Resource configuration method and device for network function virtualization environment, equipment and storage medium

Abstract

The invention discloses a resource configuration method and device for a network function virtualization environment, equipment and a storage medium. The method comprises the following steps: constructing a virtual network function VNF placement model comprising a plurality of constraint conditions; generating a particle fitness function according to the VNF placement model based on a penalty function; initializing a particle swarm, and updating the particle swarm in combination with a particle swarm algorithm and the particle fitness function to obtain a target particle swarm under a target fitness condition; determining a resource configuration matrix of the VNF according to the particle parameters corresponding to the target particle swarm; wherein each particle in the particle swarm represents a deployment scheme of a VNF. According to the method, the VNF placement model is constructed, the penalty function is introduced to generate the particle fitness function, the final resource configuration matrix of the VNF is determined based on the particle swarm, and the resources are allocated according to the resource configuration matrix, so that the utilization rate and the allocation efficiency of the resources can be improved.

Description

Technical Field

[0001] Embodiments of the present invention relate to the technical field of network function virtualization, and in particular to a resource configuration method, apparatus, device and storage medium for a network function virtualization environment. Background Art

[0002] Virtualized Network Function (VNF) placement is a key research area in Network Function Virtualization (NFV), which involves how to effectively locate and deploy VNF instances in the network to meet service requirements while optimizing resource utilization and network performance. The core goal of VNF placement is to ensure efficient operation of network services while reducing costs and enhancing Quality of Service (QoS). The complexity of this problem comes from multiple requirements and constraints, including VNF performance requirements, hardware resource availability, network latency requirements, and energy consumption constraints.

[0003] In practical applications, the VNF placement problem is usually modeled as an optimization problem, seeking to minimize the total cost or maximize the quality of service under the premise of satisfying all service requirements and constraints, and to achieve lightweight resource optimization. Solutions include heuristic algorithms, meta-heuristic methods such as genetic algorithms and particle swarm optimization, and adaptive algorithms based on machine learning, including the use of modern software-defined networking and cloud computing technologies to dynamically manage and optimize VNF placement to adapt to real-time changes in network status and fluctuations in service demand.

[0004] In modern network systems, service providers need to effectively deploy virtual network functions to meet service quality requirements, optimize resource usage, and reduce energy consumption and maintenance costs during deployment. Therefore, determining which physical or virtual servers to place virtual network functions on is a key decision, which not only affects resource utilization efficiency, but also affects the latency and data transmission efficiency of the entire network. The question is how to achieve these goals under the condition of limited physical resources. Summary of the invention

[0005] The present invention provides a resource configuration method, device, equipment and storage medium for a network function virtualization environment to solve the problem in the prior art that a resource allocation scheme for a virtual network function cannot be effectively determined.

[0006] According to one aspect of the present invention, a resource configuration method for a network function virtualization environment is provided, the method comprising:

[0007] Construct a virtual network function (VNF) placement model including multiple constraints;

[0008] generating a particle fitness function according to the VNF placement model based on a penalty function;

[0009] Initialize the particle swarm, combine the particle swarm algorithm and the particle fitness function, update the particle swarm to obtain the target particle swarm under the target fitness condition;

[0010] Determine a resource configuration matrix of the VNF according to the particle parameters corresponding to the target particle group;

[0011] Each particle in the particle group represents a deployment solution of a VNF.

[0012] According to another aspect of the present invention, a resource configuration device for a network function virtualization environment is provided, the device comprising:

[0013] A construction module, used for constructing a virtual network function VNF placement model including multiple constraints;

[0014] A generating module, configured to generate a particle fitness function according to the VNF placement model based on a penalty function;

[0015] An updating module is used to initialize the particle swarm, and update the particle swarm to obtain a target particle swarm under a target fitness condition by combining the particle swarm algorithm and the particle fitness function;

[0016] A determination module, configured to determine a resource configuration matrix of the VNF according to particle parameters corresponding to the target particle group;

[0017] Each particle in the particle group represents a deployment solution of a VNF.

[0018] According to another aspect of the present invention, there is provided an electronic device, the electronic device comprising: at least one processor; and

[0019] a memory communicatively connected to the at least one processor; wherein,

[0020] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the resource configuration method of the network function virtualization environment described in any embodiment of the present invention.

