CN107466016B - A kind of cell buffer memory device distribution method based on user mobility - Google Patents
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Abstract
The invention discloses a kind of cell buffer memory device allocation algorithm based on user mobility, belongs to wireless communication field.The data set analysis for carrying out the long-term motion track of user group in initial phase first, is divided into discrete time slot according to certain time interval for the system time of user's mobile trajectory data collection.User requests a file in each time slot, calculates overall cache hit rate；Then an integer programming problem is converted by caching assignment problem；Using genetic annealing algorithms, the optimal solution of buffer memory device capacity original allocation problem is searched in solution space.It is operated including the fitness function of special optimization design, selection operation, crossover operation etc.；If solution has restrained, distribution of the device file between small-cell base station at this time is exported, and distribute buffer memory device between small-cell base station accordingly, obtain optimal buffer memory device allocation plan, improved the cache hit rate performance of user and effectively saved equipment and be laid to this.
Description
Technical field
The invention belongs to wireless communication field, specifically a kind of cell buffer memory device distribution side based on user mobility
Method.
Background technique
In recent years, with the fast development of mobile network, more and more mobile subscribers are passed through wireless using plurality of devices
Network insertion internet, therefore, the data traffic in mobile network increase substantially, and increase the pressure of backbone network data transmission.
Small cell edge caching technology has the advantages that reduction cellular traffic and backhaul link flow, can cope with a large amount of mobile data streams
Bring challenge is measured, network performance is promoted, optimizes user experience.
Since in next generation mobile communication system, small-cell base station disposes comparatively dense, on all small-cell base stations all
The buffer memory device of arrangement enough capacity will cause huge expense, therefore, in total limited situation of buffer memory device cost, be
System, which not can guarantee, is fitted with enough buffer memory devices at each small-cell base station.How buffer memory device is optimized in small-cell base station
In distribution, i.e. addressing of the buffer memory device between small-cell base station is a critical issue.
Current research all assumes that each small-cell base station in the buffer memory capacity problem for being related to small-cell base station
It is carried out under the premise of possessing the buffer memory device of identical capacity, there is no consider buffer memory device uneven distribution；Using
The obvious disadvantage of buffer memory device mean allocation exactly cannot neatly distribute buffer memory device between small-cell base station, and in major part
In reality scene, due to the characteristic of mobile subscriber, there is a large number of users with normal in the small-cell base station installation for seldom thering is user to visit
The buffer memory device of the identical capacity of the small-cell base station of visiting is obviously unreasonable, it will leads to the waste of buffer memory device resource.
It was delivered based on M.C.Gonzalez, C.A.Hidalgo, and A.-L.Barabasi et al. in 2008
Correlative study in mono- text of Understanding Individual Human Mobility Patterns, the moving rail of the mankind
With 24 hours, for the period, and regressive trend was presented as unit of this period in mark.In addition, in the environment of cell dense deployment,
Same user can generally access several base stations, and complicated topological structure makes the assignment problem of buffer memory device become more multiple
It is miscellaneous, need the popularity Conjoint Analysis in conjunction with file.
Summary of the invention
The present invention is while excellent in order to maximize the cache hit rate performance of user in the limited situation of buffer memory device total amount
Change distribution of the buffer memory device in small-cell base station, proposes a kind of cell buffer memory device distribution side based on user mobility
Method.
Specifically: distributing field for the edge cache capacity of base station in the intensive small subzone network comprising a large amount of mobile subscribers
Scape, by analyzing the data acquisition system of the long-term motion track of user, the popularity distribution of comprehensive hot topic file will be between small-cell base station
The original allocation problem of buffer memory device and the placement problem of hot spot file combine, and are translated into an integer programming and ask
Topic, is searched for buffer memory device capacity original allocation in solution space using the Neighborhood-region-search algorithm of Global Genetic Simulated Annealing Algorithm thought and asked
The optimal solution of topic.
Specific steps are as follows:
Step 1: analyzing the long-term shifting of each user's history in the group for some user group in mobile network
Dynamic rail mark, and the time that all motion tracks occupy is discretized into time slot at regular intervals；
Motion track record is position of each user where in different time slot.
Step 2: in each time slot, file service of each user to one unit-sized of network request；
The probability of different files is requested to meet the distribution of file popularity；
Step 3: being existed according to the location of user in each time slot, file popularity, buffer memory device and device file
Distribution between small-cell base station calculates overall cache hit rate.
