Effective Task Scheduling in Cloud Computing Based on Improved Social Learning Optimization Algorithm

Transcription

1 Effectve Task Schedulng n Cloud Computng Based on Improved Socal Learnng Optmzaton Algorthm Zhzhong Lu School of Henan Polytechnc Unversty, Jaozuo, Chna lzzmff@126.com Jngxuan Qn School of Henan Polytechnc Unversty, Jaozuo, Chna qjxwud@163.com Wepng Peng School of Henan Polytechnc Unversty, Jaozuo, Chna pwp9999@hpu.edu.cn Hao Chao School of Henan Polytechnc Unversty, Jaozuo, Chna chaohao@hpu.edu.cn Abstract For the typcal optmal problem of task schedulng n cloud computng, ths paper proposes a novel resource schedulng algorthm based on Socal Learnng Optmzaton Algorthm (SLO). SLO s a new swarm ntellgence algorthm whch s proposed by smulatng the evoluton process of human ntellgence and has better optmzaton mechansm and optmzaton performance. Ths paper proposes two learnng operators for task schedulng n cloud computng after analyzng the characterstcs of the problem of task schedulng; then, by ntroducng the Small Poston Value (SPV) method, the two learnng operators wth contnuous nature essence are enabled to solve the problem of task schedulng, and then the mproved SLO s employed to solve the problem of cloud resource optmal schedulng. Fnally, the performance of mproved SLO s compared wth exstng research work on the CloudSm platform. Expermental results show that the approach proposed n ths paper has better global optmzaton ablty and convergence speed. Keywords Cloud Computng, Task Schedulng, Socal Learnng Optmzaton Algorthm, Servce of Qualty 1 Introducton Cloud computng s a busness servce model and computng model whch ntegrates grd computng, parallel computng and P2P technologes[1]. Furthermore, t s a pay-as-you-go model whch provdes resources at lower costs wth greater relablty 4

2 and delvers the resources by means of vrtualzaton technologes [2]. The goal of cloud computng s to provde on-demand computng servce wth hgh relablty, scalablty, and avalablty [3]. As the emergng trend n dstrbuted computng recently, cloud computng provded many advantage to end-users, such as data ubquty, flexblty of access, hgh avalablty of resources, and flexblty. The task schedulng problem s a key problem whch nfluences the qualty of Cloud computng, and t s an NP-hard problem [5]. Recently, some research work focuses on ths problem and propose several methods, most of these methods are desgned based on evolutonary algorthms, as ths knd of algorthm has strong heurstc algorthm optmzaton ablty. Some evolutonary algorthms (such as genetc algorthm [6-9], partcle swarm algorthm [10-12], ant colony algorthm [13, 14], bee colony [15], and cuckoo algorthm [4, 16]) has been used to solve the problem of task schedulng n cloud computng. However, above evolutonary algorthms stll have some shortcomngs, such as slow convergence speed, easy to fall nto local optmum, etc. These shortcomngs reduce the effcency of task schedulng problem solvng. To solve the task schedulng problem effcently, ths paper proposes a new approach based on mproved Socal Learnng Optmzaton Algorthm (SLO) [17]. SLO s a new evolutonary algorthm whch s proposed by smulatng the evoluton process of human ntellgence, t conssts of three co-evoluton spaces: mcro-space, learnng space and belef space. These three spaces construct a closed co-evoluton process whch has better evoluton mechansm. SLO provdes a heurstc optmzaton algorthm model for solvng optmzaton problems, and evoluton process n each space could be realzed by desgnng correspondng optmzaton operators. For solvng task schedulng problem effcently, ths work desgns some optmzaton operators (such as crossover operaton, mutaton operaton, observatonal learnng, mtaton learnng operaton, accept operaton and nfluence operaton) for dfferent spaces of SLO, moreover, Small Poston Value (SPV) method s used to dscrete the ndvduals of mproved SLO, so as to enable the mproved SLO could solve the dscrete task schedulng problem. Fnally, the performance of mproved SLO s compared wth other methods on the CloudSm platform. Expermental results show that mproved SLO has better performance than that of other evolutonary algorthms. The man contrbuton of ths work s presented as follows. It proposes optmzaton operators of the three spaces n SLO and substantate the SLO for solvng the task schedulng problem; SPV method s used to dscrete the ndvduals of mproved SLO, so as to enable the mproved SLO could solve the dscrete task schedulng problem. The structure of ths work s organzed as follows: Secton 1 llustrates the related work; Secton 2 ntroduces the task schedulng problem; Secton 3 ntroduces the SLO algorthm; Secton 4 presents the task schedulng method based on mproved SLO. Secton 5 demonstrates the expermental results; Secton 6 concludes the work. JOE Vol. 13, No. 6,

