Fine-grained slice isolation method in air-sea cross-domain network

Abstract

The invention discloses a fine-grained slice isolation method in an air-sea cross-domain network, which belongs to the technical field of network communication, and comprises the following steps: firstly collecting resource data to construct a cross-domain heterogeneous resource map; selecting an optimal slice deployment scheme through K shortest path screening and node matching decision; then realizing slice hard isolation by means of a resource data isolation technology, a network resource isolation technology and a wireless link resource isolation technology; and finally, monitoring the operation state, and triggering dynamic adjustment of the scheme when the operation state is abnormal. The system comprises three modules, and seamless switching of slices is guaranteed. Through a deterministic optimization algorithm and a multi-level isolation mechanism, accurate and efficient allocation and strict isolation of heterogeneous resources are realized, and the QoS guarantee capability of services in a high-dynamic cross-domain network and the system stability are remarkably improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of network communication, and particularly relates to a fine-grained slice isolation method in an air-sea cross-domain network. BACKGROUND

[0002] The application relates to resource management technology in an air-sea cross-domain network. With the development of air, sea and sky integrated networks, it is necessary to provide differentiated and highly reliable network services for diversified tasks such as reconnaissance, communication and remote sensing in a complex heterogeneous environment composed of satellites, unmanned aerial vehicles, ships and ground stations. Network slicing technology, as a key enabling technology, creates multiple logically isolated virtual networks on shared physical infrastructure to meet the quality of service (QoS) requirements of different services. However, the existing technology has obvious deficiencies when applied to the air-sea cross-domain scenario.

[0003] Currently, related technologies for implementing network slice resource isolation mainly include: (1) Network slicing technology based on 5G core network architecture. This technology is mainly based on network function virtualization (NFV) and software-defined networking (SDN), and divides logical independent network resources for different services (such as eMBB, uRLLC and mMTC) on a relatively fixed ground network infrastructure. However, this technology has the following problems: First, its resource model and scheduling strategy are mainly designed for ground homogeneous networks, making it difficult to model and perceive the air-sea heterogeneous resources such as satellite long-delay links, energy-limited unmanned aerial vehicles and moving trajectories of ships. Second, its orchestration mechanism assumes a relatively stable network topology, which cannot adapt to the dramatic topology changes caused by high-speed node movement and frequent link disconnection in air-sea networks, resulting in frequent failure of resource allocation strategies and inability to guarantee slice isolation effect and QoS.

[0004] (2) Traditional cross-domain resource scheduling and coordination technology. This technology usually relies on inter-domain gateway protocols (such as BGP) and pre-configured static service level agreements (SLA) for route selection and traffic engineering between different autonomous domains. However, this technology has the following problems: First, the coordination mechanism has a slow response speed, usually on the order of minutes or even hours, which cannot meet the real-time creation and second-level adjustment requirements of slices in high-dynamic scenarios. Second, each domain manages its own resources independently, lacking an end-to-end, unified resource view and control plane, which makes it extremely difficult to perform fine-grained resource reservation and isolation for cross-domain slices. When resources in a certain domain are tight, global optimization scheduling cannot be performed, which easily leads to business congestion and breaks the resource isolation between slices.

[0005] Therefore, how to realize a fine-grained slice resource isolation method capable of quickly responding to topology changes, accurately allocating heterogeneous resources and providing end-to-end hard isolation protection in a high-dynamic and heterogeneous air-sea cross-domain network is a technical problem to be solved in the field. SUMMARY

[0006] In view of the deficiencies in the background art, the purpose of the present application is to provide a fine-grained slice isolation method in an air-sea cross-domain network, collect multi-dimensional resource data, and construct a cross-domain heterogeneous resource graph model; then the slice mapping mechanism determines the optimal deployment scheme through a two-stage algorithm (KSP filters candidate paths, node resource matching and global optimal decision algorithm calculates the comprehensive score); finally, the resource isolation execution framework converts the optimal deployment scheme into configuration instructions, realizes end-to-end hard isolation through three-layer isolation technology, and dynamically re-plans migration when an event is triggered. It can provide deterministic QoS guarantee for network slicing in complex dynamic environment, effectively guaranteeing business continuity.

