Cloud Computing Resource Scheduling Method Research Based on

Improved Genetic Algorithm

Cui Yun-fei1 Li Xin-ming1, Dong Ke-wei2, Zhu Ji-lu1

1The Academy of Equipment Command & Technology, Beijing 101416, China 2China Satellite Maritime Tracking & Control Department, Jiang yin Jiangsu 214431, China

email: sducyf123@yahoo.com.cn

Key words: Cloud computing; Resource scheduling; Genetic Algorithm; CLOUDSIM

Abstract. Resource scheduling is the key port of cloud computing resource management. One

excellent method may enhance the efficiency of the whole cloud computing system, and effectively

share resource in wide area. Genetic Algorithm has adaptability, global optimization ability and

implicit parallelism, which is not in other methods. For the sake of scheduling effective resource to

accomplish relevant task, improved genetic algorithm is adopted in cloud computing resource

scheduling research. Finally, a simulation based on cloudsim is carried out, which proves the

correctness and validity of the scheduling method mentioned in this paper.

Introduction

The most important aim of cloud computing is to offer information services according to need. And

cloud computing services are made up by a set of correlative child services [1] (the logic relationship

between child services is supposed known in this paper).

The key to offer information services is how to implement resource scheduling. When

scheduling child services, each child service is supposed to be implemented by every resource that

can be used.

There areMchild services and N resources, then

1) { }1 2, , ,

MS S S S= �

: child services;

2) { }1 2, , , NR R R R= �: resources;

3) ijt :the time needed to implement child services ( 1,2, , )iS i M= � by resources ( 1,2, , )

jR j N= � .

4) iP :the time needed to implement the ithservice by standard resource.

5) rjR :operation ratio between the jth resource and standard resource.

In one scheduling, we use )( jRC to define the time to implement all local child services by

jR ,

use T to define the whole time to implement all child services, then

max( ( ))jT C R= , 1, 2, ,j N= � (1)

Cloud computing resource scheduling aims to look for min( )T . And the relationship between

services can be shown by a directed acyclic graph (as in figure 1).

Advanced Materials Research Vols. 271-273 (2011) pp 552-557Online available since 2011/Jul/04 at www.scientific.net© (2011) Trans Tech Publications, Switzerlanddoi:10.4028/www.scientific.net/AMR.271-273.552

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Figure 1 Directed Acyclic Graph

Between two connective child services in two layers, the above node is called father node when

another one is called child node. One precondition to implement services in child node is to

implement services in child node.

Improved Genetic Algorithm

Colony Initialization. For one scheduling question, including m child services and n resources, chromosomes are created by random, which means to random distribute one service to

one resource. Owing to satisfy sequence restriction of child services, we rank the child services

firstly when colony initialization.

The relationship between services can be shown by a directed acyclic graph (as in figure 1). In

one DAG, each layer has a depth value, and lesser depth value means higher PRI. Depth value is

computed by formula (2).

+ otherSparentlevel

nodefathernohasSSlevel

i

i

i，１

　　　　　　０，　＝

)))((max(

)(

， 1, 2i m= 2 (2)

Here, )( iSparent is the father nodes aggregate of iS , )))((max( iSparentlevel is the maximal

depth value of the father nodes aggregate of iS . We can get depth information of nodes in DAG

based on the method of depth search. After getting depth information, we arrange child services on

the same resource, according depth value. Then child services on the same resource will avoid

deadlock.

We will secondly arrange the child services arranged before. And the principle is father node

first and short service first [2-4]

.

For child services, which have different depth value on the same resource, the validity of the

logic between child services can be assured by arranging child services according depth value.

Meanwhile, child services, which have same depth value, can be implemented at the same time.

For child services, which can be implemented at the same time on same resource, the child

service will be first implemented that has child nodes. If there are two child services, which have

child nodes or have no child nodes, the short services will be first implemented.

For the sake of keeping colony variety and avoiding constringency prematurely, multi-colony

parallel genetic algorithm is adopted [4-6]

.

1) Initialization. There are ( 2)p p ≥ colonies, which has q chromosomes;

2) The ( 2)p p ≥ colonies carry through genetic operation;

3) When the genetic number can be divided by w (w is supposed according fact), we choose

the individuals, which have the maximal fitness in each colony. And we arrange these individuals

from the biggest one (1y ) to the least one( 2y ) according fitness;

Advanced Materials Research Vols. 271-273 553

4) We use1y replace the individual, which has the least fitness in the colony that includes 2y ;

5) Circulate till satisfying end condition.

Fitness Computing. According one-dimensional integer encoding and arrange rule, we

decode each chromosome. We will compute the whole time cost by each resource to implement all

child services on it. And the fitness is the reciprocal of the most time cost.

Suppose [ ][ ]i jfin is the time that iS is implemented on jR , then

ij[ ][ ]= [ ][ ]i j i jfin start t+ (3)

[ ][ ]i jstart is the time that iS is start implemented on jR . ijt 　is the time need to implement iS

on jR .

