CN109800071A - A kind of cloud computing method for scheduling task based on improved adaptive GA-IAGA - Google Patents

A kind of cloud computing method for scheduling task based on improved adaptive GA-IAGA Download PDF

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CN109800071A
CN109800071A CN201910004317.5A CN201910004317A CN109800071A CN 109800071 A CN109800071 A CN 109800071A CN 201910004317 A CN201910004317 A CN 201910004317A CN 109800071 A CN109800071 A CN 109800071A
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task
population
follows
fitness
chromosome
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杨磊
杜明辉
赵丽花
梁亚玲
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South China University of Technology SCUT
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South China University of Technology SCUT
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Abstract

The invention discloses a kind of cloud computing method for scheduling task based on improved adaptive GA-IAGA, using the evaluation method of the weighted optimization based on multiple target as fitness function；The mode of real number direct coding is taken to be encoded；Generate using state algorithm and at random the method generation initial population combined；Classification selection strategy is taken to carry out selection operation；Crossover operation is carried out according to crossover probability and chromosome diversity factor；Mutation operation is carried out according to dynamic variation probability；Whether terminate according to double termination condition determining programs；It finds preferred plan and is allocated.Improvement is optimized to multiple steps of traditional genetic algorithm in the present invention, improves user satisfaction and algorithm execution efficiency.

Description

A kind of cloud computing method for scheduling task based on improved adaptive GA-IAGA
Technical field
The present invention relates to cloud computing task scheduling technique field more particularly to a kind of cloud computings based on improved adaptive GA-IAGA Method for scheduling task.
Background technique
With the fast development of information age, computer technology and network technology etc. are more and more perfect, have pushed internet Application is popularized.Cloud computing be grow up on the basis of distributed computing, parallel computation, grid computing one kind it is novel Calculating mode, cloud computing obtain various resources, including various applications and IT service etc. by network.As a kind of new calculating mould Formula, the core of cloud computing are also to be connected computing resource, Service Source and storage resource etc. by network, form a resource Pond, while according to the demand of user, United Dispatching and the relevant resource of management.
In face of increasing service request, realize that the scheduling efficient and rational to task in cloud helps to be promoted user to clothes The satisfaction of business is one of the emphasis paid close attention in cloud computing application research.The cloud computing technology computing discipline emerging as one, It is the stage developed using relevant many researchs still in one to it, not perfect.What many task schedules were still continued to use is net Method in lattice calculating, though it is simple and practical, due to the considerations of lacking to factors such as the environmental characteristics of cloud computing, scheduling effect Fruit is often unsatisfactory.Therefore reasonable scheduling model how is taken out, and realizes cloud computing task using preferable algorithm Effective scheduling, meet the needs of users, have great significance to the practical cloud computing service that improves to a greater degree.
Currently, with bionic rapid development, people increasingly pay attention to bionics techniques, and thus produce new calculation Method, genetic algorithm are one of them.Genetic algorithm is classical intelligent algorithm, it can preferably solve np problem, people Be gradually concerned about the concurrency and intelligence of genetic algorithm, and be introduced into during the scheduling of resource of cloud computing, effect compares It is good.But traditional genetic algorithm, when handling cloud computing task schedule, inevitably some defects, limit it Effect.Therefore how improved adaptive GA-IAGA, the shortcomings that overcoming traditional algorithm, its reliability of lift-off and efficiency, by genetic algorithm It is dissolved into the task schedule field of cloud computing, is met the needs of users, the effect of genetic algorithm is more effectively played, becomes mesh Preceding research emphasis.
