CN110351145B - Virtualized wireless network function arrangement method based on economic benefits - Google Patents

Virtualized wireless network function arrangement method based on economic benefits Download PDF

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CN110351145B
CN110351145B CN201910648218.0A CN201910648218A CN110351145B CN 110351145 B CN110351145 B CN 110351145B CN 201910648218 A CN201910648218 A CN 201910648218A CN 110351145 B CN110351145 B CN 110351145B
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邹赛
许磊
田淋风
肖蕾
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Chongqing College of Electronic Engineering
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Abstract

The invention discloses a virtualized wireless network function compiling method based on economic benefits. The method establishes a virtualized wireless network function orchestration mathematical optimization model and provides a virtualized wireless network function orchestration process (VNFPSO) based on economic benefits. Aiming at the characteristics of openness of a virtualized wireless access network architecture, discreteness of network functions and exponential appearance of network load, the classical particle swarm optimization algorithm is corrected in aspects of inertia weight, particle variation, learning factors and the like, and the solving speed of a global approximate optimal solution is increased. The invention is beneficial to reducing the service rejection rate of the virtualized wireless access network and improving the utilization rate of network system resources.

Description

Virtualized wireless network function arrangement method based on economic benefits
Technical Field
The invention relates to the field of communication, in particular to a virtualized wireless network function arranging method based on economic benefits.
Background
With Network Function Virtualization (NFV) and Software Defined Network (SDN) processes, virtualized network function orchestration (NFVO) is considered as a soul of future networks, and will become a key for operators to flexibly manage networks and maximize the performance of new technical advantages. NFVO helps to standardize the functionality of virtual networks to improve interoperability of software-defined network elements. It can perform resource orchestration, network service orchestration, and other functional orchestrations. It can coordinate, authorize, publish, and use resources independent of any particular virtual infrastructure manager, and also provide management of VNF instances sharing NFV infrastructure resources. The European Telecommunications Standards Institute (ETSI) developed exclusively NFVO organizers. Therefore, the method has great significance and practical value for researching the virtualized wireless network function arrangement method.
Existing NFVOs are mainly classified into two categories. (1) The node functions (Middlebox) are programmed: it is a server that replaces special network hardware devices with software. An article "organizing Virtualized Network Functions" was published in the journal of IEEE Transactions on networks and Service Management by Md factory Bari and shihaber rahmann hawdhurry in 2016, and a method of using integer linear programming to organize the Virtualized Network Functions of a core Network was proposed, which reduces the Network operation cost and improves the resource utilization rate. At the same time, a heuristic algorithm of the nested packing problem is adopted to solve the VNFO problem. (2) Arranging a virtualized link (Service functional chaining): it refers to the dynamic integration of one or more service functions into a service function link, such as a content distribution network. Tarik Taleb and Adlen Kbentini published in the journal of IEEE Transactions On Wireless communications in 2016, a paper "cosmetic With Emerging Mobile Social applications Through Dynamic Service Function Chaining" and proposes a Service Function chain organization process. It first identifies the mobile web application and the users located in the same neighborhood, then defines the relevant procedures of the application for the relevant data streams and handles the establishment of multicast procedures in the mobile domain.
The existing NFVO process mainly lays out specific infrastructure for a specific application at a specific time, so as to maximize the overall value of the infrastructure, but the existing NFVO process cannot maximize the economic benefit of the infrastructure, and the purchasing cost and the operation and maintenance expenditure of the operator equipment are very high.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a virtualized wireless network function arrangement method based on economic benefits, which can reduce the service rejection rate of a virtualized wireless access network and improve the utilization rate of network system resources.
The purpose of the invention is realized as follows:
s1: identifying Web services, selecting a component of a virtualized network function according to the total amount of resources of a resource pool, and establishing a mathematical model for the arrangement process of functions required by Web application, resources corresponding to the functions required by the Web application, functions of a physical network, resources corresponding to the functions of the physical network and the virtualized network function;
s2: converting the mathematical model established in the step S1 into a particle swarm algorithm model;
s3: optimizing inertia weight parameters, particle variation parameters and learning factor parameters of the particle swarm algorithm model in the step S2;
s4: and adopting the model parameters optimized in the step S3 to perform wireless access network virtualization network function arrangement.