3 2 Related works Task schedulng s one of the most mportant and crtcal problems n cloud computng, moreover, task schedulng s also an NP-complete problem [18]. Nowadays such problems are often addressed by usng heurstc methods. Many researchers have even more proven that heurstc algorthms can better to fnd the optmal soluton for task schedulng n cloud envronment. Most of these works are focused on mnmze the makespan or save the executon cost n cloud computng. For example, Nma et al. [4] have proposed a task schedulng evolutonary algorthm n cloud computng based on Cuckoo Search Algorthm (CSA), whch used oblgate brood parastc behavor of some cuckoo speces n combnaton wth the Lévy flght behavor of some brds and frut fles, and mproves the coverage speed. Smlarly, Km et al.[19] have developed bogeography-based optmzaton (BBO) for task schedulng, ths algorthm n large sze problems s performs satsfactorly than other optmzaton problems such as genetc algorthm (GA), partcle swarm optmzaton (PSO) and smulated annealng (SA). Solmaz Abd et al. [11] have proposed a modfed PSO algorthm to allocate tasks n cloud computng envronment whch combne wth shortest job to fastest processor algorthm (SJFP) to ntalzes partcles mnmze the makespan. Ndh Bansala et al. [20] have ntroduced the cost parameter to the QoS-drven schedulng algorthm mnmzng the total allocaton cost. However, there are some other parameters whch nfluence the schedulng of tasks and utlzaton of resources. So some scholars have carred out the related research about mult-dmensonal optmzaton for cloud task schedulng problem. Jacob et al. [21] have proposed a task schedulng algorthm based on bacteral foragng optmzaton algorthm. The algorthm consdered the QoS parameters such as cost, relablty and makespan together whch s able to meet the needs of the users. Sun et al. [22] have presented a heurstc cloud resource schedulng algorthm wth applcaton preference whch based on the mmune clonal algorthm of rapd mult-objectve optmzaton. Accordng to the preference prorty of non-domnated antbodes, the algorthm can mprove the convergence ablty effcently. He et al. [23] have proposed a PSO-based adaptve mult-objectve task schedulng strategy whch consders four objectves ncludes optmal resource utlzaton, task completon tme, average cost and average energy consumpton. In order to mantan the partcle dversty, the method adopt acceleraton coeffcent to adjust parameters adaptvely and mprove populaton dversty. Recently, abundant research works have promoted the development of task schedulng to some extent, but these methods stll has some lmtatons. For nstance, the dfference of algorthm performance n dfferent cloud computng envronment would be more evdent, the soluton s easy to be run nto local optmum or the task schedulng optmzaton target s sngle and so on. To overcome the above defcences, ths paper proposes a novel task schedulng algorthm based on socal learnng optmzaton algorthm (SLO) [17], moreover, a new objectve functon consstng of task completon tme, cost and energy consumpton s proposed. Furthermore, observaton 6

4 learnng operator and mtaton learnng operator n SLO are desgned for mprovng the convergence speed and the qualty of task schedulng servces. 3 Task schedulng Problem The quaternon of task schedulng model M= ( SV,, F!, ) s made up of n tasks and m vrtual resources, where S s a set of n tasks, V s a set of m resources, F stand for the objectve functon of resource schedulng and! s the resource schedulng optmzaton algorthm. In ths paper we make the followng assumptons about cloud task schedulng model [24]: (1) The performance of the vrtual machne meets the requrements of any task; (2) Each task can only be performed by one vrtual machne; (3) The effect of task transmsson tme on model optmzaton s not consdered. Ths paper only consders the stuaton wth one user (multple users can cycle executon), the characterstcs of schedulng model are descrbed as follows: (1)The task set S = { s1, s2,!, s n } submtted by the user, whch are ndvsble and ndependent between n tasks. SL represent the length of -th task that always determnes by the user. (2)The vrtual resources setv = { v1, v2,!, v,!, vm}, where each vrtual resource v has multple attrbutes, such as computng power, memory and bandwdth, etc. In ths paper, we manly consder the computng power of vrtual machne VC = { vc, vc,!, vc,! vc } where vc represent the computng power of the vrtual 1 2 machne. m (3) Task executon tme matrx, Tme = { tme }( = 1, 2, n; j = 1, 2, m) j!! where tme j s the executon tme of task on a vrtual machne j, the tme j s calculated by formula (1): tme (4) Dstrbuton matrx = SL vc (1) j j X = x x! x m x21 x 22! x2m, where " " " " x x! x n1 n2 nm x j! 1, f a task of s wll be run on v j = "!!! # 0, otherwse We use the matrx to represent whether the task s assgned to the vrtual machne j. Where x j = 1 ndcates that the task s allocated to the vrtual resource j, otherwse not. In order to mprove the QoS n cloud computng envronments, we consdered four factors n cloud task schedulng nclude the task completon tme, cost, energy consumpton and resource utlzaton [23]. JOE Vol. 13, No. 6,