[ ][ ] ( )( )( ) [ ][ ]{ }jmTriparentfinsrejistart +−= ,max (4)

sre − is the latest leisure time on jR ; max( ( ( )))fin parent i is the time that all father nodes of

iShave been implemented; [ ][ ]m jTr is the communication delay between

jR and

mR , which

implements the last father node ofiS .

We defines ( )f x to be the fitness of chromosome x , then

)])}][[))(((,(max{/1

)]][[(/1])][[(/1)(

max

maxmax

1

11

ij

Mi

ij

MiMi

tjmTriparentfinsparere

tjistartjifinxf

++−=

+==

≤≤

≤≤≤≤

(5)

Genetic Operator. In the genetic algorithm, selection operator can maintain the stability of

colony. We select the chromosome, which has largest colony fitness, directly copied to the next

generation. Then the remaining chromosomes using roulette wheel selection operation model.

We suppose ∑ if to be the sum of fitness value of the remaining chromosomes in the colony,

and suppose fi to be the fitness value of the ith chromosome, of which the probability to be selected

is∑ i

i

ff .

In the genetic algorithm, crossover is the most important search operator. Firstly, each colony

takes genetic operation alone till the scheduled number of reproduction. Then we choose the

individuals, which have the maximal fitness in each colony. And we arrange these individuals from

the biggest one (1y ) to the least one( 2y ) according fitness. We use

1y replace the individual, which

has the least fitness in the colony that includes2y . Then circulate.

The algorithm uses a single point crossover operator. After crossover operator, variation will be

carried through according variation probability that 0.01MP = .

554 Advanced Materials and Information Technology Processing

The scheduling process of the improved genetic algorithm. We have designed cloud

computing resource scheduling process based on improved genetic algorithm (as in figure 2).

step 1 Creating DAG according logic relationship between child services;

step 2 According DAG, arranging based on that father node first and short service first;

step 3 Colony initialization. There are ( 2)p p ≥ colonies, which has q chromosomes.

step 4 Ascertain the operating parameters, as maximum number of iteration(N), crossover

probability(Pc) and variation probability(Pm).

step 5 Computing fitness; Carry through genetic operation side by side till the number of

genetic operation is w, then go to step 6;

step 6 If it is the optimization condition, go to step 8, otherwise, go to step 7;

step 7 We choose the individuals, which have the maximal fitness in each colony. And we

arrange these individuals from the biggest one (1y ) to the least one( 2y ) according

fitness, and use1y replace the individual, which has the least fitness in the colony that

includes 2y . Then go to step 5;

step 8 Select the individual that has the maximum fitness;

step 9 Get scheduling result.

step 10

Arrange

child

services

Colony

initialization

according to

real number

coding

Get

scheduling

result

no

yes

no

Each

colony

carries

through

genetic

operation

Individual

replace

Create

the

DAG of

child

services

yes Export

optimal

individual

If is the

number

of genetic

operation

w?

If is it the

scheduled

condition?

Figure 2 Cloud computing resource scheduling process based on improved genetic algorithm

Experiment based on cloudsim

Cloudsim. Cloudsim[7] is a cloud simulator that is developed and is published by Rajkumar

Buyya. It is used for cloud computing infrastructure and application services to modeling,

simulation and experimental. It also provides a resource scheduling and allocation strategy

simulation platform. We verify the given resource scheduling genetic algorithm through testing

different scheduling results of different scheduling strategies by the core module Datacenterbroke in

cloudsim.

An example is as figure3 and figure 4.

Advanced Materials Research Vols. 271-273 555

Figure 3 Schematic diagram of experimental operation

Figure 4 Schematic diagram of experimental running results

Simulation results and analysis. In order to validate the algorithm, we have compared the

improved algorithm and the basic algorithm. The numbers of cloud tasks are 10, 20, 30, 40 and 50.

The numbers of resources are 3, 5 and 8. There are 5 colonies. And each colony includes 30

chromosomes. The crossover is 0.8 while the mutation probability is 0.01. The most number of

genetic operation views is 300. The result is as table 1.

556 Advanced Materials and Information Technology Processing

Table 1 Comparison of the execution time for several scheduling algorithms

Task amount Virtual resource amount Min- min (ms) Max-min (ms) Basic GA (ms) Improved GA (ms)

10

3 137 143 146 148

5 87 87 93 91

8 53 49 56 55

20

3 281 283 293 293

5 153 148 152 146

8 105 94 102 109

30

3 431 431 420 413

5 318 302 292 285

8 172 165 163 157

40

3 587 593 564 547

5 359 342 324 309

8 236 215 196 192

50

3 738 738 697 672

5 435 429 408 381

8 297 271 254 263

When the number of virtual resources and cloud tasks is small, the efficiency of GA, Min-min and

Max-min is equivalent. When the number of virtual resources and tasks increase, the efficiency of

GA is better than Min-min and Max-min. And the improved GA is better than the basic GA.

Summary

For the problem of cloud computing scheduling, we have improved GA. Through simulation based

cloudsim, the better efficiency of improved GA is proved.

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Advanced Materials Research Vols. 271-273 557

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