CN109362093A - The method for optimizing resources of the total throughput maximization of network function virtualization - Google Patents

The method for optimizing resources of the total throughput maximization of network function virtualization Download PDF

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CN109362093A
CN109362093A CN201811537839.3A CN201811537839A CN109362093A CN 109362093 A CN109362093 A CN 109362093A CN 201811537839 A CN201811537839 A CN 201811537839A CN 109362093 A CN109362093 A CN 109362093A
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logical node
frog
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CN109362093B (en
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谢宁
张齐齐
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Shenzhen University
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    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The present disclosure describes a kind of method for optimizing resources of the total throughput maximization of network function virtualization, comprising: which network function virtualization architecture includes logical layer, virtual level, physical layer and management arranging system, logical layer includes logical node and controller, and physical layer includes physical node；Logical node and controller pass through orthogonal channel by corresponding physical node and send information signal to base station；Base station calculates payload bit number, channel usage quantity, transmission power, received signal to noise ratio, handling capacity and the total emitted energy of each logical node and each controller based on information signal；And when third quantity, payload bit number, received signal to noise ratio, first total emitted energy and second total emitted energy meet corresponding requirements and logical node and controller meet super reliability requirement, management arranging system is based on hybrid optimization algorithm and adaptively distributes transmission power and channel usage quantity, so that total throughput maximization.

Description

The method for optimizing resources of the total throughput maximization of network function virtualization
Technical field
This disclosure relates to technical field of network virtualization, and in particular to a kind of total throughout of network function virtualization is maximum The method for optimizing resources of change.
Background technique
Requirement with terminal user to the service with more diversified high data rate continues to increase, and telecommunications service mention Correspondingly and constantly it must buy, store and grasp for quotient (Telecommunications service provider, TSP) Make new physical equipment.This technical staff for not requiring nothing more than operation and managing the equipment has quick and various technical ability, but also The dense deployment network equipment is needed, this leads to high operation expenditure (Operation Expenditure, OPEX) and Capital expenditure (Capital Expenditure, CAPEX).Network function virtualization (Network Function Virtualization, NFV it) has been proposed as a kind of method for solving these challenges, it provides design, deployment and management net using virtualization technology The new method of network service, the concept of NFV are expected to serve various emerging service industries and vertical market, such as industrial automation, from It is dynamic to drive, robot, health care, virtual and augmented reality.
Resource allocation (Resource allocation, RA) is the key factor in network function virtualization (NFV) deployment One of.Since physical layer (Physical layer, PHY) resource is limited (for example, sending energy and channel use), it is used for The problem of RA problem of the PHY layer of NFV deployment has become rapid growth, especially for the communication for supporting super reliable and low latency (Ultra Reliable Low Latency Communications, URLLC).Further, since in the uplink of NFV deployment Maximization total throughout optimization problem in road (uplink, UL) transport protocol is that height is non-convex, therefore is difficult to obtain global Optimal solution, and since the Over-constrained condition in optimization problem will lead to low convergence efficiency phenomenon, traditional heuritic approach is not It can directly solve the problems, such as the resource optimization of maximization total throughout.
Summary of the invention
The disclosure is to complete in order to solve the above-mentioned existing problems, and its purpose is to provide one kind can be fast and effeciently The resource for completing the total throughput maximization of the network function virtualization of the maximization total throughout optimization problem in NFV deployment is excellent Change method.
For this purpose, being present disclose provides a kind of method for optimizing resources of the total throughput maximization of network function virtualization The method for optimizing resources of total throughput maximization including network function virtualization architecture and base station characterized by comprising institute Stating network function virtualization architecture includes logical layer, virtual level, physical layer and management arranging system, and the logical layer includes having The logical node of first quantity and controller with the second quantity, the physical layer includes the physics section with third quantity Point, the third quantity are equal to the sum of first quantity and second quantity, and the logical node and the controller are logical It crosses the virtual level and the physical node forms mapping relations, the logical node and the controller pass through corresponding described Physical node sends information signal to the base station by orthogonal channel；The base station is based on the information signal and calculates each institute State the payload bit number of logical node and each controller, channel usage quantity, transmission power, received signal to noise ratio and Handling capacity, calculates the first total emitted energy and the first total throughout of multiple logical nodes, and calculates multiple controls The total emitted energy of the second of device and the second total throughout；And when the third quantity is equal to preset service quantity, payload Bit number is equal to predetermined bit number and received signal to noise ratio is equal to default received signal to noise ratio, and the logical node and the controller are full The super reliability requirement of foot, first total emitted energy is no more than the first energy threshold and second total emitted energy is not more than Second energy threshold, when the channel usage quantity uses sum no more than channel, the management arranging system is based on mixing excellent Change algorithm and adaptively distribute the transmission power and the channel usage quantity, so that total throughput maximization, described always to gulp down The amount of spitting be the sum of first total throughout and second total throughout, wherein the hybrid optimization algorithm be multidimensional and It leapfrogs-extremal optimization algorithm using the total throughout as the mixing of adaptive value, the multidimensional includes the transmitting of each logical node The transmission power and each logical node of power and each controller and the channel of each controller use Quantity.