The mathematical expectation E of the cache hit rate of total usertotal(χ) is as follows:
T is the discrete time slot set that continuous time line divides；For the set for servicing user；For user's request
The set of popular file；Z (f) is the file popularity size of user；I is indicator function, when its expression formula is true, I=1；
No I=0.For small set of cells；Indicate user u the location of in time slot τ； For the position of all users
Set set, binary variable γp,cUser is indicated in the coverage area whether position p is in base station c, if in γp,c=1；It is no
Then, γp,c=0；Binary variable factor χc,fIndicate whether store file f in the c of base station, if it is χc,f=1；Otherwise, χc,f=
0；
Meanwhile mathematical expectation Etotal(χ) will meet following constraint condition:
Wherein, C1 indicates the All Files stored on the c of base stationIt is less than and is equal to the caching that the base station can install
Equipment maximum capacity CSmax；The buffer memory device maximum capacity CS of all base stationsmaxIt is worth all the same.
C2 indicates that system is the memory capacity summation that all base stations are distributedBeing less than can distribute equal to system
Total buffer memory device capacity C Stotal；System is that the memory capacity of base station c distribution is CS (c).
Step 4: being based on genetic algorithm and simulated annealing, optimize the mathematical expectation E of overall cache hit ratetotal
(χ) obtains optimum allocation and hot spot file optimal placement on buffer memory device of the buffer memory device on small-cell base station.
Specific step is as follows:
Step 401, by mathematical expectation EtotalBelong to the binary variable factor χ of the same distribution state in (χ)c,fArrangement
It is oneRowThe matrix χ of column generates several chromosomes according to the population quantity of setting as hereditary chromosome at random, makees
For initial population.
File j whether is stored on the element representation base station i that the i-th row jth arranges in matrix χ；When element χ (i, j) is 1,
Illustrate to store file j on the i of base station；Otherwise, not stored file on the i of base station.
Current population is set as parent population by step 402, calculates the fitness of each chromosome in parent population；
Current Population Size is N, and the chromosome in population indicates are as follows: χ1,χ2,…χn,...,χN；
S(χn) it is chromosome χnFitness value, calculate it is as follows:
χbestChromosome for the most defect individual generated in all previous iteration of genetic algorithm；With the increase of the number of iterations
Variation.
Step 403 is calculated separately every according to the fitness of each chromosome using roulette formula random selection chromosome
A chromosome is selected to be hereditary to the probability of filial generation.
For chromosome χnThe probability P of filial generation is hereditary to by selectionnIt calculates as follows:
Step 405, when chromosome meets constraint condition by the probability for selecting to be hereditary to filial generation, select the chromosome carry out
Subsequent operation；
Constraint condition are as follows:
R is the real number generated using random number seed, 0 < r < 1；
For chromosome χn, work as probability PnWhen meeting above-mentioned constraint condition, chromosome χnIt is selected；
Step 406, selection two meet the chromosome of condition, carry out intersection and mutation operation, obtain two new chromosomes；
Specific step is as follows:
Step 4061, system generate two random numbers
Step 4062 utilizes the two chromosome χ chosena,χb, judgementValue whether be 1, such as
Fruit is then to exchange χa(u, v) and χbThe value of (u, v), enters step 4063；Otherwise, return step 4061；
Chromosome χ after step 4063, verifying exchangeaWith χbWhether constraint condition is met, if so, entering step 4064
Carry out mutation operation；Otherwise, return step 4061；
Constraint condition is as follows:
The All Files stored on i-th of base station in C3 representing matrix χ are less than the caching that can be installed equal to the base station
Equipment maximum capacity CSmax；Namely chromosome χaWith χbThe All Files stored on each base station in corresponding matrix meet small
In equal to CSmax；
It is assignable equal to system total that C4 indicates that system is less than for the memory capacity summation of base stations distribution all in matrix χ
Buffer memory device capacity C Stotal；Namely system is matrix χaWith χbIn the distribution of all base stations memory capacity summation meet it is small
In equal to CStotal。
Step 4064, system generate four random numbers
Step 4065, for the chromosome χ after exchangeaJudge whetherIf it is, exchange
χa(u, v) and χaThe value of (u ', v ') is as chromosome χaVariation result χa', otherwise, return step 4064.