[0007] The technical scheme adopted by the present application is as follows: A fine-grained slice isolation method in an air-sea cross-domain network, which unifies the construction of a cross-domain heterogeneous resource graph model by constructing a cross-domain heterogeneous resource graph model for heterogeneous resources, realizes a slice intelligent mapping mechanism by using a two-stage combined optimization algorithm, and realizes end-to-end resource isolation by means of a cross-layer collaborative resource isolation execution framework, thereby providing deterministic QoS guarantee for network slicing in complex dynamic environment; comprising the following steps: Step S1. Collecting multi-dimensional resource data of nodes and links in the air-sea cross-domain network, constructing a cross-domain heterogeneous resource graph model based on a dynamic weighting graph model; Step S2. Based on the combined optimization strategy, design the slice mapping mechanism, after receiving the network QoS demand of the slice, filter the candidate paths through K shortest path, calculate the path cost and node resource matching degree comprehensive score through node resource matching and global optimal decision algorithm, select the global optimal deployment scheme; Step S3. Constructing a multi-level, end-to-end resource isolation execution framework, converting the optimal deployment scheme into configuration instructions, and realizing end-to-end hard isolation between slices by means of three-layer isolation technology; Step S4. Monitor events, and dynamically adjust the optimal deployment scheme after an abnormal event is triggered.

[0008] Preferably, the cross-domain heterogeneous resource graph model is constructed in step S1, and the specific steps are as follows: Collecting multi-dimensional resource data of nodes and links in the air-sea cross-domain network, constructing a cross-domain heterogeneous resource graph model for unified representation of cross-domain heterogeneous physical resources based on a dynamic weighting graph model, the cross-domain heterogeneous resource graph model is represented as G=(V,E), wherein V is a node set, As an edge set, this cross-domain heterogeneous resource graph model defines the attributes of the node set and edge set in multiple dimensions, as follows: Node set : Represents a physical entity, each node Associate a multidimensional attribute vector ,in Represents the total computing power of a node. Indicates available computing power. Represents total memory. Indicates available memory. Indicates current energy consumption; Edge set : Represents a physical or logical link; each edge Associate a multidimensional attribute vector ,in Indicates the total bandwidth of the link. Indicates available bandwidth. Indicates the propagation and processing delay. Indicates latency jitter. This indicates the reliability of the link.

[0009] Preferably, in step S2, the process of selecting the globally optimal deployment scheme is as follows: The network QoS requirements containing multi-dimensional resource data slices are received, and a two-stage slice mapping mechanism is used to map formal slice service requests to physical resources, as follows: Phase 1: Selection of K-shortest paths based on multi-objective constraints The first stage process includes preliminary filtering and calculation of compound path costs, as detailed below: 1) Preliminary filtering: Remove links that do not meet the QoS constraints based on the network QoS requirements of the slice; 2) Calculate the cost of the composite path: Using the K-shortest path selection algorithm, the cost Cost(P) of each candidate path P is calculated using the composite path cost formula, and the K candidate paths with the lowest costs are selected; the composite path cost formula is:

[0010] in, It is a path One of the links in; , These are links latency, available bandwidth, and reliability; This is the bandwidth of the slice request; It is a normalized weighting factor adjusted according to the business type; Phase Two: Node Resource Matching Based on Optimal Adaptation and Global Optimal Decision On the basis of the candidate paths screened out by the K shortest path, a node resource matching and global optimal decision algorithm is adopted; the algorithm calculates a comprehensive score for each candidate path by combining the path cost and the node resource matching degree to simultaneously evaluate the network cost and the node resource matching degree, and selects a global optimal deployment scheme that takes into account the network performance and the computational resource utilization, and the calculation formula is as follows:

[0011] wherein, is the composite path cost calculated in the first stage; and are the resource requirements of the slice for calculation and memory; and are the available resources of the nodes on the path; represents the resource matching degree of the node with the most tense resources on the path, and the closer the value is to 1, the more matched and smaller the redundancy of the resources are; and are weight coefficients.

