CN115190021B - Deterministic time delay service oriented slice deployment method and related equipment - Google Patents

Deterministic time delay service oriented slice deployment method and related equipment Download PDF

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CN115190021B
CN115190021B CN202210436170.9A CN202210436170A CN115190021B CN 115190021 B CN115190021 B CN 115190021B CN 202210436170 A CN202210436170 A CN 202210436170A CN 115190021 B CN115190021 B CN 115190021B
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node
physical network
slice
deployment
link
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CN115190021A (en
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欧清海
宋继高
陆正嘉
王炫中
肖云杰
林亦雷
张亚南
王茜
龚爽
喻鹏
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State Grid Corp of China SGCC
State Grid Information and Telecommunication Co Ltd
Beijing University of Posts and Telecommunications
State Grid Shanghai Electric Power Co Ltd
Beijing Zhongdian Feihua Communication Co Ltd
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State Grid Corp of China SGCC
State Grid Information and Telecommunication Co Ltd
Beijing University of Posts and Telecommunications
State Grid Shanghai Electric Power Co Ltd
Beijing Zhongdian Feihua Communication Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/08—Configuration management of networks or network elements
    • H04L41/0893—Assignment of logical groups to network elements
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network

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Abstract

The application provides a slice deployment method for deterministic delay service, which comprises the following steps: quantifying the bottom physical network resources of deterministic delay service and slicing request resources to obtain a plurality of quantized values; defining node importance of the bottom layer physical network node by using the quantized value; constructing a slice deployment model by utilizing the quantized value and the node importance; generating a plurality of initial deployment schemes by using a slice deployment model, and determining an optimal deployment scheme among the plurality of initial deployment schemes by using an improved particle swarm optimization algorithm, wherein the optimal deployment scheme is configured to deploy slices for deterministic latency services. According to the method provided by the invention, factors such as the overall service delay and the network resource utilization rate are comprehensively considered, the optimization of the slice resource allocation is completed, the reliability, stability, instantaneity and safety of deterministic delay service are effectively improved, and the problems that the delay perceptibility of the slice deployment method in the prior art is poor and the key service delay is difficult to guarantee are solved.

Description

Deterministic time delay service oriented slice deployment method and related equipment
Technical Field
The present disclosure relates to the field of communications technologies, and in particular, to a slice deployment method and related devices for deterministic latency services.
Background
In the prior art, the communication resource scheduling method and the slice deployment method are mainly focused on improving the network resource utilization rate, lack of a slice resource allocation scheme aiming at specific service requirements, have the problems of poor delay sensing capability, difficult guarantee of key service delay and the like, and are difficult to meet the requirements of parameter aggregation, communication scheduling and the like of a distributed power supply, an energy storage system, a controllable load and the like. The virtual network deployment method based on time delay perception is provided, and an end-to-end network slice facing to deterministic time delay service requirements is constructed, so that the method has very important significance for improving the reliability, instantaneity and safety of a communication system with high requirements on communication resource scheduling.
Disclosure of Invention
In view of this, the present application aims to provide a slice deployment method and related equipment for deterministic latency service.
Based on the above objects, the present application provides a slice deployment method for deterministic latency service, including: quantifying the bottom physical network resources of deterministic delay service and slicing request resources to obtain a plurality of quantized values; defining node importance of the bottom layer physical network node by using the quantized value; constructing a slice deployment model by utilizing the quantized value and the node importance; generating a plurality of initial deployment schemes by using the slice deployment model, and determining an optimal deployment scheme from the plurality of initial deployment schemes by using an improved particle swarm optimization algorithm, wherein the optimal deployment scheme is configured to deploy slices for the deterministic latency service.
Optionally, the defining the node importance of the underlying physical network node by using the quantized value includes: defining network characteristics and topology characteristics of the bottom layer physical network node by utilizing the quantized values; and defining the node importance by utilizing the network characteristics and the topology characteristics.
Optionally, the quantized value includes: the capacity of the bottom layer physical network node, the bandwidth of the bottom layer physical network link and the adjacent link set; the defining the network characteristics and the topology characteristics of the underlying physical network node by using the quantized values includes: defining the network characteristics of the bottom layer physical network node by using the bottom layer physical network node capacity, the bottom layer physical network link bandwidth and the adjacent link set; defining the topological feature by using the normalized node degree of the bottom physical network node and the normalized betweenness centrality of the bottom physical network node.
Optionally, the slice deployment model includes a target optimization function and a conditional constraint function; the constructing a slice deployment model by using the quantized values and the node importance comprises the following steps: constructing the target optimization function according to the upper limit of the total processing delay requirement of the slice and the lower limit of the total processing delay requirement of the slice by utilizing the quantized value; and constructing the condition constraint function by using the quantized value and the node importance.
Optionally, the improved particle swarm optimization algorithm comprises an iterative formula; before determining the optimal deployment scenario among the plurality of initial deployment scenarios using the improved particle swarm optimization algorithm, further comprising: and taking the deployment scheme as a target update value of the iterative formula to obtain the improved particle swarm optimization algorithm.
Optionally, the determining an optimal deployment scenario among the plurality of initial deployment scenarios using the improved particle swarm optimization algorithm comprises: determining an initial global optimum and an initial local optimum in the iterative formula according to the plurality of initial deployment schemes; iteratively updating the initial global optimum, the initial local optimum, and the plurality of initial deployment scenarios using the improved particle swarm optimization algorithm; and stopping the iterative updating and outputting the optimal deployment scheme in response to the iterative updating times reaching a first threshold value and/or the updated initial deployment scheme meeting a first condition function, wherein the first threshold value and the first condition function are preset.
Optionally, the deployment scheme includes a mapping scheme and an adjustment of the mapping scheme, and the iterative formula includes:
Wherein the method comprises the steps of，Mapping schemes respectively obtained by the kth iteration and the k+1 iteration of the ith bottom layer physical network node,/I>The adjustment of mapping schemes obtained by the kth iteration and the k+1 iteration of the ith bottom layer physical network node respectively, w is inertia weight, c1 is individual cognitive acceleration coefficient, c2 is social acceleration coefficient, and +.>For the local optimum obtained for the kth iteration, P G Is the global optimum, < >>Representing->If the corresponding mapping scheme is adjusted +.>The value in dimension t is 1, then +.>Unchanged, otherwise, there is a need to iterate k+1 times>Replacing with other mapping schemes; />Representing a differential calculation; />Representing a weighting operation.
