CN110996334A - Virtualized wireless network function arrangement strategy - Google Patents
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Abstract
The invention provides a virtualized wireless network function arrangement strategy which is beneficial to reducing rejection rate of Internet of things access service and improving utilization rate of network system resources, and the strategy comprises the following steps of S1: and establishing a chemical reaction optimization mathematical model for arranging the resources of the virtualized wireless network. S2: solving the mathematical model established in the step S1, wherein the solving comprises improving the local optimization capability of the CRO based on Gaussian disturbance, balancing the global and local search capabilities based on a random walk method, and improving the search capability and the search speed of the global approximate optimal solution of the CRO based on reinforcement learning. The invention has the beneficial effects that: the method is beneficial to reducing the rejection rate of the access service of the Internet of things, improving the utilization rate of network system resources, accelerating the solving speed of the global approximate optimal solution, improving the approximation degree of the approximate optimal solution and finally accelerating the automation and intelligentization process of the virtual network.
Description
Technical Field
The invention belongs to the field of mobile communication, and particularly relates to a resource arranging method for a virtualized network slice of a wireless mobile communication network.
Background
With the development of network technology, communication networks no longer only satisfy person-to-person communication, but extend to person-to-object and object-to-object communication. However, the performance indexes of different communication modes for network requirements are very different. Various businesses want to have a vertical proprietary network to provide services, such as the autonomous vehicle networking needs to provide real-time and highly reliable services, while the monitoring internet of things needs to have low-bandwidth and ultra-massive connections. With the emergence of ever-changing applications, the requirement degree of everything interconnection is enhanced, the access mode and the network function positioning are changed greatly, and the chimney type wireless mobile access network architecture cannot meet the development requirement of services to a certain extent. The chimney-type wireless access technology is difficult to realize efficient service support through a unified air interface and a network control protocol, and a new service type is difficult to rapidly deploy. Diversified network nodes and networking forms not only cause inconsistency of user experience, but also bring heavy burden to network operation and maintenance work. In the future, a wireless network needs to support various application scenarios such as eMBB, mMTC, URLLC, various combination requirements among eMBB, mMTC and URLLC on a unified common platform. However, the demands of various applications or services on network metrics vary greatly. In order to meet the requirements of different indexes of each service, a future virtualized wireless network management platform needs to have flexible management capability and rapid expansion and contraction capability. Meanwhile, the future wireless network not only serves individuals, but also serves vertical industries (such as public safety, intelligent factories, intelligent medical services, V2X and the like), and business models are remarkably differentiated. The differentiation of business models requires the decoupling of software and hardware of a wireless network, the virtualization and the software of network functions, the programmable and customizable support of the network functions and the provision of different network services for users in different industries by a uniform architecture in the future. Therefore, the resource arrangement becomes an important part in the arrangement of the virtualized network functions, and is also one of key technologies influencing the success or failure of the network arrangement system.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a virtualized wireless network function arrangement strategy which is beneficial to reducing the rejection rate of the access service of the Internet of things and improving the utilization rate of network system resources.
The invention is realized by the following steps:
s1: the following formula is adopted to establish a chemical reaction optimization mathematical model for arranging the resources of the virtualized wireless network,
where n is the number of functions in the resource pool, m is the number of features in the resource pool, μj,kJ-th network function f representing completion of virtual requestjWith the kth feature akThe cost required;
s2: solving the mathematical model established in the step S1, wherein the solving comprises improving the local optimization capability of the CRO based on Gaussian disturbance, balancing the global and local search capabilities based on a random walk method, and improving the search capability and the search speed of the global approximate optimal solution of the CRO based on reinforcement learning.