For the chromosome χ after exchangebIt is equally operated, obtains chromosome χb'Final variation result.
Step 407, two new chromosome χ resulting to variationa'With χb', disturbance operation is executed respectively, and judges disturbance knot
Whether fruit meets Metropolis criterion, if it is, entering step 408, otherwise, re-starts disturbance behaviour to disturbance result
Make.
Disturbance operation is specific as follows:
Step 4071, system generate four random numbers
Step 4072 is directed to mutated chromosome χa'Judge whetherIf it is, exchange
χa'(u1,v1) and χa(u2,v2) value be used as to mutated chromosome χa'Disturbance as a result, otherwise, return step 4064.
For the chromosome χ after variationb'It is equally operated, obtains chromosome χb'Final disturbance result.
Whether step 408, the current disturbance number of judgement reach iteration step length, if so, by chromosome χa'With χb'Disturbance
As a result it is put into progeny population, enters step 409；Otherwise, return step 407；
Step 409 judges whether progeny population is equal to adaptive Population Size, if so, entering step 410；Otherwise, it holds
Row step 406 continues selective staining body and is operated.
Step 410 judges there is maximum E in current progeny populationtotalWhether the chromosome of (χ) has restrained, if it is,
Terminate and export optimal result, otherwise, return step 402 carries out new round iteration:
Optimal result is how to arrange buffer memory device on small-cell base station and how to place hot spot text on buffer memory device
Part.
Step 5: arranging buffer memory device on small-cell base station according to optimal result, and placed on buffer memory device
Hot spot file.
The present invention has the advantages that
1) a kind of, cell buffer memory device distribution method based on user mobility may be implemented to hit user cache
The promotion of rate.According to simulation result as can be seen that distribution of the present invention to small-cell base station buffer memory device, effectively improves use
The cache hit rate performance at family.
2) a kind of, cell buffer memory device distribution method based on user mobility, can save buffer memory device cost, lead to
The shift position preference of analysis user is crossed, the buffer memory device for the small-cell base station for seldom having user to visit is reduced, to save
The cost of operator's laying buffer memory device.
Detailed description of the invention
Fig. 1 is the long-term motion track schematic diagram of user group's history in mobile network of the present invention；
Fig. 2 is a kind of cell buffer memory device distribution method flow chart based on user mobility of the present invention；
Fig. 3 is the process for optimizing the mathematical expectation of overall cache hit rate the present invention is based on Global Genetic Simulated Annealing Algorithm
Figure；
Fig. 4 is present invention figure compared with the variation that cache hit rate under mean allocation algorithm is distributed with the popularity of file；
Fig. 5 is that the present invention illustrates with cache hit rate under mean allocation algorithm with the relationship comparison of buffer memory capacity saturation degree
Figure.
Specific embodiment
Below in conjunction with drawings and examples, the present invention is described in further detail.
A kind of cell buffer memory device distribution method based on user mobility of the present invention, with edge cache technology
In small subzone network, firstly, the data set analysis of the long-term motion track of user group is carried out in initial phase, user is mobile
The system time of track data collection is divided into discrete time slot according to certain time interval.User asks in each time slot
A file is sought, and according to the popularity distribution of user present position, comprehensive popular file in each time slot, by cell base
The original allocation problem of buffer memory device and hot spot file are combined in the placement problem of buffer memory device between standing, and calculate overall caching
Hit rate；Then, an integer programming problem is converted by caching assignment problem；Using genetic annealing algorithms, with device file
Distribution variable between small-cell base station is chromosome, and the optimal of buffer memory device capacity original allocation problem is searched in solution space
Solution, to optimize overall cache hit rate.Including the fitness function of special optimization design, selection operation, crossover operation, change
ETTHER-OR operation, adaptive crossover and mutation probability mechanism, disturbance operation, cooling function, adaptive population quantity mechanism；If Xie Yishou
It holds back, is then considered as and is optimal distribution, export distribution of the device file between small-cell base station at this time, and accordingly in small-cell base station
Between distribute buffer memory device, obtain optimal buffer memory device allocation plan, improve the cache hit rate performance of user and effectively save
Equipment is laid to this.
As shown in Fig. 2, specific steps are as follows:
Step 1: analyzing the long-term shifting of each user's history in the group for some user group in mobile network
Dynamic rail mark, and the time that all motion tracks occupy is discretized into time slot at regular intervals；
Motion track record is position of each user where in different time slot, as shown in Figure 1.