Based on the same inventive concept, the present application also provides a slice deployment device for deterministic delay service, comprising: the quantization module is configured to quantize the bottom physical network resources and the slicing request resources of the deterministic delay service to obtain a plurality of quantized values; an importance definition module configured to define node importance of the underlying physical network node using the quantized value; a model building module configured to build a slice deployment model using the quantized values and the node importance; and the optimization module is configured to generate a plurality of initial deployment schemes by using the slice deployment model, and determine an optimal deployment scheme from the plurality of initial deployment schemes by using an improved particle swarm optimization algorithm, wherein the optimal deployment scheme is configured to deploy slices for the deterministic time delay service.
Optionally, the importance definition module includes: a feature definition unit configured to define network features and topology features of the underlying physical network nodes using the quantized values; an importance definition unit configured to define the node importance using the network feature and the topology feature.
Optionally, the quantized value includes: the capacity of the bottom layer physical network node, the bandwidth of the bottom layer physical network link and the adjacent link set; the feature definition unit includes: a network feature definition subunit configured to define the network feature of the underlying physical network node using the underlying physical network node capacity, an underlying physical network link bandwidth, and a set of contiguous links; a topology feature definition subunit configured to define the topology feature using the normalized node degree of the underlying physical network node and the normalized betweenness centrality of the underlying physical network node.
Optionally, the slice deployment model includes a target optimization function and a conditional constraint function; the model building module is further configured to: constructing the target optimization function according to the upper limit of the total processing delay requirement of the slice and the lower limit of the total processing delay requirement of the slice by utilizing the quantized value; and constructing the condition constraint function by using the quantized value and the node importance.
Optionally, the improved particle swarm optimization algorithm comprises an iterative formula; the apparatus further comprises: an algorithm improvement module configured to update a deployment scenario as a target update value of the iterative formula to arrive at the improved particle swarm optimization algorithm.
Optionally, the optimization module is further configured to: determining an initial global optimum and an initial local optimum in the iterative formula according to the plurality of initial deployment schemes; iteratively updating the initial global optimum, the initial local optimum, and the plurality of initial deployment scenarios using the improved particle swarm optimization algorithm; and stopping the iterative updating and outputting the optimal deployment scheme in response to the iterative updating times reaching a first threshold value and/or the updated initial deployment scheme meeting a first condition function, wherein the first threshold value and the first condition function are preset.
Optionally, the deployment scheme includes a mapping scheme and an adjustment of the mapping scheme, and the iterative formula includes:
wherein,mapping schemes respectively obtained by the kth iteration and the k+1 iteration of the ith bottom layer physical network node,/I>The adjustment of mapping schemes obtained by the kth iteration and the k+1 iteration of the ith bottom layer physical network node respectively, w is inertia weight, c1 is individual cognitive acceleration coefficient, c2 is social acceleration coefficient, and +. >For the local optimum obtained for the kth iteration, P G Is the global optimum, < >>Representing the mapping scheme/>If the corresponding mapping scheme is adjusted +.>The value in dimension t is 1, then +.>Unchanged, otherwise, there is a need to iterate k+1 times>Replacing with other mapping schemes; />Representing a differential calculation; />Representing a weighting operation.
Based on the same inventive concept, the application also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor is used for realizing any slice deployment method facing deterministic delay service when executing the program.
Based on the same inventive concept, the application also provides a non-transitory computer readable storage medium, which stores computer instructions, wherein the computer instructions are used for making a computer execute any one of the deterministic latency service oriented slice deployment methods.
From the above, it can be seen that the slice deployment method for deterministic latency service provided by the present application includes: quantifying the bottom physical network resources of deterministic delay service and slicing request resources to obtain a plurality of quantized values; defining node importance of the bottom layer physical network node by using the quantized value; constructing a slice deployment model by utilizing the quantized value and the node importance; generating a plurality of initial deployment schemes by using the slice deployment model, and determining an optimal deployment scheme from the plurality of initial deployment schemes by using an improved particle swarm optimization algorithm, wherein the optimal deployment scheme is configured to deploy slices for the deterministic latency service. According to the method provided by the invention, factors such as the overall service delay and the network resource utilization rate are comprehensively considered, the optimization of the slice resource allocation is completed, the reliability, stability, instantaneity and safety of deterministic delay service are effectively improved, and the problems that the delay perceptibility of the slice deployment method in the prior art is poor and the key service delay is difficult to guarantee are solved.
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In order to more clearly illustrate the technical solutions of the present application or related art, the drawings that are required to be used in the description of the embodiments or related art will be briefly described below, and it is apparent that the drawings in the following description are only embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort to those of ordinary skill in the art.
Fig. 1 is a flow chart of a deterministic latency service oriented slice deployment method according to an embodiment of the present application;
fig. 2 is a schematic diagram illustrating a part of a detailed flow of a deterministic latency service-oriented slice deployment method according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a deterministic latency service oriented slice deployment device according to an embodiment of the present application;
fig. 4 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present application.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail below with reference to the accompanying drawings.
It should be noted that unless otherwise defined, technical or scientific terms used in the embodiments of the present application should be given the ordinary meaning as understood by one of ordinary skill in the art to which the present application belongs. The terms "first," "second," and the like, as used in embodiments of the present application, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", etc. are used merely to indicate relative positional relationships, which may also be changed when the absolute position of the object to be described is changed.
The distributed energy is widely applied and popularized in the power generation field with the advantages of flexibility, environmental protection, economy and the like, and in order to fully utilize the distributed energy to generate power and enable the distributed energy to be well fused with the traditional power market, the prior art proposes to aggregate various distributed energy together to form a virtual power plant which integrally participates in a power system.
The virtual power plant utilizes the communication technology to realize the aggregation and coordination optimization of distributed energy, the communication system plays an important role in the coordination control of source-network-load-storage of the virtual power plant, and a bidirectional channel is provided for real-time, reliable and safe transmission of various acquisition, monitoring and control data, so that the virtual power plant has strict requirements on the scheduling of communication resources. As described in the background section, the communication resource scheduling method and the network slice deployment method in the prior art are difficult to meet the communication requirements of the virtual power plant service.