Further, the step S1 includes the following steps,
s101, modeling the virtual feature cost of the virtual function, including,
j network function f of a virtual requestjWith the kth feature akThe amount of resources required is represented by the following equation:
ηd＝σb×ηs+σp×ηp+σit×ηit
δsrepresenting combinations of functional modules xj',k'Coefficient of (d)pRepresenting combinations of functional modules xj',k'Coefficient of (d)itRepresenting combinations of functional modules xj',k'ηsIs the unit price of the corresponding resource, ηpIs the unit price of the corresponding resource, ηitIs the unit price of the corresponding resource ηdIs the combined cost of the various resources. it denotes service resources, s denotes bandwidth resources, p denotes power domain resources, Mcotj,kRepresenting a plurality of items having an attribute ajVirtual function module f ofiThe cost, σ, paid for using the same resource togetherbIs a weight coefficient, σpIs a weight coefficient, σitIs a weight coefficient, and the constraint relation is that the weight coefficient is more than or equal to 0 and more than or equal to sigmab,σp,σit≤1，σb+σp+σit＝1，
S102, the functions selected in the virtualized network function set and the quantity and the characteristics of the resources required by each function are expressed by the following constraint conditions:
Rj,k.it≤N×xj,k.it，
Rj,k.p≤N×xj,k.p，
Rj,k.s≤N×xj,k.s，
wherein R represents a virtual request and x represents a selected module;
the virtual service orchestration is represented by the following constraints:
where s ', p ', it ' denote the relevant resources that have been used. all represents all resources;
s103: j network function f of a virtual requestjWith the kth feature akThe amount of resources required is represented by the following equation:
the following mathematical model was established:
fi→fi+yrepresenting 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 that exists between,
s104: adding virtual function module f in solving processiThe cost is expressed by the following formula:
μj,k'＝μj,k+μj+y,k，
chemical reaction optimization mathematical model for expressing virtualized wireless network resource layout by adopting following formula
Further, the step S2 includes the following steps,
s201: let ω (i) be the structure of the ith molecule, and adopt KE as a means of measuring the state of the molecule to represent the ability of a molecule to escape from the current state to reach a worse molecular structure, the initial value of KE is "0", buff is the buffer energy, generated by molecule null collision, and is responsible for by the global function, and the initial value is "0";
s202: let' be the structure of the molecule after the impact, indicate all objects, ω (i). Best is the structure of the ith molecule with the lowest current potential energy, ω. Gbest indicates the molecular structure with the lowest current global potential energy, firstly, the structure with the lowest potential energy of the current molecule i is utilized, gaussian is adopted for a perturbation, and then a random walk model is used for walking between the structure with the lowest current potential energy of the ith molecule and the molecular structure with the lowest global potential energy after the structure with the lowest current potential energy of the ith molecule is perturbed by gaussian to obtain the structure of the molecule after the impact:
wherein the content of the first and second substances,
is Gaussian disturbance, and rand is a random number;
the conditions under which the molecules undergo a wall-collision reaction are expressed by the following formula:
PEω(i)+KEω(i)≥PEω(i)'
the kinetic energy KE of the resulting molecule is expressed using the following formula:
KEω(i)'＝(PEω(i)+KEω(i)-PEω(i)')×q，
wherein q is a loss coefficient, and (1-q) represents the loss proportion of KE in the wall collision process;
s203: make ω'1，ω'2Is the structure of the decomposed molecule, adopts the following formula to perform a disturbance on omega by adopting Gauss, then performs random walk,
the conditions under which the molecules undergo decomposition reaction are expressed by the following formula:
the kinetic energy KE calculation formula of the resulting molecule is expressed by the following formula
Where temp is a temporary variable;
s204: two molecules omega1，ω2Randomly selecting values of the same positions for exchange, and randomly adding a random number to each molecular structure to ensure that the random number is omega'1，ω'2Is the structure of the exchanged molecule, and is represented by the following formula ω'1，ω'2：
Wherein the content of the first and second substances,
represents from ω2Replacing omega by k bits at any place1The corresponding value.
Represents from ω1Replacing omega by k bits at any place2Rand (ω) is a randomly generated molecular structure.
