CN102685779A - Method for optimizing wireless network business resource management - Google Patents

Method for optimizing wireless network business resource management Download PDF

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CN102685779A
CN102685779A CN2012101307431A CN201210130743A CN102685779A CN 102685779 A CN102685779 A CN 102685779A CN 2012101307431 A CN2012101307431 A CN 2012101307431A CN 201210130743 A CN201210130743 A CN 201210130743A CN 102685779 A CN102685779 A CN 102685779A
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solution
chromosome
node
network
population
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黄东
黄林果
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Abstract

The invention provides a method for optimizing the wireless network business resource management, which is characterized in that the high-efficient transmission of the business resource is realized through the steps of building a service processing time optimization model, initializing the chromosomes, executing the local searching of the chromosome and the like.

Description

A kind of method for optimizing wireless network traffic resource management
Technical field
The present invention relates to wireless communication technology field, more particularly to Optimum Theory and wireless network.
Background technology
In recent years, with the expansion developed rapidly with network application scope of network technology, the characteristic of network traffics there occurs the change of essence.The experimental results show that modern packet switching network has the characteristic completely different with traditional telephone network.Traditional communication network traffic is short related, i.e., different characteristics are presented in service traffics structure on different time scales, and when timing statisticses yardstick is larger or when linking number tends to be infinite, service traffics are intended to smoothly.And modern network flow is statistical self-similarity (self-similarity), i.e., the service traffics sequence observed on different time scales has identical statistical property.Just because of the network traffics characteristic different from traditional Poisson distributions so that the network mechanism such as traffic shaping, resource allocation, queue management, queue scheduling should be otherwise varied with traditional method.In design planning network switch fabric, it should the characteristics such as the self similarity of network traffics, long related, many points of shapes are taken into full account, so as to provide the user more preferable service quality.
Network service quality QoS (Quality of Service) is the major issue that network design must take into consideration.Existing Internet provides " doing one's best " (Best Effort) service, under this service model, all Business Streams " are made no exception " competition network resource, router all uses the scheduling strategy of first in first out (First In First Out) to all IP bags, IP bags are sent to destination as possible, reliability, delay to transmission etc. can not provide any guarantee.But with Internet development, the increase of network user's number, the service quality difference required for different user is larger.Therefore, designed in the network switching equipment and meet the dispatching method of QoS of survice requirement and have become current focus, typical QoS realizes that framework is as shown in Figure 1.
Therefore, it is necessary to design a kind of efficient service resources optimization method so that Internet resources are able to optimal utilization.
The content of the invention