[0021] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the resource configuration method for the network function virtualization environment described in any embodiment of the present invention when executed.

[0022] A resource configuration method, device, equipment and storage medium for a network function virtualization environment according to an embodiment of the present invention, the method includes: constructing a virtual network function VNF placement model including multiple constraints; generating a particle fitness function based on the VNF placement model based on a penalty function; initializing a particle swarm, combining a particle swarm algorithm and the particle fitness function, updating the particle swarm to obtain a target particle swarm under target fitness conditions; determining a resource configuration matrix of the VNF according to the particle parameters corresponding to the target particle swarm; wherein each particle in the particle swarm represents a deployment plan of a VNF. The method constructs a VNF placement model, introduces a penalty function to generate a particle fitness function, determines the final VNF resource configuration matrix based on the particle swarm, and allocates resources according to the resource configuration matrix, thereby improving resource utilization and allocation efficiency, and solves the problem that the resource allocation plan of the virtual network function cannot be effectively determined in the prior art.

[0023] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0025] Figure 1 A schematic diagram of a process flow of a resource configuration method for a network function virtualization environment provided in Embodiment 1 of the present invention;

[0026] Figure 2 A schematic diagram of the relationship between an inertia factor and fitness provided in an embodiment of the present invention;

[0027] Figure 3 A schematic diagram of a resource allocation matrix provided in an embodiment of the present invention;

[0028] Figure 4 A schematic diagram of the structure of a resource configuration device for a network function virtualization environment provided in Embodiment 2 of the present invention;

[0029] Figure 5 Schematic diagram of the structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only an embodiment of a part of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work should belong to the scope of protection of the present invention. It should be understood that the various steps recorded in the method implementation of the present invention can be performed in different orders and / or in parallel. In addition, the method implementation may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0031] The term "including" and its variations used herein are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0032] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, any variations of the terms "including" and "having" are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0033] It should be noted that the modifications of "one" and "plurality" mentioned in the present invention are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0034] The names of the messages or information exchanged between multiple devices in the embodiments of the present invention are only used for illustrative purposes, and are not used to limit the scope of these messages or information.

[0035] Embodiment 1

[0036] Figure 1A flow chart of a resource configuration method for a network function virtualization environment provided in Embodiment 1 of the present invention is applicable to a situation where a deployment plan for a virtual network function is determined in a network function virtualization environment. The method can be executed by a resource configuration device for a network function virtualization environment, wherein the device can be implemented by software and / or hardware and is generally integrated on an electronic device. In this embodiment, the electronic device includes but is not limited to: computers and other devices.

[0037] like Figure 1 As shown, a resource configuration method for a network function virtualization environment provided by Embodiment 1 of the present invention includes the following steps:

[0038] S110. Construct a virtual network function VNF placement model including multiple constraints.

[0039] The constraint conditions may include resource capacity constraint conditions, service quality constraint conditions, binary constraint conditions, etc. The virtual network function VNF placement model may refer to a model for optimizing the deployment location of virtual network functions in network function virtualization.

[0040] In this embodiment, a VNF placement model including multiple constraints can be constructed.

[0041] In one embodiment, the constraints include resource capacity constraints, quality of service constraints, and binary constraints;

[0042] The resource capacity constraint condition is:

[0043] ;

[0044] in, is the amount of computing resources required for virtual network function j, is a binary constraint, , is the total computing resource of physical node i, and n is the number of physical nodes in the network function virtualization environment;

[0045] The quality of service constraints are:

[0046] ;

[0047] in, is the quality of service that virtual network function j can provide when deployed on physical node i, is the minimum value of the quality of service requirement that the virtual network function needs to meet, and m is the number of virtual network functions in the network function virtualization environment.

[0048] The resource capacity constraint condition may be a condition to ensure that the virtual network function deployed on any physical server node (i.e., physical node) in the NFV environment does not exceed the resource capacity of the node, and the resource capacity may be CPU, memory, bandwidth, etc. The service quality constraint condition may be a condition to ensure that the service quality provided by the virtual network function deployed on any physical server node in the NFV environment is not lower than the threshold of the user's service quality requirement. The binary constraint may be a binary decision variable indicating whether a virtual network function is deployed on a physical node.

