A New Task Schedulng Algorthm Based on Improved Genetc Algorthm n Cloud Computng Envronment Congcong Xong, Long Feng, Lxan Chen A New Task Schedulng Algorthm Based on Improved Genetc Algorthm n Cloud Computng Envronment 1 Congcong Xong, 2 Long Feng, 3 Lxan Chen *1 College of Computer Scence and Informaton Engneerng, Tanjn Unversty of Scence & Technology, Tanjn 300222, Chna, E-mal: xongcc@tust.edu.cn 2 College of Computer Scence and Informaton Engneerng, Tanjn Unversty of Scence & Technology, Tanjn 300222, Chna, E-mal:fenglong0826@163.com 3 Informatzaton Constructon and Management Offce, Tanjn Unversty of Scence & Technology, Tanjn 300222, Chna, E-mal: clx@tust.edu.cn Abstract Task schedulng and resource allocaton are two core technques n cloud computng. In order to use resource effcently n heterogeneous envronment, the paper presents an new task schedulng algorthm based on Genetc Algorthm (GA). The model consders four aspects of the task schedulng: task fnshed tme, task expenses, bandwdth and relablty n cloud computng envronment. And the optmal target of the model s to acheve mn-tme, mn-cost, max-bandwdth and max-relablty. Besdes, the new algorthm adopts rule-bound crossover and mutaton operaton to mprove ndvdual qualty. The results of smulaton experments valdate that compared wth the exstng GA, the new GA ntroduced the Qualty of Servce (QoS) can reflect users satsfacton of the schedulng results n the round and solve the task schedulng problems n cloud computng envronment effectvely. 1. Introducton Keywords: Genetc Algorthm (GA), cloud computng, task schedulng There are lots of computer resource n the cloud envronment, ncludng CPU, memory, bandwdth and so on. And the method to schedule tasks effectvely has become one hotspot n the feld of computer scence. The task scheduler n cloud computng envronment s to determne a proper assgnment of resources to the tasks of jobs to complete all the jobs receved from users. Untl now, researchers have proposed some statc, dynamc and mxed forms of resource schedulng strategy n cloud computng envronment, such as: FIFO (Frst In and Frst Out) and ts smple extensons, ISH[1], ETF[1], GA-based task schedulng and so on[2-5]. The frst two algorthms are smple and belong to the statc strategy, but usually wth poor performances. Because the resources pool quotas and job queues are partly depended on artfcal settngs. However, GA-based task schedulng algorthms belong to heurstc ntellgent algorthm, whle there are always some problems such as low convergence, one-sded target and so on. Consderng the shortage of the exstng task schedulng algorthms n cloud computng, a new task schedulng based on mproved genetc algorthm s presented. The well-dstrbuted strategy, whch makes ndvduals dstrbute unformly n the soluton space by usng the chromosome matchng rate when the ntal populaton s generated, s proposed to avod the premature convergence effectvely. And the Qualty of Servce (QoS) s ntroduced to mprove the ftness functon, whch can not only fnd the correspondng relaton between the task and the vrtual machne quckly and effectvely, but also can reflect users satsfacton on the schedulng results. 2. Task schedulng There are many smlartes as well as dfferences on resource schedulng between n cloud computng and other envronments. The most remarkable dfference s the object of schedulng. The objects of tradtonal resource schedulng are the threads and tasks runnng on entty resources whch belong to the fne graned schedulng. But the objects of schedulng n the cloud envronment are vrtual machnes whch belong to the coarse graned schedulng. There are lots of computaton resources, store resources and other resources n cloud computng Advances n nformaton Scences and Servce Scences(AISS) Volume5, Number3, Feb 2013 do: 10.4156/AISS.vol5.ssue3.5 32