In the disclosure, information is sent to base station based on network function virtualization architecture, logical node and the controller Signal, base station receives information signal and calculates relevant parameter, when third quantity, payload bit number, received signal to noise ratio, first When total emitted energy and second total emitted energy meet corresponding requirements and logical node and controller satisfaction super reliability requirement, pipe Reason arranging system is based on hybrid optimization algorithm and adaptively distributes transmission power and channel usage quantity, so that total throughout is maximum Change.The maximization total throughout optimization problem that thereby, it is possible to be completed quickly and effectively in NFV deployment, and have preferable global Search capability.
In the optimization method involved in the disclosure, optionally, the total throughout meets The handling capacity of i-th of logical node and j-th of controller is respectively to meetWith Wherein, kLiFor the payload bit number of i-th of logical node, kCjFor the payload bit number of j-th of controller, Pt,LiFor The transmission power of i-th of logical node, Pt,CjFor the transmission power of j-th of controller, nLiChannel for i-th of logical node makes With quantity, nCjFor the channel usage quantity of j-th of controller.Thereby, it is possible to obtain total throughout.
In the optimization method involved in the disclosure, optionally, the super reliability requirement meetsWithAndWherein, pe,LiFor the packet error probability of i-th of logical node, pe,CjFor j-th of controller Packet error probability,It is the packet error probability upper limit of any one logical node,It is any one described control The packet error probability upper limit of device.As a result, the connection between any one controller and base station have than any one logical node and The higher reliability of connection between base station.
In the optimization method involved in the disclosure, optionally, the launch energy of i-th of logical node and j-th of controller Amount is respectively to meet ELi=Pt,LinLiAnd ECj=Pt,CjnCj, wherein ELiAnd ECjUnit be all WHzs, Pt,LiIt is i-th The transmission power of logical node, Pt,CjFor the transmission power of j-th of controller, nLiNumber is used for the channel of i-th of logical node Amount, nCjFor the channel usage quantity of j-th of controller, the channel is met using sumM is First quantity, N are second quantity.Thereby, it is possible to obtain the emitted energy of each logical node and each controller And channel uses sum.
It is optionally, described to mix the-extremal optimization algorithm input parameter packet that leapfrogs in the optimization method involved in the disclosure Include the payload bit number k of the first quantity M, the second quantity N, each logical nodeLi, each control The payload bit number k of deviceCj, each logical node received signal to noise ratio γLi, each controller reception letter It makes an uproar and compares γCj, the logical node the packet error probability upper limitThe packet error probability upper limit of the controller First energy thresholdSecond energy thresholdSum n is used with the channel∑, output parameter includes always handling up Measure RΣ, each logical node transmission power Pt,Li, the controller transmission power Pt,Cj, each logical node letter Road usage quantity nLiWith the channel usage quantity n of each controllerCj.As a result, mixing leapfrog-extremal optimization algorithm can It is optimized based on above-mentioned input parameter, and obtains the output parameter of optimization.
In the optimization method involved in the disclosure, optionally, it is described mixing leapfrog-extremal optimization algorithm include: setting Initiation parameter；The random population p generated including F frog；The X that the position of every frog is t=2 (M+N) with dimensioni It indicates；Adaptive value f (the X of every frog is calculated by assessment algorithmi)；Judge whether to meet convergence criterion；It is quasi- when meeting convergence When then, obtains optimal output parameter and terminate process；When being unsatisfactory for convergence criterion, the corresponding adaptive value of F frog is pressed According to descending sort；P group frog and submodule are constructed because of complex；For circulation is carried out to every group of frog, each time in submodule in circulation Because of local search in complex, the fitness of every frog is calculated using the assessment algorithm, and pole is carried out to every frog again Value optimization, the fitness of every frog are obtained by assessment algorithm；It shuffles to all frogs.Thereby, it is possible to ensure quickly and Stable convergence.
In the optimization method involved in the disclosure, optionally, the input parameter of the assessment algorithm is i-th frog Position Xi, output parameter is the adaptive value f (X of the position of i-th frogi), calculate the fitness f (X of the position of every frogi)； WhereinT indicates penalty coefficient, and meets T=2 × 104；WhenOrWhen, f (Xi)=f (Xi)-T；WhenOrWhen, f (Xi)=f (Xi)-T；WhenWhen, f (Xi)=f (Xi)-T；Otherwise f (X is returnedi).Every blueness is obtained based on assessment algorithm as a result, The adaptive value of the frog.
In the optimization method involved in the disclosure, optionally, the process of the extremal optimization includes: that random generate has The individual of multiple component parts；Calculate the adaptive value of each component part of each individual；It is optimal that current individual, which is arranged, Individual；Judge whether to meet established standards, terminates process when meeting；When being unsatisfactory for established standards, current individual is calculated The adaptive value of each component part；Search the component part that adaptive value is less than or equal to the adaptive value of the current individual；It obtains Target individual；Using target individual as new current individual；When target individual adaptive value be less than optimum individual adaptive value, then Target individual is as optimum individual.Thereby, it is possible to improve local search ability.
In the optimization method involved in the disclosure, optionally, the adaptive value λ of each component partijMeetWherein, Δ xijIt is the offset between the current location and new position of i-th frog.Thereby, it is possible to The adaptive value of each component part after being mutated.