Summary of the invention
It is an object of the invention to provide a kind of cloud computing method for scheduling task based on improved adaptive GA-IAGA, including following step It is rapid:
S0. it pre-processes: proposing particular problem description or model, determine chromosome coding mode, determine that chromosome is preferably commented Valence method and fitness function；
S1. it initializes: setting maximum number of iterations or other termination conditions, population scale, crossover probability, mutation probability etc. Parameter；
S2. it generates initial population: state algorithm and random algorithm is combined, form first generation population, and iteration meter Number device zero；
S3. it calculates initial population fitness: calculating the fitness value for going out all individuals in contemporary population, and it is optimal to find value Fitness value, as current contemporary optimal solution and current globally optimal solution；
S4. judge whether to terminate: being judged whether to meet two termination conditions according to the information that previous step obtains, if meeting Output global optimum's individual is optimal solution, and algorithm terminates；Otherwise enter in next step；
S5. selection operation: taking classification selection strategy, and advantage individual is avoided to be lost during evolving by generation；
S6. the individual of current population crossover operation: is selected according to crossover probability to execute improved crossover operation；
S7. improved mutation operation mutation operation: is executed according to the individual of the current population of dynamic variation probability selection；
S8. it generates next-generation population: passing through step S5, S6, S7, generate the population of a new generation；
S9. it calculates current population's fitness: first calculating all ideal adaptation angle value of contemporary population, and find out wherein most That big label is optimum individual.It is compared to each other with global optimum individual again, if its value is more excellent, by it As global optimum's individual；Otherwise it does not change；
S10. algorithm terminates: judge whether two conditions for meeting iteration ends, if it is not, iteration count adds 1 certainly, algorithm Return back to S5；If so, output is as a result, algorithm terminates；
S11. it exports optimal solution: according to optimal solution, task being assigned to virtual machine, and export optimal solution；
The particular problem of the step S0 describes or model is as follows:
The different cloud task (Tasks) of M length is distributed to N number of performance different virtual machine (VMs) to execute, it is reasonable to find Allocation plan so that allocation result meet the cloud computing task schedule decision tree based on multiple target, problem include it is following about Beam condition is using MapReduce model, and overall tasks have been split into several small tasks, and nothing between each subtask It is directly linked；Two are mapped to virtual machine for all cloud computing resources；Three be the delay and loss for ignoring data transmission；Four be to mention The number of tasks of friendship is greater than virtual machine number；
The coding mode about chromosome of the step S0 is as follows:
Take the mode of real number direct coding: every chromosome length (i.e. contained gene dosage) is equal to task quantity M, base The value of cause is the serial number { 0,1,2 ..., N-1 } of virtual machine, therefore each gene position means that task symbol, gene Value, which means that distribute to, is worth virtual machine marked as this.It clearer can show the mapping relations between Task and VM in this way.
Of the invention is further described, chromosome optimizing evaluation method in the step S0 are as follows: adding based on multiple target Optimizing evaluation method, the method overall merit task completion time are weighed, the factor of three aspect of amount consumption and load balancing uses Person can determine corresponding weight according to demand, obtain corresponding fitness function, specific as follows:
(1) it about the fitness function component F 1 of task completion time, is obtained by following procedure:
Execution time TaskTime (i, j) of the note task i on virtual machine j are as follows:
Wherein Task_length (i) indicates the length of task i, and VM_Mips (j) indicates the execution speed of virtual machine j.
After task is distributed, each VM executes task execution time the sum of of the time for all distribution on the VM, it may be assumed that
Wherein Num (j) indicates the task quantity distributed on j-th of VM.