[0012] Preferably, in the step S3, the multi-level, end-to-end resource isolation execution framework includes a calculation resource isolation technology, a network resource isolation technology and a wireless link resource isolation technology, the global optimal deployment scheme selected in the step S2 is converted into a configuration instruction executable for the underlying heterogeneous device, and is issued and executed to realize end-to-end isolation from calculation, wired network to wireless link, and to realize hard isolation between slices, and the specific implementation is as follows: 1) Resource data isolation technology: through the invocation of a container engine API by an orchestrator, and by utilizing the namespace and control group mechanism of a Linux kernel, an independent process and network protocol stack view are created for each slice, and the CPU time slice and memory usage of the slice are limited; 2) Network resource isolation technology: based on a software defined network architecture, a central controller issues a flow table rule to a switching node supporting an OpenFlow protocol; the flow table rule guides the traffic of different slices to different queues and virtual channels based on the slice ID and five-tuple information, and configures a bandwidth guarantee and priority policy; 3) Wireless link resource isolation technology: for satellite and unmanned aerial vehicle wireless communication scenarios, through linkage with a bottom MAC protocol controller, orthogonal frequency division multiple access and time division multiple access technologies are adopted to allocate mutually orthogonal time-frequency resource blocks for different slices, so as to eliminate signal interference at the physical layer.

[0013] Preferably, the monitoring event specifically includes: After the resource isolation execution framework is completed, the running state of the slice and the network resource state are monitored in real time, when abnormal events such as link interruption, performance decline, node failure and slice demand change occur, the dynamic re-planning migration is triggered, the slice mapping mechanism of step S2 is automatically re-executed, and a new optimal deployment scheme is found based on the updated resource graph; then, step S3 is re-executed, the configuration of the related device is updated, and seamless switching and continuity of the slice are realized.

[0014] Compared with the prior art, the present application proposes a fine-grained slice isolation method in an air-sea cross-domain network, which has the following advantages: (1) The problem of incompatibility of heterogeneous resource models and inaccurate perception is solved. In view of the problem that the 5G slice technology in the background technology is difficult to model the air-sea heterogeneous resources uniformly, the present application constructs a unified cross-domain heterogeneous resource graph (CD-HRG), and accurately quantizes and dynamically updates the heterogeneous attributes such as long delay link of satellite and energy limitation of unmanned aerial vehicle. This enables the system to comprehensively and real-timely perceive the real state of the entire cross-domain network, providing accurate data basis for subsequent resource allocation, and fundamentally solving the problem of unreasonable resource allocation caused by model mismatch; (2) The problem of slow response and low efficiency of resource scheduling in a high dynamic environment is overcome; in view of the problem that the traditional cross-domain coordination technology in the background technology has slow response and cannot adapt to the dramatic changes in network topology, the slice mapping mechanism based on the combination optimization strategy is adopted, which can complete the calculation and decision of complex slice requests within seconds; this mechanism can quickly respond to dynamic events such as node movement and link interruption, and re-plan the optimal resource deployment scheme, ensuring the continuity of the service and the stability of the QoS, which is significantly better than the response speed of minutes or even hours of the traditional technology; (3) End-to-end fine-grained resource isolation is realized, and the QoS guarantee capability is improved; in view of the problem that the resource isolation granularity is coarse and the cross-domain coordination is weak in the background technology, resulting in the problem that the QoS cannot be guaranteed, a multi-level, end-to-end resource isolation execution framework is adopted, realizing the overall hard isolation from computing, network to wireless link; through the cooperation of SDN, containerization and physical layer resource division, it is ensured that the resources (such as CPU, bandwidth, time slot) of a slice cannot be occupied by other slices, even when the resources in a certain domain are tight, the global view can be used for scheduling, so as to provide predictable and highly reliable end-to-end QoS guarantee, solving the problem of isolation failure caused by lack of global coordination in the traditional technology; (4) The global optimization capability and resource utilization rate of the system are enhanced; through unified resource view and centralized optimization decision, the barrier of independent management of each autonomous domain and non-intercommunication of information in the traditional technology is broken; this enables the system to allocate resources from the global perspective, avoids local congestion and resource waste, and significantly improves the resource utilization efficiency of the entire air-sea cross-domain network, which provides the possibility of carrying more and higher quality services on limited resources. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 The figure is a schematic diagram of the overall framework of the fine-grained slice isolation method in the air-sea cross-domain network. DETAILED DESCRIPTION