The technical problems to be solved by the invention are：Solve the relatively low problem of wireless network traffic resources management efficiency.
The present invention provides a kind of method for optimizing wireless network traffic resource management to solve above-mentioned technical problem, it is characterised in that：
A, set up traffic handling time Optimized model； 
B, chromosome population initialized；
C, execution chromosome Local Search.
In the step A, traffic handling time Optimized model is set up
Figure 2012101307431100002DEST_PATH_IMAGE001
Figure 549441DEST_PATH_IMAGE002
Wherein
Figure 2012101307431100002DEST_PATH_IMAGE003
The maximum time spent for the network processes Business Stream using traffic differentiation unit,
Figure 13920DEST_PATH_IMAGE004
For
Figure 213957DEST_PATH_IMAGE006
Individual Business Stream,
Figure 2012101307431100002DEST_PATH_IMAGE007
For the of network
Figure 472901DEST_PATH_IMAGE008
Individual node,For in node
Figure 492809DEST_PATH_IMAGE007
Upper business
Figure 495400DEST_PATH_IMAGE004
Earliest arrival time,
Figure 549944DEST_PATH_IMAGE010
,
Figure 2012101307431100002DEST_PATH_IMAGE011
For node
Figure 245367DEST_PATH_IMAGE007
To business
Figure 486993DEST_PATH_IMAGE004
Treatment time,
Figure 27696DEST_PATH_IMAGE012
For to business
Figure 936746DEST_PATH_IMAGE004
Operation sequence number,For business
Figure 537491DEST_PATH_IMAGE004
Number of processes,
Figure 834DEST_PATH_IMAGE014
,
Figure 2012101307431100002DEST_PATH_IMAGE015
For the node number in network,
Figure 876386DEST_PATH_IMAGE016
For the number of Business Stream, the service differentiation cellular construction of network is as shown in Figure 4.
In the step B, chromosome population is initialized, and cross processing is carried out to chromosome, its sub-step is：A. randomly choosing two parent chromosomes X and Y is used to produce daughter chromosome Z；B. forWith
Figure 374363DEST_PATH_IMAGE018
, generate random number
Figure 2012101307431100002DEST_PATH_IMAGE019
, order
Figure 411590DEST_PATH_IMAGE020
If,
Figure 2012101307431100002DEST_PATH_IMAGE021
, then
Figure 627807DEST_PATH_IMAGE022
If,
Figure 2012101307431100002DEST_PATH_IMAGE023
, then
Figure 41471DEST_PATH_IMAGE024
；If c.
Figure 2012101307431100002DEST_PATH_IMAGE025
Or
Figure 393955DEST_PATH_IMAGE026
In the presence of, then Z is back to, and stop calculating, ifOr
Figure 40017DEST_PATH_IMAGE026
It is not present, then goes to sub-step d；D. for node
Figure 2012101307431100002DEST_PATH_IMAGE027
, perform following steps：1. node V is removed from child chromosome Z；2. from minimum node ID
Figure 257372DEST_PATH_IMAGE028
Search is proceeded by, sees whether can all nodes be accessed.Order
Figure 2012101307431100002DEST_PATH_IMAGE029
For the node set that can be accessed,For the node set that can not be accessed, if
Figure 2012101307431100002DEST_PATH_IMAGE031
, then child chromosome is connected for two nodes, and stops calculating, if, then step is gone to 3.；3. network connection is repaired, the link of a minimum cost is set
Figure 2012101307431100002DEST_PATH_IMAGE033
So that node
Figure 34201DEST_PATH_IMAGE034
With
Figure 2012101307431100002DEST_PATH_IMAGE035
It is added into child chromosome Z；4. when increase link
Figure 258509DEST_PATH_IMAGE033
Afterwards, make
Figure 116743DEST_PATH_IMAGE036
And
Figure 2012101307431100002DEST_PATH_IMAGE037
, and continue since node j to scan for network node, when
Figure 401094DEST_PATH_IMAGE031
When, stop search；5. child chromosome Z is back to, its flow is as shown in Figure 3.
In the step C, chromosome Local Search is performed.Its sub-step is：A. make
Figure 548042DEST_PATH_IMAGE038
；B. generate
Figure 2012101307431100002DEST_PATH_IMAGE039
RANDOM SOLUTION, and be copied into, and it is estimated；C. solved for each
Figure 2012101307431100002DEST_PATH_IMAGE041