The then task completion time of certain allocation plan are as follows:
Thus, the fitness function component F 1 about task completion time are as follows:
F1=Fitnesstime=Timetotal；
(2) it about the fitness function component F 2 of energy consumption, is obtained by following procedure:
The specific consumption for remembering VM (j) is Power (j), then the energy input of VM (j) are as follows:
The total energy consumption of all virtual machines of certain allocation plan are as follows:
Thus, the fitness function component F 2 about energy consumption are as follows:
F2=Fitnesspower=Powertotal；
(3) it about the fitness function component F 3 of load balancing, is obtained by following procedure:
The execution time average of all VMs are as follows:
Load balancing function are as follows:
Fitness function about target 3 (load balancing) at this time are as follows:
F3=Fitnessload=LoadBalance；
(4) the weighted optimization evaluation method based on multiple target, comprehensive fitness degree function are as follows:
F=α F1+βF2+γF3；
Wherein alpha+beta+γ=1, and α, beta, gamma ∈ [0,1]
For the step S1 in initialization, setting maximum number of iterations is MaxGen, and other termination conditions are global optimum Chromosome is in continuous X generation without more excellent solution, population scale Scale, crossover probability Pc, mutation probability Pm。
Of the invention is further described, the step S2 is when generating initialization population, first with a variety of static methods Choosing generates chromosome dyad, then generates other chromosomes at random according to coding rule, thus forms a population.State algorithm The characteristics of be execute speed be exceedingly fast, solution can be quickly found, these solution typically more excellent solution.This operates introducing that can be purposive The more excellent solution in part, increases the probability of advantage chromosome, can provide initial population more better than random selection initial population.Example Such as: using state algorithm, such as max_min, min_min scheduling algorithm first acquires chromosome dyad, then generates other dyes at random Thus colour solid forms initial population.
Of the invention is further described, the step S3 can be obtained when calculating fitness according to following formula:
F=α F1+βF2+γF3；(formula 12)；Wherein alpha+beta+γ=1, and α, beta, gamma ∈ [0,1]
Specific weight α, beta, gamma are selected according to actual needs by user.Such as when only considering the deadline, it can use: α =1, β=0, γ=0；If it is considered that deadline and energy consumption are more important, load balance is more secondary, then can use: α=0.4, β=0.4, γ=0.2, and so on.
Of the invention is further described, the step S4 is when judging termination condition, using two kinds of termination conditions, respectively It is as follows:
If global optimum's chromosome regard this solution as optimal solution in the continuous X generation more excellent solution of nothing, terminator；
If two do not occur situation (1), maximum number of iterations MaxGen is gone to always, and export globally optimal solution,
When judging termination condition, (1) is first determined whether, then judge (2).Such termination condition is added, can be reduced not Necessary the number of iterations improves executing efficiency.
As an improvement of the present invention, the step S5 takes classification back-and-forth method, specifically such as when carrying out selection operation Under:
Find out the mean value of all chromosome fitness in populationFitness value is less than in the population (being better than) average value, that is, meetChromosome it is selected, remaining chromosome is according to individual adaptation degree in current population The details of value operate to execute roulette selection.The select probability of each chromosome when executing roulette selection operation are as follows:Wherein Scale is population scale, and the method effectively prevents excellent individual by generation evolution It is lost in the process；It can accelerate convergence rate.
Of the invention is further described, when the step S6 carries out crossover operation, takes two neighboring position one in population It is right, the mode of random single point crossing, with probability PcIntersected.The similarity of each two chromosome of centering is checked before intersecting (same position gene equal probabilities), do not intersect when similarity is larger (more than 60%), otherwise normal to intersect.It can avoid in this way " inbreeding " generates more different individuals, it is ensured that the diversification of group.
Of the invention is further described, when the step S7 carries out mutation operation, mutation probability is function Pm=0.15- 0.1e-(i/1000), wherein i is the number of iterations.It can guarantee that mutation probability is not too big in this way, and can be adaptive.
Of the invention is further described, step S11 assigns the task to corresponding void according to the optimal solution for inputting algorithm Quasi- machine, terminates program.
By adopting the above technical scheme, it has the following beneficial effects:
The present invention has comprehensively considered the plurality of target demand of user, makes user that can adjust objective appraisal according to the demand of oneself Function (fitness function), improves user satisfaction；And the Optimal improvements by being carried out to the multiple steps of traditional genetic algorithm, Improve algorithm execution efficiency；Use the weighted optimization evaluation method based on multiple target as the fitness function of genetic algorithm, It is capable of the effect of the multiple targets of overall merit, and selects optimal scheduling scheme accordingly；To several processes of traditional genetic algorithm: raw Optimal improvements appropriate have been carried out at initial population, selection, intersection, variation, termination condition etc., have improved efficiency of algorithm.