To meet the delay sensitivity requirement of the virtual power plant service, one embodiment of the present application proposes a slice deployment method for deterministic delay service, as shown in fig. 1, the method includes:
and step S101, quantifying bottom physical network resources of deterministic delay service and slicing request resources to obtain a plurality of quantized values.
Step S102, defining the node importance of the bottom layer physical network node by using the quantized value. In the node mapping process, the priority of node mapping is determined by the node importance, so that efficient allocation of slice resources is further ensured.
And step S103, constructing a slice deployment model by using the quantized values and the node importance.
Step S104, generating a plurality of initial deployment schemes by using the slice deployment model, and determining an optimal deployment scheme from the plurality of initial deployment schemes by using an improved particle swarm optimization algorithm, wherein the optimal deployment scheme is configured to deploy slices for the deterministic latency service. The particle swarm optimization algorithm has the advantages of high operation speed, simple setting parameters and capability of completing efficient allocation of slice resources, and the improved particle swarm optimization algorithm is made based on deterministic time delay service, so that the whole scheme is more in accordance with the technical target to be achieved.
According to the method, the node importance concept is added into the improved particle swarm optimization algorithm, the factors such as the overall service delay and the network resource utilization rate are comprehensively considered, the optimization of the slice resource allocation is completed, the reliability, stability, instantaneity and safety of deterministic delay service are effectively improved, and the problems that the delay perception capability is poor and the key service delay is difficult to guarantee in the slice deployment method in the prior art are solved.
In a specific embodiment, the underlying physical network resources of the deterministic latency service quantized in step S101 are specifically: by weighted undirected graph G p (N p ,L p ,C p ,B p ,l p ) Representing an underlying physical network, where N p Representing a collection of all physical nodes in a physical network that provide resources such as computation, storage, functional services, etc.; l (L) p Representing a contiguous set of links providing physical network bandwidth resources; c (C) p Representing the underlying physical network capacity; b (B) p Representing the underlying physical network link bandwidth; l (L) p Representing the physical link length.
A specific embodiment of the present application regards a network slice s as an end-to-end virtual consisting of a set of virtual network functions (VNFs, virtualize Network Function) meeting corresponding traffic demands and virtual links between VNFsAnd (5) simulating a network. The slice request resources for quantifying deterministic delay traffic in step S101 are specifically: by weighted undirected graph G v (N v ,L v F) denotes a network slice, where N v A set of virtual nodes representing slices; l (L) v Representing a set of virtual links in a slice; f is the request traffic for the network slice.
Above N p ,L p ,C p ,B p ,l p ,N v ,L v F is the quantized value described in the embodiments of the present application.
In some embodiments, as shown in fig. 2, the step S102 includes:
Step S201, defining network characteristics and topology characteristics of the bottom layer physical network node by using the quantized value; the network characteristics comprise node capacity and node forwarding rate, and the topology characteristics comprise node degree and connectivity of the underlying physical network nodes, wherein. Connectivity is represented by the median centrality. The median centrality is a measure of graph centrality based on shortest paths, with at least one shortest path between nodes for each pair of nodes in the connected graph, such that the number of edges (for an unweighted graph) or the sum of edge weights (for a weighted graph) that the path passes through is minimized.
Step S202, defining the node importance by using the network feature and the topology feature, where the node importance is expressed as:
wherein NF is i As a function of the characteristics of the network,is the topological feature.
In some embodiments, as shown in fig. 2, the step S201 includes:
step S2011, defining the network characteristics of the underlying physical network node by using the capacity of the underlying physical network node, the bandwidth of the underlying physical network link, and the set of adjacent links, where the network characteristics are expressed as:
NF i reflecting the CPU resources and bandwidth resources of the underlying physical network node. In the above formula, C i The capacity of the underlying physical network node for the underlying physical network node i,for the contiguous set of links of the underlying physical network node i,for the bandwidth of the underlying physical network link,/-a->For the contiguous link set of the underlying physical network node i>Is allocated to the link bandwidth resource.
Step S2012, defining the topology feature by using the normalized node degree of the underlying physical network node and the normalized betweenness centrality of the underlying physical network node, where the topology feature is expressed as:
wherein d' i For normalizing the node degree of the bottom layer physical network node i, b' i The medium centrality of the bottom physical network node i is normalized;
specifically, d' i Expressed as:
wherein N is the total number of nodes of the underlying physical network, d i The node degree is the node degree of the bottom layer physical network node i;
b′ i expressed as:
wherein b i Is the betweenness centrality of the bottom layer physical network node i, b i The possibility that the bottom physical network node i is connected to other nodes is quantized, N is the total number of nodes of the bottom physical network, s-t is the path of the bottom physical network node s to the bottom physical network node t, namely the path of the node pair s-t, sigma st (i) Representing the shortest path number passing through the bottom physical network node i in the path of s-t; when each node pair in the underlying physical network has at least one shortest path through node i, then b i Take the maximum valueThus b can be obtained i ′ Is an expression of (2).
In some embodiments, the slice deployment model includes an objective optimization function and a conditional constraint function;
the step S103 includes:
and step 301, constructing the target optimization function according to the upper limit of the total processing time delay requirement of the slice and the lower limit of the total processing time delay requirement of the slice by utilizing the quantized value. In this step, a binary variable description slice mapping relationship to the underlying physical network needs to be introduced first, including:
representation ofIf the u node in the slice maps to the s node of the underlying physical network, +.>1, otherwise 0;
representing that if virtual links (u, v) in a slice map onto underlying physical network links (s, t)Otherwise, 0;
representing the total processing delay of the mapping of virtual links (u, v) in a slice to links (s, t) in the underlying physical network, expressed in particular as:
wherein,the bandwidth requirement expressed as virtual links (u, v) in the slice; />Link capacity for physical links (s, t); / >Representing a link length of a physical link (i, j), wherein the physical link (i, j) is a minimum sub-link of a segment of the physical link (s, t); />Link bandwidth allocated for physical link (i, j); the variable θ is a weight factor used to balance network characteristics. From the time delay formula, it can be seen that the object isThe lower the physical link bandwidth capacity, or the longer its physical link length, the higher the total processing delay incurred.
Based onThe target optimization function of the slice deployment model facing the deterministic delay business requirement can be obtained:
wherein N is p N being the set of the underlying physical network nodes v T, a set of virtual nodes in the slice max Upper limit of total processing time delay requirement for the slice, T min And for the lower limit of the total processing time delay requirement of the slice, the total processing time delay obtained by the target optimization function is infinitely close to the intermediate value of the upper limit and the lower limit of the total processing time delay requirement.