, using binary system system of selection from
Figure 822870DEST_PATH_IMAGE040
Middle one solution Y of random selection, and cross processing is carried out to X and Y, a new solution Z is obtained, if Z and setIn solution it is different, Z is added to
Figure 2012101307431100002DEST_PATH_IMAGE043
, and update set
Figure 177945DEST_PATH_IMAGE044
；D. Local Search is carried out to population, to each solution
Figure 275214DEST_PATH_IMAGE041
, a partial operation number is randomly choosed, and this operand is used for X, new solution Z is produced, if Z and set
Figure 311303DEST_PATH_IMAGE042
In solution it is different, Z is added to
Figure 937456DEST_PATH_IMAGE043
, and update set
Figure 58996DEST_PATH_IMAGE044
；E. Local Search table is set up, for each solution, a partial operation number is randomly choosed, and this operand is used for X, new solution Z is produced, if Z is optimal solution, set is copied intoIn, if Z is not optimal solution, abandoned；F. parent population and progeny population are mixed, for producing population
Figure 116131DEST_PATH_IMAGE046
, and utilize mixed population
Figure 913185DEST_PATH_IMAGE040
Update, to each solution
Figure 53180DEST_PATH_IMAGE041
Reasonability be estimated, and by reasonability to set
Figure 961093DEST_PATH_IMAGE040
Classified, wherein the Rationality Assessment method solved is
Figure 971774DEST_PATH_IMAGE048
,For
Figure 2012101307431100002DEST_PATH_IMAGE051
For the adaptation coefficient of the infeasible solution of chromosome,
Figure 470889DEST_PATH_IMAGE052
Initial value be 1,
Figure 2012101307431100002DEST_PATH_IMAGE053
,
Figure 98179DEST_PATH_IMAGE054
；G. first is usedCross processing of the solution for chromosome, and by other solutions from setRemove；If h. in the presence of exceeding
Figure 2012101307431100002DEST_PATH_IMAGE055
Solution be estimated, or nearestIt is not updated for set E in chromosome, then stops calculating, and is back to sub-step c, wherein
Figure 928415DEST_PATH_IMAGE039
For
Figure 443710DEST_PATH_IMAGE051
For the initial population invariable number of chromosome,
Figure 2012101307431100002DEST_PATH_IMAGE057
For maximum population invariable number,For minimum population number,For any normalization integer between a and b,
Figure 269900DEST_PATH_IMAGE019
For any normalized value between 0 and 1,
Figure 75045DEST_PATH_IMAGE055
For the maximal solution of permission,
Figure 659611DEST_PATH_IMAGE060
Not updateSolution maximum number,
Figure 499391DEST_PATH_IMAGE061
For optimal feasible solution,
Figure 511209DEST_PATH_IMAGE062
For
Figure 2012101307431100002DEST_PATH_IMAGE063
The rational assessment average of individual chromosome,
Figure 69229DEST_PATH_IMAGE064
For cause for
Figure 2012101307431100002DEST_PATH_IMAGE065
, equation
Figure 191906DEST_PATH_IMAGE066
Set up and right
Figure 2012101307431100002DEST_PATH_IMAGE067
, equation
Figure 151772DEST_PATH_IMAGE068
The network state variables of establishment,For
Figure 334491DEST_PATH_IMAGE070
The state decision variable of individual chromosome,
Figure 2012101307431100002DEST_PATH_IMAGE071
,
Figure 114229DEST_PATH_IMAGE072
It is for network state
Figure 2012101307431100002DEST_PATH_IMAGE073
When business weight coefficient,
Figure 40596DEST_PATH_IMAGE074
,
Figure 2012101307431100002DEST_PATH_IMAGE075
For total business demand of network,
Figure 854968DEST_PATH_IMAGE076
For linkThe probability of failure,
Figure 2012101307431100002DEST_PATH_IMAGE077
For linkBusiness demand,
Figure 674523DEST_PATH_IMAGE078
For link
Figure 874560DEST_PATH_IMAGE033
Reliable probability,For 0-1 state vector of network,
Figure 2012101307431100002DEST_PATH_IMAGE079
For link
Figure 622253DEST_PATH_IMAGE033
State vector, total flow is as shown in Figure 2.
 