[0016] The technical solutions in the embodiments of the present application will be further described in detail below with reference to the drawings in the embodiments of the present application. It should be noted that the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0017] In order to make the purposes, technical solutions and advantages of the present application clearer, the embodiments of the present application will be further described in detail below with reference to the drawings of the specification: In order to more clearly understand the above-mentioned purposes, features and advantages of the present application, the advantages of the present application will be further illustrated by comparing embodiments with reference to the drawings and specific embodiments.

[0018] The present application proposes a fine-grained slice isolation method in an air-sea cross-domain network, as shown in Figure 1 The method constructs a cross-domain heterogeneous resource graph model by unifying heterogeneous resources, realizes a slice intelligent mapping mechanism by using a two-stage combined optimization algorithm, and realizes end-to-end resource isolation by means of a cross-layer collaborative resource isolation execution framework, thereby providing deterministic QoS guarantee for network slices in a complex dynamic environment; the steps of the method are described in detail: Step S1. Collecting multi-dimensional resource data of air-sea cross-domain network nodes and links, constructing a cross-domain heterogeneous resource graph model based on a dynamic weighting graph model; Step S2. Based on the combined optimization strategy, designing a slice mapping mechanism, after receiving the network QoS demand of the slice, screening candidate paths through K shortest path, calculating the path cost and node resource matching degree comprehensive score through node resource matching and global optimal decision algorithm, and selecting the global optimal deployment scheme; Step S3. Constructing a multi-level, end-to-end resource isolation execution framework, converting the optimal deployment scheme into configuration instructions, and realizing end-to-end hard isolation between slices by means of three-layer isolation technology; Step S4. Monitor events, and dynamically adjust the optimal deployment scheme after triggering by abnormal events.

[0019] Specifically, the step S1 constructs a cross-domain heterogeneous resource graph (CD-HRG) model, specifically as follows: Collecting multi-dimensional resource data of node and link information in air-sea cross-domain network; The multi-dimensional resource data includes ① node information: through the agent program deployed on ships, unmanned aerial vehicles, satellite gateways and ground stations, the resource state is reported periodically (for example, once every second); for example, the agent of the unmanned aerial vehicle will report its current available computing power, remaining memory, battery capacity, etc.; ② link information: through the software-defined network controller, network monitoring tool and link prediction model, the link state is obtained in real time, such as the available bandwidth, propagation delay and link reliability of the wireless link between the ship and the unmanned aerial vehicle, and the space link between the unmanned aerial vehicle and the satellite; The collected information is modeled as a dynamic weighted graph model, and a cross-domain heterogeneous resource graph model for uniformly representing cross-domain heterogeneous physical resources such as air, sea and sky is constructed based on the dynamic weighted graph model, and the cross-domain heterogeneous resource graph model represents G=(V, E), wherein is the node set, is the edge set, the cross-domain heterogeneous resource graph model quantitatively defines the attributes of the node set and the edge set, and the specific content is as follows: Node set : represents physical entities such as unmanned aerial vehicles , satellites , ships , etc., each node is associated with a multi-dimensional attribute vector , wherein represents the total computing power of the node, represents the available computing power, represents the total memory, represents the available memory, represents the current energy consumption; Edge set : represents physical or logical links; each edge is associated with a multi-dimensional attribute vector , wherein represents the total bandwidth of the link, represents the available bandwidth, represents the propagation and processing delay, represents the delay jitter, represents the link reliability (such as the probability of successful transmission).