The conditions under which the exchange reaction of the molecules takes place are expressed by the following formula:
temp2＝buff×rand，
temp2 is a temporary variable;
the kinetic energy KE of the exchanged molecules is obtained by the following formula:
buff＝buff-temp2，
s205: and (3) synthesis reaction: two molecules omega1，ω2The values of the same location are added and modulo the highest value of that location. Let ω ' be the structure of the molecule after exchange, and ω ' is represented by the following formula '
ω'＝ω1+ω2，
The conditions under which the molecules undergo synthesis are expressed by the following formula:
temp2＝buff×rand，
PEω1+KEω1+PEω2+KEω2+temp2≥PEω'，
the kinetic energy KE of the resulting molecule is obtained using the following formula,
KEω'＝(PEω1+KEω1+PEω2+KEω2-PEω')×q，
buff＝buff-temp2，
s206: the state where each molecule is chemically reacted is set to S ═ S in the state set of Q-learning method1,…,St,…STPi is a behavior set of the Q-learning method, where pi ═ a +1, a ≦ a-1, and 0 ≦ a ≦ T, where a is "0", only the row a ≦ a +1 motion, when a is T, the initial value of a is T, T is the number of times the molecules have chemically reacted, T is the number of times the overall iteration has occurred, the benefit per time is expressed as γ ═ PE (ω') -PE (ω) |, the cost per time is the value at which buff increases when an invalid collision or an invalid decomposition occurs, and the Q value is updated using the following formula:
where σ is the learning rate, β is the discount factor,
is a benefit in memory;
the value of q is adjusted by the following formula:
wherein λ is a coefficient of exponential distribution.
S207: analyzing each molecule in the population pop to determine whether the molecule meets the collision reaction condition, if so, generating the collision reaction, and after the collision reaction, judging the PEω((i)≥PEω(i)'If the value is larger than the threshold value, the value is omega (i)', otherwise, the reaction is invalid wall collision, the energy in wall collision is converted into the energy of the buffer zone, and the following formula is adopted to express the energy,
buff＝buff+(PEω(i)+KEω(i)-PEω(i)')×(1-q)；
when ineffective wall collision occurs, the molecules continue to collide with the wall and reach PEω(i)<PEω(i)'Until the end;
each molecule in the population pop is analyzed for whether a decomposition reaction condition is satisfied, and if so, a decomposition reaction occurs. After the decomposition reaction, judgment was made
Or
If greater than, ω (i) becomes min (ω (i)1',ω(i)2') while adding a max (ω (i) to ω (pop +1) ═ max (ω (i)1',ω(i)2') otherwise the reaction is ineffective decomposition, and the energy at the time of wall collision is converted into buffer zone energy, and the energy is expressed by the following formula:
buff＝buff+(PEω(i)+KEω(i)-PEω(i)1'-PEω(i)2')×(1-q)，
when a non-effective collision occurs, the molecules continue to decompose and reach
Or
Until now, the decomposed macromolecule ω (pop +1) ═ max (ω (i)1',ω(i)2') carrying out a wall-collision reaction and to PEω(pop+1)<PEω(pop+1)'Until now, 1 was added to the population on the basis of the original population, and pop ═ pop + 1.
Optionally selecting one molecule for analysis of each molecule in the population pop, and judging whether the exchange reaction condition is met or not, if not, selecting one molecule for analysis, otherwise, carrying out the exchange reaction;
and (3) optionally analyzing each molecule in the population pop, and judging whether the binding reaction condition is met or not, if not, selecting another molecule for analysis, otherwise, performing the binding reaction, and subtracting 1 from the population on the original basis, wherein the pop is equal to pop-1.
The invention has the beneficial effects that: the method is beneficial to reducing the rejection rate of the access service of the Internet of things, improving the utilization rate of network system resources, accelerating the solving speed of the global approximate optimal solution, improving the approximation degree of the approximate optimal solution and finally accelerating the automation and intelligentization process of the virtual network.
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FIG. 1 is a flow chart of the present invention.
Detailed Description
The resource arrangement of the virtualized network function is a combined optimization problem, and the virtualized wireless access network architecture has heterogeneity, distributivity, dynamics and openness; the method has the advantages that due to the characteristics of discreteness of network functions, exponential network load, rapid difference of new service emergence and the like, the arrangement of network virtualization resources becomes very complex, and the method is an NP complete problem. For the characteristics of the virtualized wireless access network management platform, the resource arrangement of the virtualized network function is generally solved by adopting a heuristic algorithm. However, without free lunch, the metaheuristics of all search extrema are exactly the same on average performance of all possible objective functions. Therefore, in the industrial application of resource arrangement of the virtualized network function, the optimization algorithm must take the search speed into consideration when being good at global search. A Chemical Reaction Optimization algorithm (CRO) is inspired by the interaction between molecules in Chemical Reaction to seek the lowest potential energy phenomenon in a potential energy surface, adopts four elementary reactions, follows the first law and the second law of thermodynamics, and has the characteristics of simplicity, universality, strong robustness, self-learning, self-organization, self-adaptation and the like. The algorithm solves the problems of combination optimization and function optimization, particularly the single-target optimization problem of a high-dimensional multi-modal function, has high convergence speed and strong robustness, and can effectively avoid falling into local optimization. In a broad sense, the chemical reaction optimization is an algorithm framework, only general operation agents (molecules) and energy management schemes need to be defined, the molecular properties of the chemical reaction optimization can be correspondingly changed according to the requirements of users, and the population scale can be adjusted in real time. Therefore, the algorithm has strong flexibility and can be self-adapted to different optimization problems.