Step 2: in each time slot, file service of each user to one unit-sized of network request；
The probability of different files is requested to meet the distribution of file popularity；
Step 3: being existed according to the location of user in each time slot, file popularity, buffer memory device and device file
Distribution between small-cell base station calculates overall cache hit rate.
The mathematical expectation E of the cache hit rate of total usertotal(χ) is as follows:
T is the discrete time slot set that continuous time line divides；For the set for servicing user, quantity is For
The set of the popular file of user's request；Quantity isIt is equal in magnitude；Z (f) is the file popularity size of user；I is to refer to
Show function, when its expression formula is true, I=1；No I=0.For small set of cells, quantity is Indicate user u in the time
The location of when slot τ；For the location sets of all users, binary variable γp,cIndicate user is in the position p
In the no coverage area in base station c, if in γp,c=1；Otherwise, γp,c=0；Binary variable factor χc,fIndicate base station c
Inside whether file f is stored, if it is χc,f=1；Otherwise, χc,f=0；
Meanwhile mathematical expectation Etotal(χ) will meet following constraint condition:
Wherein, C1 indicates the All Files stored on the c of base stationIt is less than and is equal to the caching that the base station can install
Equipment maximum capacity CSmax；The buffer memory device maximum capacity CS of all base stationsmaxIt is worth all the same.
C2 indicates that system is the memory capacity summation that all base stations are distributedBeing less than can distribute equal to system
Total buffer memory device capacity C Stotal；System is that the memory capacity of base station c distribution is CS (c)；Unit is a popular file
Size.
Step 4: being based on genetic algorithm and simulated annealing, optimize the mathematical expectation E of overall cache hit ratetotal
(χ) obtains optimum allocation and hot spot file optimal placement on buffer memory device of the buffer memory device on small-cell base station.
As shown in Figure 3, the specific steps are as follows:
Step 401, by mathematical expectation EtotalBelong to the binary variable factor χ of the same distribution state in (χ)c,fArrangement
It is oneRowThe matrix χ of column generates several chromosomes according to the population quantity of setting as hereditary chromosome at random, makees
For initial population.
For the mathematical expectation E of cache hit ratetotal(χ), χ are independent variable；By adjusting
To optimize Etotal(χ)；χc,fIt is binary variable, meets the chromosomal foci characteristic in genetic algorithm, without introducing additional coding
Mode.In view of the constraint condition of formula (2) and formula (3), the present invention uses the hereditary chromosome of matrix form, will belong to same
The χ variable of distribution state is arranged as oneRowThe matrix of column, is indicated with χ.In matrix χ the i-th row jth arrange element χ (i,
J) indicate whether store file j on the i of base station；When element χ (i, j) is 1, illustrate to store file j on the i of base station；Otherwise, base
It stands not stored file on i.
The constraint condition of formula (2) and formula (3) is converted are as follows:
The All Files stored on i-th of base station in C3 representing matrix χ are less than the caching that can be installed equal to the base station
Equipment maximum capacity CSmax；Namely chromosome χaWith χbThe All Files stored on each base station in corresponding matrix meet small
In equal to CSmax；
It is assignable equal to system total that C4 indicates that system is less than for the memory capacity summation of base stations distribution all in matrix χ
Buffer memory device capacity C Stotal；Namely system is matrix χaWith χbIn the distribution of all base stations memory capacity summation meet it is small
In equal to CStotal。
Current population is set as parent population by step 402, calculates the fitness of each chromosome in parent population；
Current Population Size is N, and each chromosome in population indicates are as follows: χ1,χ2,…χn,...,χN；
S(χn) it is chromosome χnIdeal adaptation angle value, calculate it is as follows:
χbestChromosome for the most defect individual generated in all previous iteration of genetic algorithm；It is a dynamic value, with repeatedly
The increase of generation number and change.
Step 403 is calculated separately every according to the fitness of each chromosome using roulette formula random selection chromosome
A chromosome is selected to be hereditary to the probability of filial generation.
