CN110851257A - Genetic and differential hybrid evolution cloud computing task scheduling algorithm based on early-stage catastrophe strategy - Google Patents

Genetic and differential hybrid evolution cloud computing task scheduling algorithm based on early-stage catastrophe strategy Download PDF

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CN110851257A
CN110851257A CN201911082199.6A CN201911082199A CN110851257A CN 110851257 A CN110851257 A CN 110851257A CN 201911082199 A CN201911082199 A CN 201911082199A CN 110851257 A CN110851257 A CN 110851257A
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catastrophe
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energy consumption
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王淑栋
李艳青
庞善臣
王珣
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China University of Petroleum East China
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    • G06F9/4843—Task transfer initiation or dispatching by program, e.g. task dispatcher, supervisor, operating system
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Abstract

The invention provides a genetic and differential hybrid evolution cloud computing task scheduling algorithm based on a previous catastrophe strategy aiming at the problems of task deadline and minimized energy consumption in cloud computing task scheduling, and aims to meet the task completion deadline and minimize the total energy consumption. The traditional heuristic algorithm solves the task scheduling problem, is easy to fall into local optimum and causes weak global searching capability. According to the method, the deadline and the energy consumption are quantized into a fitness function, a genetic algorithm and a differential evolution algorithm are mixed, a catastrophe strategy is introduced, the search range is expanded, and the problem of premature evolution algorithm is effectively solved. Experimental results show that the algorithm can effectively reduce energy consumption and effectively improve the local optimal problem.