A great deal of time is consumed in the arrangement process; meanwhile, the agent model assisted evolution algorithm is a main idea for solving the time-consuming optimization problem. The SAEA is adopted to correct all stages of initialization, wall collision, decomposition, exchange, synthesis and target value estimation of the CRO, and the calculation times of a real target are reduced to the maximum extent by evaluating the individual advantages and disadvantages in a multi-dimensional space formed by a Gaussian process model predicted value and an error value.
As shown in fig. 1, the present invention provides a virtualized wireless network function orchestration policy, comprising the following steps:
s1: based on the characteristics of the virtualized wireless network resource arrangement, the requirements of the chemical reaction optimization model are combined, and a chemical reaction optimization mathematical model for the virtualized wireless network resource arrangement is established as follows:
n is the number of functions in the resource pool. m is the number of features in the resource pool. Mu.sj,kJ-th network function f representing completion of virtual requestjWith the kth feature akThe cost required.
S2: and (3) correcting each stage of initialization, wall collision, decomposition, exchange, synthesis and target value estimation of the CRO by adopting SAEA, and solving the mathematical model established by S1, wherein the method specifically comprises the following steps: improving the local optimization capability of the CRO based on Gaussian (Gaussian) disturbance; the global and local search capabilities are balanced based on a random walk approach. And the search capability and the search speed of the global approximate optimal solution of the CRO are improved based on reinforcement learning.
Further, the step S1 includes the following steps,
s101, modeling the virtual feature cost of the virtual function, including,
j network function f of a virtual requestjWith the kth feature akThe required amount of resources may be provided by one physical AP, or may be provided by a plurality of physical APs, or may only require one physical AP to provide a portion of the resources, as shown in the following equation:
when a share of virtual resources is provided by a physical node: the cost is the sum of the product of the unit price of each resource and the required quantity of the resource and the combined cost of each resource. Deltas，δp，δitRepresenting combinations of functional modules xj',k'ηs，ηp，ηitIs the unit price of the corresponding resource ηdIs the combined cost of the various resources. it represents service resources, s represents bandwidth resources, p represents power domain resources, and N represents the number of nodes.
ηd＝σb×ηs+σp×ηp+σit×ηit(3)
σb，σp，σitIs a weight coefficient, and the constraint relation is that the weight coefficient is more than or equal to 0 and more than or equal to sigmab,σp,σit≤1，σb+σp+σit＝1.
When a share of virtual resources is provided by multiple physical nodes: its cost is the sum of the costs of N nodes plus the combined cost of N nodesj,k。costj,kA plurality of fingers having a characteristic ajVirtual function module f ofiThe price paid for parallel use.
When multiple virtual resources are provided by one physical node: its cost is the sum of the costs of 1/N nodes plus the combined cost Mcost of 1/N nodesj,k.Mcostj,kRefers to a plurality of characters having an attribute of ajVirtual function module f ofiThe cost of using the same resource
As can be seen from the formula (1-3),
according to the system model, it can be known that the number of resources and the feature requirement of the functions selected from the virtualized network function set and each function are greater than or equal to the number of resources corresponding to the virtual request, and the following constraint conditions exist:
Rj,k.it≤N×xj,k.it (4)
Rj,k.p≤N×xj,k.p (5)
Rj,k.s≤N×xj,k.s (6)
r denotes a virtual request and x denotes a selected module. 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. Thus, it is an NP-hard problem. In order to reduce the difficulty of solving and simultaneously embody the resource shortage, the following constraint conditions are added:
where s ', p ', it ' denote the relevant resources that have been used. all represents all resources.