[0049] In this embodiment, the constraints may include resource capacity constraints, service quality constraints and binary constraints, and a VNF placement model may be constructed through the above constraints.

[0050] S120. Generate a particle fitness function based on the penalty function according to the VNF placement model.

[0051] Among them, the penalty function can refer to a method of transforming a constrained optimization problem into an unconstrained problem, and the constraints are handled by introducing a penalty function and a penalty factor. The function of the penalty function is to assign a maximum value to infeasible points or points that attempt to cross the boundary and escape the feasible domain, that is, to transform the constrained optimization problem into an unconstrained optimization problem. The particle fitness function can be a function that evaluates the quality of the particle solution.

[0052] In this embodiment, a penalty function may be introduced to generate a particle fitness function based on the constraints in the VNF placement model.

[0053] In one embodiment, the generation of a particle fitness function based on the VNF placement model based on a penalty function includes: establishing an optimization objective function for VNF placement based on the VNF placement model; introducing a penalty function, and generating a particle fitness function based on the penalty function and the optimization objective function.

[0054] The optimization objective function may be a function that takes the target to be achieved as the optimization direction, and the target may be resource optimization, load balancing optimization, etc., which is not limited in this embodiment.

[0055] In this embodiment, the optimization objective function of VNF placement can be established through the constraints in the VNF placement model, and the particle fitness function is generated based on the penalty function and the optimization objective function by introducing the penalty function. The model constructed in this embodiment can meet the resource capacity constraints and service quality requirements of the server, there are multiple nonlinear constraints, and the optimization objective is a nonlinear function, which is suitable for reflecting the constraints on the optimization objective function through the penalty function.

[0056] S130, initializing the particle swarm, combining the particle swarm algorithm and the particle fitness function, updating the particle swarm to obtain a target particle swarm under target fitness conditions.

[0057] Among them, particle swarm can refer to a group of multiple particles in particle swarm optimization. Particle Swarm Optimization (PSO) is an optimization tool based on swarm intelligence. In PSO, each solution is regarded as a "particle", and the particles "fly" in the solution space to find the optimal solution through individual and group experience. Particles have two properties: position and speed. The position represents a potential solution, while the speed determines the direction and distance of the particle search for a new solution. Particle swarm algorithm can refer to an algorithm for updating the parameters of particles. The target fitness condition can be the fitness condition that the particle swarm needs to meet set by the user. The specific fitness can be set according to the actual situation. The target particle swarm can be a particle swarm that meets the conditions.

[0058] In this embodiment, the particle swarm can be initialized, the parameters of each particle can be updated through the particle swarm algorithm, the fitness of each particle can be calculated through the particle fitness function, and the particle swarm whose fitness meets the target fitness condition can be used as the target particle swarm. When the particle swarm is initialized, the size of the particle swarm, i.e., the number of particles, can be set, the dimension of the solution (i.e., the position dimension of each particle) can be defined according to the number of physical servers and the number of virtual network functions, and the maximum number of iterations can be set, the particle position can be randomly initialized, and the initial velocity of the particle can be set to 0 or a small random value, which determines the direction and amplitude of the particle's exploration of the new position.

[0059] The virtual network function configuration problem solved by this embodiment involves multiple resources and multiple virtual network function deployment decisions, forming a high-dimensional and complex search space, which is suitable for global optimization through a particle swarm algorithm.

[0060] S140. Determine a resource configuration matrix of the VNF according to the particle parameters corresponding to the target particle group.

[0061] Each particle in the particle group represents a deployment scheme of a VNF. The particle parameter may be a parameter of the particle. The resource configuration matrix (positions matrix) may be a matrix for indicating how to configure the resources of the VNF.

[0062] In this embodiment, the resource configuration matrix of the VNF can be determined by the particle parameters corresponding to the target particle group.

[0063] In this embodiment, the CPU, memory and storage resources of the physical server nodes in the NFV environment are limited, and each virtual network function has certain service quality requirements (delay, throughput, etc.). When deploying the virtual network function on the physical server node, the cost is calculated only once so that the maximum cost value can be solved by the max function. The virtual network functions can be classified by type so that each virtual network function has the same resource consumption, energy consumption and maintenance cost, and the same virtual network function can be deployed on different nodes.