Step S302, constructing the condition constraint function by using the quantized value and the node importance; the conditional constraint function includes:
wherein,the one-to-one correspondence between link mappings and corresponding node mappings is ensured;the bandwidth of the physical link (s, t) is ensured to meet the bandwidth requirement of the virtual link (u, v) mapped on the physical link (s, t); / >The computing resource of the physical node s is ensured to meet the resource requirement of the virtual node u mapped on the computing resource;the flow conservation is ensured, and the inflow flow of the middle physical node v is equal to the outflow flow thereof; /> Ensuring that the total delay of slice deployment is between the upper limit and the lower limit of the delay requirement; />For evaluating whether the physical node s corresponding to the deployment virtual node u is available or not by using the node importance, and NI th A preset node importance threshold value; />Is a variable constraint.
According to the method, a slice deployment model consisting of a target optimization function and a conditional constraint function thereof is obtained, the resource allocation problem described by the model is an NP-hard problem, and although an accurate solution (such as a branch-and-bound method) of the model can calculate the optimal solution, the algorithm complexity is high and the solution time is long. The embodiment of the application provides an improved particle swarm optimization algorithm, which belongs to a heuristic optimization algorithm, has the advantages of high efficiency, high execution speed and the like, can enable the obtained deployment scheme to reach the deterministic time delay range of service requirements on the premise of meeting bandwidth requirements, and simultaneously improves the network resource utilization rate and load balance as much as possible.
The resource allocation of a slice can be generally solved by a two-stage mapping method (i.e., node mapping and link mapping). In this embodiment, in order to simplify the model, the node mapping may be determined first, and then, according to the mapped node, the shortest path satisfying the bandwidth requirement is selected in the underlying physical network as the corresponding mapping link, so that only the resource mapping allocation problem of the node needs to be focused on.
In some embodiments, the improved particle swarm optimization algorithm comprises an iterative formula; prior to step S104, the method further comprises:
taking a deployment scheme (namely a mapping scheme and adjustment of the mapping scheme) as a target update value of the iterative formula to obtain the improved particle swarm optimization algorithm.
The embodiment of the application is based on an improved particle swarm optimization algorithm, and the algorithm is suitable for discrete solution by redefining the motion parameters and operation modes of particles in a search space, wherein the specific variable parameters are defined as follows:
X i ＝(x i1 ,x i2 ,…,x in ) T an n-dimensional column vector representing the ith node mapping scheme. n is the number of virtual nodes; each dimension in the vector represents a physical network node number, such as x, to which one virtual node in the slice is mapped in the underlying physical network in And the number of the bottom physical network node mapped by the nth virtual node in the ith mapping scheme is represented.
V i ＝(v i1 ,v i2 ,…,v in ) T An n-dimensional column vector represents an adjustment to the ith node mapping scheme. For vector V i V of each dimension in (a) ik ，v ik When=0, the kth node in the scheme needs to reselect a new bottom physical node to adjust the mapping, otherwise, no adjustment is needed.
The iterative formula includes:
wherein, Mapping schemes respectively obtained by the kth iteration and the k+1 iteration of the ith bottom layer physical network node,/I>The adjustment of mapping schemes obtained by the kth iteration and the k+1 iteration of the ith bottom layer physical network node respectively, w is inertia weight, c1 is individual cognitive acceleration coefficient, c2 is social acceleration coefficient, and +.>For the local optimum obtained for the kth iteration, P G Is the global optimum, < >>Representing->If the corresponding mapping scheme is adjusted +.>The value in dimension t is 1, then +.>Unchanged, otherwise, there is a need to iterate k+1 times>Replacing with other mapping schemes; />Representing a differential calculation, e.g. if + ->The value of the dimension t is equal, the operation result is 1, and otherwise, the value is 0; />The weight calculation is represented by a weighted addition of n-dimensional column vectors, and the weights are determined by w, c1, and c 2.
In some embodiments, the generating a plurality of initial deployment scenarios using the slice deployment model comprises:
first of all,based on the objective optimization function, a fitness function f (X i ) Fitness function f (X i ) The same principle as the objective optimization function is as follows:
wherein the method comprises the steps ofRepresenting the node pairs (x) in the underlying physical network to which adjacent virtual nodes (k, k+1) in the slice are mapped i,k, x i,k+1 ) The total time delay of the links between the two;
and generating a plurality of feasible solutions meeting the basic service bandwidth requirement by using the fitness function and the condition constraint function as an initial deployment scheme.
In some embodiments, the step S104 includes:
step S401, determining an initial global optimal value and an initial local optimal value in the iterative formula according to the plurality of initial deployment schemes. In a specific embodiment, an initial global optimal value is obtained by calculating an initial deployment scheme with the smallest fitness function value, and an initial local optimal value is obtained by the initial value of each initial deployment scheme.
Step S401, iteratively updating the initial global optimal value, the initial local optimal value and the plurality of initial deployment schemes by using the improved particle swarm optimization algorithm; and stopping the iterative updating and outputting the optimal deployment scheme in response to the iterative updating times reaching a first threshold value and/or the updated initial deployment scheme meeting a first condition function, wherein the first threshold value and the first condition function are preset. In a specific embodiment, the first threshold is 15-30 times, and the first condition function is
The above updated local optimum value P i Is too much to (a)Cheng Juti is: based on fitness function f (X i ) When f (X i )<f(P i ) Let P i ＝X i The method comprises the steps of carrying out a first treatment on the surface of the The global optimum P is updated G The process of (1) is specifically as follows: based on fitness function f (X i ) Let P G ＝P i |min i {f(X i ) -a }; the updating of the plurality of initial deployment schemes is performed using an iterative formula of the improved particle swarm optimization algorithm described above.
It should be noted that, the method of the embodiments of the present application may be performed by a single device, for example, a computer or a server. The method of the embodiment can also be applied to a distributed scene, and is completed by mutually matching a plurality of devices. In the case of such a distributed scenario, one of the devices may perform only one or more steps of the methods of embodiments of the present application, and the devices may interact with each other to complete the methods.