Detailed description of the invention
Fig. 1 is flowage structure schematic diagram of the invention；
Specific embodiment
The present invention will be further described with reference to the accompanying drawing.
A kind of cloud computing method for scheduling task based on improved adaptive GA-IAGA, steps are as follows:
S0. it pre-processes: proposing particular problem description or model, determine chromosome coding mode, determine that chromosome is preferably commented Valence method and fitness function；
Problem description are as follows: the different cloud task (Tasks) of M length is distributed into N number of performance different virtual machine (VMs) and is held Row, finds reasonable allocation plan, so that allocation result meets the cloud computing task schedule decision tree based on multiple target.
The chromosome coding mode takes the mode of real number direct coding, and chromosome length is number of tasks M, each position On genic value indicate the corresponding resource number of the task.
Fitness function is the weighted optimization evaluation function based on multiple target, it may be assumed that
F=α F1+βF2+γF3；Wherein alpha+beta+γ=1, and α, beta, gamma ∈ [0,1]
Specific weight α, beta, gamma are selected according to actual needs by user, such as herein desirable α=0.4, and β=0.4, γ= 0.2, it indicates in evaluation of result, deadline and energy consumption are more important, and load balancing is more secondary, and so on.
S1. initialize: setting maximum number of iterations is MaxGen, and other termination conditions are global optimum's chromosome continuous In X generation, is without more excellent solution (such as settable X=MaxGen × 10%), population scale Scale, crossover probability Pc, mutation probability Etc. parameters be Pm。
S2. generate initial population: first with a variety of static methods select generate chromosome dyad, then according to coding rule with Machine generates other chromosomes, thus forms a population, and iteration count is zeroed.
The characteristics of state algorithm is to execute speed to be exceedingly fast, and can quickly find solution, these solutions are typically all more excellent solution.This operation The more excellent solution of introducing portion that can be purposive increases the probability of advantage chromosome, can provide than random selection initial population more Good initial population.Such as: using state algorithm, such as max_min, min_min scheduling algorithm first acquires chromosome dyad, then Other chromosomes are generated at random, thus form initial population.
S3. it calculates initial population fitness: calculating the fitness value for going out all individuals in contemporary population, and it is optimal to find value Fitness value, as current contemporary optimal solution and current globally optimal solution；Fitness function formula is by base in step S0 It is provided in the weighted optimization evaluation function of multiple target.
S4. judge whether to terminate: being judged whether to meet two termination conditions according to the information that previous step obtains, if meeting Output global optimum's individual is optimal solution, and algorithm terminates；Otherwise enter in next step.
Two kinds of termination conditions are as follows respectively:
(1) if global optimum's chromosome is in the continuous X generation more excellent solution of nothing, terminator, and it regard this solution as optimal solution
(2) if not occurring situation (1), maximum number of iterations MaxGen is gone to always, and export globally optimal solution.
When judging termination condition, (1) is first determined whether, then judge (2).
S5. selection operation: taking classification selection strategy, and advantage individual is avoided to be lost during evolving by generation.
Classification selection method particularly includes: find out the mean value of all chromosome fitness in populationIt should Fitness value is less than (being better than) average value in population, that is, meetsChromosome it is selected, remaining chromosome is according to working as The details of ideal adaptation angle value operate in preceding population to execute roulette selection.Each chromosome when executing roulette selection operation Select probability are as follows:
S6. the individual of current population crossover operation: is selected according to crossover probability to execute improved crossover operation；
Crossover probability and method are as follows: take two neighboring position in population a pair of, the mode of random single point crossing, with probability P c Intersected.Check the similarity (same position gene equal probabilities) of each two chromosome of centering before intersecting, similarity compared with Do not intersect when big (more than 60%), it is otherwise normal to intersect.It can avoid " inbreeding " in this way, generate more different individuals, Ensure the diversification of group.