In the optimization method involved in the disclosure, optionally, the mixing leapfrogs the-runing time of extremal optimization algorithm Meeting complexity isWherein, extremal optimization process jumps condition NEOValue be set as 1 to 100 it Between any number, NiteIt is the number of iterations of extremal optimization, and NiteIt is 2, lmaxIndicate the greatest iteration time of shuffled frog leaping algorithm Number, t is dimension.Thereby, it is possible to obtain runing time complexity.
This disclosure relates to the-resource of the maximization total throughout of extremal optimization algorithm (MSFLA-EO) that leapfroged based on mixing Optimization method has superior stability and ability of searching optimum, according to various performance parameters to the method for optimizing resources of proposition into Emulation is gone, the method for optimizing resources that the results show is proposed for maximizing total throughout has significant effect.
Detailed description of the invention
Fig. 1 is to show the schematic diagram of the system model of NFV framework involved in the example of the disclosure.
Fig. 2 is that the resource for the total throughput maximization for showing the virtualization of network function involved in the example of the disclosure is excellent The flow chart of change method.
Fig. 3 is that the resource for the total throughput maximization for showing the virtualization of network function involved in the example of the disclosure is excellent The flow chart of the hybrid optimization algorithm of change method.
Fig. 4 is that the resource for the total throughput maximization for showing the virtualization of network function involved in the example of the disclosure is excellent The flow chart of the extremal optimization process of change method.
Fig. 5 is that the resource for the total throughput maximization for showing the virtualization of network function involved in the example of the disclosure is excellent The waveform diagram of the total throughout under different payload bit numbers of change method.
Fig. 6 is that the resource for the total throughput maximization for showing the virtualization of network function involved in the example of the disclosure is excellent The waveform diagram of the total throughout under different received signal to noise ratio of change method.
Specific embodiment
Hereinafter, explaining the preferred embodiment of the disclosure in detail with reference to attached drawing.In the following description, for identical Component assign identical symbol, the repetitive description thereof will be omitted.Scheme in addition, attached drawing is only schematical, the mutual ruler of component Very little shape of ratio or component etc. can be with actual difference.
It should be noted that term " includes " and " having " and their any deformation in the disclosure, such as wrapped Include or the process, method, system, product or equipment of possessed a series of steps or units are not necessarily limited to be clearly listed that A little step or units, but may include or with being not clearly listed or for these process, methods, product or equipment Intrinsic other step or units.
Fig. 1 is to show the schematic diagram of the system model of NFV framework involved in the example of the disclosure.This disclosure relates to one The method for optimizing resources of the total throughput maximization of kind network function virtualization, being includes network function virtualization architecture and base station Total throughput maximization method for optimizing resources.
In some instances, as shown in Figure 1, network function virtualization architecture includes logical layer, virtual level, physical layer and pipe Manage arranging system.Logical layer may include the logical node with the first quantity and the controller with the second quantity.Multiple controls The spring function to realize network coexists in device processed.Wherein, the first quantity for example can be M, and the second quantity for example can be N It is a.
In some instances, physical layer may include the physical node with third quantity.Third quantity is equal to the first number The sum of amount and the second quantity.Third quantity for example can be X.Wherein, X=M+N.Physical node for example can be VNF operation Hardware device.Physical layer can also include physical link.It is logical that physical link for example can be wired or wireless reality Believe equipment.In addition, since NFV can dispose redundancy to restore from software or hardware fault, the money in physical node Source can serve multiple logical nodes and controller.Wherein, logical node and controller can pass through virtual level and physics section Point forms mapping relations.
In some instances, function needed for management arranging system (NFV MANO) can provide configuration VNF and relevant operation Energy.Management arranging system can also be responsible for the resource allocation of whole network.For example, resource may include sending power and channel to make Use number.
Fig. 2 is that the resource for the total throughput maximization for showing the virtualization of network function involved in the example of the disclosure is excellent The flow chart of change method.This disclosure relates to network function virtualization total throughput maximization method for optimizing resources can letter Referred to as optimization method.
In some instances, as shown in Fig. 2, being based on above-mentioned NFV framework, optimization method may include transmission phase (step S100), calculation stages (step S200) and optimizing phase (step S300) are received.
In the step s 100, logical node and controller can pass through orthogonal channel to base station by corresponding physical node Send information signal.Orthogonal channel for example can be orthogonal band channel or orthogonal timeslots channel.Thereby, it is possible to ignore information letter Cochannel interference in number transmittance process.
In some instances, the connection between any one controller and base station can have than any one logical node and base The higher reliability of connection between standing.Thereby, it is possible to reduce controller to reduce brought by the connection failure with base station at random The problems such as failure, intentional attack, software fault.
In step s 200, base station can calculate effective load of each logical node and each controller based on information signal Lotus bit number, channel usage quantity, received signal to noise ratio and handling capacity.Wherein, logical node may include M.I-th of logic section The relevant parameter of point can be indicated by subscript Li.For example, kLiFor the payload bit number of i-th of logical node, nLiIt is i-th The channel usage quantity of a logical node, Pt,LiFor the transmission power of i-th of logical node, γLiFor connecing for i-th logical node Receive signal-to-noise ratio.Controller may include N number of.The relevant parameter of j-th of controller can be indicated by subscript Cj.For example, kCjFor jth The payload bit number of a controller, nCjFor the channel usage quantity of j-th of controller, Pt,CjFor the hair of j-th of controller Penetrate power, γCjFor the received signal to noise ratio of j-th of controller.In addition, channel can be expressed as using sum
In addition, in some instances, the first handling capacity R of i-th of logical nodeLiIt can satisfy With the second handling capacity R of j-th of controllerCjIt can satisfyThereby, it is possible to obtain each logical node The first handling capacity and each controller the second handling capacity.Thereby, it is possible to obtain each logical node and each controller Emitted energy, channel use sum and handling capacity.