[0064] A resource configuration method for a network function virtualization environment provided in a first embodiment of the present invention includes: constructing a virtual network function VNF placement model including multiple constraints; generating a particle fitness function based on the VNF placement model based on a penalty function; initializing a particle swarm, combining a particle swarm algorithm and the particle fitness function, updating the particle swarm to obtain a target particle swarm under target fitness conditions; determining a resource configuration matrix of the VNF based on the particle parameters corresponding to the target particle swarm; wherein each particle in the particle swarm represents a deployment plan of a VNF. The method constructs a VNF placement model, introduces a penalty function to generate a particle fitness function, determines the final VNF resource configuration matrix based on the particle swarm, and allocates resources based on the resource configuration matrix, thereby improving resource utilization and allocation efficiency, and solves the problem that the resource allocation plan of the virtual network function cannot be effectively determined in the prior art.

[0065] Based on the above embodiment, a variant embodiment of the above embodiment is proposed. It should be noted that in order to make the description concise, only the differences from the above embodiment are described in the variant embodiment.

[0066] In one embodiment, the optimization objective function is:

[0067] ;

[0068] Among them, d ij is the energy consumption of deploying virtual network function j on physical node i, e ij is the direct resource consumption of deploying virtual network function j on physical node i, m i is the maintenance cost of physical node i, α, β, γ, and λ are weight factors used to balance different cost factors in the objective function, α is the weight factor of resource consumption cost, β is the weight factor of energy consumption cost, γ is the weight factor of maintenance cost, and λ is the weight factor of service quality.

[0069] In this embodiment, Refers to the excess reward for quality of service, which means that the quality of service provided by the virtual network function j deployed on the physical node i exceeds the minimum requirement. Greater than hour, A positive value indicates that the service quality exceeds the basic requirements. The sign of the weight factor λ is negative in order to convert the excess service quality into a positive contribution, so that when the service quality exceeds the minimum requirement, it is subtracted from the total objective function value, resulting in a smaller optimization objective function value. At this time, λ is used to balance the importance of service quality improvement on the total objective function. Increasing the value of λ will enhance the motivation to optimize the excess service quality.

[0070] In one embodiment, the penalty function is:

[0071] ;

[0072] in, is the penalty function weighting factor used to balance resource capacity and service quality, is the amount of computing resources required for virtual network function j on physical node i;

[0073] The particle fitness function is:

[0074] ;

[0075] in, Optimization objective function for VNF placement.

[0076] In this embodiment, the optimization objective function is obtained The penalty function is introduced based on To transform the original nonlinear multi-constrained problem into an unconstrained optimization problem, the penalty function And optimize the objective function Combined to form particle fitness function .

[0077] In one embodiment, the particle swarm algorithm and the particle fitness function are combined to update the particle swarm to obtain a target particle swarm under a target fitness condition, including: obtaining the initial particle parameters of each particle in the particle swarm; determining the fitness of each particle through the particle fitness function and the particle parameters; when the iteration termination condition is not satisfied in the current iteration, the particle parameters of each particle in the particle swarm are updated based on the fitness and the particle swarm algorithm, and the new fitness of each particle is calculated to continue the iteration; when the iteration termination condition is met, the particle swarm at this time is used as the target particle swarm.

[0078] The particle parameters may include the particle position, velocity, and number of ions. Fitness may refer to the degree of performance of an individual (or solution) in the process of solving a problem. The iteration termination condition may be that the iteration reaches the maximum number of iterations, or the fitness reaches the fitness threshold.

[0079] In this embodiment, the initial particle parameters of each particle in the particle swarm can be obtained, and the fitness of each particle can be calculated through the particle parameters and the particle fitness function. If it is determined that the iteration does not meet the iteration termination condition according to the fitness of this iteration and the number of iterations, the particle parameters of each particle in the particle swarm can be updated through the fitness and particle swarm algorithm, and the new fitness can be calculated, and the optimal position of the particle corresponding to the historical best fitness of the particle is updated, and the iteration is continued until the iteration termination condition is reached; otherwise, the current particle swarm can be directly used as the target particle swarm.