S7. improved mutation operation mutation operation: is executed according to the individual of the current population of dynamic variation probability selection.Become Different probability is function Pm=0.15-0.1e-(i/1000), wherein i is the number of iterations.It can guarantee that mutation probability is not too big in this way, and It can be adaptive.
S8. it generates next-generation population: passing through step S5, S6, S7, generate the population of a new generation.
S9. it calculates current population's fitness: first calculating all ideal adaptation angle value of contemporary population, and find out wherein most That big label is optimum individual.It is compared to each other with global optimum individual again, if its value is more excellent, by it As global optimum's individual；Otherwise it does not change；Fitness function formula is by the weighted optimization based on multiple target in step S0 Evaluation function provides.
S10. algorithm terminates: judge whether two conditions for meeting iteration ends, if it is not, iteration count adds 1 certainly, algorithm Return back to S5；If so, output is as a result, algorithm terminates.Two conditions are provided by step S4.
S11. it exports optimal solution: according to optimal solution, task being assigned to virtual machine, and export optimal solution.
The particular problem of the step S0 describes or model is as follows:
The different cloud task (Tasks) of M length is distributed to N number of performance different virtual machine (VMs) to execute, it is reasonable to find Allocation plan so that allocation result meet the cloud computing task schedule decision tree based on multiple target, problem include it is following about Beam condition is using MapReduce model, and overall tasks have been split into several small tasks, and nothing between each subtask It is directly linked；Two are mapped to virtual machine for all cloud computing resources；Three be the delay and loss for ignoring data transmission；Four be to mention The number of tasks of friendship is greater than virtual machine number；
The coding mode about chromosome of the step S0 is as follows:
Take the mode of real number direct coding: every chromosome length (i.e. contained gene dosage) is equal to task quantity M, base The value of cause is the serial number { 0,1,2 ..., N-1 } of virtual machine, therefore each gene position means that task symbol, gene Value, which means that distribute to, is worth virtual machine marked as this.It clearer can show the mapping relations between Task and VM in this way.
Take the mode of real number direct coding: every chromosome length (i.e. contained gene dosage) is equal to task quantity M, base The value of cause is the serial number { 0,1,2 ..., N-1 } of virtual machine, therefore each gene position means that task symbol, gene Value, which means that distribute to, is worth virtual machine marked as this.It clearer can show the mapping relations between Task and VM in this way.
For example: it is assumed that virtual machine quantity is 5, number of tasks is 10, then the gene number of its every chromosome is 10, genic value be serial number { 0,1,2,3,4 } corresponding to 5 virtual machines one of them.If be encoded to (2,1,3,0,4,1,3, 2,4,2) chromosome, then its indicate virtual machine and task corresponding relationship are as follows:
VM0:Task { 3 }
VM1:Task { 1,5 }
VM2:Task { 0,7,9 }
VM3:Task { 2,6 }
VM4:Task { 4,8 }
Chromosome optimizing evaluation method in the step S0 are as follows: the weighted optimization evaluation method based on multiple target, the method Overall merit task completion time, the factor of three aspect of amount consumption and load balancing, user can determine phase according to demand The weight answered obtains corresponding fitness function, specific as follows:
(1) it about the fitness function component F 1 of task completion time, is obtained by following procedure:
Execution time TaskTime (i, j) of the note task i on virtual machine j are as follows:
Wherein Task_length (i) indicates the length of task i, and VM_Mips (j) indicates the execution speed of virtual machine j.