[0020] Specifically, in the step S2, the process of selecting the globally optimal deployment scheme is as follows: When receiving network QoS requirements containing multi-dimensional resource data slices, the network QoS requirements of the slices include end-to-end latency, bandwidth, reliability, and node computing and memory resource requirements. Based on the resource graph in the cross-domain heterogeneous resource graph model constructed in step S1, a two-stage slice mapping mechanism is used to efficiently and accurately map the formal slice service request (SLA) to physical resources. The specific process is as follows: Phase 1: K-Shortest Path (KSP) Selection Algorithm Based on Multi-Objective Constraints The first stage process includes preliminary filtering and calculation of compound path costs, as detailed below: 1. Preliminary filtering: Based on the network QoS requirements of the slice, remove links in the resource graph that do not meet the basic requirements of the slice (such as available bandwidth being lower than the requested value, reliability being lower than the threshold); 2. Calculate the cost of the composite path: An improved K-shortest path selection algorithm is adopted, the core of which lies in a composite path cost formula specifically designed for heterogeneous networks; the cost Cost(P) of each candidate path P is calculated using the composite path cost formula, and K candidate paths with the lowest costs are selected; the composite path cost formula is:

[0021] in, It is a path One of the links in; These are links latency, available bandwidth, and reliability; This is the bandwidth of the slice request; It is a normalized weighting factor adjusted according to the business type; This formula unifies the costs of latency, bandwidth availability (in reciprocal form, the smaller the available bandwidth, the higher the cost), and reliability (in logarithmic form, converted into additive cost), thereby selecting the K lowest-cost candidate paths that are optimal at the network layer. Phase Two: Node Resource Matching and Global Optimal Decision Algorithm Based on Best-Fit Based on the candidate paths selected by the K-shortest path algorithm, a node resource matching and global optimal decision-making algorithm is adopted; this algorithm is used for each candidate path. A comprehensive score is calculated by combining path cost and node resource matching degree. To simultaneously evaluate the matching degree between network cost and node resources, and select the globally optimal deployment scheme that balances network performance and computing resource utilization, the calculation formula is as follows:

[0022] in, is the composite path cost calculated in the first stage; and is the resource requirement of the slice pair in terms of computation and memory; and is the available resource of the node on the path; represents the resource matching degree of the most resource-constrained node (bottleneck node) on the path, and the closer the value is to 1, the more matched the resource is and the smaller the redundancy is, which is used to evaluate whether the computation and memory resources of the node (in this case, the UAV) carrying the virtual network function on the path are sufficient and not excessively redundant; and is a weight coefficient; the algorithm selects the globally optimal deployment scheme that takes into account network performance and computation resource utilization by maximizing the comprehensive score Optimal scheme decision: the system selects the "path-node" combination with the highest comprehensive score as the final deployment scheme; for example, the final decision may be to select the path "ship -> UAV A -> satellite 1 -> ground station B" for the data flow, and to specify that the video compression function is deployed on the UAV A. Specifically, in the step S3, the multi-level, end-to-end resource isolation execution framework includes a computation resource isolation technology, a network resource isolation technology, and a wireless link resource isolation technology, converts the globally optimal deployment scheme selected in the step S2 into configuration instructions executable by the underlying heterogeneous devices, and issues and executes the configuration instructions to achieve end-to-end isolation from computation, wired network to wireless link, and to achieve hard isolation between slices, as follows:

[0023] ① Computation resource isolation technology: a new container is created to run the video compression function by calling the container engine API (such as Docker) through the orchestrator, and the central processor time slice and memory usage of the container are strictly limited through the cgroups technology to ensure that it does not exceed the computation resources of the UAV, and the Linux kernel's namespace (Namespaces) and control group (cgroups) mechanism are used to create independent processes and network protocol stack views for each slice, which are isolated from other applications and have precise CPU time slice and memory usage limits; ② Network resource isolation technology: based on the software-defined network (SDN) architecture, precise flow table rules are issued to the switch nodes supporting the OpenFlow protocol through a software-defined network (SDN) controller; these rules guide the traffic of different slices to different queues and virtual channels based on slice ID, five-tuple, etc., and open a virtual channel with a bandwidth guarantee of 20 Mbps and a high priority for the data flow of the slice, and configure strict bandwidth guarantees and priority policies; ​③Wireless link resource isolation technology: for the wireless link between the ship and the unmanned aerial vehicle A, through linkage with the bottom layer MAC protocol controller, orthogonal frequency division multiple access (OFDMA) and time division multiple access (TDMA) and other technologies are adopted to allocate mutually orthogonal time-frequency resource blocks for different slices, so as to eliminate signal interference from the physical layer.

[0024] Specifically, the monitoring event specifically includes: After the resource isolation execution framework is completed, the running state of the slice and the network resource state are monitored in real time, when abnormal events such as link interruption, performance decline, node failure and slice demand change occur, the event triggers dynamic re-planning migration, and the slice mapping mechanism of step S2 is automatically re-executed, a new optimal deployment scheme is found based on the updated resource atlas; then, step S3 is re-executed, the configuration of the related equipment is updated, seamless switching and continuity of the slice are realized; The link interruption: if the unmanned aerial vehicle A flies out of the communication range of the satellite 1, the link will be updated in the atlas accordingly; The performance decline: if the link between the ship and the unmanned aerial vehicle is disturbed, the available bandwidth decreases to below the required value; Through the cooperative work of the above four steps, the embodiment of the application constructs a complete closed-loop system from resource modeling, intelligent decision-making to isolation execution, which can provide key business with strictly isolated network slice services with deterministic quality of service guarantee in a high-dynamic, heterogeneous air-sea cross-domain network.

[0025] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications to the embodiments once they know the basic inventive concept. Therefore, the appended claims are intended to be interpreted as including all the preferred embodiments and all the changes and modifications falling within the scope of the present application.

[0026] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application also intends to include these modifications and variations.

Claims

1. A method for fine-grained slice isolation in an air-sea cross-domain network, characterized in that, Specifically comprising: Step S1. Collecting multi-dimensional resource data of air-sea cross-domain network nodes and links, and constructing a cross-domain heterogeneous resource graph model based on a dynamic weighting graph model; Step S2. Based on a combination optimization strategy, designing a slice mapping mechanism, after receiving the network QoS requirements of the slice, screening candidate paths through K shortest path, calculating path cost and node resource matching degree comprehensive score through node resource matching and global optimal decision algorithm, and selecting a global optimal deployment scheme; The process of selecting a global optimal deployment scheme is as follows: Receiving network QoS requirements of a slice containing multi-dimensional resource data, and using a two-stage slice mapping mechanism to map formal slice service requests to physical resources, specifically as follows: First stage: K shortest path screening based on multi-objective constraints The process of the first stage includes preliminary filtering and calculating composite path cost, specifically as follows: 1) Preliminary filtering: for the network QoS requirements of the slice, remove links that do not meet the QoS constraints; 2) Calculate the composite path cost: use the K shortest path screening algorithm to calculate the cost Cost(P) of each candidate path P through the composite path cost formula, and select the K candidate paths with the lowest cost; the composite path cost formula is: ； wherein, is a path of one link in the path; , are the latency, available bandwidth and reliability of the link , respectively; is the bandwidth of the slice request; is a normalized weight factor adjusted according to the traffic type; Second stage: node resource matching based on best adaptation and global optimal decision On the basis of the candidate paths screened out by the K shortest path algorithm, a node resource matching and global optimal decision algorithm is adopted The algorithm calculates the comprehensive score by combining the path cost and the node resource matching degree to simultaneously evaluate the network cost and the node resource matching degree, and selects a globally optimal deployment scheme that takes into account the network performance and the computing resource utilization rate, and the calculation formula is: ； wherein, is the composite path cost calculated in the first stage; and is the resource requirement of the slice pair for computation and memory; and is the available resource of the node on the path; represents the resource matching degree of the node with the most stressed resource on the path, and the value closer to 1 indicates that the resource is more matched and the redundancy is smaller; and is the weight coefficient; Step S3. Constructing a multi-level, end-to-end resource isolation execution framework, converting the optimal deployment scheme into configuration instructions, and realizing end-to-end hard isolation between slices by means of three-layer isolation technology; Step S4. Monitoring events, and dynamically adjusting the optimal deployment scheme after an abnormal event is triggered.