In combination with formula (7-9), formula (2) is converted to:
since there may be dependencies between functional modules. f. ofi→fi+yRepresenting 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.
Virtual function module fiAnd a virtual function module fi+yThe exclusion relationship exists, and can be embodied in the service request. Therefore, in the process of solving,
the 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:
μj,k'＝μj,k+μj+y,k(13)
the formula (1) is converted into:
further, the step S2 includes the following steps,
the local search capability of CRO is mainly determined by the collision reaction and decomposition reaction of molecules; the global search capability of CRO is mainly determined by the exchange reaction and synthesis reaction of molecules. The CRO is integrated with some heuristic algorithms, so that the global and local searching capability is balanced, and the solving speed is increased. And improving the local optimization capability of the CRO based on a Gaussian random walk model. And the maximum Hamming distance is used for improving the global optimization capability of the CRO. The calculation times of the real target are reduced to the maximum extent by evaluating the individual advantages and disadvantages in a multidimensional space formed by the predicted value and the error value of the Gaussian process model.
S201: let ω (i) be the structure of the ith molecule. KE may be used as a measure of the state of a molecule, which represents the ability of a molecule to escape from the current state to a worse molecular structure (a new solution, with a higher value for the fitness function). The initial value of KE is "0". buff is the buffer energy, generated by molecular null collisions, and is accounted for by the global function, with an initial value of "0".
S202: wall collision reaction:
the molecules hit the walls of the container and some of the structure of the molecules changes. Let' be the structure of the molecule after impact, indicate all objects, ω (i). Best be the structure of the ith molecule with the lowest current potential energy, and ω. Gbest indicate the structure of the molecule with the lowest current global potential energy. Firstly, a structure with the lowest potential energy of the current molecule i is utilized, and Gaussian is adopted for carrying out disturbance; and then the structure with the lowest current potential energy of the ith molecule is disturbed by Gauss and then walks away from the molecular structure with the lowest global potential energy through a random walk model (random walk approach), so that the structure of the impacted molecule can be obtained:
wherein the content of the first and second substances,
for gaussian perturbations, rand is a random number. The conditions under which the molecules undergo a wall-collision reaction are:
PEω(i)+KEω(i)≥PEω(i)'(16)
according to the law of conservation of energy, the calculation formula of kinetic energy KE of the resultant molecule can be obtained
KEω(i)'＝(PEω(i)+KEω(i)-PEω(i)')×q (17)
Wherein q is a loss coefficient, and (1-q) represents the loss proportion of KE in the wall collision process.
S203: the molecule is broken down into two molecules. Make ω'1，ω'2Is the structure of the decomposed molecule. A Gaussian is adopted for omega to carry out disturbance, and then random walk is carried out, then
The conditions under which the decomposition reaction of the molecules takes place are:
according to the law of conservation of energy, the calculation formula of kinetic energy KE of the resultant molecule can be obtained
Where temp is a temporary variable.
S204: two molecules omega1，ω2Values of some identical positions are randomly chosen to be exchanged. In order to better obtain a global approximate optimal solution, when molecules are exchanged, a random number is randomly added to each molecular structure. Make ω'1，ω'2Is the structure of the exchanged molecule, then
Wherein the content of the first and second substances,
represents from ω2Replacing omega by k bits at any place1The corresponding value.
Represents from ω1Replacing omega by k bits at any place2Rand (ω) is a randomly generated molecular structure.
The conditions under which the exchange reaction of the molecules takes place are:
temp2＝buff×rand (26)
temp2 is a temporary variable, and the kinetic energy KE calculation formula of the exchanged molecules can be obtained according to the law of conservation of energy
buff＝buff-temp2 (31)
S205: and (3) synthesis reaction: two molecules omega1，ω2The values of the same location are added and modulo the highest value of that location. Let ω' be the structure of the exchanged molecule, then
ω'＝ω1+ω2(32)
The conditions under which the molecules undergo synthesis reaction are:
temp2＝buff×rand (33)
PEω1+KEω1+PEω2+KEω2+temp2≥PEω'(34)
according to the law of conservation of energy, the calculation formula of kinetic energy KE of the resultant molecule can be obtained
KEω'＝(PEω1+KEω1+PEω2+KEω2-PEω')×q (35)
buff＝buff-temp2 (36)
S206: adjusting CRO parameters based on Q-learning method:
in order to accelerate the convergence speed and obtain a global approximate optimal solution and reduce the times of invalid collision and invalid decomposition, a Q-learning method is adopted to determine the value of Q.