Description

Genetic and differential hybrid evolution cloud computing task scheduling algorithm based on early-stage catastrophe strategy
Technical Field
The invention belongs to the two fields of cloud computing and scheduling algorithms, and particularly relates to a cloud computing genetic and differential hybrid evolution task scheduling algorithm for minimizing energy consumption based on deadline.
Background
For task scheduling in cloud computing, task scheduling is a key technology of cloud computing, how to reasonably distribute tasks and optimize a scheduling strategy, and the important problem of research is that all tasks are completed within an expected time and the cost is low. The traditional heuristic algorithm solves the task scheduling problem, is easy to fall into local optimum and causes weak global searching capability. Therefore, a genetic and differential hybrid evolution cloud computing task scheduling algorithm based on the early catastrophe strategy is provided, and it is necessary to minimize energy consumption on the premise of meeting the deadline.
Disclosure of Invention
The invention aims to: aiming at the problem of minimizing energy consumption in time-constrained cloud computing task scheduling, a genetic and differential hybrid evolution cloud computing task scheduling algorithm based on a pre-stage catastrophe strategy is provided, and the aim is to minimize the total energy consumption while meeting the task deadline in each cloud.
In order to achieve the purpose, the technical scheme adopted by the invention comprises the following parts:
1. a genetic and differential hybrid evolution cloud computing task scheduling algorithm based on a pre-catastrophe strategy is disclosed.
And for the tasks submitted by the users, minimizing energy consumption on the basis of meeting the deadline as much as possible, taking the energy consumption as an objective function, and scheduling by utilizing an improved genetic and differential hybrid evolution algorithm based on the early catastrophe strategy. The improved genetic and differential hybrid evolution cloud computing task scheduling algorithm based on the early-stage catastrophe strategy is as follows:
step 1: chromosome coding, initialization parameters.
Step 2: and calculating the fitness.
And step 3: judging whether the optimal individual fitness of the t-1 generation is equal to the t generation, if so, reducing the catastrophe threshold by one, and then turning to the step 4; otherwise, continuing.
And 4, step 4: an improved selection operation is performed.
And 5: and generating a random number, and performing variant cross operation of differential evolution according to a part of the random number, and performing cross variant operation of a genetic algorithm according to a part of the random number.
Step 6: t +1, a population of children is generated and it is determined whether the catastrophe threshold cat is equal to 0 (before the t/2 iteration). If equal to 0, then a catastrophic operation is performed, otherwise, the process goes to step 7.
And 7: and if the iteration times reach the maximum, outputting, otherwise, turning to the step 2.
2. The genetic and differential hybrid evolution cloud computing task scheduling algorithm based on the early-stage catastrophe strategy according to claim 1, so as to meet an objective function of minimizing energy consumption on the basis of deadline, and is characterized in that the objective function is designed as follows:
Figure BDA0002264318010000021
the penalty factor punish represents whether the deadline is met, wherein aijIndicating whether task i is executing on virtual machine j, ETCijRepresenting the actual execution time, expT, of task i on virtual machine jiIndicating the task deadline. Since the ultimate goal is to minimize energy consumption, the objective function we have designed is:wherein p isjRepresents the CPU power of virtual machine j, vms is the number of virtual machines, and Ntsk is the number of tasks.
3. The selective cross mutation operation of the algorithm of claim 1, wherein:
selecting operation: before the groups in the population are selected according to roulette, the optimal individuals are directly stored until
One generation, while halving the suboptimal individual selection probability.
Cross variation in genetic algorithms: in the cross mutation operation, the similarity of cross fragments is too high, and mutation may occur
The gene values are the same. For this purpose, a threshold value is set for the crossover operation, which can only be executed if a certain similarity is exceeded
Performing cross operation; if the two gene values are the same, the first random number is added with 1 to be compared with the other one
The individual gene sites perform mutation operations and, if still the same, the random number continues to be incremented by 1.
4. The genetic and differential hybrid evolution cloud computing task scheduling algorithm based on the early-stage catastrophe strategy according to claim 1, wherein the catastrophe operation is characterized in that: in the set iteration number of the first 2/G, if the optimal individuals in the 4/G which appear continuously are consistent, the mutation probability is increased to be twice of the original mutation probability.
The optimized scheduling method provided by the invention has the following advantages and beneficial effects: according to the method, the problems of task deadline and energy consumption are considered, then the task in the cloud is scheduled by using an improved genetic and differential hybrid algorithm under the constraint of the deadline and aiming at optimizing the energy consumption, and the method can effectively shorten the total energy consumption of the virtual machine.
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FIG. 1 is a flow chart of a genetic and differential hybrid evolution cloud computing task scheduling algorithm based on a pre-stage catastrophe strategy according to the present invention;
Detailed Description
In order to enable those skilled in the art to better understand the technical problems, technical solutions and technical effects in the present application, the following describes in detail a genetic and differential evolution cloud computing task scheduling algorithm based on a catastrophe strategy according to the present invention with reference to the accompanying drawings and the detailed description.
FIG. 1 shows the steps of the present invention:
step 1: m users submit different computing tasks to the cloud, and the total task amount is Ntsk. Where each task has two attributes: t isi(datai,expTi)。dataiIndicates the size of the task, expTiIndicating the deadline of the task. And the cloud end schedules the Ntsk tasks to different virtual machines according to the scheduling strategy.
The invention assumes that there are m virtual machines VMs ═ { vm ═ m-1,vm2,K,vmmAll under the same data center, then all virtual machine bandwidths (i.e., bw) are the same.
Step 2.1: initializing relevant parameters, wherein the relevant parameters comprise: iteration times t, maximum iteration times G, mutation probability m and cross probability c of a genetic algorithm, cross probability CR of a differential evolution algorithm and a scaling factor F. Population size Ntsk, catastrophe threshold cat; the chromosomes are encoded. (real number coding)
Step 2.2: and calculating the fitness.
Designing a penalty factor punish:
Figure BDA0002264318010000031
represents whether a deadline is met, wherein aijIndicating whether task i is executing on virtual machine j,
Figure BDA0002264318010000032
representing the actual execution time, f, of task i on virtual machine jjRepresenting the computing power of a virtual machine, expTiIndicating the task deadline. Since the ultimate goal is to minimize energy consumption, the objective function we have designed is:wherein p isjRepresents the CPU power, vm, of virtual machine jmIs the number of virtual machines.
And finally, the fitness of the individual is taken as the Fit value.
Step 2.3: judging whether the optimal individual fitness of the t-1 generation is equal to the t generation, if so, reducing the catastrophe threshold by one, and then turning to the step 2.4; otherwise, continuing.
Step 2.4: an improved selection operation is performed.
Selecting operation: a combination of roulette and reservation of the best individual is used. The optimal individuals are directly stored in the next generation without participating in selection operation, the rest individuals are selected by roulette to generate offspring populations, and meanwhile, the selection probability of suboptimal individuals is halved.
Step 2.5: and generating a random number, and performing variant cross operation of differential evolution according to a part of the random number, and performing cross variant operation of a genetic algorithm according to a part of the random number.
Description of the drawings: and performing operation according to the generated random number. The variation and crossover of the differential evolution is not further improved, but the crossover operation of the genetic algorithm is improved as follows: in the cross mutation operation, the similarity of the cross fragments may be too high, and the values of the mutated genes may be the same. Therefore, threshold setting is carried out on the crossing operation, and the crossing operation can be executed only when certain similarity is exceeded; if the two gene values are the same, the first random number is added with 1 to perform mutation operation with the other gene point, and if the two gene values are still the same, the random number is added with 1.
Step 2.6: t +1, a population of children is generated and it is determined whether the catastrophe threshold cat is equal to 0 (before the t/2 iteration). If equal to 0, then a catastrophe operation is performed, otherwise, step 2.7 is diverted. Wherein the catastrophe operation is as follows: in the set iteration number of the first 2/G, if the optimal individuals in the 4/G which appear continuously are consistent, the mutation probability is increased to be twice of the original mutation probability.
Step 2.7: if the iteration times reach the maximum, outputting the optimal solution, otherwise, turning to the step 2.2.
The above examples are only used to illustrate the present invention and not to limit the technical solutions described in the present invention, and it should be understood by those skilled in the art that, the genetic and differential hybrid evolution cloud computing task scheduling algorithm based on the early catastrophe strategy disclosed in the above invention may be further modified on the basis of the above without departing from the broad distance, and these modifications are also regarded as protection of the present invention.