A New Task Schedulng Algorthm Based on Improved Genetc Algorthm n Cloud Computng Envronment Congcong Xong, Long Feng, Lxan Chen envronment. The task schedulng algorthm not only needs to be congruent wth the deadlne of the jobs, but also concern the users expectatons on the bandwdth and cost of the resources. So, the task schedulng n cloud computng envronment should be a mult-objectve schedulng. Mapreduce model dvdes jobs nto several nterdependent tasks after users submt jobs nto the cloud computng envronment. The execute process of tasks can be represented by a Drected Acyclc Graph (DAG) whch s shown n Fgure 1.The nodes are the tasks to be executed. The drected edges show the dependent relatonshps of the tasks. T4 T1 T5 T2 T8 T6 T3 T7 Fgure 1. An example of a DAG The task schedulng n cloud computng envronment s to execute N nterdependent tasks T { T1, T2,, TN } on M resources ( P { P1, P2,, PM } ) effcently, and the result of t should satsfy users expectatons. Expected Tme to Compute (ETC) matrx ETC [, represents the expected computaton tme of the task on the resource j. If the task T cannot execute on the resource P j, ETC [,. The total fnsh tme of the task T could be obtaned from Equaton 1. ETF M s mn ETC[, (1) j j1 Then the total executon tme of all the tasks could be expressed as follows: Where, 1,2,, N j 1,2,, M N tme maxetf (2). Network transmsson decded by bandwdth has a sgnfcant effect on those applcatons whch communcate wth others frequently or contan a large amount of nformaton. Gven that BW s the bandwdth of the resource, then the total used bandwdth of all the tasks can be wm defned as follows: 1 Execute _ bw TaskNum( J m ) TaskTatal ( m) ln BW TaskTotal ( m1) wm (3) Prce constrant s one of the most normal QoS constrants at present. Snce the charge of resources s measured by unt, the task T could be defned as: 33
A New Task Schedulng Algorthm Based on Improved Genetc Algorthm n Cloud Computng Envronment Congcong Xong, Long Feng, Lxan Chen EC [, / qc 1 cpu/ num q2c mem/ MB q3c stor/ MB q4c bw Mbps (4) where C s the unt prce of resources, and P s the number of resources. Then, the total prce of all the tasks could be obtaned from Equaton 5. N M j 1 1 cost mnec[, (5) Assumng that Fal [ s the breakdown rate of resources obtaned by resource montor system. The user expected functon about the completon rate can be obtaned by Equaton 6. succ (1 Fal[, ) N 1 DEFINITION 1 The executon of a task s sad to acheve user satsfacton when the resource consumpton of the task s near to the value users have expected. s the real resource consumpton of the task. s the resource consumpton of the task whch s the user expectaton value. Formally, the user satsfacton functon could be expressed as: W ln AR / ER (7) Where s a balance constant, and 0 1. The value of user satsfacton functon s 0, when AR s equal to ER, whch ndcates the schedulng result has acheve users satsfacton. And fw 0, t means the real resource consumpton of the task exceeds users expectatons. Whle f W 0, the result s totally contrary to the former one. Gven the user expected cost Expect_cost, user expected computaton tme Expect_tme, user expected bandwdth Expect_bw and set user expected completon rate Expect_succ whch are set by users. A weghted objectve functon can be used as the ftness functon whch s defned as: (6) f tme bw cost succ 1ln 2 ln 3 ln 4 ln (8) Expect_ tme Expect _ bw Expect_cost Expect_ succ where ndcates the weght of the QoS constrant and 1 4 1. As varous applcatons may requre dfferent QoS, weght coeffcent vectors can be set dfferently to satsfy ther demands. 3. Genetc algorthm Genetc Algorthm (GA) n partcular became popular through the work of John Holland n the early 1970s [6]. GA generates solutons to optmzaton problems usng technques nspred by natural evoluton [7]. And t becomes a wdely used global optmzaton algorthm n many felds wth ts remarkable characterstcs of hgh-effcency, stablty, sutablty for parallel processng [8]. There are always some problems such as premature convergence n the Basc Genetc Algorthm (BGA). Several algorthms and methods have been proposed to solve the task 34