In step s 200, first total emitted energy and first that base station can also calculate multiple logical nodes is always handled up Amount, and calculate the second total emitted energy and the second total throughout of multiple controllers.
Specifically, base station can calculate the emitted energy E of i-th of logical nodeLiWith the emitted energy of j-th of controller ECj.Wherein, ELiAnd ECjUnit be WHzs, ELi=Pt,LinLiAnd ECj=Pt,CjnCj.Hair based on each logical node The emitted energy of energy and each controller is penetrated, base station can calculate the total emitted energy and all controllers of all logical nodes Total emitted energy.
In addition, in some instances, total throughout can be the sum of the first total throughout and the second total throughout, and always gulp down The amount of spitting meets
In step S300, management arranging system carries out self-adjusted block to need the condition met may include third quantity It is equal to predetermined bit number equal to preset service quantity, payload bit number and received signal to noise ratio is equal to default received signal to noise ratio. That is, third quantity X can be the preset service quantity of setting.
In addition, management arranging system, which carries out self-adjusted block, needs to meet logical node and the super reliability requirement of controller. Namely super reliability requirement meetsWithAndSuch asWith Wherein, pe,LiFor the packet error probability of i-th of logical node, pe,CjFor the packet error probability of j-th of controller,It is The packet error probability upper limit of any one logical node,It is any one controller packet error probability upper limit.It is any as a result, The higher reliability of the connection having than between any one logical node and base station is connected between a controller and base station.
In some instances, management arranging system progress self-adjusted block also needs to meet first total emitted energy and is not more than First energy thresholdAnd second total emitted energy be not more than the second energy thresholdChannel usage quantity makes no more than channel With total nΣ.That is, WithIt patrols respectively Collect the first energy threshold of nodeWith the second energy threshold of controller
In some instances, management arranging system can send self-adjusted block instruction, adaptively to distribute transmitting function Rate and channel usage quantity.Base station can receive point that self-adjusted block instruction carries out transmission power and channel usage quantity Match.
In some instances, base station can realize power and the control to each logical node by automated power control The control of the power of device processed.For example, the radiofrequency signal of the transceiver station of base station is sequentially input the filtering with filter function Device and frequency converter, and then intermediate-freuqncy signal is obtained, then this intermediate-freuqncy signal is input in the automatic power control module of base station to function Rate is controlled.Wherein, automatic power control module includes A/D converter, goes direct current component, power estimation unit and power anti- Present adjustment unit.
In some instances, the automated power control process of automatic power control module includes: by intermediate-freuqncy signal by A/D Converter obtains digital signal, which goes direct current component to obtain the digital intermediate frequency letter of zero-mean by variable points Number, which estimates using the power that the power estimation unit of point-variable obtains signal, the power estimation value New gain coefficient value is obtained by Feedback of Power adjustment unit, new gain coefficient is applied to the clipping adjustment in subsequent time period Process maintains the output of digital medium-frequency signal near firm power.
In some instances, base station can be stabilized the signal received and retransmit away, can effectively subtract in this way Less or the loss of signal of communication in wireless transmissions is avoided, guarantees the communication quality of user.
In some instances, base station can be used frequency division multiplexing mode and realize distribution to channel usage quantity.In physics The available bandwidth of channel is more than under bandwidth situation needed for single information signal (each physical node send information signal), can be with The total bandwidth of the physical channel is divided into several subchannels identical with single information signal bandwidth is transmitted.Believe in every height The information signal that a corresponding physical node is sent is transmitted on road, to realize the multiple information letters of simultaneous transmission in the same channel Number (multiple signals).Before multiple signals carry out frequency division multiplexing, need to remove the frequency spectrum of each road signal by frequency spectrum shift technology It moves on in the different sections of physical channel frequency spectrum, so that the bandwidth of each information signal does not overlap.After carrying out frequency spectrum shift, need Each signal is modulated with different carrier frequencies.Each signal is centered on its corresponding carrier frequency, in certain bandwidth Subchannel on transmitted.In addition, interfering with each other in order to prevent, needs to bring using interference protection measure and each is isolated Subchannel.
In step S300, managing arranging system can be based on hybrid optimization algorithm certainly in the case where meeting above-mentioned condition Transmission power and channel usage quantity are adaptively distributed, so that total throughput maximization.Wherein, hybrid optimization algorithm is multidimensional And it leapfrogs-extremal optimization algorithm using total throughout as the mixing of adaptive value.- extremal optimization algorithm the general specifically, mixing leapfrogs Improved extremal optimization (Extreme value optimization, EO) replaces the RANDOM SOLUTION of shuffled frog leaping algorithm.The disclosure By extremal optimization in conjunction with shuffled frog leaping algorithm, due to stronger local search ability.