When evolving in per generation, current optimal base is added into filial generation first because of χbest；Then it is directed to chromosome χnBy selection heredity
To the probability P of filial generationnIt calculates as follows:
Step 405, when chromosome meets constraint condition by the probability for selecting to be hereditary to filial generation, select the chromosome carry out
Subsequent operation；
Constraint condition are as follows:
R is the real number generated using random number seed, 0 < r < 1；
For chromosome χn, work as probability PnWhen meeting above-mentioned constraint condition, chromosome χnIt is selected；
Step 406, selection two meet the chromosome of condition, carry out the intersection and mutation operation of genetic algorithm, obtain two
A new chromosome；
Specific step is as follows:
Step 4061, system generate two random numbers
Step 4062 utilizes the two chromosome χ chosen from parent populationa,χb, judgementValue
It whether is 1, if it is, exchange χa(u, v) and χbThe value of (u, v), enters step 4063；Otherwise, return step 4061；
Chromosome χ after step 4063, verifying exchangeaWith χbWhether the constraint condition of formula (4) and (5) is met, if so,
Enter step 4064 carry out mutation operations；Otherwise, return step 4061；
Step 4064, system generate four random numbers
Step 4065, for the chromosome χ after exchangeaJudge whetherIf it is, exchange
χa(u, v) and χaThe value of (u ', v ') is as chromosome χaVariation result χa', otherwise, return step 4064.
For the chromosome χ after exchangebIt is equally operated, obtains chromosome χb'Final variation result.
Corresponding to chromosome χa, crossover probability PcrossWith mutation probability PmutateBe provided that
Wherein, FavgFor the fitness average value of current population, Pc1For the upper bound of the crossover probability of setting；Pc2For setting
The lower bound of crossover probability, Pm1For the upper bound of the mutation probability of setting；Pm2For the lower bound of the mutation probability of setting.P is taken hereinc1
=0.9, Pc2=0.5, Pm1=0.2, Pm2=0.02.And when carrying out crossover operation to two chromosomes, crossover probability is selected as
Two chromosomes correspond to the smaller value of crossover probability.
Step 407, two new chromosome χ resulting to variationa'With χb', disturbance operation is executed respectively, and judges disturbance knot
Whether fruit meets Metropolis criterion, if it is, entering step 408, otherwise, re-starts disturbance behaviour to disturbance result
Make.
After the crossover operation and mutation operation for having executed genetic algorithm every time generate new individual, in fixed step-length L,
Random change chromosome χ, which is operated, using disturbance calculates solid energy G (χ)=1/F at this time when chromosome χ temperature is T
(χ), in next iteration, makes solid be in a new state χ ', calculates this with the decline of temperature T using disturbing function
When solid energy G (χ ').And according to Metropolis criterion and probability PMetropolisIt chooses whether to receive new state χ ' work
For current solid state.In addition, the disturbance operation that the present invention uses is identical as the mutation operation of the foregoing description.
Probability PMetropolisIt calculates as follows:
Wherein, Δ G=G (χ ')-G (χ), i.e., the difference of new solid energy and old solid energy.
In simulated annealing link, initial temperature T0Are as follows:
Wherein, FminAnd FmaxThe minimum value and maximum value of fitness value respectively in initial population, s are one adjustable normal
Number.
Cooling function changes system Current Temperatures T during each iteration, and the present invention is in index cooling function
On the basis of improve, if set generation temperature be T ', have
Wherein, the present embodiment takes α=0.95, K=1.1, M=20.
Disturbance operation is specific as follows:
Step 4071, system generate four random numbers
Step 4072 is directed to mutated chromosome χa'Judge whetherIf it is, exchange
χa'(u1,v1) and χa(u2,v2) value be used as to mutated chromosome χa'Disturbance as a result, otherwise, return step 4064.
For the chromosome χ after variationb'It is equally operated, obtains chromosome χb'Final disturbance result.
Whether step 408, the current disturbance number of judgement reach iteration step length, if so, by chromosome χa'With χb'Disturbance
As a result it is put into progeny population, enters step 409；Otherwise, return step 407；
Step 409 judges whether progeny population is equal to adaptive Population Size, if so, entering step 410；Otherwise, it holds
Row step 406 continues selective staining body and is operated.
Step 410 judges there is maximum E in current progeny populationtotalWhether the chromosome of (χ) has restrained, if it is,
Terminate and export optimal result, otherwise, return step 402 carries out new round iteration:
Optimal result is how to arrange buffer memory device on small-cell base station and how to place hot spot text on buffer memory device
Part.