A New Task Schedulng Algorthm Based on Improved Genetc Algorthm n Cloud Computng Envronment Congcong Xong, Long Feng, Lxan Chen schedulng problems. Most of them, however, only unlaterally consder reducng the completon tme and the average completon tme, such as [2,9-11] or reflectng users ntegrated requrements on the network bandwdth, prce and so on. Therefore, researches focusng on all of the aspects are needed. Accordng to the basc algorthm dea of GA and characterstcs of task schedulng n cloud computng envronment, a new optmzaton method based on the BGA for solvng the above-mentoned problems s presented. The mproved algorthm can avod the premature convergence effectvely by usng the chromosome matchng rate. In addton, consderng the defnton of QoS, ts ftness functon can take users expectatons ncludng the servce response tme, network bandwdth, task expenses and relablty as the standard to measure the schedulng results. 3.1. Encodng of chromosomes Bnary encodng and float encodng are the most common types of encodng. Consderng the dvson of jobs n cloud computng envronment, ths paper adopts an ndrect encodng type: resource-task encodng. The total number of all the tasks s the length of each chromosome. The value of each gene s the resource number at the same locus. For example,gven the length of each chromosome 10 and the value range of each gene s from 1 to 3. The chromosome {2,3,1,2,3,2,1,2,2,1} means that the frst task s carred out on the second vrtual machne (resource), and the second task s carred out on the thrd vrtual machne, and so on. Therefore, three tasks have been sent to the frst vrtual machne to be executed: T 3, T 7 and T 10.Fve tasks have been sent to the second vrtual machne and two to the thrd vrtual machne. 3.2. Intal populaton generatng The ntal populaton has great nfluence on convergence of GA. The populaton sze s usually set to be between 50 and 160.Gven the populaton sze S, the length of each chromosome N, then the ntal populaton s generated randomly. 3.3. Ftness functon Durng each successve generaton, ndvdual solutons are selected through a ftness functon. It measures the qualty of the represented soluton. So, the ftness functon s a crucal part of GAs. The ftness functon s always desgned to be a one-sded target functon n the tradtonal GA, whch s not sutable for cloud computng.the satsfacton of the servce n cloud computng envronment can be measured by Qualty of Servce (QoS). Consderng the commercal objectve of cloud computng and QoS model, the ftness functon can be set as Equaton 8. 3.4. Operators 3.4.1. Selecton Operaton The objectve of selecton operaton s to make the better solutons have a hgher probablty to be transmtted to the next generaton. The value of selecton rate can be defned as: f ( ) p( ) (9) S f k k1 35
A New Task Schedulng Algorthm Based on Improved Genetc Algorthm n Cloud Computng Envronment Congcong Xong, Long Feng, Lxan Chen where f s the ftness value of the ndvdual. The roulette wheel selecton schema s adopted to mplement the selecton step. The cumulatve probablty of the ndvdual could be obtaned from Equaton 11. 3.4.2. Crossover and Mutaton P ( ) p( k) (10) s Crossover s known as a basc genetc operator. It partally exchanges nformaton between the two selected chromosomes [6,12]. Once the strng s pcked at random to be subjected to crossover from the populaton, t randomly chooses several crossover ponts and exchanges the alleles wth ts mate to form two new strngs. For example, two crossng chromosomes- and can exchange one or more alleles. Mutaton helps avod stckng at the local optmum and guarantee the populaton dversty. Chromosome reversal strategy s used as the mutaton methodology n ths paper. The chromosome randomly selects ts substrng and nverts t. 3.5. Proposed Algorthm Procedure The man procedure of the new algorthm s descrbed as follows. Step1: Generate an ntal populaton P(0) usng the matchng rate. Step2: Sort the ftness values of the chromosomes n ascendng order, k=0. Step3: Choose two chromosomes usng the roulette wheel and prepare them for crossover and mutaton. Step4: Use crossover and mutaton to create a new populaton P(k+1), k=k+1. Step5: If the maxmum number of generatons or a convergence s not reached, then return to Step 2. 4. Expermental results and evaluaton A smulaton experment s desgned to compare the schedulng performance of the BGA and Improved Genetc Algorthm (IGA). The experments have been carred out on the smulaton platform named CloudSm. The ntal parameters of the algorthms are as follows: maxmum number of generatons 80, resource number 20, crossover probablty 0.8, mutaton probablty 0.2. The value range of task number s from 20 to 100, and the weght coeffcent array { 1, 2, 3, 4 } s set to{0.6,0.1,0.3,0}( 4 s set to 0 because of the breakdown rate of resources obtaned by the platform CloudSm). The fnshed tme of two algorthms s shown n fgure 2.And the ftness value of two algorthms s shown n fgure 3. k1 Fgure 2. Fnshed tme of two algorthms 36