Further, the step S1 includes the following steps,
mathematical modeling of functions required for the Web application includes S101,
defining the virtual function request function of application i as Ri＝(Fi,QoSi)，
Wherein, FiIs a set of virtual functions requested by an application i,
Fi＝{f1,fj,fn}
Fiis expressed in the form of: where fj＝{id,name,description,note}
Wherein j represents FiN represents FiThe nth function of (1), i.e. the number of functions in the functional resource pool, j, n is a natural number, j<n，
Id represents a virtual function identification number,
the Name represents the Name of the virtual function,
description denotes the Description of the virtual function,
note represents comment information;
QoSivirtual function f being a request of an application ijCorresponding attribute, QoSiIs expressed in the form of:
Figure BDA0002134266040000031
wherein f isjRepresenting a particular virtual function, akRepresenting a virtual function fjWith an attribute, k representing a virtual function fjM represents a virtual function fjIt represents information resource, b represents bandwidth resource, p is power resource in radio frequency;
the physical network resources are mathematically modeled S102, including,
the cost of defining a unit spectrum resource is csApplying i virtual functions fjAttribute akThe number of required bandwidth resources is
Figure BDA0002134266040000041
s denotes a frequency spectrum, B is a size of a unit bit rate,
defining a wireless access link ηsThe cost of unit spectral efficiency is:
Figure BDA0002134266040000042
wherein, γiIs the signal-to-noise ratio, defining the cost of power resources in a unit radio frequency as cpWireless access link ηpThe cost of power resource efficiency per unit radio frequency is ηp＝pk×cp，
The cost of defining unit information resource is citService ηitThe cost of consuming information resources is ηp＝itk×cit，
Deployment benefits η of VNFsdAt a cost of ηd＝σb×ηs+σp×ηp+σit×ηit，
Wherein σb、σp、σitIs a weight coefficient, 0 ≦ sigmab,σp,σitLess than or equal to 1, and sigmab+σp+σit＝1；
And S103, performing mathematical modeling on the virtualized network function orchestration process, including,
constructing a mathematical model of a virtualized network function arrangement process:
Figure BDA0002134266040000043
wherein mui,jIs a cost function, representing having an attribute akVirtual function module f ofjThe cost to be paid for is that,
xj',k'indicating a selected functional module, Ri,j,k→it≤N×xj',k'→it，Ri,j,k→p≤N×xj',k'→p，Ri,j,k→b≤N×xj',k'→ b denotes selecting N with attribute akVirtual function module f ofjThe IT resource value, the frequency spectrum resource and the transceiving power resource are larger than or equal to the resource value requested by the application i, N is a natural number,
Figure BDA0002134266040000051
representing virtual function modules fiAnd a virtual function module fi+yThere is a dependency relationship, fi≠fi+yRepresenting virtual function modules fiAnd a virtual function module fi+yThere is an exclusive relationship, costj,kRefers to a plurality of characters having an attribute of ajVirtual function module f ofiCost of parallel use, Mcotj,kRefers to a plurality of characters having an attribute of ajVirtual function module f ofiThe cost, δ, paid for using the same resource togethers，δp，δitFor combining into functional modules xj',k'The coefficient of (a);
and S104, optimizing the mathematical model of the virtualized network function orchestration process, comprising the following steps,
s1041: adding constraint conditions:
Figure BDA0002134266040000052
Figure BDA0002134266040000053
where s ', p ', it ' denote the relevant resources that have been used, all denote all resources;
s1042: the mathematical model of the virtualized network function arrangement process is optimized into
Figure BDA0002134266040000054
In the optimization process, the following formula is adopted to add a virtual function module fiCost of, muj,k'＝μj,k+μj+y,kY is a temporary variable;
s1043: the mathematical model of the programming process of the simulated network functions is optimized into
Figure BDA0002134266040000061
Further, the step S2 includes the following steps,
s201: in the total dimension D ═ m × n of the virtual service arrangement problem solution space, arbitrarily taking L particles to form a group, i is smaller than L, wherein the ith particle is described by adopting two indexes in the k generation, and the value of the virtual function attribute is taken as the position of PSO and is expressed as
Figure BDA0002134266040000062
The D-dimensional vector of (2) and the speed of change of the virtual function attribute value as the flight speed of the PSO, which is expressed as
Figure BDA0002134266040000063
Wherein t represents t flights of the particle;
s202: the optimal position of the individual history when the ith particle is searched to the kth generation is
Figure BDA0002134266040000064
The historical optimal position g of the whole particle swarm up to the k generation is searched
Figure BDA0002134266040000065
At the k +1 th generation, the iterative update formula of the j-th dimension velocity and position of the ith particle is as follows:
Figure BDA0002134266040000066
where ω is the inertial weight used to measure the effect of the velocity at the previous time on the next move, c1And c2Is a learning factor, r1And r2Is [0, 1 ]]The random number in (c).