5 Factor 1: TIME express the task completon tme. In cloud computng envronments, RET ndcates the executon tme of vrtual resource j whch can be calculated j n by a functon RETj = " xj! tme. Because the vrtual resources are run n parallel, the j = 1 completon tme of all tasks s obtaned accordng to (3): TIME m = max RET (3) j= 1 j Factor 2: C means the cost n task schedulng. For llustrate the superorty of the algorthm better n task schedulng, we assume that the tasks are mutually ndependent. Matrx RC{ j}( j = 1, 2,!, m) s used to express the cost of vrtual resource j per tme unt. The total executon cost for all cloud tasks can be calculated accordng to formula (4): m " j ( ) (4) C = RET! RC j j= 1 Factor 3: The energy consumpton used matrx E= { e}( = 1, 2,!, nj ; = 1, 2,!, m) to express. Where e j represents the energy consumpton when task runs on resource j. The total energy consumpton EC for fnsh all tasks are calculated by (5); n m EC =!! x * e (5) j = 1 j= 1 j Factor 4: RU represents the resource utlzaton n cloud computng task schedulng problem. When resource utlzaton n cloud computng s hgher, the less energy consumpton. Therefore, mprovng the utlzaton effcency of cloud resources can effectvely reduce the cost of cloud servce provders. The resource utlzaton [26] s accordng to the formula (6): j= 1 ( j ) ( ) m RU = 1!" Total! RET m* Total (6) Above all, we need to fnd the most reasonable schedulng scheme and determne the fnal decson matrx. The goal of task schedulng n ths paper s mnmze the task completon tme, cost, energy consumpton and resource utlzaton. The resource utlzaton s proportonal to the executon tme; therefore we only need to consder three man factors: task completon tme, cost and energy consumpton. The objectve functon of the problem of task schedulng s defned as (7): ( )! 1! 2! 3 C F x = " Total + " EC + " (7) j 8

6 Where!1,!2,!3 respectvely represent the weghts of the task completon tme, the cost and the energy consumpton n the objectve functon, and! 1 +! 2 +! 3 =1,! 1 "0,!2"0,! 3 "0. 4 Task schedulng Problem As we know, n all groups of organsms, the ntellgence of humans s the hghest among all of the anmal groups. Humans always enhance ther ntellgence by learnng from others behavor; ths s the key reason why human ntellgence s hgher than that of other anmals. In human socety, through observaton and the mtaton of others behavors, humans can mprove ther ntellgence quckly; ths behavor has been called mtaton learnng or socal learnng. Socal learnng optmzaton algorthm Paradgm (SLO) [17] s a swarm ntellgence algorthm whch nspred by the evoluton process of human ntellgence. The framework of SLO s constructed based on the framework and key functons of the culture algorthm (CA) [25]. On the bass of the framework of CA, a new mcro-space s added to the bottom of the CA framework to smulate ndvdual genetc evoluton. The populaton space of CA s taken as the learnng space of SLO, whle the top layer of the CA framework s taken as the belef space. The framework of SLO s presented n Fg. 1. Belef Space Update() Accept() Learnng Space ( XXX ) Influence() Provde Mcro Space Evaluate() (XXX) Fg. 1. Framework of SLO SLO conssts of three co-evoluton spaces: the bottom s the mcro-space, where ndvduals genetc evoluton takes places; the mddle layer s the learnng space, where ndvduals enhance ther ntellgence through learnng and where better knowledge s extracted and delvered to the top layer; the top layer s the belef space, where culture s formed through knowledge accumulaton and s used to gude ndvduals evoluton regularly n the macro-space. The general algorthmc structure of the SLO s descrbed n Alg_1. JOE Vol. 13, No. 6,