A New Task Schedulng Algorthm Based on Improved Genetc Algorthm n Cloud Computng Envronment Congcong Xong, Long Feng, Lxan Chen As t s shown n Fgure2, the average fnsh tme of the BGA at the prelmnary stage s less than that of IGA. However, as the number of generatons ncreases, the advantages of the IGA become more and more obvous. The reason s that the crossover and mutaton of the IGA mprove ts global search ablty. The ftness value could reflect users satsfacton of the schedulng result. The schedulng result s congruent wth users satsfacton when the ftness value s 0. If the ftness value s bgger than 0, t means the schedulng result exceeds users expectatons. It can be seen from the expermental results that the IGA has hgher ftness value than standardzed Genetc Algorthm, whch means the schedulng result of IGA can satsfy users expectatons better. Fgure 3. Ftness value of two algorthms 5. Conclusons Resource schedulng becomes more complex as the ntroducton of vrtualzaton technology n cloud computng[13]. Ths paper presented an mproved task schedulng algorthm based on the basc genetc algorthm combned QoS for the task schedulng n cloud computng, wth the objectve to satsfy users expectatons on servce response tme, network bandwdth, task expenses and relablty. The results show that the new IGA based task schedulng algorthm not only can be able to get hgher resources utlzaton, but also has the ablty to reflect the conformty between the schedule result and users expectatons. In addton, the next step s to focus on the study of dynamc queue schedulng algorthm to the realzaton of a unversal task schedulng algorthm and combne wth other related algorthm to make comprehensve comparsons accordng to the dfferent performance ndex. 6. References [1] Kwok Y K, Ahmad I, Statc schedulng algorthms for allocatng drected task graphs to multprocessors, ACM Computng Surveys, Vol.31, No.4, pp.406 ~ 471, 1999. [2] Chenghua Sh, Xaomn Wang, Schedulng Model of Dspatchng Ready Mxed Concrete Trucks Based on GA, AISS, Vol. 4, No. 8, pp. 131 ~ 136, 2012. [3] JanPng Wang, YanL Zhu, HongYu Feng, A Mult-Task Schedulng Method Based on Ant Colony Algorthm Combned QoS n Cloud Computng, AISS, Vol. 4, No. 11, pp. 185 ~ 192, 2012. [4] Paton N W, de Aragao M A T, Lee K, et al, Optmzng utlty n cloud computng through automatc workload executon, IEEE Data Eng Bull, Vol.32, No.1, pp.51 ~ 58, 2009. 37
A New Task Schedulng Algorthm Based on Improved Genetc Algorthm n Cloud Computng Envronment Congcong Xong, Long Feng, Lxan Chen [5] Luyun Xu, Yunsheng Zhang, Xa-an B, A New Model and Queue Management Algorthm for Congeston Control n Cloud Servce, AISS, Vol. 4, No. 11, pp. 320 ~ 327, 2012. [6] RUDOLPH G, Convergence analyss of canoncal genetc algorthms, IEEE Trans on Neural Networks, Vol.5, No.1, pp.96-101, 1994. [7] D Martno V, Mllott M, Suboptmal schedulng n a grd usng genetc algorthms, Parallel Computng, Vol.30, pp.553-565, 2004. [8] Correa R.C., Ferrera A., Rebreyend P., Schedulng multprocessor tasks wth genetc algorthms, IEEE Transactons on Parallel and Dstrbuted Systems, Vol.10, No.8, pp.825-837. [9] JnFeng Wang, KaYu Chu, An Applcaton of Genetc Algorthms for the Flexble Job-shop Schedulng Problem, IJACT, Vol. 4, No. 3, pp. 271 ~ 278, 2012. [10] Salcedo-Sanz S.,Bousono-Calzon C.,Fgueras-Vdal A.R., A mxed neural-genetc algorthm for the broadcast schedulng problem, IEEE Transactons on Wreless Communcatons, Vol.2, No.2, pp.277-283. [11] Zomaya A.Y., Ward C., Macey B., Genetc Schedulng for parallel processor systems: comparatve studes and performance ssues, IEEE Transactons on Parallel and Dstrbuted Systems, Vol.10, No.8, pp.795-812, 1999. [12] Arnold D V, Hans-Georg B, A General Nose Model and Its Effects on Evoluton Strategy Performance, IEEE Transacton on Evolutonary Computaton, Vol.10, No.4, pp.380-391, 2006. [13] Janfeng Zhao, Wenhua Zeng, Mu Lu, Guangmng L, A model of Vrtual Resource Schedulng n Cloud Computng and Its Soluton usng EDAs, JDCTA, Vol. 6, No. 4, pp. 102 ~ 113, 2012. 38