After task is distributed, each VM executes task execution time the sum of of the time for all distribution on the VM, it may be assumed that
Wherein Num (j) indicates the task quantity distributed on j-th of VM,
The then task completion time of certain allocation plan are as follows:
Thus, the fitness function component F 1 about task completion time are as follows:
F1=Fitnesstime=Timetotal；
(2) it about the fitness function component F 2 of energy consumption, is obtained by following procedure:
The specific consumption for remembering VM (j) is Power (j), then the energy input of VM (j) are as follows:
The total energy consumption of all virtual machines of certain allocation plan are as follows:
Thus, the fitness function component F 2 about energy consumption are as follows:
F2=Fitnesspower=Powertotal；
(3) it about the fitness function component F 3 of load balancing, is obtained by following procedure:
The execution time average of all VMs are as follows:
Load balancing function are as follows:
Fitness function about target 3 (load balancing) at this time are as follows:
F3=Fitnessload=LoadBalance；
(4) the weighted optimization evaluation method based on multiple target, comprehensive fitness degree function are as follows:
F=α F1+βF2+γF3；
Wherein alpha+beta+γ=1, and α, beta, gamma ∈ [0,1]
For the step S1 in initialization, setting maximum number of iterations is MaxGen, and other termination conditions are global optimum Chromosome is in continuous X generation without more excellent solution, population scale Scale, crossover probability Pc, mutation probability Pm。
The step S2 is selected first with a variety of static methods when generating initialization population and is generated chromosome dyad, then It generates other chromosomes at random according to coding rule, thus forms a population.The characteristics of state algorithm is to execute speed to be exceedingly fast, Solution can be quickly found, these solutions are typically all more excellent solution.This operates the more excellent solution of introducing portion that can be purposive, increases advantage dye The probability of colour solid can provide initial population more better than random selection initial population.Such as: using state algorithm, such as Max_min, min_min scheduling algorithm first acquire chromosome dyad, then generate other chromosomes at random, thus form initial kind Group.
Of the invention is further described, the step S3 can be obtained when calculating fitness according to following formula:
F=α F1+βF2+γF3；(formula 12)；Wherein alpha+beta+γ=1, and α, beta, gamma ∈ [0,1]
Specific weight α, beta, gamma are selected according to actual needs by user.Such as when only considering the deadline, it can use: α =1, β=0, γ=0；If it is considered that deadline and energy consumption are more important, load balance is more secondary, then can use: α=0.4, β=0.4, γ=0.2, and so on.
The step S4 is as follows respectively using two kinds of termination conditions when judging termination condition:
If global optimum's chromosome regard this solution as optimal solution in the continuous X generation more excellent solution of nothing, terminator；
If two do not occur situation (1), maximum number of iterations MaxGen is gone to always, and export globally optimal solution,
When judging termination condition, (1) is first determined whether, then judge (2).Such termination condition is added, can be reduced not Necessary the number of iterations improves executing efficiency.
The step S5 takes classification back-and-forth method when carrying out selection operation, specific as follows:
Find out the mean value of all chromosome fitness in populationFitness value is less than in the population (being better than) average value, that is, meetChromosome it is selected, remaining chromosome is according to individual adaptation degree in current population The details of value operate to execute roulette selection.The select probability of each chromosome when executing roulette selection operation are as follows:Wherein Scale is population scale, and the method effectively prevents excellent individual by generation evolution It is lost in the process, convergence rate can be accelerated.
When the step S6 carries out crossover operation, take two neighboring position in population a pair of, the mode of random single point crossing, With probability PcIntersected.The similarity (same position gene equal probabilities) of each two chromosome of centering is checked before intersecting, Do not intersect when similarity is larger (more than 60%), it is otherwise normal to intersect.It can avoid " inbreeding " in this way, generate more different Individual, it is ensured that the diversification of group.
When the step S7 carries out mutation operation, mutation probability is function Pm=0.15-0.1e-(i/1000), wherein i is repeatedly Generation number.It can guarantee that mutation probability is not too big in this way, and can be adaptive.
Step S11 assigns the task to respective virtual machine according to the optimal solution for inputting algorithm, terminates program.
The foregoing describe basic principles and main features of the invention, It should be understood by those skilled in the art that of the invention It is not restricted to the described embodiments, the above embodiments and description only illustrate the principle of the present invention, is not departing from Under the premise of spirit and scope of the invention, various changes and improvements may be made to the invention, these changes and improvements both fall within requirement In the scope of the invention of protection, invents claimed range and be defined by the appending claims and its equivalent thereof.