In some instances, mixing leapfrog-extremal optimization algorithm input parameter may include the first quantity M, the second quantity N, the payload bit number k of each logical nodeLi, each controller payload bit number kCj, each logical node Received signal to noise ratio γLi, each controller received signal to noise ratio γCj, logical node the packet error probability upper limitControl The packet error probability upper limit of deviceFirst energy thresholdSecond energy thresholdSum n is used with channel∑.By This, mixing leapfrogs-and extremal optimization algorithm can be optimized based on above-mentioned input parameter, and obtain the output parameter of optimization.
Fig. 3 is that the resource for the total throughput maximization for showing the virtualization of network function involved in the example of the disclosure is excellent The flow chart of the hybrid optimization algorithm of change method.
In some instances, as shown in figure 3, mixing leapfrog-extremal optimization algorithm may include setting initiation parameter (step Rapid S310).Initiation parameter for example can be above-mentioned mixing and leapfrog-input the parameter of extremal optimization algorithm.Initiation parameter Be also possible to initial population F, group p, every group of frog quantity q and EO process jump condition NEODeng.
In some instances, as shown in figure 3, mixing leapfrog-extremal optimization algorithm can also include be randomly generated population (by F frog represents) (step S320).Namely mixing leapfrog-extremal optimization algorithm can generate group including F frog at random Group p, the X that the position of every frog is t=2 (M+N) with dimensioniIt indicates.In step s 320, F can be initial population, q can To be the quantity of every group of frog.Initial population F=pq can be by the frog P={ X that generates at random1,X2,...XFGenerate.One In a little examples, p=20, q=10 and F=200 can be set.In addition, Xi=[xi1,xi2,...,xit] be i-th of frog position It sets, to solve the problems, such as that t is tieed up.In the disclosure, dimension t meets t=2 (M+N).Multidimensional t may include each logical node Transmission power, the transmission power of each controller, the channel usage quantity of each logical node and each controller channel make Use quantity.
In some instances, as shown in Figure 3 mixing leapfrog-extremal optimization algorithm can also include the suitable of every frog of assessment Response (step S330).Namely mixing leapfrog-extremal optimization algorithm can calculate the adaptive value f of every frog by assessment algorithm (Xi).Wherein, adaptive value f (Xi) meetThat is, total throughout RΣFor adaptive value.It adapts to Value is also referred to as fitness.
In some instances, the input parameter of assessment algorithm is the position X of i-th frogi, output parameter is i-th blueness Adaptive value f (the X of the position of the frogi).Assessment algorithm specifically includes the fitness f (X for calculating the position of every frogi)；WhereinWhenOrWhen, f (Xi)=f (Xi)-T；WhenOrWhen, f (Xi)=f (Xi)-T；WhenWhen, f (Xi)=f (Xi)-T；Otherwise f (X is returnedi).Wherein, T indicates penalty coefficient, and meets T=2 × 104.It is obtained as a result, based on assessment algorithm The adaptive value of every frog.
In some instances, as shown in figure 3, mixing leapfrog-extremal optimization algorithm can also comprise determining whether meet receive It holds back criterion (step S340) and when meeting convergence criterion, obtains optimal output parameter and terminate process (step S350).Its In, output parameter may include total throughout RΣ, each logical node transmission power Pt,Li, controller transmission power Pt,Cj、 The channel usage quantity n of each logical nodeLiWith the channel usage quantity n of each controllerCj。
In some instances, as shown in figure 3, mixing leapfrog-extremal optimization algorithm can also include quasi- when be unsatisfactory for convergence When then, F frog (step S360) is sorted in descending order.Also i.e. by the corresponding adaptive value of F frog according to descending sort.
In some instances, as shown in figure 3, mixing leapfrog-extremal optimization algorithm can also include structure group and submodule because Complex (step S370).By submodule, because of complex, that can prevent shuffled frog leaping algorithm (SFLA) in local optimum as a result, Position stops.
In some instances, as shown in figure 3, mixing leapfrog-extremal optimization algorithm can also include carrying out to every group of frog For circulation because of local search in complex and carries out extremal optimization (interim EO to every frog in submodule in circulation each time Process) (step S380).The fitness of every frog is calculated in recycling each time in step S380 using assessment algorithm.
In some instances, as shown in figure 3, mixing leapfrog-extremal optimization algorithm can also include to all frogs carry out Shuffle (step S390).Thereby, it is possible to ensure quick and stable convergence.
In step S380, submodule is because usually following triangular probability selection rule in complex.Worst frog l in every group The moving distance of secondary iteration is updated to Di,w(l)=r (Xi,b-Xi,w(l)), wherein r is a random number and r ∈ [0,1], and Xi,bIt is frog position best in the group.In some instances, near optimum position, it is understood that there may be than current optimum position The other positions for accommodating more foods but being located at except the currently range between worst position and optimum position.In order to include this Situation simultaneously extends possible search range, can be by Di,w(l)=r (Xi,b-Xi,w(l)) it is revised as Di,w(l)=wcr (Xi,b-Xi,w (l)).Wherein, c is jump sense of vision factor and c >=1.In addition, c cannot infinitely expand.C can be set to 1≤c≤3 as a result,.Example Such as, c=1.5.Increase the formula D of ci,w(l)=wcr (Xi,b-Xi,w(l)) hop range of each step of frog can be increased, extended The jump visual field of frog, and enhance the optimization ability of algorithm.lmaxIndicate the maximum number of iterations allowed in shuffled frog leaping algorithm, Such as lmax=1000.