7 Alg_1. Algorthmc structure of SLO Step1: Intalzaton Generate ndvduals n the mcro-space; REPEAT Step2: Indvdual Genetc Evoluton Phase Carry out ndvdual genetc evoluton n the mcro-space and provde ndvduals to the learnng space; Step3: Indvdual Learnng Phase For each ndvdual, carry out the mtaton learnng and observatonal learnng; Step4: Culture Influence Phase Establshes the culture; apply culture to regularly nfluence the ndvdual genetc evoluton n the mcro- space; Memorze the best soluton acheved so far; UNTIL (the termnaton condton s satsfed); 5 Task Schedulng Based on mproved SLO SLO s an optmzaton model, when usng t to solve optmzaton problems, t needs to desgn concrete operators n each spaces. For solvng the task schedulng problem effcently, ths work desgns several operators for each spaces of SLO. 5.1 Operators n there space of SLO Operators n the Mcro-space: The evoluton process n mcro-space s manly based on genetc algorthm, whch s used to mtate human ntellgence evoluton, and ths process s dvded nto three operatons: selecton, crossover and mutaton. Intalzaton operaton: Intal populaton n the Mcro-space are randomly generated, the value of each dmenson s obtaned accordng to formula (8): POP = POP + ( POP! POP )* rand (8) j mn max mn where POP represents the j-th dmenson of the ndvduals POP j, =1,2,,popsze; j=1,2,,n, n whch popsze ndcates the ndvdual quantty, n s the number of tasks and rand s a random number between 0 and 1. POP max = 5, POP mn =! 0.5. Indvduals generated by formula (8) s a contnuous varable, but the task schedulng n cloud computng s a dscrete problem, so ths paper ntroduce the small poston value (SPV) rule [27] to convert the contnuous ndvdual POP pop, pop,, pop! =!,!,!,!. =! to a vec- = [ 1 2! n] to dscrete ndvdual [ 1 2 n] Formula (9) s used to change the dscrete ndvdual! [! 1,! 2,,! n] tor R = [ r, r,!, r ] whch s used to calculate the process tmes of tasks. 1 2 n, 10

8 R j =! mod m+ 1 (9) j Here m represents the amount of resource. An example to llustrate the converson process for 10 tasks and 4 resources s llustrated n Table 1. Table 1. Convert POP to R by usng SPV rule Dmenson j Varable POP ! R Varable pop ! R Selecton Operaton: In ths paper, the roulette wheel s used to select excellent ndvdual from the populaton. Assume that the populaton sze s P, for an ndvdual POP whose ftness value s f and ts selecton probablty Select can be calculated by (10). After obtaned the selecton probablty of each ndvdual, then, the selecton operaton can carry out based on these probabltes. popsze / j= 1 j Select = f! f (10) Crossover Operaton: For the crossover operaton, assume that two dfferent ndvduals whch are randomly selected from the populaton are POP and POP, through j crossover operaton, two new dfferent ndvduals are generated, ths process can be llustrated as Fg. 2. Randomly generate the sngle pont cross poston k Fg. 2. schematc dagram of crossover operator JOE Vol. 13, No. 6,