The state where each molecule is chemically reacted is set to S ═ S in the state set of Q-learning method1,…,St,…STAnd pi is an action set of the Q-learning method, where pi is { a +1, a-1}, and 0 ≦ a ≦ T, where a is "0", only the row a ≦ a +1 action, and when a is T, only the row a ≦ a-1 action. The initial value of a is t, i.e. a equals t. T is the number of times the molecule undergoes a chemical reaction and T is the number of times the overall iteration occurs. The gain at each time is γ ═ PE (ω') -PE (ω) |. The cost per time l is buff at the time of invalid collision or invalid decompositionAn increased value. The Q value updating formula is as follows:
where σ is the learning rate (learning rate) and β is the discount factor (discount factor), it can be seen from the formula that the larger the learning rate σ, the less the effect of retaining the previous training, the larger the discount factor β,
the greater the effect that is played.
Is a benefit in memory.
And adjusting the value of Q based on a Q-learning method, so that the value in the early stage is larger, and the value in the later stage is smaller.
The formula for q is:
wherein λ is a coefficient of exponential distribution.
Q-learning is a value-based algorithm in a reinforcement learning algorithm, wherein Q is Q (S, a), namely in the S State (S belongs to S) at a certain moment, the expectation that the profit can be obtained by taking the Action a (a belongs to A) is taken, and the environment can feed back the corresponding rewardr according to the Action of agent, so the main idea of the algorithm is to construct a Q-table by State and Action to store a Q value, and then the Action capable of obtaining the maximum profit is selected according to the Q value.
S207: specific implementation of the step S2
The CROROS algorithm is realized by firstly initializing the number pop of chemical reaction molecule groups and the times T of generating overall iteration; and then initializing the virtual request R and initializing the virtualized network function and the virtualized network resource of the network management system platform.
And adjusting the value of the parameter Q of the CROROS algorithm based on a Q-learning method.
Each molecule in the population pop is analyzed whether the wall-collision reaction condition is met, and if so, the wall-collision reaction occurs. After the wall-collision reaction, the PE was judgedω(i)≥PEω(i)'If the energy is larger than the predetermined value, ω (i) ═ ω (i)', otherwise, the reaction is invalid to touch the wall, and the energy at the time of touching the wall is converted into the energy of the buffer zone according to the principle of conservation of energy, as shown in the following formula.
buff＝buff+(PEω(i)+KEω(i)-PEω(i)')×(1-q) (39)
When ineffective wall collision occurs, the molecules continue to collide with the wall and reach PEω(i)<PEω(i)'Until now.
Each molecule in the population pop is analyzed for whether a decomposition reaction condition is satisfied, and if so, a decomposition reaction occurs. After the decomposition reaction, judgment was made
Or
If greater than, ω (i) becomes min (ω (i)1',ω(i)2') while adding a max (ω (i) to ω (pop +1) ═ max (ω (i)1',ω(i)2') to a host; otherwise, the reaction is ineffective decomposition, and the energy in collision with the wall is converted into the energy of the buffer zone according to the principle of energy conservation, as shown in the following formula.
buff＝buff+(PEω(i)+KEω(i)-PEω(i)1'-PEω(i)2')×(1-q) (40)
When a non-effective collision occurs, the molecules continue to decompose and reach
Or
Until now, the decomposed macromolecule ω (pop +1) ═ max (ω (i)1',ω(i)2') carrying out a wall-collision reaction and to PEω(pop+1)<PEω(pop+1)'Until now, 1 was added to the population on the basis of the original population, and pop ═ pop + 1.
And optionally selecting one molecule for analysis of each molecule in the population pop, and judging whether the exchange reaction condition is met or not, if not, selecting one molecule for analysis, otherwise, carrying out the exchange reaction.
And (3) optionally analyzing each molecule in the population pop, and judging whether the binding reaction condition is met or not, if not, selecting another molecule for analysis, otherwise, performing the binding reaction, and subtracting 1 from the population on the original basis, wherein the pop is equal to pop-1.