In some instances, parameter w has significant impact to the convergence behavior of shuffled frog leaping algorithm, is used to preferably control The relationship between local search and global search in frog jump process processed.Indicate that the powerful overall situation is searched when parameter w is larger Suo Nengli and weaker local search ability indicate that weaker ability of searching optimum and powerful local are searched when parameter w is smaller Suo Nengli.Parameter w meetsIn some instances, w is setmin=0.8 and wmax= 2.5, w is in a linear fashion from w with iterations going onmaxGradually decrease to wmin。
In some instances, Xi,wIt (l) is the frog position in i-th group of the l times iteration with worst adaptive value, It is updated to Xi,w(l+1)=Xi,w(l)+Di,w(l), if the adaptive value of new frog position is better than original adaptive value after updating, newly Position will replace old position.Otherwise, Di,w(l)=wcr (Xi,b-Xi,w(l)) X ini,bBy XbIt replaces, wherein XbIt is entire population number Best frog position in amount.If not observing any improvement in the updated, random solution X will be implementedi,w(l).? This operation is repeated in each group, until reaching specified iteration.
Fig. 4 is that the resource for the total throughput maximization for showing the virtualization of network function involved in the example of the disclosure is excellent The flow chart of the extremal optimization process of change method.
In some instances, as shown in figure 4, carrying out the process of extremal optimization to every frog again in step S380.Pole Value optimization can eliminate the worst component (namely component part) in optimum individual.
In some instances, as shown in figure 4, the extremal optimization process of step S380 may include random generation individual X= [x1,x2,...,xt] (step S381).Namely extremal optimization process can generate the individual with multiple component parts at random.? In step S381, individual can be expressed as X=[x1,x2,...,xt], xiFor each component part of individual X.For example, individual X Each component part may, for example, be the transmission power P of each logical nodet,Li, each controller transmission power Pt,Cj, it is each The channel usage quantity n of a logical nodeLiWith the channel usage quantity n of each controllerCj。
In some instances, as shown in figure 4, extremal optimization process may include that evaluation adapts to value f (Xi) (step S382).Namely extremal optimization process can calculate each component part adaptive value of each individual.Value is adapted to be referred to as Adaptive value or fitness.Each component part adaptive value meetsWherein Δ xijIt is working as i-th frog Offset between front position and new position, the adaptive value can by i-th of component part is mutated and is made it is all its He is kept fixed and obtains component part.
In some instances, as shown in figure 4, extremal optimization process may include setting optimal solution Xb=Xi(step S383). Namely it is optimum individual that current individual, which can be set, in extremal optimization process.Extremal optimization process can also comprise determining whether to meet Preset standard (step S384).Wherein, preset standard can be referred to as established standards.When meeting established standards When, export adaptive value f (Xi) and terminate process.
In some instances, as shown in figure 4, extremal optimization process may include when being unsatisfactory for established standards, assessment is every A decision variable xiFitness (step S385).Wherein, i takes natural number, and decision variable is also referred to as component part.Therefore, pole Value optimization process can calculate the adaptive value of each component part of current individual.The adaptive value of each component part meetsWherein, Δ xijIt is the offset between the current location and new position of i-th frog.Thereby, it is possible to The adaptive value of each component part after being mutated.
In some instances, as shown in figure 4, extremal optimization process may include finding to meet λj≤λiXj(step S386).Namely the component part that adaptive value is less than or equal to the adaptive value of current individual is searched in step S386.In addition, xj It can also be expressed as x 'i。
In some instances, as shown in figure 4, extremal optimization process may include obtaining X ' (step S387).Wherein, X ' table Show target individual.In step S387, the component part x of target individualjIt can be by x 'i=xi+ηδiChange its state.Its In, x 'iAnd xiIndicate i-th of ingredient of mutation front and back individual X.δiIt indicates to generate random number.For example, δiIt can be standard Cauchy Stochastic variable or standard gaussian stochastic variable.δiIt can also be mixed Gaussian and Cauchy function operator.Variable η be one amplification because Son is usually linearly reduced with the increase of mutation quantity.For example, the value of setting η is linearly reduced to 0.1 from 1.
In some instances, as shown in figure 4, extremal optimization process may include unconditionally accepting X=X ' (step S388). Namely in step S388, by target individual X ' as new current individual.
In some instances, if as shown in figure 4, extremal optimization process may include f (X) < f (Xb), then Xb=X (step S389).Namely in step S389, when the adaptive value f (X) of target individual is less than the adaptive value f (X of optimum individualb), then target Individual is used as optimum individual.Thereby, it is possible to improve local search ability.
In step S390, after carrying out deep search in each group, entire frog population is shuffled and classified.Root Best frog position is recorded according to adaptive value.Then group is repartitioned, and executes local area deep-searching again.