9 After the crossover operaton, carry out the greedy operaton that s to keep up two better ndvduals among the four ndvduals. Mutaton Operaton: In ths work, the sngle pont mutaton method s adopted, that s, randomly generate a new gene n the range of [ POPmn, POP max ] and to replace the randomly selected gene pont n the ndvdual. Then, evaluate the new ndvdual and old ndvdual, carry out the greedy operaton and keep the better one. Operators n the Learnng-space: In the learnng space, socal learnng optmzaton algorthm s smulatng the learnng process about human socety and promotng populaton evoluton by learnng from the best ndvduals. The learnng operators of the learnng-space nclude observaton learnng and mtaton learnng. Observaton learnng: In socety, ndvduals wll retan a porton of ther knowledge when they observng and learnng from elte ndvduals. Inspred by ths phenomenon, ths work ntroduces the nerta coeffcent to observaton learnng operator, so as to avod jumpng nto the local optmum, enhance the searchng ablty and convergence speed of SLO. Let POP = ( pop 1, pop 2,!, popk,!, popn ) ndcates any ndvdual n a populaton, cg cg cg cg cg POP = ( pop, pop,!, pop,!, pop ) represents the current global optmal soluton, 1 2 cg pop, pop [ POP, POP ] j mn max j n 1 2 k n * * * * *! and POP = ( pop, pop,!, pop,!, pop ) ndcate the new ndvdual produced by the observaton learnng. Observaton learnng operatons are defned as (11) pop =!# pop + pop #(1$ sn( ")) (11) * cg k k k k * * Where pop represents the k-th dmenson of new ndvduals k POP ;! ndcates the learnng nerta weght and! "(0,1), ths work set! = 0.5 ; sn(!) denotes the learnng dsturbance factor and!#[2,"] ;! " x ndcates the self-retanng part of an k ndvdual n the observaton learnng; x cg "(1# sn(!)) denotes the part that an ndvdual through learnng obtanng from the current global optmal ndvdual. * When the value of pop out of the k range[ POPmn, POP max ], we modfy the value of the dmenson varable by formula (12): pop * k! POPmax f popk > POP = " # POPmn f popk < POP max mn (12) Learnng: Imtaton learnng s the process of repeatng others' behavor conscously or unconscously n the human socety, then realze the constant evoluton of ndvduals. Based on ths prncple, ths work desgns the mtaton learnng operator based on the optmal ndvdual and two dfferent normal ndvduals who are random selected form the populaton. Moreover, the learnng process s also characterzed by volatlty and nstablty, therefore, dynamc learnng operaton s ntroduced to reduce the nterference of ndvdual learnng from others n mtaton learnng process, so as to mprove the convergence speed. The mtaton learnng operaton s defned as (13). 12

10 pop = pop + F!( pop + ( pop " pop )) (13) * cg k k k r1k r2k * Where pop s the k-th dmenson of new produced ndvdual after mtaton k learnng; F![0,1] represents scalng factor; r 1 and r 2 are random number between [1, n ] and r 1! r 2 ; pop s the k-th dmenson of random ndvdual rk POP ; 1 r 1 pop s rk 2 the k-th dmenson of random ndvdual POP ; s the k-th dmenson of the current best ndvdual cg pop ; k rk 1 rk 2 r 2 cg popk ( pop! pop ) ndcates the dynamc learnng step whch s automatc change wth the ncrease of the number of teratons. POP, POP, we should If the new ndvdual s k-th dmenson out of the range [ ] reset the value of the varable accordng to the formula (12). After carryng out the mtaton learnng operaton, the greedy operaton s performed, and the ndvduals wth better ftness are retaned. Operaton n the Belef Space: There are two operators n the belef space, they are Update operator and Influence operator. Update operator s used to update the knowledge n the belef space; the Influence operator s used to gude the evoluton of the mcro space wth the knowledge n the belef space when the algorthm teratng every # tmes. These two operators are defned as follows. Update (): Replace the poor ndvduals n the belef space wth the new better ndvduals who were selected from the learnng space. Influence (): Use ndvduals n the belef space to substtute the poor ndvduals n the mcro-space and randomly generate some ndvduals to replace some orgnal ndvduals. mn max 5.2 Applyng the styles to an exstng paper Based on the computng process of SLO and the desgned operators, the process of task schedulng based on mproved SLO s descrbed as Alg_2. JOE Vol. 13, No. 6,

11 Alg_2 Task schedulng based on SLO Input: populaton sze popsze; mutaton probablty PM,crossover probablty PC; F!"h!"!! Output: the optmal result; Step1: Intalzaton Generate ndvduals n the mcro-space; Step2: Dscretzaton wth SPV For(=1 to popsze) Convert the contnuous ndvdual POP to the dscrete ndvdual! accordng to the SPV rule; Then, transform the! to processor's vector R accordng to formula (9). Last, calculatng each ndvdual s ftness value by usng formula (7);} EndFor REPEAT Step3: Indvdual Genetc Evoluton Phase Carry out the roulette selecton operaton; For(=1 to popsze) // popsze s the scale of the ndvduals Carry out the crossover operaton and keep the better ndvdual; EndFor For(=1 to popsze) Carry out the mutaton operaton and keep the better ndvdual; EndFor Repet step2,carry out the best ftness by usng formula(7); Step4: Indvdual Learnng Phase For(=1 to popsze) Carry out the observatonal learnng accordng to formula (10) and keep the better ndvdual; EndFor For(=1 to popsze) Carry out the mtaton learnng accordng to formula (12) and keep the better ndvdual; EndFor Step5: Culture Influence Phase If(the generaton tme == k1) Carry out the Influence() functon; EndIf If(the generaton tme == k2) Generate q new ndvduals and replace q old ndvduals randomly; EndIf Memorze the best soluton acheved so far; UNTIL (the termnaton condton s satsfed); 14