It should be noted that some embodiments of the present application are described above. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments described above and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
Based on the same inventive concept, corresponding to the method of any embodiment, the present application further provides a deterministic latency service oriented slice deployment device, as shown in fig. 3, including:
the quantization module 10 is configured to quantize the underlying physical network resources and the slice request resources of the deterministic latency service to obtain a plurality of quantized values;
an importance definition module 20 configured to define node importance of the underlying physical network node using the quantized value;
a model construction module 30 configured to construct a slice deployment model using the quantized values and the node importance;
an optimization module 40 configured to generate a plurality of initial deployment schemes using the slice deployment model and to determine an optimal deployment scheme among the plurality of initial deployment schemes using a modified particle swarm optimization algorithm, the optimal deployment scheme configured to deploy slices for the deterministic latency service.
According to the device provided by the application, the node importance concept is added into the improved particle swarm optimization algorithm, the factors such as the overall service delay and the network resource utilization rate are comprehensively considered, the optimization of the slice resource allocation is completed, the reliability, stability, instantaneity and safety of deterministic delay service are effectively improved, and the problems that the delay perception capability is poor and the key service delay is difficult to guarantee in the slice deployment method in the prior art are solved.
In some embodiments, the importance definition module 20 includes:
a feature definition unit configured to define network features and topology features of the underlying physical network nodes using the quantized values;
an importance definition unit configured to define the node importance using the network feature and the topology feature.
In some embodiments, the quantized values include: the capacity of the bottom layer physical network node, the bandwidth of the bottom layer physical network link and the adjacent link set;
the feature definition unit includes:
a network feature definition subunit configured to define the network feature of the underlying physical network node using the underlying physical network node capacity, an underlying physical network link bandwidth, and a set of contiguous links;
a topology feature definition subunit configured to define the topology feature using the normalized node degree of the underlying physical network node and the normalized betweenness centrality of the underlying physical network node.
In some embodiments, the slice deployment model includes an objective optimization function and a conditional constraint function;
the model building module 30 is further configured to: constructing the target optimization function according to the upper limit of the total processing delay requirement of the slice and the lower limit of the total processing delay requirement of the slice by utilizing the quantized value; and constructing the condition constraint function by using the quantized value and the node importance.
In some embodiments, the improved particle swarm optimization algorithm comprises an iterative formula;
the apparatus further comprises:
an algorithm improvement module configured to update a deployment scenario as a target update value of the iterative formula to arrive at the improved particle swarm optimization algorithm.
In some embodiments, the optimization module 40 is further configured to: determining an initial global optimum and an initial local optimum in the iterative formula according to the plurality of initial deployment schemes; iteratively updating the initial global optimum, the initial local optimum, and the plurality of initial deployment scenarios using the improved particle swarm optimization algorithm; and stopping the iterative updating and outputting the optimal deployment scheme in response to the iterative updating times reaching a first threshold value and/or the updated initial deployment scheme meeting a first condition function, wherein the first threshold value and the first condition function are preset.
In some embodiments, the deployment scheme includes a mapping scheme and an adjustment of the mapping scheme, and the iterative formula includes:
wherein,mapping schemes respectively obtained by the kth iteration and the k+1 iteration of the ith bottom layer physical network node,/I >Respectively the ith bottom physical network nodeAdjustment of mapping scheme obtained by k and k+1 iterations of points, w is inertia weight, c1 is individual cognitive acceleration coefficient, c2 is social acceleration coefficient, and->For the local optimum obtained for the kth iteration, P G Is the global optimum, < >>Representing->If the corresponding mapping scheme is adjusted +.>The value in dimension t is 1, then +.>Unchanged, otherwise, there is a need to iterate k+1 times>Replacing with other mapping schemes; />Representing a differential calculation; />Representing a weighting operation.
For convenience of description, the above devices are described as being functionally divided into various modules, respectively. Of course, the functions of each module may be implemented in the same piece or pieces of software and/or hardware when implementing the present application.
The device of the foregoing embodiment is configured to implement the corresponding deterministic latency service-oriented slice deployment method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiment, which are not described herein.
Based on the same inventive concept, the application also provides an electronic device corresponding to the method of any embodiment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the slice deployment method facing deterministic latency service according to any embodiment when executing the program.
Fig. 4 shows a more specific hardware architecture of an electronic device according to this embodiment, where the device may include: a processor 1010, a memory 1020, an input/output interface 1030, a communication interface 1040, and a bus 1050. Wherein processor 1010, memory 1020, input/output interface 1030, and communication interface 1040 implement communication connections therebetween within the device via a bus 1050.
The processor 1010 may be implemented by a general-purpose CPU (Central Processing Unit ), microprocessor, application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, etc. for executing relevant programs to implement the technical solutions provided in the embodiments of the present disclosure.
The Memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory ), static storage device, dynamic storage device, or the like. Memory 1020 may store an operating system and other application programs, and when the embodiments of the present specification are implemented in software or firmware, the associated program code is stored in memory 1020 and executed by processor 1010.
The input/output interface 1030 is used to connect with an input/output module for inputting and outputting information. The input/output module may be configured as a component in a device (not shown) or may be external to the device to provide corresponding functionality. Wherein the input devices may include a keyboard, mouse, touch screen, microphone, various types of sensors, etc., and the output devices may include a display, speaker, vibrator, indicator lights, etc.
Communication interface 1040 is used to connect communication modules (not shown) to enable communication interactions of the present device with other devices. The communication module may implement communication through a wired manner (such as USB, network cable, etc.), or may implement communication through a wireless manner (such as mobile network, WIFI, bluetooth, etc.).
Bus 1050 includes a path for transferring information between components of the device (e.g., processor 1010, memory 1020, input/output interface 1030, and communication interface 1040).
It should be noted that although the above-described device only shows processor 1010, memory 1020, input/output interface 1030, communication interface 1040, and bus 1050, in an implementation, the device may include other components necessary to achieve proper operation. Furthermore, it will be understood by those skilled in the art that the above-described apparatus may include only the components necessary to implement the embodiments of the present description, and not all the components shown in the drawings.
The electronic device of the foregoing embodiment is configured to implement the corresponding deterministic latency service-oriented slice deployment method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiment, which are not described herein.
Based on the same inventive concept, corresponding to the method of any embodiment, the application further provides a non-transitory computer readable storage medium, wherein the non-transitory computer readable storage medium stores computer instructions, and the computer instructions are used for making the computer execute the slice deployment method for deterministic latency service according to any embodiment.
