node according to the obtained minimum cost and maximum flow.
  • the last partial residual network of the solution algorithm is shown in Figure 4.
  • the capacity and weight of the edge from the service node N1 to the edge server node E1 are (0, 100), indicating that the service node has been
  • the point N1 is placed on the edge server node E1, and the corresponding reverse edge (1, -100) is generated.
  • the service node Nn is allocated to the edge server node E2.
  • the number of user visits to service nodes N1 and Nn changes and the number of visits is greater than the threshold, then the corresponding edges in the residual network need to be modified.
  • the modified residual network is shown in Figure 5.
  • Step S542 when the number of service nodes changes, delete service nodes or add service nodes, and update the locations of service nodes, specifically including:
  • the service node and related edges are added to the residual network, the minimum cost path from the service node to the edge server node is selected, and the flow is added to reach the maximum flow, thereby realizing all services. Node placement; add migration costs on the edge where service nodes connect to edge server nodes;
  • a new minimum cost maximum flow is found, and the service node is migrated and placed.
  • the elimination circle method is also used to find a new minimum cost maximum flow.
  • Step S543 When the bearing capacity of the edge server node changes, update the location of the service node, which specifically includes:
  • Step S5431 When the capacity of the edge server node is reduced, check whether the flow from the edge server node is larger than the capacity of the corresponding edge after the reduction, specifically:
  • the flow corresponding to the edge with the highest cost in the edge server node will be returned; starting from the corresponding edge service node, select to end The shortest path to the point and increase the flow, and add the migration cost on the edge connecting the service node to the edge server node;
  • Step S5432 When the capacity of the edge server node increases or a new edge server node is added, the residual network is modified, and the migration cost is added on the edge connecting the service node to the edge server node; Starting from the server node, use the circle elimination method to find a new minimum cost and maximum flow, and adjust the service node.
  • the multi-service migration method establishes a minimum cost maximum flow model by constructing a network topology map and generating a shortest path distance matrix. Solve to get the minimum cost and maximum flow, migrate and place the service node, and update the position of the service node every time period. Real-time monitoring and statistics are performed on the access information of each service, and the corresponding service nodes and edge nodes are adjusted when necessary.
  • the embodiment of the present invention can achieve a more balanced placement of virtual services on edge nodes under the condition that only a small part of the access cost is increased, and the balance degree can be adjusted, which is positively correlated with the increased access cost, and can be It is very flexibly adjusted according to the actual needs of users, and can also be adjusted according to the load of each edge node, and can well cope with various situations in large-scale dynamic networks.
  • an embodiment of the present invention further provides a load balancing multi-service migration system based on minimum cost and maximum flow.
  • the system includes the following:
  • Topology map building module It is used to build a network topology map based on the geographical location and connection relationship of edge servers.
  • Matrix generation module used to calculate the shortest path distance between each pair of edge servers in the network topology diagram, and generate a shortest path distance matrix.
  • Model building module used to establish a minimum-cost maximum flow model according to the distance matrix, combined with the access information of each service, the load capacity of each edge server and its relationship with the service delay, and the model includes the source node , service nodes, edge server nodes and edge nodes.
  • Service node migration module used to solve the model to obtain the minimum cost and maximum flow, migrate the service nodes, and place them on the corresponding edge server nodes.
  • Service node update module used to update the access information of each service, adjust the weight of the edge connecting the edge server node to the end node, and update the position of the service node on the edge server node.
  • Monitoring and adjustment module It is used to monitor and count the access information of each service in real time, and adjust the corresponding service nodes and edge server nodes when the change rate of the access information exceeds the set threshold.
  • the service node update module includes:
  • Cost update sub-module used to update the access cost and migration cost of each service node placed on the edge server node in the residual network of the model solving algorithm.
  • Node deletion and addition sub-module It is used to perform corresponding deletion and addition of service nodes and edge server nodes in the residual network according to the updated access information.
  • Weight adjustment sub-module Adjust the weight of the edge connecting the edge server node to the endpoint according to the current edge network index changes and the load of each edge server node.
  • Node update sub-module use the incremental minimum cost and maximum flow algorithm to solve, and update the position of the service node on the edge server node according to the minimum cost and maximum flow obtained by the solution.
  • the system provided in the embodiment of the present invention is specifically used to execute the foregoing method embodiment, which is not repeated in this embodiment of the present invention.
  • FIG. 9 is a schematic diagram of the physical structure of an electronic device according to an embodiment of the present invention.
  • the electronic device may include: a processor (processor) 301, a communications interface (Communications Interface) 302, and a memory (memory) 303 and a communication bus 304 , wherein the processor 301 , the communication interface 302 , and the memory 303 communicate with each other through the communication bus 304 .
  • the processor 301 can call a computer program stored in the memory 303 and run on the processor 301 to execute the methods provided by the above embodiments, for example, including:
  • a minimum cost maximum flow model is established, and the model includes source nodes, service nodes, edge server nodes and endpoints;
  • Update the access information for each service adjust the weight of the edge connecting the edge server node to the endpoint, and update the position of the service node on the edge server node;
  • Real-time monitoring and statistics are performed on the access information of each service.
  • the change rate of the access information exceeds the set threshold, the corresponding service nodes and edge server nodes are adjusted.
  • the above-mentioned logic instructions in the memory 303 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product.
  • the technical solutions of the embodiments of the present invention are essentially, or the parts that make contributions to the prior art or the parts of the technical solutions can be embodied in the form of software products, and the computer software products are stored in a storage medium , including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.
  • the aforementioned storage medium includes: U disk, mobile hard disk, Read-Only Memory (ROM, Read-Only Memory), Random Access Memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program codes .
  • Embodiments of the present invention further provide a non-transitory computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, it is implemented to perform the methods provided by the foregoing embodiments, for example, including:
  • a minimum cost maximum flow model is established, and the model includes source nodes, service nodes, edge server nodes and endpoints;
  • Update the access information for each service adjust the weight of the edge connecting the edge server node to the endpoint, and update the position of the service node on the edge server node;
  • Real-time monitoring and statistics are performed on the access information of each service.
  • the change rate of the access information exceeds the set threshold, the corresponding service nodes and edge server nodes are adjusted.
  • the device embodiments described above are only illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in One place, or it can be distributed over multiple network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution in this embodiment. Those of ordinary skill in the art can understand and implement it without creative effort.
  • each embodiment can be implemented by means of software plus a necessary general hardware platform, and certainly can also be implemented by hardware.
  • the above-mentioned technical solutions can be embodied in the form of software products in essence or the parts that make contributions to the prior art, and the computer software products can be stored in computer-readable storage media, such as ROM/RAM, magnetic A disc, an optical disc, etc., includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform the methods described in various embodiments or some parts of the embodiments.
  • the method and system for multi-service migration based on load balancing with minimum cost and maximum flow provided by the embodiments of the present invention can obtain an optimal placement and migration strategy that minimizes the overall service access cost, and can also achieve a small increase in access when only a small part of the service access cost is minimized.
  • the placement of services on edge nodes is more balanced, and the degree of balance can be adjusted.
  • the time complexity is much lower than the previous method, and the strategy can be solved in a relatively short time, which is a good response. The sudden increase in the number of service accesses and the abnormal situation.