Further, the step S3 includes the following steps,
s301: dynamically adjusting an inertia weight omega of a traditional PSO algorithm, improving the resolution of convergence and reducing the flight speed of particles when the particle position is close to the individual historical optimal position or the historical optimal position of the whole particle swarm, accelerating the convergence speed and improving the flight speed of the particles when the particle position is far away from the individual historical optimal position or the historical optimal position of the whole particle swarm, wherein the inertia weight omega is given by the following formula:
Figure BDA0002134266040000071
wherein, tv1And tv2Is a threshold value, c1iAnd c2iIs the learning factor of the ith time, r1iAnd r2iIs the random number of the ith time;
s302: design of variant particles, comprising the steps of:
s3021: if it is not
Figure BDA0002134266040000072
Then determine psiIs a variant particle, wherein tp is a threshold value,
s3022: setting the smooth moving distance of the variant particle as
Figure BDA0002134266040000073
Wherein Bd (k) is the smooth moving distance of the variation particle of the kth generation, and L is the number of intervals between two particles;
s3023: setting the variation equation of the particles as
Figure BDA0002134266040000074
The invention has the beneficial effects that:
1. the invention can reduce the equipment purchasing cost and the operation and maintenance expenditure of operators under the condition of ensuring the flexibility and the openness, and researches the wireless virtualization network resource arrangement without considering the sequence of the network functions of each virtualization from the aspect of economic benefit. In the actual operation and maintenance of operators, the change of the network and the adjustment requirement of resources are usually from a service level, meanwhile, the control and analysis of the service do not need to know the resource level, the two have complex relation, and the virtualized network function arrangement can realize the 'customized' realization of the applied functions and the maximization of the economic benefit of the infrastructure.
2. The invention is beneficial to reducing the service rejection rate of the virtualized wireless access network and improving the utilization rate of network system resources.
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FIG. 1 is a flow chart of an embodiment of the present invention.
FIG. 2 is a system architecture diagram according to an embodiment of the present invention.
Detailed Description
The invention provides a virtualized wireless network function orchestration process (VNFPSO) based on economic benefits by establishing a virtualized wireless network function orchestration mathematical optimization model. Aiming at the characteristics of openness of a virtualized wireless access network architecture, discreteness of network functions and appearance of network load index, the classical particle swarm optimization algorithm is corrected in aspects of inertia weight, particle variation, learning factors and the like, the solving speed of a global approximate optimal solution is increased, the service rejection rate of the virtualized wireless access network is reduced, and the utilization rate of network system resources is improved.
As shown in fig. 1, the present invention provides a method for arranging wireless network functions based on virtualization of economic benefits, comprising the following steps,
s1: identifying Web services, selecting a component of a virtualized network function according to the characteristics of the services and the total resource amount of a resource pool, and establishing a mathematical model for Web application, physical network resources and a virtualized network function arrangement process respectively;
s2: converting the mathematical model established in the step S1 into a particle swarm algorithm model;
s3: optimizing inertia weight parameters, particle variation parameters and learning factor parameters of the particle swarm algorithm model in the step S2;
s4: and adopting the model parameters optimized in the step S3 to perform wireless access network virtualization network function arrangement.
Step S1 is explained in detail below:
step S1 includes, for the virtualized network function arrangement needs to identify the Web service first, and then select the necessary components and links of the virtualized network function for arrangement according to the total amount of resources in the resource pool, and establish mathematical models for the Web application, the physical network resources, and the virtualized network function arrangement process, respectively.