The computer readable media of the present embodiments, including both permanent and non-permanent, removable and non-removable media, may be used to implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device.
The computer instructions stored in the storage medium of the foregoing embodiments are used to make the computer execute the slice deployment method for deterministic latency service according to any one of the foregoing embodiments, and have the beneficial effects of the corresponding method embodiments, which are not described herein.
Those of ordinary skill in the art will appreciate that: the discussion of any of the embodiments above is merely exemplary and is not intended to suggest that the scope of the application (including the claims) is limited to these examples; the technical features of the above embodiments or in the different embodiments may also be combined within the idea of the present application, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of brevity.
Additionally, well-known power/ground connections to Integrated Circuit (IC) chips and other components may or may not be shown within the provided figures, in order to simplify the illustration and discussion, and so as not to obscure the embodiments of the present application. Furthermore, the devices may be shown in block diagram form in order to avoid obscuring the embodiments of the present application, and this also takes into account the fact that specifics with respect to implementation of such block diagram devices are highly dependent upon the platform on which the embodiments of the present application are to be implemented (i.e., such specifics should be well within purview of one skilled in the art). Where specific details (e.g., circuits) are set forth in order to describe example embodiments of the application, it should be apparent to one skilled in the art that embodiments of the application can be practiced without, or with variation of, these specific details. Accordingly, the description is to be regarded as illustrative in nature and not as restrictive.
While the present application has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of those embodiments will be apparent to those skilled in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.
The present embodiments are intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims. Accordingly, any omissions, modifications, equivalents, improvements and/or the like which are within the spirit and principles of the embodiments are intended to be included within the scope of the present application.

Claims (4)

1. The slice deployment method for deterministic delay service is characterized by comprising the following steps:
quantifying the bottom physical network resources of deterministic delay service and slicing request resources to obtain a plurality of quantized values;
defining node importance of the bottom layer physical network node by using the quantized value;
constructing a slice deployment model by utilizing the quantized value and the node importance;
generating a plurality of initial deployment schemes by using the slice deployment model, and determining an optimal deployment scheme in the plurality of initial deployment schemes by using an improved particle swarm optimization algorithm, wherein the optimal deployment scheme is configured to deploy slices for the deterministic latency service;
The defining the node importance of the underlying physical network node by using the quantized value comprises the following steps:
defining network characteristics and topology characteristics of the bottom layer physical network node by utilizing the quantized values;
defining the node importance by using the network characteristics and the topology characteristics;
the quantized values include: the capacity of the bottom layer physical network node, the bandwidth of the bottom layer physical network link and the adjacent link set;
the defining the network characteristics and the topology characteristics of the underlying physical network node by using the quantized values includes:
defining the network characteristics of the bottom layer physical network node by using the bottom layer physical network node capacity, the bottom layer physical network link bandwidth and the adjacent link set;
defining the topological feature by using the normalized node degree of the bottom layer physical network node and the normalized betweenness centrality of the bottom layer physical network node;
the slice deployment model comprises a target optimization function and a condition constraint function;
the constructing a slice deployment model by using the quantized values and the node importance comprises the following steps:
constructing the target optimization function according to the upper limit of the total processing delay requirement of the slice and the lower limit of the total processing delay requirement of the slice by utilizing the quantized value;
Constructing the condition constraint function by utilizing the quantized value and the node importance;
the improved particle swarm optimization algorithm comprises an iterative formula;
before determining the optimal deployment scenario among the plurality of initial deployment scenarios using the improved particle swarm optimization algorithm, further comprising:
taking a deployment scheme as a target update value of the iterative formula to obtain the improved particle swarm optimization algorithm;
the determining an optimal deployment scenario among the plurality of initial deployment scenarios using the improved particle swarm optimization algorithm comprises:
determining an initial global optimum and an initial local optimum in the iterative formula according to the plurality of initial deployment schemes;
iteratively updating the initial global optimum, the initial local optimum, and the plurality of initial deployment scenarios using the improved particle swarm optimization algorithm;
stopping the iterative updating and outputting the optimal deployment scheme in response to the iterative updating times reaching a first threshold value and/or the updated initial deployment scheme meeting a first condition function, wherein the first threshold value and the first condition function are preset;
the deployment scheme comprises a mapping scheme and adjustment of the mapping scheme, and the iterative formula comprises:
Wherein,mapping schemes respectively obtained by the kth iteration and the k+1 iteration of the ith bottom layer physical network node,/I>The adjustment of mapping schemes obtained by the kth iteration and the k+1 iteration of the ith bottom layer physical network node respectively, w is inertia weight, c1 is individual cognitive acceleration coefficient, c2 is social acceleration coefficient, and +.>For the local optimum obtained for the kth iteration, P G Is the global optimum, < >>Representing->If the corresponding mapping scheme is adjusted +.>The value in dimension t is 1, then +.>Unchanged, otherwise, there is a need to iterate k+1 times>Replacing with other mapping schemes; />Representing a differential calculation; />Representing a weighting operation;
the node importance is expressed as:
wherein NF is i As a function of the characteristics of the network,is the topological feature; d' i For normalizing the node degree of the bottom layer physical network node i, b' i The medium centrality of the bottom physical network node i is normalized;
the network characteristics are expressed as:
C i the capacity of the underlying physical network node for the underlying physical network node i,for the contiguous link set of the underlying physical network node i +.>For the bandwidth of the underlying physical network link,/-a->For the contiguous link set of the underlying physical network node i >Is a sum of link bandwidth resources of the network;
d′ i expressed as:
wherein N is the total number of nodes of the underlying physical network, d i The node degree is the node degree of the bottom layer physical network node i;