Based on genetic algorithm and simulated annealing, optimize the mathematical expectation E of overall cache hit ratetotal(χ's) is total
Body algorithm flow is summarized as follows:
Step 1: input initialization population invariable number Smax, adaptive minimum population number Smin, auto-adaptive parameter M, initial temperature system
Number s, warm factor alpha, scrambling COEFFICIENT K, simulated annealing step-length L, crossover probability P are moved backc1With Pc2, mutation probability Pm1With Pm2。
Step 2: initialization population and the fitness value that each individual is calculated according to fitness function.And it is counted according to formula (13)
Calculate initial temperature T0。
Step 3: current population being set as father group, starts to evolve, first current optimal base because of χbestIt is put into progeny population.
Step 4: selecting two chromosomes according to selection operation, record its fitness value, and according to crossover probability Pc1With Pc2、
Mutation probability Pm1With Pm2Calculate adaptive crossover and mutation probability PcrossAnd Pmutate, and cross and variation behaviour is carried out according to this probability
Make.
Step 5: resulting chromosome being done into disturbance operation, calculates new adaptive value, and determine according to Metropolis criterion
It is fixed whether to receive new state.
Step 6: if if current iteration number is less than simulated annealing step-length L otherwise return step 5 puts new chromosome
Enter in progeny population.
Step 7: if current progeny population is less than, otherwise return step 4 updates current progeny population fitness value, and
Current optimal base is updated because of χbest。
Step 8: judging current optimal solution χbestWhether restrain, if not converged, return step 3, otherwise, output are current
Optimal solution χbestAnd terminate algorithm.
At algorithm initial stage, initial population quantity is set as the larger value Smax, can guarantee the gene diversity at initial stage in this way
Property, and in genetic algorithm iterative process, if undergoing M genetic iteration and χbestIt does not change, then SmaxReduce 1, until subtracting
It is small to arrive minimum population quantity SminUntil.Thus can reduce the algorithm later period there is no optimal solution solution space in carry out it is unnecessary
Wasted calculation amount is searched for, the execution efficiency of algorithm is improved.
Step 5: arranging buffer memory device on small-cell base station according to optimal result, and placed on buffer memory device
Hot spot file.
Embodiment:
The present embodiment considers following scene: using 92 experiment users of (KAIST) in Korea Advanced Institute of Science and Technology at 1 month
Interior motion track is as mobile trajectory data source.It, will be adjacent slight using equally distributed small-cell base station deployment way
Distance between area base station is set as 40 meters, and the covering radius of each small-cell base station signal is set as 100 meters.Each base station can at most pacify
The size of the buffer memory device capacity of dress is 5 file units.In terms of user file request, it is assumed that file popularity coincidence coefficient
It is distributed for the Zipf of α, and the sum of popular file set is 100.In addition, being analysis system cache hit rate and total buffer memory capacity
CStotalThe relationship of size, definition buffer memory capacity saturation degree η are total buffer memory capacity CStotalWith small-cell base station quantityThe ratio between,
Unit is file, i.e.,
In order to prove the performance of buffer memory device distribution mechanism proposed in this paper, the buffer memory device of mean allocation is selected to distribute
Mechanism compares.
Mean allocation algorithm: total buffer memory device is averagely allocated to each small-cell base station, does not consider user mobility.
As shown in figure 4, setting buffer memory capacity saturation degree η=1.As seen from the figure, with the increase of Zipf breadth coefficient α, two
The cache hit rate performance of kind algorithm also increases with it, and at different Zipf breadth coefficient α, the caching of genetic annealing algorithms
Hit rate performance suffers from certain promotion compared with mean allocation algorithm.
The cache hit rate performance pair of genetic annealing algorithms and mean allocation algorithm under different buffer memory capacity saturation degrees
Than as shown in Figure 5, wherein setting Zipf breadth coefficient α=1.It can be found that with the increase of buffer memory capacity saturation degree, two kinds
The cache hit rate performance of algorithm has promotion.Also, compared to mean allocation algorithm, genetic annealing algorithms are in low buffer memory capacity
Performance boost in the case where saturation degree 120%, and the performance boost 80% in the case where high level cache capacity saturation degree.This
It is because genetic annealing algorithms adjust the distribution of buffer memory device according to the long-term mobile trajectory data of user group, in total caching
In the case that place capacity is certain, more buffer memory devices are distributed to the more small-cell base station by access chance, and
Simultaneously in the case where user can access the intensive scene of multiple small-cell base stations, cache contents are carried out in conjunction with the distribution of file popularity
Optimization.It can be seen that genetic annealing algorithms are compared to mean allocation algorithm in the lower situation of buffer memory capacity saturation degree
There is better cache hit rate performance boost effect.