The step S1 specifically includes the following steps,
s101, Web application mathematical modeling
The virtual function request function for application i is defined as: ri＝(Fi,QoSi)
Wherein, FiIs a set of virtual functions requested by an application i. FiIs expressed in the form of: fi＝{f1,fj,fn}
where fj＝{id,name,description,note}。
QoSiVirtual function f being a request of an application ijThe corresponding attribute. QoS (quality of service)iIs expressed in the form of:
Figure BDA0002134266040000091
wherein f isjRepresenting a particular virtual function, akRepresenting a virtual function fjWith an attribute, k representing a virtual function fjM represents a virtual function fjIt represents information resource, b represents bandwidth resource, p power resource in radio frequency, n is number of functions in resource pool;
s102, mathematical modeling of physical network resources
The cost of defining a unit spectrum resource is cs. Application i virtual function fjAttribute akNumber of required bandwidth resources
Figure BDA0002134266040000101
B is the size of the unit bit rate defining the radio access link ηsThe cost of unit spectral efficiency is:
Figure BDA0002134266040000102
γiis the signal to noise ratio. The cost of defining power resources in a unit radio frequency is cpA wireless access link ηpThe cost of power resource efficiency per unit radio frequency is ηp＝pk×cp。
The cost of defining unit information resource is citService ηitThe cost of consuming information resources is ηp＝itk×cit。
The deployment benefit of the VNF is the joint use cost of the spectrum resource, the power resource and the information resource, the deployment benefit η of the VNFdAt a cost of ηd＝σb×ηs+σp×ηp+σit×ηit。
Wherein σb、σpIs a weight coefficient, 0 ≦ σb,σp,σitLess than or equal to 1, and sigmab+σp+σit＝1。
S103, mathematical modeling of the function arrangement process of the virtual network
The mathematical model of the virtual network function arrangement process is
Figure BDA0002134266040000103
Wherein mui,jIs a cost function which represents having an attribute of akVirtual function module f ofjThe cost to be paid. x is the number ofj',k'Indicating the selected functional module. Ri,j,k→it≤N×xj',k'→it，Ri,j,k→p≤N×xj',k'→p，Ri,j,k→b≤N×xj',k'→ b denotes selecting N with attribute akVirtual function module f ofjIT resource value ofThe frequency spectrum resource and the transceiving power resource are larger than or equal to the resource value requested by the application i.
Figure BDA0002134266040000113
Representing virtual function modules fiAnd a virtual function module fi+yThere is a dependency if fiIf present, then fi+yMust be present. f. ofi≠fi+yRepresenting virtual function modules fiAnd a virtual function module fi+yThere is an exclusive relationship if fiIf present, then fi+yMust not be present. costj,kRefers to a plurality of characters having an attribute of ajVirtual function module f ofiThe price paid for parallel use. Mcostj,kRefers to a plurality of characters having an attribute of ajVirtual function module f ofiThe cost of using the same resource together. Deltas，δp，δitThe dentes are combined into a functional module xj',k'The coefficient of (a).
S104, optimizing the mathematical model of the virtual network function arrangement process
S1041: virtual service orchestration essentially selects a sub-virtual function from a set of virtual functions. When the construction costs are equal, the specific selection scheme has diversity. In order to reduce the difficulty of solving and simultaneously embody the resource shortage, the following constraint conditions are added:
Figure BDA0002134266040000111
Figure BDA0002134266040000112
where s ', p ', it ' denote the relevant resources that have been used. all represents all resources.
S1042: the mathematical model of the planning process of the network function is optimized for the first time
Figure BDA0002134266040000121
Virtual function module fiAnd a virtual function module fi+yHaving an exclusive relationship, i.e. in the service requestIs shown. Therefore, in the process of solving,
Figure BDA0002134266040000122
if fi≠fi+ythe constraints may be removed. Simultaneous virtual function module fiAnd a virtual function module fi+yThe dependency relationship exists, and only the virtual function module f is added in the solving processiThe cost, increment, is shown as: mu.sj,k'＝μj,k+μj+y,k。
S1043: the mathematical model of the simulation network function arrangement process is finally optimized into
Figure BDA0002134266040000123
Step S2 is explained below:
step S2 includes converting the mathematical model established at S1 into a classical particle swarm algorithm.