12 6 Experment performance analyses In order to verfy the effcency of the approach proposed n ths work, CloudSm platform s adopted to smulate the task schedulng experment. The expermental envronment s PC wth the followng confguratons, OS: Wndows 7; CPU: Inter 4, 3.20GHZ; Memory:4G. The smulaton experment s conducted by extendng the DataCenterBroker class n the CloudSm platform. Ths work take the partcle swarm optmzaton algorthm(pso)[23] and genetc algorthm(ga)[28] as the compared object and analyze the performance of mproved SLO on the four factors nvolved n the ftness functon, such as task completon tme, cost, energy consumpton and resource utlzaton. 6.1 Values of parameters Under the same expermental condtons, the parameters of three algorthms are set as follows: populaton sze P=10, task length SL, vrtual machne executon capablty VC, energy consumpton matrx E, and cost matrx RC unfed randomly generated by MATLAB. The random range of SL s[ MI,50000MI ], the range of VC value s [ 200,1000 ], the value range of E s[ 1,10 ], the range of value of RC s[ 1, 5 ]. And the weght of each factor n the objectve functon! 1 =0.5!! 2 =0.2!! 3 = 0.3; Crossover rate PC=0.6 and mutaton rate PM=0.08 n SLO and GA; C1=C2 =2 n PSO. 6.2 Experment results and analyses To verfy the effectveness of mproved SLO algorthm n solvng cloud task schedulng, we adopted two contrastve experment. Each algorthm runs 10 tmes to elmnate the nterference of random factors to the expermental results and the average value s taken as the comparson. (1)Experment 1: Compare the performance of three algorthm on a smaller scale In ths experment, set 20 tasks and 5 vrtual computng resources n the cloudng computng envronment. Then accordng to the objectve functon formula (7), calculate the task completon tme, cost, energy consumpton and the ftness of the optmal soluton of the three algorthms. The expermental results are shown n Fg.3 Fg.6, the x-axs represents the teraton number, and the vertcal axs represents the performance comparson Fg. 3 shows the results of task completon tme. From Fg. 3 t can be found that, the task completon tme of the mproved SLO s less than the other two algorthms when the three algorthms are terated for 60 tmes. Ths result proves that mproved SLO have quckly convergence speed than GA and PSO n solvng the task schedulng problem. Fg.4 llustrates the comparson results of cost. Compared wth other two algorthms, the mproved SLO can reduce the cost n solvng task schedulng problem. So, based on the results showed n Fg. 3 and Fg. 4, we can get that, mproved SLO not only optmzes the executon tme, but also optmzes the executon cost of task schedulng. JOE Vol. 13, No. 6,

13 Taks completon tme(sec) Iteratons GA-based AMTS PSO-based AMTS SLO-based AMTS Cost Iteratons GA-based AMTS PSO-based AMTS SLO-based AMTS Fg.3 The task completon tme for 20 tasks, 5 resource Fg.4 The cost for 20 tasks, 5 resource GA-based AMTS PSO-based AMTS SLO-based AMTS Energy consumpton GA-based AMTS 96 PSO-based AMTS SLO-based AMTS Iteratons Ftness Iteratons Fg.5 The energy consumpton for 20 tasks, 5 resource Fg.6 The ftness for 20 tasks, 5 resource Fg. 5 shows the energy consumpton of three algorthms. As the ftness functon conssts of three parts wth dfferent weghts, ncludes task completon tme, cost and energy consumpton and the proporton of energy consumpton s smaller when algorthm tends to the optmal ftness leads to the fluctuaton of energy consumpton. In ths experment, ftness value represents the evaluaton value of resource schedulng scheme of each algorthm. Expermental results n Fg. 6 proves that SLO has a faster convergence rate, and can obtan better solutons for task schedulng problem. (2)Experment 2: Compare the performance of three algorthm on a large scale Dfferent from experment 1!ths experment sets 150 tasks and 20 vrtual machnes n the CloudSm platform. Experment 2 adopts the same objectve functon as experment 1. Expermental results as shown n Fg. 7 Fg.10, where the x-axs represents the teraton number, the vertcal axs represents the performance comparson. 16