Claims (9)

1. a kind of cloud computing method for scheduling task based on improved adaptive GA-IAGA, which comprises the following steps:
S0. it pre-processes: proposing particular problem description or model, determine chromosome coding mode, determine chromosome optimizing evaluation side Method and fitness function；
S1. initialize: setting maximum number of iterations or other termination conditions, population scale, crossover probability, mutation probability etc. are joined Number；
S2. it generates initial population: state algorithm and random algorithm is combined, form first generation population, and iteration count Zero；
S3. it calculates initial population fitness: calculating in contemporary population and go out the fitness value of all individuals, and find value adaptive optimal control Angle value, as current contemporary optimal solution and current globally optimal solution；
S4. judge whether to terminate: being judged whether to meet two termination conditions according to the information that previous step obtains, be exported if meeting Global optimum's individual is optimal solution, and algorithm terminates；Otherwise enter in next step；
S5. selection operation: taking classification selection strategy, and advantage individual is avoided to be lost during evolving by generation；
S6. the individual of current population crossover operation: is selected according to crossover probability to execute improved crossover operation；
S7. improved mutation operation mutation operation: is executed according to the individual of the current population of dynamic variation probability selection；
S8. it generates next-generation population: passing through step S5, S6, S7, generate the population of a new generation；
S9. it calculates current population's fitness: first calculating all ideal adaptation angle value of contemporary population, and find out maximum That label is optimum individual, then it is compared to each other with global optimum individual, if its value is more excellent, as Global optimum's individual；Otherwise it does not change；
S10. algorithm terminates: judging whether two conditions for meeting iteration ends, if it is not, iteration count is from adding 1, algorithm is turned round To S5；If so, output is as a result, algorithm terminates；
S11. it exports optimal solution: according to optimal solution, task being assigned to virtual machine, and export optimal solution；
The particular problem of the step S0 describes or model is as follows:
The different cloud task (Tasks) of M length is distributed to N number of performance different virtual machine (VMs) to execute, finds reasonable point With scheme, so that allocation result meets the cloud computing task schedule decision tree based on multiple target, problem includes following constraint item Part is using MapReduce model, and overall tasks have been split into several small tasks, and without directly between each subtask Association；Two are mapped to virtual machine for all cloud computing resources；Three be the delay and loss for ignoring data transmission；Four be to submit Number of tasks is greater than virtual machine number；
The coding mode about chromosome of the step S0 is as follows:
Take the mode of real number direct coding: every chromosome length (i.e. contained gene dosage) is equal to task quantity M, gene Value is the serial number { 0,1,2 ..., N-1 } of virtual machine, therefore each gene position means that task symbol, and genic value is just Expression, which is distributed to, is worth virtual machine marked as this.It clearer can show the mapping relations between Task and VM in this way.
2. a kind of cloud computing method for scheduling task based on improved adaptive GA-IAGA according to claim 1, which is characterized in that Chromosome optimizing evaluation method in the step S0 are as follows: the weighted optimization evaluation method based on multiple target, the method overall merit Task completion time, the factor of three aspect of amount consumption and load balancing, user can determine corresponding weight according to demand, Corresponding fitness function is obtained, specific as follows:
(1) it about the fitness function component F 1 of task completion time, is obtained by following procedure:
Execution time TaskTime (i, j) of the note task i on virtual machine j are as follows:
Wherein Task_length (i) indicates the length of task i, and VM_Mips (j) indicates the execution speed of virtual machine j.
After task is distributed, each VM executes task execution time the sum of of the time for all distribution on the VM, it may be assumed that
Wherein Num (j) indicates the task quantity distributed on j-th of VM.