The step S2 specifically includes the following steps,
s201: in the total dimension D (mn) of the solution space of the virtual service arrangement problem, arbitrary L particles are taken to form a group, wherein the ith (i) is<L) particles can be described in the k-th generation by two indices: the value of the virtual function attribute can be viewed as the location of the PSO, expressed as
Figure BDA0002134266040000124
A D-dimensional vector of (1); the change speed of the virtual function attribute value can be regarded as the flight speed of the PSO, and is expressed as
Figure BDA0002134266040000125
D-dimensional vector of (1).
S202: the optimal position of the individual history when the ith particle is searched to the kth generation is
Figure BDA0002134266040000131
The historical optimal position of the whole particle swarm up to the k generation is searched
Figure BDA0002134266040000132
Then at the k +1 th generation, the iterative update formula of the j-th dimension velocity and position of the ith particle is as follows:
Figure BDA0002134266040000133
where ω is the inertial weight, which measures the effect of the velocity at the previous time on the next move. c. C1And c2Is a learning factor, r1And r2Is [0, 1 ]]The random number in (c).
Step S3 is explained below:
step S3 includes modifying the inertial weight, the particle variation, and the learning factor of the particle swarm algorithm in step S2.
The step S3 specifically includes the following steps,
s301: and dynamically adjusting the inertia weight omega of the traditional PSO algorithm according to the searching process. When the particle position approaches the individual historical optimum position or the historical optimum position of the entire particle swarm, the resolution of convergence needs to be improved, reducing the flight speed of the particle. When the particle position is far away from the individual historical optimal position or the historical optimal position of the whole particle swarm, the convergence speed needs to be increased, and the flight speed of the particles needs to be increased. To ensure good local search capability and convergence speed of the algorithm, the inertial weight ω is given by:
Figure BDA0002134266040000141
wherein, tv1And tv2Is a threshold value, c1iAnd c2iIs the learning factor of the ith time, r1iAnd r2iIs the ith random number.
S302: the design of the variant particles was as follows:
s3021: if it is not
Figure BDA0002134266040000142
Then determine psiIs a variant particle, where tp is the threshold.
S3022: the smooth moving distance of the variant particle is
Figure BDA0002134266040000143
Wherein Bd (k) is the smooth moving distance of the variant particle of the k generation.
S3023: the variation equation of the particle is
Figure BDA0002134266040000144
The beneficial effect of the invention is that,
1. the invention can reduce the equipment purchasing cost and the operation and maintenance expenditure of operators under the condition of ensuring the flexibility and the openness, and researches the wireless virtualization network resource arrangement without considering the sequence of the network functions of each virtualization from the aspect of economic benefit. In the actual operation and maintenance of operators, the change of the network and the adjustment requirement of resources are usually from a service level, meanwhile, the control and analysis of the service do not need to know the resource level, the two have complex relation, and the virtualized network function arrangement can realize the 'customized' realization of the applied functions and the maximization of the economic benefit of the infrastructure.
2. The invention is beneficial to reducing the service rejection rate of the virtualized wireless access network and improving the utilization rate of network system resources.
Finally, it is noted that the above-mentioned preferred embodiments illustrate rather than limit the invention, and that, although the invention has been described in detail with reference to the above-mentioned preferred embodiments, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the scope of the invention as defined by the appended claims.