b′ i expressed as:
wherein b i For the medium centrality of the bottom physical network node i, s→t is the path of the bottom physical network node s to the bottom physical network node t, sigma st (i) Representing the shortest path number passing through the bottom physical network node i in the path of s-t;
the constructing the target optimization function according to the upper limit of the total processing time delay requirement of the slice and the lower limit of the total processing time delay requirement of the slice by using the quantized value comprises the following steps: introducing a mapping relation from a binary variable description slice to a bottom physical network;
representing +.A ∈node in a slice is mapped to an s-node of the underlying physical network>1, otherwise 0;
representing that if virtual links (u, v) in a slice map onto underlying physical network links (s, t)Otherwise, 0;
representing the total processing delay of the virtual links (u, v) in the slice mapped to links (s, t) in the underlying physical network, expressed as:
wherein,the bandwidth requirement expressed as virtual links (u, v) in the slice; />Link capacity for physical links (s, t); />Representing a link length of a physical link (i, j), wherein the physical link (i, j) is a minimum sub-link of a segment of the physical link (s, t); / >Link bandwidth allocated for physical link (i, j); the variable theta is a weight factor and is used for balancing network characteristics;
based onObtaining a deterministic delay service-oriented requirement by a delay formula of (1)Target optimization function of the slice deployment model:
wherein N is p N being the set of the underlying physical network nodes v T, a set of virtual nodes in the slice max Upper limit of total processing time delay requirement for the slice, T min A lower limit of total processing delay requirement for the slice;
the conditional constraint function includes:
wherein,the one-to-one correspondence between link mappings and corresponding node mappings is ensured;the bandwidth of the physical link (s, t) is ensured to meet the bandwidth requirement of the virtual link (u, v) mapped on the physical link (s, t); />The computing resource of the physical node s is ensured to meet the resource requirement of the virtual node u mapped on the computing resource; />The flow conservation is ensured, and the inflow flow of the middle physical node v is equal to the outflow flow thereof; /> Ensuring that the total delay of slice deployment is between the upper limit and the lower limit of the delay requirement; />For evaluating whether the physical node s corresponding to the deployment virtual node u is available or not by using the node importance, and NI th A preset node importance threshold value; /> Is a variable constraint;
The generating a plurality of initial deployment schemes by using the slice deployment model comprises:
based on the objective optimization function, a fitness function f (X i )：
Wherein the method comprises the steps ofRepresenting the node pairs (x) in the underlying physical network to which adjacent virtual nodes (k, k+1) in the slice are mapped i,k, x i,k+1 ) The total time delay of the links between the two;
the first threshold value is 15-30 times, and the first conditional function is
The above updated local optimum value P i The process of (1) is specifically as follows: based on fitness function f (X i ) When f (X i )<f(P i ) Let P i ＝X i The method comprises the steps of carrying out a first treatment on the surface of the The global optimum P is updated G The process of (1) is specifically as follows: based on fitness function f (X i ) Let P G ＝P i |min i {f(X i ) -a }; the updating of the plurality of initial deployment schemes is performed using an iterative formula of the improved particle swarm optimization algorithm described above.
2. A deterministic latency service oriented slice deployment apparatus, comprising:
the quantization module is configured to quantize the bottom physical network resources and the slicing request resources of the deterministic delay service to obtain a plurality of quantized values;
an importance definition module configured to define node importance of the underlying physical network node using the quantized value;
a model construction module configured to construct a slice deployment model using the quantized values and the node importance, the slice deployment model including a target optimization function and a conditional constraint function;
An optimization module configured to generate a plurality of initial deployment schemes using the slice deployment model, and to determine an optimal deployment scheme among the plurality of initial deployment schemes using an improved particle swarm optimization algorithm, the optimal deployment scheme configured to deploy slices for the deterministic latency service;
the importance definition module includes:
a feature definition unit configured to define network features and topology features of the underlying physical network nodes using the quantized values;
an importance definition unit configured to define the node importance using the network feature and the topology feature;
the feature definition unit includes:
a network feature definition subunit configured to define the network feature of the underlying physical network node using the underlying physical network node capacity, an underlying physical network link bandwidth, and a set of contiguous links;
a topology feature definition subunit configured to define the topology feature using the normalized node degree of the underlying physical network node and the normalized betweenness centrality of the underlying physical network node;
the model building module is further configured to: constructing the target optimization function according to the upper limit of the total processing time delay requirement of the slice and the lower limit of the total processing time delay requirement of the slice by utilizing the quantized value, and constructing the condition constraint function by utilizing the quantized value and the node importance;
The improved particle swarm optimization algorithm includes an iterative formula, the apparatus further comprising:
an algorithm improvement module configured to take a deployment scenario as a target update value of the iterative formula to obtain the improved particle swarm optimization algorithm;
the optimization module is further configured to: determining an initial global optimum and an initial local optimum in the iterative formula according to the plurality of initial deployment schemes; iteratively updating the initial global optimum, the initial local optimum, and the plurality of initial deployment scenarios using the improved particle swarm optimization algorithm; stopping the iterative updating and outputting the optimal deployment scheme in response to the iterative updating times reaching a first threshold value and/or the updated initial deployment scheme meeting a first condition function, wherein the first threshold value and the first condition function are preset;
the deployment scheme comprises a mapping scheme and adjustment of the mapping scheme, and the iterative formula comprises:
wherein,mapping schemes respectively obtained by the kth iteration and the k+1 iteration of the ith bottom layer physical network node,/I>Mapping schemes obtained by the kth and k+1 iterations of the ith underlying physical network node respectively Adjusting, wherein w is inertia weight, c1 is individual cognitive acceleration coefficient, c2 is social acceleration coefficient,/L>For the local optimum obtained for the kth iteration, P G Is the global optimum, < >>Representing->If the corresponding mapping scheme is adjusted +.>The value in dimension t is 1, then +.>Unchanged, otherwise, there is a need to iterate k+1 times>Replacing with other mapping schemes; />Representing a differential calculation; />Representing a weighting operation;