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  • Computer Networks & Wireless Communication (AREA)
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  • Quality & Reliability (AREA)
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Abstract

La présente invention concerne le domaine technique de l'information. Sont divulgués un procédé et un système de migration multiservice à équilibrage de charges basés sur un flux maximal à coût minimal. Ledit procédé consiste à : construire une carte topologique de réseau selon la position géographique et la relation de connexion de serveurs périphériques ; calculer la distance de trajet le plus court entre chaque paire de serveurs périphériques et générer une matrice de distances de trajets les plus courts ; établir un modèle de flux maximal à coût minimal, résoudre le modèle pour obtenir un flux maximal à coût minimal et placer un nœud de service sur un nœud correspondant de serveur périphérique ; mettre à jour des informations d'accès de chaque service, régler le poids périphérique, connecté à un nœud terminal, du nœud de serveur périphérique et mettre à jour la position du nœud de service sur le nœud de serveur périphérique ; et surveiller et collecter des statistiques des informations d'accès en temps réel et, lorsque le taux de variation des informations d'accès dépasse un seuil défini, régler le nœud de service et le nœud de serveur périphérique lui correspondant. La présente invention permet un placement plus équilibré, flexible et réglable d'un service virtuel sur un nœud périphérique, ce qui garantit une réponse rapide à un utilisateur en améliorant la qualité de service.
PCT/CN2020/138815 2020-12-10 2020-12-24 Procédé et système de migration multiservice à équilibrage de charges basés sur un flux maximal à coût minimal Ceased WO2022120959A1 (fr)

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CN202011436474.2A CN112601232B (zh) 2020-12-10 2020-12-10 基于最小费用最大流的负载均衡的多服务迁移方法及系统

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