The present invention introduces the mobility of user in the distribution of small-cell base station buffer memory device to optimize distribution, and uses
It is solved based on the algorithm model of genetic simulated annealing, so that decision goes out optimal allocation plan, improves the cache hit of user
Rate performance saves equipment and is laid to this.
Claims (2)
1. a kind of cell buffer memory device distribution method based on user mobility, which is characterized in that specific steps are as follows:
Step 1: analyzing the long-term moving rail of each user's history in the group for some user group in mobile network
Mark, and the time that all motion tracks occupy is discretized into time slot at regular intervals；
Step 2: in each time slot, file service of each user to one unit-sized of network request；
The probability of different files is requested to meet the distribution of file popularity；
Step 3: according to the location of user in each time slot, file popularity, buffer memory device and device file slight
Distribution between area base station calculates overall cache hit rate；
The mathematical expectation E of the cache hit rate of total usertotal(χ) is as follows:
T is the discrete time slot set that continuous time line divides；For the set for servicing user；For the prevalence of user's request
The set of file；Z (f) is the file popularity size of user；I is indicator function, when its expression formula is true, I=1；No I=
0；For small set of cells；Indicate user u the location of in time slot τ； For the position collection of all users
It closes, binary variable γp,cUser is indicated in the coverage area whether position p is in base station c, if in γp,c=1；Otherwise,
γp,c=0；Binary variable factor χc,fIndicate whether store file f in the c of base station, if it is χc,f=1；Otherwise, χc,f=0；
Mathematical expectation Etotal(χ) will meet following constraint condition:
Wherein, C1 indicates the All Files stored on the c of base stationIt is less than and is equal to the buffer memory device that the base station can install
Maximum capacity CSmax；The buffer memory device maximum capacity CS of all base stationsmaxIt is worth all the same；
C2 indicates that system is the memory capacity summation that all base stations are distributedIt is less than assignable equal to system total
Buffer memory device capacity C Stotal；System is that the memory capacity of base station c distribution is CS (c)；
Step 4: being based on genetic algorithm and simulated annealing, optimize the mathematical expectation E of overall cache hit ratetotal(χ),
Obtain optimum allocation and hot spot file optimal placement on buffer memory device of the buffer memory device on small-cell base station；
Specific step is as follows:
Step 401, by mathematical expectation EtotalBelong to the binary variable factor χ of the same distribution state in (χ)c,fIt is arranged as one
It is aRowThe matrix χ of column generates several chromosomes, as first according to the population quantity of setting as hereditary chromosome at random
Beginning population；
File j whether is stored on the element representation base station i that the i-th row jth arranges in matrix χ；When element χ (i, j) is 1, explanation
File j is stored on the i of base station；Otherwise, not stored file on the i of base station；
Current population is set as parent population by step 402, calculates the fitness of each chromosome in parent population；
Current Population Size is N, and the chromosome in population indicates are as follows: χ1,χ2,…χn,...,χN；
S(χn) it is chromosome χnFitness value, calculate it is as follows:
χbestChromosome for the most defect individual generated in all previous iteration of genetic algorithm；Change with the increase of the number of iterations；
Step 403 calculates separately each dye using roulette formula random selection chromosome according to the fitness of each chromosome
Colour solid is selected to be hereditary to the probability of filial generation；
For chromosome χnThe probability P of filial generation is hereditary to by selectionnIt calculates as follows:
Step 405, when chromosome meets constraint condition by the probability for selecting to be hereditary to filial generation, select the chromosome carry out it is subsequent
Operation；
Constraint condition are as follows:
R is the real number generated using random number seed, 0 < r < 1；
For chromosome χn, work as probability PnWhen meeting above-mentioned constraint condition, chromosome χnIt is selected；
Step 406, selection two meet the chromosome of condition, carry out intersection and mutation operation, obtain two new chromosomes；
Concrete operations are as follows:
Step 4061, system generate two random numbers
Step 4062 utilizes the two chromosome χ chosena,χb, judgementValue whether be 1, if so,
Then exchange χa(u, v) and χbThe value of (u, v), enters step 4063；Otherwise, return step 4061；
Chromosome χ after step 4063, verifying exchangeaWith χbWhether constraint condition is met, if so, entering step 4064 progress
Mutation operation；Otherwise, return step 4061；
Constraint condition is as follows:
The All Files stored on i-th of base station in C3 representing matrix χ are less than the buffer memory device that can be installed equal to the base station
Maximum capacity CSmax；Namely chromosome χaWith χbThe All Files that store on each base station in corresponding matrix, which meet, to be less than etc.