2. The method of claim 1, wherein, In the step S1 of constructing a cross-domain heterogeneous resource graph model, specifically as follows: The multi-dimensional resource data of node and link information in the air-sea cross-domain network is collected, a cross-domain heterogeneous resource graph model for uniformly representing cross-domain heterogeneous physical resources is constructed based on a dynamic weighting graph model, and the cross-domain heterogeneous resource graph model is represented as G=(V,E), wherein V is a node set, E is an edge set, and the cross-domain heterogeneous resource graph model quantitatively defines the attributes of the node set and the edge set in multiple dimensions, as follows: set of nodes : represents a physical entity, each node is associated with a multidimensional attribute vector wherein represents the total computing power of the node, represents the available computing power, represents the total memory, represents the available memory, represents the current energy consumption; edge set : represents a physical or logical link; each edge is associated with a multidimensional attribute vector where denotes the total bandwidth of the link, denotes the available bandwidth, denotes the propagation and processing delay, denotes the delay jitter, denotes the link reliability.

3. The method of claim 1, wherein, In the step S3, the multi-level, end-to-end resource isolation execution framework includes computing resource isolation technology, network resource isolation technology and wireless link resource isolation technology, converts the global optimal deployment scheme selected in step S2 into configuration instructions executable for underlying heterogeneous devices, and executes them to realize end-to-end isolation from computing, wired network to wireless link, realize hard isolation between slices, specifically as follows: 1) Resource data isolation technology: call the container engine API through the orchestrator, and use the namespace and control group mechanism of the Linux kernel to create an independent process and network protocol stack view for each slice, and limit its CPU time slice and memory usage; 2) Network resource isolation technology: based on the software-defined network architecture, the central controller issues flow table rules to the switch nodes supporting the OpenFlow protocol; the flow table rules are based on slice ID and five-tuple information, and guide the traffic of different slices to different queues and virtual channels, and configure bandwidth guarantee and priority strategy; 3) Wireless link resource isolation technology: for satellite and unmanned aerial vehicle wireless communication scenarios, through linkage with the underlying MAC protocol controller, orthogonal frequency division multiple access and time division multiple access technology are used to allocate mutually orthogonal time-frequency resource blocks for different slices, to eliminate signal interference at the physical layer.

4. The method of claim 1, wherein, The monitoring events specifically include: After the resource isolation execution framework is completed, the running state of the slice and the network resource state are monitored in real time. When abnormal events such as link interruption, performance decline, node failure and slice demand change occur, dynamic re-planning migration is triggered, the slice mapping mechanism of step S2 is automatically re-executed, a new optimal deployment scheme is found based on the updated resource atlas, then step S3 is re-executed, the configuration of the related device is updated, and seamless switching and continuity of the slice are realized.

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