In some instances, mixing leapfrog-runing time of extremal optimization algorithm meets complexity and isWherein, extremal optimization process jumps condition NEOValue can be set to any between 1 to 100 Numerical value, for example, can be with NEO=10.NiteIt is the number of iterations of extremal optimization, and NiteIt can be set to 2.lmaxIndicate that mixing leapfrogs The maximum number of iterations of algorithm.T is dimension.Thereby, it is possible to obtain runing time complexity.
Fig. 5 is that the resource for the total throughput maximization for showing the virtualization of network function involved in the example of the disclosure is excellent The waveform diagram of the total throughout under different payload bit numbers of change method.Fig. 6 is to show showing for the disclosure Example involved in network function virtualization total throughput maximization method for optimizing resources under different received signal to noise ratio Total throughout waveform diagram.
In some instances, as shown in figure 5, waveform A is that received signal to noise ratio meets γL1=γL2=γC1=γC2=20 (dB) waveform diagram when.Waveform B is that received signal to noise ratio meets γL1=10 (dB) and γL2=γC1=γC2Wave when=20 (dB) Shape figure.Waveform C is that received signal to noise ratio meets γL1=γC1=10 (dB) and γL2=γC2Waveform diagram when=20 (dB).
The condition of the satisfaction of waveform diagram shown in fig. 5 includes that logical node is identical with the payload bit number of controller, That is kLi=kCj.In addition the condition of the satisfaction of waveform diagram shown in fig. 5 further includes the first energy thresholdMeetThe Two energy thresholdsMeetChannel meets n using sum∑=300.According to Fig. 5 gained, total throughout R∑With kLi=kCjThe increase of value and increase.With kLi=kCjValue further increases, total throughout R∑Its maximum of infinite approach can be achieved Value.
Waveform C is in kLi=kCjUnder conditions of=24 (bytes), assessment algorithm does not have feasible solution.This is because kLi=kCj Value is bigger, and required physical layer resources are more.Specifically, in the incipient stage of Fig. 5, kLi=kCjThe increase of value is gulped down always The amount of spitting R∑On provide the contribution of more multiple dominant.Then, the increase of channel access times becomes in total throughout R∑On it is leading Factor.IfAssessment algorithm does not have feasible solution.In addition, from figure 5 it can be seen that waveform A Under the result is that best because the channel SNRs of logical node and controller are best.
In some instances, as shown in fig. 6, waveform D isAnd n∑Waveform when=300 Figure.Waveform E isAnd n∑Waveform diagram when=300.Waveform F is And n∑Waveform diagram when=300.Waveform G isAnd n∑Waveform diagram when=300.Waveform M is And n∑Waveform diagram when=400.Waveform N isAnd n∑=200 When waveform diagram.
The condition of the satisfaction of waveform diagram shown in fig. 6 includes that each received signal to noise ratio is identical.For example, γL1=γL2.Fig. 6 Shown in waveform total throughout R∑Value is with γL1=γL2The increase of value and increase.Wherein, waveform E's the result is that best, wave Shape D's the result is that worst.
In the disclosure, information signal is sent to base station based on network function virtualization architecture, logical node and controller, Base station receives information signal and calculates relevant parameter, when third quantity, payload bit number, received signal to noise ratio, first total hair It penetrates energy and second total emitted energy meets corresponding requirements and when logical node and controller meet super reliability requirement, management volume Heat-extraction system is based on hybrid optimization algorithm and adaptively distributes transmission power and channel usage quantity, so that total throughput maximization. The maximization total throughout optimization problem that thereby, it is possible to be completed quickly and effectively in NFV deployment, and there is preferable global search Ability.
This disclosure relates to the-maximization total throughout optimization side of extremal optimization algorithm (MSFLA-EO) that leapfroged based on mixing Method has superior stability and ability of searching optimum, is imitated according to method for optimizing resources of the various performance parameters to proposition Very, the method for optimizing resources that the results show is proposed has significant effect for maximizing total throughout.
Although being illustrated in conjunction with the accompanying drawings and embodiments to the disclosure above, it will be appreciated that above description The disclosure is not limited in any form.Those skilled in the art can without departing from the connotation and range of the disclosure To be deformed and be changed to the disclosure as needed, these deformations and variation are each fallen in the scope of the present disclosure.

Claims (10)

1. a kind of method for optimizing resources of the total throughput maximization of network function virtualization, being includes network function virtualization frame The method for optimizing resources of the total throughput maximization of structure and base station, which is characterized in that
Include:
The network function virtualization architecture includes logical layer, virtual level, physical layer and management arranging system, the logical layer packet The logical node with the first quantity and the controller with the second quantity are included, the physical layer includes the object with third quantity Node is managed, the third quantity is equal to the sum of first quantity and second quantity, the logical node and the control Device forms mapping relations, the logical node and the controller by the virtual level and the physical node and passes through accordingly The physical node sends information signal to the base station by orthogonal channel；
The base station calculates the payload ratio of each logical node and each controller based on the information signal Special number, channel usage quantity, transmission power, received signal to noise ratio and handling capacity calculate first total hair of multiple logical nodes Energy and the first total throughout are penetrated, and calculates the second total emitted energy and the second total throughout of multiple controllers；And
When the third quantity is equal to preset service quantity, payload bit number is equal to predetermined bit number and received signal to noise ratio etc. In default received signal to noise ratio, the logical node and the controller meet super reliability requirement, first total emitted energy No more than the first energy threshold and second total emitted energy is not more than the second energy threshold, and the channel usage quantity is little When channel is using sum, the management arranging system is based on hybrid optimization algorithm and adaptively distributes the transmission power and institute Channel usage quantity is stated, so that total throughput maximization, the total throughout is that first total throughout and described second are total The sum of handling capacity,
Wherein, the hybrid optimization algorithm is multidimensional and is leapfroged-extremal optimization using the total throughout as the mixing of adaptive value Algorithm, the multidimensional include the transmission power of each logical node and the transmission power of each controller and each institute State the channel usage quantity of logical node and each controller.