In CSmax；
C4 indicates that system is that the memory capacity summation of all base station distribution in matrix χ is less than assignable equal to system total delay
Deposit place capacity CStotal；Namely system is matrix χaWith χbIn the memory capacity summation of all base stations distribution meet and be less than etc.
In CStotal；
Step 4064, system generate four random numbers
Step 4065, for the chromosome χ after exchangeaJudge whetherIf it is, exchange χa
(u, v) and χaThe value of (u ', v ') is as chromosome χaVariation result χa', otherwise, return step 4064；
For the chromosome χ after exchangebIt is equally operated, obtains chromosome χb'Final variation result；
Step 407, two new chromosome χ resulting to variationa'With χb', respectively execute disturbance operation, and judge disturbance the result is that
It is no to meet Metropolis criterion, if it is, entering step 408, otherwise, disturbance operation is re-started to disturbance result；
Disturbance operation is specific as follows:
First, system generates four random numbers
Then, it is directed to mutated chromosome χa'Judge whetherIf it is, exchange χa'(u1,v1)
With χa(u2,v2) value be used as to mutated chromosome χa'Disturbance as a result, otherwise, re-starting mutation operation；
For the chromosome χ after variationb'It is equally operated, obtains chromosome χb'Final disturbance result；
Whether step 408, the current disturbance number of judgement reach iteration step length, if so, by chromosome χa'With χb'Disturbance result
It is put into progeny population, enters step 409；Otherwise, return step 407；
Step 409 judges whether progeny population is equal to adaptive Population Size, if so, entering step 410；Otherwise, step is executed
Rapid 406, continue selective staining body and is operated；
Step 410 judges there is maximum E in current progeny populationtotalWhether the chromosome of (χ) has restrained, if it is, terminating
And optimal result is exported, otherwise, return step 402 carries out new round iteration:
Optimal result is how to arrange buffer memory device on small-cell base station and how to place hot spot file on buffer memory device；
Step 5: arranging buffer memory device on small-cell base station according to optimal result, and hot spot is placed on buffer memory device
File.
2. a kind of cell buffer memory device distribution method based on user mobility as described in claim 1, which is characterized in that
Motion track record described in step 1 is position of each user where in different time slot.
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| CN108174395B (en) * | 2018-01-15 | 2020-10-20 | 南京邮电大学 | Base station cache management method and system based on transfer action evaluation learning framework |
| CN108112039B (en) * | 2018-02-05 | 2021-04-27 | 东南大学 | Heterogeneous cellular network caching method based on retransmission and user movement |
| CN108551472B (en) * | 2018-03-20 | 2021-01-19 | 南京邮电大学 | Content cache optimization method based on edge calculation |
| CN108540549B (en) * | 2018-04-03 | 2020-12-22 | 西南交通大学 | User mobility-oriented network edge cache selection method |
| CN108738048B (en) * | 2018-04-25 | 2021-02-26 | 杭州电子科技大学 | Active storage method of maximized fairness base station based on genetic algorithm |
| CN108769729B (en) * | 2018-05-16 | 2021-01-05 | 东南大学 | Cache arrangement system and cache method based on genetic algorithm |
| CN109413694B (en) * | 2018-09-10 | 2020-02-18 | 北京邮电大学 | Small cell caching method and device based on content popularity prediction |
| CN109714790A (en) * | 2019-01-23 | 2019-05-03 | 南京邮电大学 | A kind of edge cooperation caching optimization method based on user mobility prediction |
| CN112468597B (en) * | 2020-12-11 | 2021-05-28 | 深圳市知小兵科技有限公司 | Data center resource allocation method and device based on artificial intelligence |
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