Claims (1)

1. A virtualized wireless network function arrangement method based on economic benefit is characterized by comprising the following steps,
s1: identifying Web services, selecting a component of a virtualized network function according to the total amount of resources of a resource pool, and establishing a mathematical model for functions, physical network resources and a virtualized network function arrangement process required by Web application respectively;
s2: converting the mathematical model established in the step S1 into a particle swarm algorithm model;
s3: optimizing inertia weight parameters, particle variation parameters and learning factor parameters of the particle swarm algorithm model in the step S2;
s4: adopting the model parameters optimized in the step S3 to perform wireless access network virtualization network function arrangement;
the step S1 includes the steps of,
s101: mathematical modeling of the functionality required for a Web application includes,
defining the virtual function request function of application i as Ri＝(Fi，QoSi)，
Wherein, FiIs a set of virtual functions requested by an application i,
Fi＝{f1，fj，fn}
Fiis expressed in the form of: where fj＝{id，name，description，note}
Wherein j represents FiN represents FiThe nth function of (1), j and n are natural numbers, and j is less than n;
QoSivirtual function f being a request of an application ijCorresponding attribute, QoSiIs expressed in the form of:
QoSi＝{a1，ak，am}
where ak＝{itk，bk，pk}，
wherein f isjRepresenting a particular virtual function, akRepresenting a virtual function fjWith an attribute, k representing a virtual function fjM represents a virtual function fjThe m-th attribute of (1), it represents information resource, b represents bandwidth resource, p is power resource in radio frequency, id represents virtual function identification number, name represents virtual function name, description represents description of virtual function, note represents annotation information;
the physical network resources are mathematically modeled S102, including,
the cost of defining a unit spectrum resource is csApplying i virtual functions fjAttribute akThe number of required bandwidth resources is
Figure FDA0002367671850000021
s denotes a frequency spectrum, B is a size of a unit bit rate,
the cost for defining the unit spectrum benefit of the wireless access link is as follows:
Figure FDA0002367671850000022
wherein, γiIs the signal-to-noise ratio, defining the cost of power resources in a unit radio frequency as cpThe cost of power resource efficiency in radio access link unit radio frequency is ηp＝pk×cp，
The cost of defining unit information resource is citThe cost of service consumption information resource is ηit＝itk×citThe cost of the benefit of the deployment of the VNF is ηd＝σb×ηs+σp×ηp+σit×ηit，
Wherein σb、σp、σitIs a weight coefficient, 0 ≦ sigmab,σp,σitLess than or equal to 1, and sigmab+σp+σit＝1；
And S103, performing mathematical modeling on the virtualized network function orchestration process, including,
constructing a mathematical model of a virtualized network function arrangement process:
Figure FDA0002367671850000023
s.t.Ri,j,k→it≤N×xj',k'→it
Ri,j,k→p≤N×xj',k'→p
Ri,j,k→b≤N×xj',k'→b
Figure FDA0002367671850000024
Figure FDA0002367671850000025
Figure FDA0002367671850000026
wherein mui,jIs a cost function, representing having an attribute akVirtual function module f ofjThe cost, x, to payj',k'Indicating a selected functional module, Ri,j,k→it≤N×xj',k'→it，Ri,j,k→p≤N×xj',k'→p，Ri,j,k→b≤N×xj',k'→ b denotes selecting N with attribute akVirtual function module f ofjThe IT resource value, the spectrum resource and the transceiving power resource are larger than the resource value requested by the application i, N is a natural number,
Figure FDA0002367671850000031
representing virtual function modules fiAnd a virtual function module fi+yThere is a dependency relationship, fi≠fi+yRepresenting virtual function modules fiAnd a virtual function module fi+yThere is an exclusive relationship, costj,kRefers to a plurality of characters having an attribute of ajVirtual function module f ofiCost of parallel use, Mcotj,kRefers to a plurality of characters having an attribute of ajVirtual function module f ofiThe cost, δ, paid for using the same resource togethers，δp，δitFor combining into functional modules xj',k'The coefficient of (a);
and S104, optimizing the mathematical model of the virtualized network function orchestration process, comprising the following steps,
s1041: adding constraint conditions:
Figure FDA0002367671850000032
Figure FDA0002367671850000033
where s ', p ', it ' denote the relevant resources that have been used, all denote all resources;
s1042: the mathematical model of the virtualized network function arrangement process is optimized into
Figure FDA0002367671850000034
In the optimization process, the following formula is adopted to add a virtual function module fiCost of, muj,k'＝μj,k+μj+y,kY is a temporary variable;
s1043: optimizing a virtualized network function orchestration process mathematical model to
Figure FDA0002367671850000041
s.t.Ri,j,k→it≤N×xj',k'→it
Ri,j,k→p≤N×xj',k'→p
Ri,j,k→b≤N×xj',k'→b。
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