2. optimization method as described in claim 1, which is characterized in that
The total throughout meetsThe handling capacity of i-th of logical node and j-th of controller point Wei it not meetWithWherein, kLiFor the payload ratio of i-th of logical node Special number, kCjFor the payload bit number of j-th of controller, Pt,LiFor the transmission power of i-th of logical node, Pt,CjFor jth The transmission power of a controller, nLiFor the channel usage quantity of i-th of logical node, nCjIt is used for the channel of j-th of controller Quantity.
3. optimization method as described in claim 1, which is characterized in that
The super reliability requirement meetsWithAndWherein, pe,LiFor i-th of logic The packet error probability of node, pe,CjFor the packet error probability of j-th of controller,It is any one described logical node The packet error probability upper limit,It is the packet error probability upper limit of any one controller.
4. optimization method as described in claim 1, which is characterized in that
The emitted energy of i-th of logical node and j-th of controller is respectively to meet ELi=Pt,LinLiAnd ECj=Pt,CjnCj, In, ELiAnd ECjUnit be all WHzs, Pt,LiFor the transmission power of i-th of logical node, Pt,CjFor j-th controller Transmission power, nLiFor the channel usage quantity of i-th of logical node, nCjIt is described for the channel usage quantity of j-th of controller Channel is met using sumM is first quantity, and N is second quantity.
5. optimization method as described in claim 1, which is characterized in that
The mixing leapfrogs-and extremal optimization algorithm input parameter includes the first quantity M, the second quantity N, each described The payload bit number k of logical nodeLi, each controller payload bit number kCj, each logical node Received signal to noise ratio γLi, each controller received signal to noise ratio γCj, the logical node the packet error probability upper limitThe packet error probability upper limit of the controllerFirst energy thresholdSecond energy threshold Sum n is used with the channel∑, output parameter includes total throughout RΣ, each logical node transmission power Pt,Li, institute State the transmission power P of controllert,Cj, each logical node channel usage quantity nLiIt is used with the channel of each controller Quantity nCj。
6. optimization method as claimed in claim 5, which is characterized in that
The mixing leapfrogs-and extremal optimization algorithm includes:
Initiation parameter is set；The random population p generated including F frog；The position of every frog is t=2 with dimension (M+N) XiIt indicates；Adaptive value f (the X of every frog is calculated by assessment algorithmi)；Judge whether to meet convergence criterion；When full When sufficient convergence criterion, obtains optimal output parameter and terminate process；It is when being unsatisfactory for convergence criterion, F frog is corresponding Adaptive value is according to descending sort；P group frog and submodule are constructed because of complex；For circulation is carried out to every group of frog, is recycled each time In in submodule the fitness of every frog is calculated using the assessment algorithm because of local search in complex, and again to every blueness The frog carries out extremal optimization, and the fitness of every frog is obtained by assessment algorithm；It shuffles to all frogs.
7. optimization method as claimed in claim 6, which is characterized in that
The input parameter of the assessment algorithm is the position X of i-th frogi, output parameter is the adaptation of the position of i-th frog Value f (Xi),
Calculate the fitness f (X of the position of every frogi)；WhereinT indicates penalty coefficient, And meet T=2 × 104；
WhenOrWhen, f (Xi)=f (Xi)-T；
WhenOrWhen, f (Xi)=f (Xi)-T；
WhenWhen, f (Xi)=f (Xi)-T；
Otherwise f (X is returnedi)。
8. optimization method as claimed in claim 6, which is characterized in that
The process of the extremal optimization includes: the random individual for generating and having multiple component parts；Calculate each of each individual The adaptive value of component part；Setting current individual is optimum individual；Judge whether to meet established standards, terminate when meeting Process；When being unsatisfactory for established standards, the adaptive value of each component part of current individual is calculated；Adaptive value is searched to be less than or wait In the component part of the adaptive value of the current individual；Obtain target individual；Using target individual as new current individual；Work as mesh The adaptive value of mark individual is less than the adaptive value of optimum individual, then target individual is as optimum individual.
9. optimization method as claimed in claim 8, which is characterized in that
The adaptive value λ of each component partijMeetWherein, Δ xijIt is the current of i-th frog Offset between position and new position.
10. optimization method as claimed in claim 6, which is characterized in that
The mixing leapfrogs-and the runing time of extremal optimization algorithm meets complexity and isWherein, Extremal optimization process jumps condition NEOValue is set as any number between 1 to 100, NiteIt is the number of iterations of extremal optimization, and NiteIt is set as 2, lmaxIndicate the maximum number of iterations of shuffled frog leaping algorithm, t is dimension.
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