the node importance is expressed as:
wherein NF is i As a function of the characteristics of the network,is the topological feature; d' i For normalizing the node degree of the bottom layer physical network node i, b' i The medium centrality of the bottom physical network node i is normalized;
the network characteristics are expressed as:
C i the capacity of the underlying physical network node for the underlying physical network node i,for the contiguous link set of the underlying physical network node i +.>For the bandwidth of the underlying physical network link,/-a->For the contiguous link set of the underlying physical network node i>Is a sum of link bandwidth resources of the network;
d′ i expressed as:
wherein N is the total number of nodes of the underlying physical network, d i The node degree is the node degree of the bottom layer physical network node i;
b′ i Expressed as:
wherein b i For the medium centrality of the bottom physical network node i, s→t is the path of the bottom physical network node s to the bottom physical network node t, sigma st (i) Representing the shortest path number passing through the bottom physical network node i in the path of s-t;
the model construction module is further configured to introduce a mapping relationship from the binary variable description slice to the underlying physical network;
wherein,representing +.A ∈node in a slice is mapped to an s-node of the underlying physical network>1, otherwise 0;
representing that if virtual links (u, v) in a slice map onto underlying physical network links (s, t)Otherwise, 0;
representing the total processing delay of the virtual links (u, v) in the slice mapped to links (s, t) in the underlying physical network, expressed as:
wherein,the bandwidth requirement expressed as virtual links (u, v) in the slice; />Link capacity for physical links (s, t); />Representing a link length of a physical link (i, j), wherein the physical link (i, j) is a minimum sub-link of a segment of the physical link (s, t); />Link bandwidth allocated for physical link (i, j); the variable theta is a weight factor and is used for balancing network characteristics;
based onObtaining a target optimization function of a slice deployment model facing to deterministic delay service requirements:
Wherein N is p N being the set of the underlying physical network nodes v T, a set of virtual nodes in the slice max Upper limit of total processing time delay requirement for the slice, T min A lower limit of total processing delay requirement for the slice;
the conditional constraint function includes:
wherein,the one-to-one correspondence between link mappings and corresponding node mappings is ensured;the bandwidth of the physical link (s, t) is ensured to meet the bandwidth requirement of the virtual link (u, v) mapped on the physical link (s, t); />The computing resource of the physical node s is ensured to meet the resource requirement of the virtual node u mapped on the computing resource; />The flow conservation is ensured, and the inflow flow of the middle physical node v is equal to the outflow flow thereof; /> Ensuring that the total delay of slice deployment is between the upper limit and the lower limit of the delay requirement; />For evaluating whether the physical node s corresponding to the deployment virtual node u is available or not by using the node importance, and NI th A preset node importance threshold value; /> Is a variable constraint;
the optimization module is further configured to obtain a fitness function f (X) i )：
Wherein the method comprises the steps ofRepresenting the node pairs (x) in the underlying physical network to which adjacent virtual nodes (k, k+1) in the slice are mapped i,k, x i,k+1 ) The total time delay of the links between the two;
The first threshold value is 15-30 times, and the first conditional function is
The above updated local optimum value P i The process of (1) is specifically as follows: based on fitness function f (X i ) When f (X i )<f(P i ) Let P i ＝X i The method comprises the steps of carrying out a first treatment on the surface of the The global optimum P is updated G The process of (1) is specifically as follows: based on fitness function f (X i ) Let P G ＝P i |min i {f(X i ) -a }; the updating of the plurality of initial deployment schemes is performed using an iterative formula of the improved particle swarm optimization algorithm described above.
3. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of claim 1 when executing the program.
4. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of claim 1.
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Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109347657A (en) * 2018-09-12 2019-02-15 石家庄铁道大学 The virtual data domain construction method of scientific and technological business is supported under SDN mode
CN110149226A (en) * 2019-05-14 2019-08-20 电子科技大学中山学院 Improved particle swarm algorithm for multi-controller deployment problem in software defined network
CN110881199A (en) * 2019-12-06 2020-03-13 国网河南省电力公司许昌供电公司 Dynamic allocation method, device and system for network slice resources
CN111629390A (en) * 2020-04-30 2020-09-04 北京邮电大学 Network slice arranging method and device
WO2020200479A1 (en) * 2019-04-05 2020-10-08 Nokia Technologies Oy Feasibility check for network slice instantiation
CN112615730A (en) * 2020-11-23 2021-04-06 北京邮电大学 Resource allocation method and device based on block chain network slice proxy
CN112822050A (en) * 2021-01-05 2021-05-18 北京信息科技大学 Method and apparatus for deploying network slices
CN113079053A (en) * 2021-05-07 2021-07-06 广东电网有限责任公司电力调度控制中心 Virtual resource reconfiguration method and system based on particle swarm theory under network slice
CN113300861A (en) * 2020-02-24 2021-08-24 中国电信股份有限公司 Network slice configuration method, device and storage medium
CN114244720A (en) * 2021-12-17 2022-03-25 湘潭大学 Multi-controller deployment method based on improved particle swarm algorithm in SDN environment
CN114390489A (en) * 2022-03-04 2022-04-22 重庆邮电大学 Service deployment method for end-to-end network slice

Patent Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109347657A (en) * 2018-09-12 2019-02-15 石家庄铁道大学 The virtual data domain construction method of scientific and technological business is supported under SDN mode
WO2020200479A1 (en) * 2019-04-05 2020-10-08 Nokia Technologies Oy Feasibility check for network slice instantiation
CN110149226A (en) * 2019-05-14 2019-08-20 电子科技大学中山学院 Improved particle swarm algorithm for multi-controller deployment problem in software defined network
CN110881199A (en) * 2019-12-06 2020-03-13 国网河南省电力公司许昌供电公司 Dynamic allocation method, device and system for network slice resources
CN113300861A (en) * 2020-02-24 2021-08-24 中国电信股份有限公司 Network slice configuration method, device and storage medium
CN111629390A (en) * 2020-04-30 2020-09-04 北京邮电大学 Network slice arranging method and device
CN112615730A (en) * 2020-11-23 2021-04-06 北京邮电大学 Resource allocation method and device based on block chain network slice proxy
CN112822050A (en) * 2021-01-05 2021-05-18 北京信息科技大学 Method and apparatus for deploying network slices
CN113079053A (en) * 2021-05-07 2021-07-06 广东电网有限责任公司电力调度控制中心 Virtual resource reconfiguration method and system based on particle swarm theory under network slice
CN114244720A (en) * 2021-12-17 2022-03-25 湘潭大学 Multi-controller deployment method based on improved particle swarm algorithm in SDN environment
CN114390489A (en) * 2022-03-04 2022-04-22 重庆邮电大学 Service deployment method for end-to-end network slice

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
5G网络切片中基于离散粒子群和Kruskal算法的跨域虚拟网络映射;王晓雷等;《万方数据库》;全文 *
HARNESS: High Availability Supportive Self Reliant Network Slicing in 5G Networks;Shwetha Vittal等;《IEEE Transactions on Network and Service Management》;全文 *
H-CRAN网络下联合拥塞控制和资源分配的网络切片动态资源调度策略;唐伦;魏延南;谭颀;唐睿;陈前斌;;《电子与信息学报》(05);全文 *
基于粒子群优化算法的5G网络切片功能迁移机制;陈强等;网络与信息安全学报;20180815(第08期);全文 *

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