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 (3)
1. A virtualized wireless network function orchestration policy comprising the steps of,
s1: the following formula is adopted to establish a chemical reaction optimization mathematical model for arranging the resources of the virtualized wireless network,
where n is the number of functions in the resource pool, m is the number of features in the resource pool, μj,kIndicating completion of the virtual requestjA network function fjWith the kth feature akThe cost required;
s2: solving the mathematical model established in the step S1, wherein the solving comprises improving the local optimization capability of the CRO based on Gaussian disturbance, balancing the global and local search capabilities based on a random walk method, and improving the search capability and the search speed of the global approximate optimal solution of the CRO based on reinforcement learning.
2. The virtualized wireless network function orchestration policy of claim 1, wherein the step S1 comprises the steps of,
s101, modeling the virtual feature cost of the virtual function, including,
will virtualize a requestjA network function fjWith the kth feature akThe amount of resources required is represented by the following equation:
ηd＝σb×ηs+σp×ηp+σit×ηit，
δsrepresenting combinations of functional modules xj',k'Coefficient of (d)pRepresenting combinations of functional modules xj',k'Coefficient of (d)itRepresenting combinations of functional modules xj',k'ηsIs the unit price of the corresponding resource, ηpIs the unit price of the corresponding resource, ηitIs the unit price of the corresponding resource ηdIs the combined cost of the various resources. it denotes service resources, s denotes bandwidth resources, p denotes power domain resources, Mcotj,kRepresenting a plurality of items having an attribute ajVirtual function module f ofiThe cost, σ, paid for using the same resource togetherbIs a weight coefficient, σpIs a weight coefficient, σitIs a weight coefficient, and the constraint relation is that the weight coefficient is more than or equal to 0 and more than or equal to sigmab,σp,σit≤1，σb+σp+σit＝1；
S102, the functions selected in the virtualized network function set and the quantity and the characteristics of the resources required by each function are expressed by the following constraint conditions:
Rj,k.it≤N×xj,k.it，
Rj,k.p≤N×xj,k.p，
Rj,k.s≤N×xj,k.s，
wherein R represents a virtual request and x represents a selected module;
the virtual service orchestration is represented by the following constraints:
where s ', p ', it ' denote the relevant resources that have been used, all denote all resources;
s103: will virtualize a requestjA network function fjWith the kth feature akThe amount of resources required is represented by the following equation:
the following mathematical model was established:
fi→fi+yrepresenting 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;
s104: adding virtual function module f in solving processiThe cost is expressed by the following formula:
μj,k'＝μj,k+μj+y,k，
chemical reaction optimization mathematical model for expressing virtualized wireless network resource layout by adopting following formula
3. The virtualized wireless network function orchestration policy of claim 1, wherein the step S2 comprises the steps of,
s201: let ω (i) be the structure of the ith molecule, and adopt KE as a means of measuring the state of the molecule to represent the ability of a molecule to escape from the current state to reach a worse molecular structure, the initial value of KE is "0", buff is the buffer energy, generated by molecule null collision, and is responsible for by the global function, and the initial value is "0";
s202: let' be the structure of the molecule after the impact, indicate all objects, ω (i). Best is the structure of the ith molecule with the lowest current potential energy, ω. Gbest indicates the molecular structure with the lowest current global potential energy, firstly, the structure with the lowest potential energy of the current molecule i is utilized, gaussian is adopted for a perturbation, and then a random walk model is used for walking between the structure with the lowest current potential energy of the ith molecule and the molecular structure with the lowest global potential energy after the structure with the lowest current potential energy of the ith molecule is perturbed by gaussian to obtain the structure of the molecule after the impact:
wherein the content of the first and second substances,
is gaussian perturbation, rand is a random number,
the conditions under which the molecules undergo a wall-collision reaction are expressed by the following formula:
PEω(i)+KEω(i)≥PEω(i)'
the kinetic energy KE of the resulting molecule is expressed using the following formula:
KEω(i)'＝(PEω(i)+KEω(i)-PEω(i)')×q，
wherein q is a loss coefficient, and (1-q) represents the loss proportion of KE in the wall collision process;
s203: make ω'1，ω'2Is the structure of the decomposed molecule, adopts the following formula to perform a disturbance on omega by adopting Gauss, then performs random walk,
the conditions under which the molecules undergo decomposition reaction are expressed by the following formula:
the kinetic energy KE calculation formula of the resulting molecule is expressed by the following formula
Where temp is a temporary variable;
s204: two molecules omega1，ω2Randomly selecting values of the same positions for exchange, and randomly adding a random number to each molecular structure to ensure that the random number is omega'1，ω'2Is the structure of the exchanged molecule, and is represented by the following formula ω'1，ω'2：
Wherein the content of the first and second substances,
represents from ω2Replacing omega by k bits at any place1The corresponding value.