Claims (4)

1. A genetic and differential hybrid evolution cloud computing task scheduling algorithm based on a pre-catastrophe strategy is disclosed.
And for the tasks submitted by the users, minimizing energy consumption on the basis of meeting the deadline as much as possible, taking the energy consumption as an objective function, and scheduling by utilizing an improved genetic and differential hybrid evolution algorithm based on the early catastrophe strategy. The improved genetic and differential hybrid evolution cloud computing task scheduling algorithm based on the early-stage catastrophe strategy is as follows:
step 1: chromosome coding, initialization parameters.
Step 2: and calculating the fitness.
And step 3: judging whether the optimal individual fitness of the t-1 generation is equal to the t generation, if so, reducing the catastrophe threshold by one, and then turning to the step 4; otherwise, continuing.
And 4, step 4: an improved selection operation is performed.
And 5: and generating a random number, and performing variant cross operation of differential evolution according to a part of the random number, and performing cross variant operation of a genetic algorithm according to a part of the random number.
Step 6: t +1, a population of children is generated and it is determined whether the catastrophe threshold cat is equal to 0 (before the t/2 iteration). If equal to 0, then a catastrophic operation is performed, otherwise, the process goes to step 7.
And 7: and if the iteration times reach the maximum, outputting, otherwise, turning to the step 2.
2. The genetic and differential hybrid evolution cloud computing task scheduling algorithm based on the early-stage catastrophe strategy according to claim 1, so as to meet an objective function of minimizing energy consumption on the basis of deadline, and is characterized in that the objective function is designed as follows:
Figure FDA0002264316000000011
the penalty factor represents whether a deadline is met, wherein aijIndicating whether task i is executing on virtual machine j, ETCijRepresenting the actual execution time, expT, of task i on virtual machine jiIndicating the task deadline. Since the ultimate goal is to minimize energy consumption, the objective function we have designed is:
wherein p isjRepresents the CPU power of virtual machine j, vms is the number of virtual machines, and Ntsk is the number of tasks.
3. The selective cross mutation operation of the algorithm of claim 1, wherein:
selecting operation: before the groups in the population are selected according to roulette, the optimal individuals are directly stored to the next generation, and meanwhile, the selection probability of the suboptimal individuals is halved.
Cross variation in genetic algorithms: in the cross mutation operation, the similarity of the cross fragments may be too high, and the values of the mutated genes may be the same. Therefore, threshold setting is carried out on the crossing operation, and the crossing operation can be executed only when certain similarity is exceeded; if the two gene values are the same, the first random number is added with 1 to perform mutation operation with the other gene point, and if the two gene values are still the same, the random number is added with 1.
4. The genetic and differential hybrid evolution cloud computing task scheduling algorithm based on the early-stage catastrophe strategy according to claim 1, wherein the catastrophe operation is characterized in that:
in the set iteration number of the first 2/G, if the optimal individuals in the 4/G which appear continuously are consistent, the mutation probability is increased to be twice of the original mutation probability.
The optimized scheduling method provided by the invention has the following advantages and beneficial effects: according to the method, the problems of task deadline and energy consumption are considered, then the task in the cloud is scheduled by using an improved genetic and differential hybrid algorithm under the constraint of the deadline and aiming at optimizing the energy consumption, and the method can effectively shorten the total energy consumption of the virtual machine.
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