[0080] Each particle in this embodiment represents a possible virtual network function deployment solution, and the position of each particle conforms to the binary characteristics of the virtual network function deployment ( =1 means deployment, =0 means not deployed), the fitness of each particle can be calculated through the parameters of the particle, that is, the fitness of each particle For each particle after the position is updated, the fitness is calculated , if the updated particle fitness is less than the original particle fitness , then the historical best fitness of the current particle is updated to , and update the optimal position of the individual particle to , otherwise the historical best fitness of the current particle remains as The optimal position of the individual particle remains unchanged. constant.

[0081] In one embodiment, the particle swarm algorithm includes a velocity update algorithm and a position update algorithm;

[0082] The speed update algorithm is:

[0083] ;

[0084] in, represents the velocity of particle i in the tth iteration, represents the speed of particle i in the t+1th iteration, w is the inertia factor, which represents the influence of the fitness of historical particles, c1 represents the learning factor of individual particles, c2 represents the learning factor of the particle group, r1 and r2 are random numbers, represents the optimal influence of individual particles, represents the global optimal impact;

[0085] The location update algorithm is:

[0086] .

[0087] The speed update algorithm may be an algorithm for updating the particle speed, the position update algorithm may be an algorithm for updating the particle position, and the historical particle fitness may be the fitness of the particle in the previous iteration.

[0088] In this embodiment, in each iteration of the particle swarm algorithm, the particle speed and position can be updated. To ensure that the updated position is still in binary form, the position after each iteration can be binarized by a Sigmoid function. The expression of the Sigmoid function is:

[0089] ;

[0090] Then update the particle position to:

[0091] .

[0092] The embodiment of the present invention provides a reasonable virtual network function placement model based on the resource capacity and service quality of physical server nodes in the NFV environment, and combines the penalty function method and particle swarm algorithm to accurately deploy and globally optimize the virtual network functions, thereby improving the resource utilization and cost-effectiveness of the NFV environment.

[0093] The embodiments of the present invention provide several specific implementation methods based on the technical solutions of the above embodiments.

[0094] As a specific implementation method of this embodiment, in order to find the optimal virtual network function configuration solution, the inertia weight w of the particle swarm algorithm is first traversed, the initial value is set to 0.2, the interval is set to 0.2, and the minimum value is found by comparing the best fitness score under each w to obtain the optimal inertia factor. A better virtual network function deployment solution is obtained through the inertia factor. The model can solve any number of nodes and decision variable dimensions. For ease of representation, the number of physical nodes is set to 4 and the number of VNFs to be deployed is set to 9.

[0095] Figure 2 A schematic diagram of the relationship between an inertia factor and fitness provided in an embodiment of the present invention is shown in FIG. Figure 2 As shown in the figure, through the coefficient simulation experiment, it can be seen that when w=0.6, the optimal fitness value is significantly smaller than other values. Based on this, the optimal inertia factor is 0.6.

[0096] Figure 3 A schematic diagram of a resource allocation matrix provided by an embodiment of the present invention is shown in FIG. Figure 3 As shown, the inertia factor of 0.6 is input into the particle swarm algorithm for solution, and the result is as follows Figure 3The logical array with 4 rows and 9 columns shown is the resource allocation matrix (positions logical matrix), where each row represents a physical node and each column represents a virtual network function. The first row represents the deployment of virtual network functions 2, 6, 7, and 8 on physical node one, the second row represents the deployment of virtual network functions 3, 4, 5, and 9 on physical node two, the third row represents the deployment of virtual network functions 1, 2, 3, 5, 6, and 8 on physical node three, and the fourth row represents the deployment of virtual network functions 1, 4, 7, 8, and 9 on physical node four.

[0097] Embodiment 2

[0098] Figure 4 A structural diagram of a resource configuration device for a network function virtualization environment provided in Embodiment 2 of the present invention, wherein the device can be used to determine a deployment plan for a virtual network function in a network function virtualization environment, wherein the device can be implemented by software and / or hardware and is generally integrated on an electronic device.

[0099] like Figure 4 As shown, the device comprises:

[0100] A construction module 210 is used to construct a virtual network function VNF placement model including multiple constraints;

[0101] A generating module 220, configured to generate a particle fitness function according to the VNF placement model based on a penalty function;

[0102] An updating module 230 is used to initialize a particle swarm, and update the particle swarm in combination with the particle swarm algorithm and the particle fitness function to obtain a target particle swarm under a target fitness condition;

[0103] A determination module 240 is used to determine a resource configuration matrix of the VNF according to the particle parameters corresponding to the target particle group;

[0104] Each particle in the particle group represents a deployment solution of a VNF.