Represents from ω1Replacing omega by k bits at any place2Rand (ω) is a randomly generated molecular structure,
the conditions under which the exchange reaction of the molecules takes place are expressed by the following formula:
temp2＝buff×rand，
temp2 is a temporary variable;
the kinetic energy KE of the exchanged molecules is obtained by the following formula:
buff＝buff-temp2，
s205: and (3) synthesis reaction: two molecules omega1，ω2The values of the same location are added and modulo the highest value of that location. Let ω ' be the structure of the molecule after exchange, and ω ' is represented by the following formula '
ω’＝ω1+ω2，
The conditions under which the molecules undergo synthesis are expressed by the following formula:
temp2＝buff×rand，
PEω1+KEω1+PEω2+KEω2+temp2≥PEω'，
the kinetic energy KE of the resulting molecule is obtained using the following formula,
KEω'＝(PEω1+KEω1+PEω2+KEω2-PEω')×q，
buff＝buff-temp2，
s206: the state where each molecule is chemically reacted is set to S ═ S in the state set of Q-learning method1,…,St,…STP is a behavior set of the Q-learning method, where p ═ a +1, a ═ a-1, and 0 ≦ a ≦ T, where a is "0", only the row a ≦ a +1 motion, when a is T, the initial value of a is T, T is the number of times the molecules have chemically reacted, T is the number of times the ensemble has been generated, the gain per time is represented by γ ═ PE (ω') -PE (ω) |, the cost per time is the value at which buff increases when an invalid collision or an invalid decomposition occurs, and the Q value is updated using the following formula:
where σ is the learning rate, β is the discount factor,
is a benefit in memory;
the value of q is adjusted by the following formula:
wherein λ is a coefficient of exponential distribution.
S207: analysis of each molecule in the population pop for satisfaction of the wall-collision responseConditions, if satisfied, generating a wall collision reaction, after the wall collision reaction, judging PEω(i)≥PEω(i)'If the value is larger than the threshold value, the value is omega (i)', otherwise, the reaction is invalid wall collision, the energy in wall collision is converted into the energy of the buffer zone, and the following formula is adopted to express the energy,
buff＝buff+(PEω(i)+KEω(i)-PEω(i)')×(1-q)；
when ineffective wall collision occurs, the molecules continue to collide with the wall and reach PEω(i)<PEω(i)'Until the end;
each molecule in the population pop is analyzed for whether a decomposition reaction condition is satisfied, and if so, a decomposition reaction occurs. After the decomposition reaction, judgment was made
Or
If greater than, ω (i) becomes min (ω (i)1',ω(i)2') while adding a max (ω (i) to ω (pop +1) ═ max (ω (i)1',ω(i)2') otherwise the reaction is ineffective decomposition, and the energy at the time of wall collision is converted into buffer zone energy, and the energy is expressed by the following formula:
buff＝buff+(PEω(i)+KEω(i)-PEω(i)1'-PEω(i)2')×(1-q)，
when a non-effective collision occurs, the molecules continue to decompose and reach
Or
Until now, the decomposed macromolecule ω (pop +1) ═ max (ω (i)1',ω(i)2') carrying out a wall-collision reaction and to PEω(pop+1)<PEω(pop+1)'Adding 1 to the original population, pop is pop +1,
optionally selecting one molecule for analysis of each molecule in the population pop, and judging whether the exchange reaction condition is met or not, if not, selecting one molecule for analysis, otherwise, carrying out the exchange reaction;
and (3) optionally analyzing each molecule in the population pop, and judging whether the binding reaction condition is met or not, if not, selecting another molecule for analysis, otherwise, performing the binding reaction, and subtracting 1 from the population on the original basis, wherein the pop is equal to pop-1.
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