The then task completion time of certain allocation plan are as follows:
Thus, the fitness function component F 1 about task completion time are as follows:
F1=Fitnesstime=Timetotal；
(2) it about the fitness function component F 2 of energy consumption, is obtained by following procedure:
The specific consumption for remembering VM (j) is Power (j), then the energy input of VM (j) are as follows:
The total energy consumption of all virtual machines of certain allocation plan are as follows:
Thus, the fitness function component F 2 about energy consumption are as follows:
F2=Fitnesspower=Powertotal；
(3) it about the fitness function component F 3 of load balancing, is obtained by following procedure:
The execution time average of all VMs are as follows:
Load balancing function are as follows:
Fitness function about target 3 (load balancing) at this time are as follows:
F3=Fitnessload=LoadBalance；
(4) the weighted optimization evaluation method based on multiple target, comprehensive fitness degree function are as follows:
F=α F1+βF2+γF3；
Wherein alpha+beta+γ=1, and α, beta, gamma ∈ [0,1]
For the step S1 in initialization, setting maximum number of iterations is MaxGen, and other termination conditions are global optimum's dyeing Body is in continuous X generation without more excellent solution, population scale Scale, crossover probability Pc, mutation probability Pm。
3. a kind of cloud computing method for scheduling task based on improved adaptive GA-IAGA according to claim 1, which is characterized in that The step S2 is selected first with a variety of static methods when generating initialization population and is generated chromosome dyad, then according to coding Regular random generates other chromosomes, thus forms a population.
4. a kind of cloud computing method for scheduling task based on improved adaptive GA-IAGA according to claim 1, which is characterized in that The step S3 can be obtained when calculating fitness according to following formula:
F=α F1+βF2+γF3；(formula 12)；Wherein alpha+beta+γ=1, and α, beta, gamma ∈ [0,1]；
Specific weight α, beta, gamma are selected according to actual needs by user.
α=1, β=0, γ=0；If it is considered that deadline and energy consumption are more important, load balance is more secondary, then can use: α =0.4, β=0.4, γ=0.2, and so on.
5. a kind of cloud computing method for scheduling task based on improved adaptive GA-IAGA according to claim 1, which is characterized in that The step S4 is as follows respectively using two kinds of termination conditions when judging termination condition:
If global optimum's chromosome regard this solution as optimal solution in the continuous X generation more excellent solution of nothing, terminator；
If two do not occur situation (1), maximum number of iterations MaxGen is gone to always, and export globally optimal solution；
When judging termination condition, (1) is first determined whether, then judge (2).Such termination condition is added, it is unnecessary to reduce The number of iterations, improve executing efficiency.
6. a kind of cloud computing method for scheduling task based on improved adaptive GA-IAGA according to claim 1, which is characterized in that The step S5 takes classification back-and-forth method when carrying out selection operation, specific as follows:
Find out the mean value of all chromosome fitness in populationFitness value is less than (excellent in the population In) average value, that is, meetChromosome it is selected, remaining chromosome is according to ideal adaptation angle value in current population Details come execute roulette selection operation.The select probability of each chromosome when executing roulette selection operation are as follows:Wherein Scale is population scale.
7. a kind of cloud computing method for scheduling task based on improved adaptive GA-IAGA according to claim 1, which is characterized in that When the step S6 carries out crossover operation, take two neighboring position in population a pair of, the mode of random single point crossing, with probability Pc Intersected；Check the similarity (same position gene equal probabilities) of each two chromosome of centering before intersecting, similarity compared with Do not intersect when big (more than 60%), it is otherwise normal to intersect.
8. a kind of cloud computing method for scheduling task based on improved adaptive GA-IAGA according to claim 1, which is characterized in that When the step S7 carries out mutation operation, mutation probability is function Pm=0.15-0.1e-(i/1000), wherein i is the number of iterations, can Guarantee that mutation probability is not too big, and can be adaptive.
9. a kind of cloud computing method for scheduling task based on improved adaptive GA-IAGA according to claim 1, which is characterized in that Step S11 assigns the task to respective virtual machine according to the optimal solution for inputting algorithm, terminates program.
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Application publication date: 20190524