[0105] This embodiment provides a resource configuration device for a network function virtualization environment, including: a construction module for constructing a virtual network function VNF placement model including multiple constraints; a generation module for generating a particle fitness function based on the VNF placement model based on a penalty function; an update module for initializing a particle swarm, combining a particle swarm algorithm and the particle fitness function, and updating the particle swarm to obtain a target particle swarm under a target fitness condition; a determination module for determining a resource configuration matrix of the VNF according to particle parameters corresponding to the target particle swarm; wherein each particle in the particle swarm represents a deployment scheme of the VNF. By constructing a VNF placement model, introducing a penalty function to generate a particle fitness function, determining the final VNF resource configuration matrix based on the particle swarm, and allocating resources according to the resource configuration matrix, the utilization rate and allocation efficiency of resources can be improved, and the problem that the resource allocation scheme of the virtual network function cannot be effectively determined in the prior art is solved.

[0106] Furthermore, the constraints include resource capacity constraints, service quality constraints and binary constraints;

[0107] The resource capacity constraint condition is:

[0108] ;

[0109] in, is the amount of computing resources required for virtual network function j, is a binary constraint, , is the total computing resource of physical node i, and n is the number of physical nodes in the network function virtualization environment;

[0110] The quality of service constraints are:

[0111] ;

[0112] in, is the quality of service that virtual network function j can provide when deployed on physical node i, is the minimum value of the quality of service requirement that the virtual network function needs to meet, and m is the number of virtual network functions in the network function virtualization environment.

[0113] Furthermore, the generating module 220 is specifically used for:

[0114] Establishing an optimization objective function for VNF placement according to the VNF placement model;

[0115] A penalty function is introduced, and a particle fitness function is generated based on the penalty function and the optimization objective function.

[0116] Furthermore, the optimization objective function is:

[0117] ;

[0118] Among them, d ij is the energy consumption of deploying virtual network function j on physical node i, e ij is the direct resource consumption of deploying virtual network function j on physical node i, m i is the maintenance cost of physical node i, α, β, γ, and λ are weight factors used to balance different cost factors in the objective function, α is the weight factor of resource consumption cost, β is the weight factor of energy consumption cost, γ is the weight factor of maintenance cost, and λ is the weight factor of service quality.

[0119] Furthermore, the penalty function is:

[0120] ;

[0121] in, is the penalty function weighting factor used to balance resource capacity and service quality, is the amount of computing resources required for virtual network function j on physical node i;

[0122] The particle fitness function is:

[0123] ;

[0124] in, Optimization objective function for VNF placement.

[0125] Furthermore, the update module 230 is specifically configured to:

[0126] Obtaining initial particle parameters of each particle in the particle group;

[0127] Determining the fitness of each particle by using the particle fitness function and the particle parameters;

[0128] When the iteration termination condition is not met in the current iteration, the particle parameters of each particle in the particle swarm are updated based on the fitness and the particle swarm algorithm, and the new fitness of each particle is calculated to continue the iteration;

[0129] When the iteration termination condition is reached, the particle swarm at this time is taken as the target particle swarm.

[0130] Furthermore, the particle swarm algorithm includes a velocity update algorithm and a position update algorithm;

[0131] The speed update algorithm is:

[0132] ;

[0133] in, represents the velocity of particle i in the tth iteration, represents the speed of particle i in the t+1th iteration, w is the inertia factor, which represents the influence of the fitness of historical particles, c1 represents the learning factor of individual particles, c2 represents the learning factor of the particle group, r1 and r2 are random numbers, represents the optimal influence of individual particles, represents the global optimal impact;

[0134] The location update algorithm is:

[0135] .

[0136] The resource configuration device for the network function virtualization environment described above can execute the resource configuration method for the network function virtualization environment provided by any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0137] Embodiment 3

[0138] Figure 5 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0139] like Figure 5 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0140] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0141] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a resource configuration method for a network function virtualization environment.