Beneficial effects of the present invention are：A kind of method for optimizing wireless network traffic resource management is provided, by setting up traffic handling time Optimized model, chromosome population is initialized, the steps such as chromosome Local Search are performed, realizing the high efficiency of transmission of service resources.
Brief description of the drawings
Fig. 1 realizes framework for typical QoS；
Fig. 2 is total schematic flow sheet；
Fig. 3 is that chromosome population carries out initialization cross processing schematic flow sheet；
Fig. 4 is the service differentiation cellular construction schematic diagram of network.

Claims (4)

1. a kind of method for optimizing wireless network traffic resource management, solves the relatively low problem of wireless network traffic resources management efficiency, comprises the following steps：
A, set up traffic handling time Optimized model； 
B, chromosome population initialized；
C, execution chromosome Local Search.
2. method according to claim 1, is characterized in that for the step A：Set up traffic handling time Optimized model
Figure 350796DEST_PATH_IMAGE001
Figure 874182DEST_PATH_IMAGE002
WhereinThe maximum time spent for the network processes Business Stream using traffic differentiation unit,
Figure 811231DEST_PATH_IMAGE004
ForIndividual Business Stream,
Figure 883092DEST_PATH_IMAGE006
For the of network
Figure 260984DEST_PATH_IMAGE007
Individual node,
Figure 861729DEST_PATH_IMAGE008
For in nodeUpper business
Figure 200624DEST_PATH_IMAGE004
Earliest arrival time,
Figure 433022DEST_PATH_IMAGE009
,
Figure 735827DEST_PATH_IMAGE010
For node
Figure 686466DEST_PATH_IMAGE006
To business
Figure 568971DEST_PATH_IMAGE004
Treatment time,
Figure 461403DEST_PATH_IMAGE011
For to business
Figure 403951DEST_PATH_IMAGE004
Operation sequence number,
Figure 841885DEST_PATH_IMAGE012
For business
Figure 59240DEST_PATH_IMAGE004
Number of processes,
Figure 266231DEST_PATH_IMAGE013
,
Figure 379680DEST_PATH_IMAGE014
For the node number in network,
Figure 836069DEST_PATH_IMAGE015
For the number of Business Stream.
3. method according to claim 1, is characterized in that for the step B：Chromosome population is initialized, and cross processing is carried out to chromosome, its sub-step is：A. randomly choosing two parent chromosomes X and Y is used to produce daughter chromosome Z；B. for
Figure 60377DEST_PATH_IMAGE016
With
Figure 387453DEST_PATH_IMAGE017
, generate random number
Figure 937383DEST_PATH_IMAGE018
, order
Figure 349910DEST_PATH_IMAGE019
If,
Figure 377909DEST_PATH_IMAGE020
, thenIf,
Figure 545902DEST_PATH_IMAGE022
, then；If c.
Figure 277415DEST_PATH_IMAGE024
Or
Figure 579083DEST_PATH_IMAGE025
In the presence of, then Z is back to, and stop calculating, if
Figure 205237DEST_PATH_IMAGE024
Or
Figure 857935DEST_PATH_IMAGE025
It is not present, then goes to sub-step d；D. for node
Figure 227736DEST_PATH_IMAGE026
, perform following steps：1. node V is removed from child chromosome Z；2. from minimum node IDSearch is proceeded by, sees whether can all nodes be accessed, is made
Figure 712124DEST_PATH_IMAGE028
For the node set that can be accessed,
Figure 320960DEST_PATH_IMAGE029
For the node set that can not be accessed, if
Figure 760032DEST_PATH_IMAGE030
, then child chromosome is connected for two nodes, and stops calculating, if
Figure 770713DEST_PATH_IMAGE031
, then step is gone to 3.；3. network connection is repaired, the link of a minimum cost is setSo that node
Figure 365959DEST_PATH_IMAGE033
With
Figure 77564DEST_PATH_IMAGE034
It is added into child chromosome Z；4. when increase link
Figure 473910DEST_PATH_IMAGE032
Afterwards, make
Figure 612767DEST_PATH_IMAGE035
And
Figure 196195DEST_PATH_IMAGE036
, and continue since node j to scan for network node, when
Figure 711490DEST_PATH_IMAGE030
When, stop search；5. it is back to child chromosome Z.
4. method according to claim 1, is characterized in that for the step C：Chromosome Local Search is performed, its sub-step is：A. make
Figure 962343DEST_PATH_IMAGE037
；B. generate
Figure 803260DEST_PATH_IMAGE038
RANDOM SOLUTION, and be copied into
Figure 873984DEST_PATH_IMAGE039
, and it is estimated；C. solved for each
Figure 927391DEST_PATH_IMAGE040
, using binary system system of selection from
Figure 298329DEST_PATH_IMAGE039
Middle one solution Y of random selection, and cross processing is carried out to X and Y, a new solution Z is obtained, if Z and set
Figure 778989DEST_PATH_IMAGE041
In solution it is different, Z is added to
Figure 868168DEST_PATH_IMAGE042
, and update set
Figure 459686DEST_PATH_IMAGE043