14 Task completon tme(sec) Iteratons GA-based AMTS PSO-based AMTS SLO-based AMTS Cost Iteratons GA-based AMTS PSO-based AMTS SLO-based AMTS Fg.7 The task completon tme for 150 tasks, 20 resource Fg.8 The cost for 150 tasks, 20 resource Energy consumpton Iteratons GA-based AMTS PSO-based AMTS SLO-based AMTS Ftness Iteratons GA-based AMTS PSO-based AMTS SLO-based AMTS Fg.9 The energy consumpton for 150 tasks, 20 resource Fg.10 The ftness for 150 tasks, 20 resource Fg.7 shows the comparson results of task completon tme of the three algorthms. Compared wth other algorthms, Fg.7 llustrates SLO can get the better soluton wth shorter task completon tme. From the comparson results n Fg. 8, we can fnd that when the task sze s larger, compared wth other algorthms, the mproved SLO s stll able to get better mplementaton cost. In addton, although the cost curve of SLO has some fluctuated wth the ncrease of teraton tmes, SLO s converge to the less cost gradually. Based on the expermental results n Fg. 7 and Fg. 8, t can fnd that the mproved SLO has the better performance n balancng the executon cost and task completon tme. Fg. 9 llustrates the comparson results of energy consumpton and proves the same concluson as experment 1. Fg.10 represents the ftness value of task schedulng scheme searched by three algorthms. From Fg. 10, t can be found that, SLO has better optmzatons performance and get the better results n task schedulng problem when compared wth GA and PSO. Furthermore, n order to verfy the solvng effcency of the proposed method, we separately calculate the ftness values of the three algorthms n the same experment condtons but dfferent run-tme condtons. The expermental results are shown n Table 2 and Table 3. JOE Vol. 13, No. 6,

15 Table 2. Research results comparson under the smaller task scale Algorthm Runtmes GA ftness PSO ftness SLO ftness 10ms ms ms ms ms ms ms ms Table 3. Research results comparson under the larger task scale Algorthm Runtmes GA ftness PSO ftness SLO ftness 50ms ms s s s s s From table 2 we can fnd that the dfference of search results s smaller between three algorthms when the runnng tme s less than 50ms. Wth the ncrease of runnng tme, PSO gets nto a local optmum. However the genetc algorthm and the mproved SLO algorthm are well convergent untl the best global optmum soluton s found, and the convergence rate of SLO s the fastest, at the same tme, the optmal soluton found by the mproved SLO s better than the optmal solutons found by the other two algorthms. Wth the ncrease of tasks and vrtual resources, table 3 shows the ftness value obtaned by the three algorthms were slowly decreased when the runnng tme s less than 1s. PSO get nto local optmum after 1.5s, but SLO can skp the local optmum, fnally gets the optmal schedulng scheme. It means that the computatonal effcency of SLO s hgher than PSO and GA. Expermental results n table 2 and 3 prove that the mproved SLO has better convergence speed and search ablty n solvng the task schedulng problem, meanwhle t also can balance the task completon tme and the cost well. In the end, on base of analyss experment result, schedulng performance of the mproved SLO s better than GA-based AMTS algorthm and PSO-based AMTS algorthm n cloud task schedulng problems. Improved SLO not only effectvely reduce the cloud task completon tme and the cost of the data center, but also provde users wth a better user experence. Wth the ncrease of tasks and vrtual resources, 18