[0142] In some embodiments, the resource configuration method of the network function virtualization environment may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the resource configuration method of the network function virtualization environment described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the resource configuration method of the network function virtualization environment in any other appropriate manner (e.g., by means of firmware).

[0143] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0144] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0145] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0146] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0147] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0148] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0149] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0150] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A resource configuration method for a network function virtualization environment, characterized in that: The method comprises: Construct a virtual network function (VNF) placement model including multiple constraints; generating a particle fitness function according to the VNF placement model based on a penalty function; Initialize the particle swarm, combine the particle swarm algorithm and the particle fitness function, update the particle swarm to obtain the target particle swarm under the target fitness condition; Determine a resource configuration matrix of the VNF according to the particle parameters corresponding to the target particle group; Each particle in the particle group represents a deployment solution of a VNF.

2. The method according to claim 1, characterized in that The constraints include resource capacity constraints, service quality constraints and binary constraints; The resource capacity constraint condition is: ； in, is the amount of computing resources required for virtual network function j, is a binary constraint, , is the total computing resource of physical node i, and n is the number of physical nodes in the network function virtualization environment; The quality of service constraints are: ； in, is the quality of service that virtual network function j can provide when deployed on physical node i, is the minimum value of the quality of service requirement that the virtual network function needs to meet, and m is the number of virtual network functions in the network function virtualization environment.

3. The method according to claim 1, characterized in that The generating a particle fitness function according to the VNF placement model based on a penalty function includes: Establishing an optimization objective function for VNF placement according to the VNF placement model; A penalty function is introduced, and a particle fitness function is generated based on the penalty function and the optimization objective function.

4. The method according to claim 3, characterized in that The optimization objective function is: ； Among them, d ij is the energy consumption of deploying virtual network function j on physical node i, e ij is the direct resource consumption of deploying virtual network function j on physical node i, m i is the maintenance cost of physical node i, α, β, γ, and λ are weight factors used to balance different cost factors in the objective function, α is the weight factor of resource consumption cost, β is the weight factor of energy consumption cost, γ is the weight factor of maintenance cost, and λ is the weight factor of service quality.

5. The method according to claim 3, characterized in that: The penalty function is: ； in, is the penalty function weighting factor used to balance resource capacity and service quality, is the amount of computing resources required for virtual network function j on physical node i; The particle fitness function is: ； in, Optimization objective function for VNF placement.

6. The method according to claim 1, characterized in that The particle swarm algorithm and the particle fitness function are combined to update the particle swarm to obtain a target particle swarm under a target fitness condition, including: Obtaining initial particle parameters of each particle in the particle swarm; Determining the fitness of each particle by using the particle fitness function and the particle parameters; When the iteration termination condition is not met in the current iteration, the particle parameters of each particle in the particle swarm are updated based on the fitness and the particle swarm algorithm, and the new fitness of each particle is calculated to continue the iteration; When the iteration termination condition is reached, the particle swarm at this time is taken as the target particle swarm.

7. The method according to claim 6, characterized in that The particle swarm algorithm includes a speed update algorithm and a position update algorithm; The speed update algorithm is: ； in, represents the velocity of particle i in the tth iteration, represents the speed of particle i in the t+1th iteration, w is the inertia factor, which represents the influence of the fitness of historical particles, c1 represents the learning factor of individual particles, c2 represents the learning factor of the particle group, r1 and r2 are random numbers, represents the optimal influence of individual particles, represents the global optimal impact; The location update algorithm is: 。 8. A resource configuration device for a network function virtualization environment, characterized in that: The device comprises: A construction module, used for constructing a virtual network function VNF placement model including multiple constraints; A generating module, configured to generate a particle fitness function according to the VNF placement model based on a penalty function; An updating module is used to initialize the particle swarm, and update the particle swarm to obtain a target particle swarm under a target fitness condition by combining the particle swarm algorithm and the particle fitness function; A determination module, configured to determine a resource configuration matrix of the VNF according to particle parameters corresponding to the target particle group; Each particle in the particle group represents a deployment solution of a VNF.

9. An electronic device, characterized in that: The device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the resource configuration method for the network function virtualization environment described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the resource configuration method for a network function virtualization environment according to any one of claims 1 to 7 when executed.

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