；D. Local Search is carried out to population, to each solution, a partial operation number is randomly choosed, and this operand is used for X, new solution Z is produced, if Z and setIn solution it is different, Z is added to, and update set
Figure 777218DEST_PATH_IMAGE043
；E. Local Search table is set up, for each solution
Figure 125678DEST_PATH_IMAGE044
, a partial operation number is randomly choosed, and this operand is used for X, new solution Z is produced, if Z is optimal solution, set is copied into
Figure 948141DEST_PATH_IMAGE042
In, if Z is not optimal solution, abandoned；F. parent population and progeny population are mixed, for producing population
Figure 215174DEST_PATH_IMAGE045
, and utilize mixed populationUpdate
Figure 614112DEST_PATH_IMAGE046
, to each solution
Figure 607475DEST_PATH_IMAGE040
Reasonability be estimated, and by reasonability to set
Figure 892963DEST_PATH_IMAGE039
Classified, wherein the Rationality Assessment method solved is
Figure 629975DEST_PATH_IMAGE047
,
Figure 153360DEST_PATH_IMAGE048
For
Figure 114363DEST_PATH_IMAGE049
For the adaptation coefficient of the infeasible solution of chromosome,
Figure 355989DEST_PATH_IMAGE050
Initial value be 1,
Figure 631112DEST_PATH_IMAGE051
,
Figure 805741DEST_PATH_IMAGE052
；G. first is usedCross processing of the solution for chromosome, and by other solutions from set
Figure 869829DEST_PATH_IMAGE039
Remove；If h. in the presence of exceeding
Figure 479802DEST_PATH_IMAGE053
Solution be estimated, or nearest
Figure 977780DEST_PATH_IMAGE054
It is not updated for set E in chromosome, then stops calculating, and is back to sub-step c, wherein
Figure 749427DEST_PATH_IMAGE038
For
Figure 231224DEST_PATH_IMAGE049
For the initial population invariable number of chromosome,
Figure 113729DEST_PATH_IMAGE055
For maximum population invariable number,
Figure 466213DEST_PATH_IMAGE056
For minimum population number,
Figure 939920DEST_PATH_IMAGE057
For any normalization integer between a and b,
Figure 377854DEST_PATH_IMAGE018
For any normalized value between 0 and 1,
Figure 798471DEST_PATH_IMAGE053
For the maximal solution of permission,
Figure 802199DEST_PATH_IMAGE058
Not update
Figure 915649DEST_PATH_IMAGE059
Solution maximum number,
Figure 840879DEST_PATH_IMAGE059
For optimal feasible solution,
Figure 596346DEST_PATH_IMAGE060
ForThe rational assessment average of individual chromosome,
Figure 942194DEST_PATH_IMAGE061
For cause for
Figure 885879DEST_PATH_IMAGE062
, equationSet up and right, equation
Figure 816292DEST_PATH_IMAGE065
The network state variables of establishment,For
Figure 813384DEST_PATH_IMAGE067
The state decision variable of individual chromosome,
Figure 115052DEST_PATH_IMAGE068
,It is for network state
Figure 393904DEST_PATH_IMAGE070
When business weight coefficient,
Figure 763705DEST_PATH_IMAGE071
,For total business demand of network,For link
Figure 591350DEST_PATH_IMAGE032
The probability of failure,
Figure 764842DEST_PATH_IMAGE074
For link
Figure 306682DEST_PATH_IMAGE032
Business demand,
Figure 274638DEST_PATH_IMAGE075
For link
Figure 370770DEST_PATH_IMAGE032
Reliable probability,
Figure 610603DEST_PATH_IMAGE070
For 0-1 state vector of network,
Figure 475790DEST_PATH_IMAGE076
For linkState vector.
CN2012101307431A 2012-04-29 2012-04-29 Method for optimizing wireless network business resource management Pending CN102685779A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106453338A (en) * 2016-10-21 2017-02-22 高道华 Resource optimization method of wireless mesh network in cloud environment

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2005031501A2 (en) * 2003-09-22 2005-04-07 Kim Hyeung-Yun Sensors and systems for structural health monitoring
CN101840200A (en) * 2010-03-19 2010-09-22 华侨大学 Adaptive processing method for optimizing dynamic data in dispatching control
CN102238686A (en) * 2011-07-04 2011-11-09 南京邮电大学 Wireless sensor network routing method for modeling quantum genetic algorithm

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2005031501A2 (en) * 2003-09-22 2005-04-07 Kim Hyeung-Yun Sensors and systems for structural health monitoring
CN101840200A (en) * 2010-03-19 2010-09-22 华侨大学 Adaptive processing method for optimizing dynamic data in dispatching control
CN102238686A (en) * 2011-07-04 2011-11-09 南京邮电大学 Wireless sensor network routing method for modeling quantum genetic algorithm

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106453338A (en) * 2016-10-21 2017-02-22 高道华 Resource optimization method of wireless mesh network in cloud environment

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Application publication date: 20120919