16 the proposed method stll has good convergence ablty and optmzaton ablty. At the same tme, the expermental results also verfy that the mproved SLO algorthm proposed n ths paper has strong ablty to skp the local optmum and faster convergence speed. 7 Conclusons In order to effcently solve the task schedulng problem n cloud computng, ths paper proposes a novel task schedulng algorthm based on the mproved SLO. We desgns some optmzaton operators (such as crossover operaton, mutaton operaton, observatonal learnng, mtaton learnng operaton, accept operaton and nfluence operaton) for dfferent spaces of SLO, moreover, Small Poston Value (SPV) method s used to dscrete the ndvduals of mproved SLO, so as to enable the mproved SLO could solve the dscrete task schedulng problem. Fnally, the performance of mproved SLO s compared wth other methods on the CloudSm platform. Expermental results show that mproved SLO has better performance than that of other evolutonary algorthms. Moreover, the mproved SLO algorthm can also be used to solve other optmzaton problems. 8 Acknowledgment Ths work s supported by: The Natonal Natural Scence Foundaton Fund Chna (No ; No ; No ); Major projects of scence and technology n Shandong Provnce (2015ZDXX0201B02); Bass and fronter of Henan Provnce ( ); Soft scence research project of Henan Provnce ( ); Zhengzhou leadng talent project (131PLJRC645); The authors would lke to thank the anonymous revewers for ther valuable feedback on ths work. 9 References [1] Jula A, Sundararajan E, Othman Z. Cloud computng servce composton: A systematc lterature revew [J]. Expert Systems wth Applcatons, 2014, 41(8): [2] L X, Xu J, Yang Y. A Chaotc Partcle Swarm Optmzaton-Based Heurstc for Market- Orented Task-Level Schedulng n Cloud Workflow Systems [J]. Computatonal Intellgence & Neuroscence, 2015, 2015: [3] Zhan Z H, Lu X F, Gong Y J, et al. Cloud Computng Resource Schedulng and a Survey of Its Evolutonary Approaches [J]. Acm Computng Surveys, 2015, 47(4): [4] Jafar Navmpour N, Sharf Mlan F. Task Schedulng n the Cloud Computng Based on the Cuckoo Search Algorthm [J]. Internatonal Journal of Modellng and Optmzaton, 2015,5(1): JOE Vol. 13, No. 6,

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18 [21] Jacob L, Jeyakrshanan V, Sengottuvelan P. Resource Schedulng n Cloud usng Bacteral Foragng Optmzaton Algorthm[J]. Internatonal Journal of Computer Applcatons, 2014, 92(1): [22] Sun D W, Chang G R, Feng-Yun L I, et al. Optmzng Mult-Dmensonal QoS Cloud Resource Schedulng by Immune Clonal wth Preference [J]. Ten Tzu Hsueh Pao/acta Electronca Snca, 2011, 39(8): [23] He H, Xu G, Pang S, et al. AMTS: Adaptve mult-objectve task schedulng strategy n cloud computng [J]. Wreless Communcaton Over Zgbee for Automotve Inclnaton Measurement Chna Communcatons, 2016, 13(4): cc [24] Lu W, Jn H, Lu B. Cloud computng resource schedulng based on mproved quantum genetc algorthm [J]. Journal of Computer Applcatons, 2013, 33(8): [25] Peng B. Knowledge and populaton swarms n cultural algorthms for dynamc envronments [M]. Wayne State Unversty, [26] Yan-Fang L I, Jang X F. Grd Resources Schedulng Algorthm Based on Dscrete Partcle Swarm and Tabu Search [J]. Computer & Modernzaton, [27] Fath Tasgetren M, Lang Y, Sevkl M, et al. Partcle swarm optmzaton and dfferental evoluton for the sngle machne total weghted tardness problem [J]. 2006, 44(22): [28] Srnvas M A P L. Genetc Algorthms: A Survey [J]. Computer, 1994, 6(27): Authors Zhzhong Lu was born n Shangshu county, Zhoukou Cty, n receved the PhD degree n computer scence from the HoHa Unversty, Nan jng, Chna, n He s currently an lecturer n the Henan Polytechnc Unversty and Post doctoral n Harbn Insttute Of Technology. Hs man research nterests are n Swarm ntellgence algorthm, Web servce computng and Cloud Computng. Jngxuan Qn was born n Lnfen cty, Shanx provnce!n Currently, she s a graduate student wth the School of Computer scence and technology n the Henan Polytechnc Unversty. Her current man research nterests nclude Swarm ntellgence algorthm and Cloud Computng. Wepng Peng was born n TanMen Cty, Hube Provnce n He receved the Ph.D. degree n Sgnal and Informaton Processng from the Bejng Unversty of Posts and Telecommuncatons, Bejng, Chna, n He s currently an assocate professor at the School of Computer Scence and Technology n Henan Polytechnc Unversty. Hs man research nterests nclude nformaton securty, data leakage preventon and securty and applcaton of IOT. Chao Hao was born n XuChang Cty, Henan Provnce n He s currently an lecturer n the Henan Polytechnc Unversty, Jao zuo, 45400, P.R. Chna. Hs man research nterests are n nformaton securty. Artcle submtted 24 January Publshed as resubmtted by the authors 14 Mach JOE Vol. 13, No. 6,