Resource Scheduling Scheme Based on Improved Frog Leaping Algorithm in Cloud Environment

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1 Informaton technologes Resource Schedulng Scheme Based on Improved Frog Leapng Algorthm n Cloud Envronment Senbo Chen 1, 2 1 School of Computer Scence and Technology, Nanjng Unversty of Aeronautcs and Astronautcs, Nanjng , Jangsu, Chna 2 School of Computer Scence and Technology, Nantong Unversty, Nantong , Jangsu, Chna Abstract Ths artcle ams to optmze resource schedulng n cloud envronment. Frstly, the proposed scheme takes nto consderaton the lmtatons of orgnal shuffled frog leapng algorthm hle usng ts mproved verson to optmze resource schedulng n cloud envronment. Secondly, by ntroducng the dea of partcle updatng n local research, the mproved scheme carres on ell n chaotc perturbatons of optmal ndvduals hen the convergence speed s acceleratng at the same tme. Thus, the probablty of local optmal emergence s gradually reduced. Fnally, the scheme embodes CloudSnm platform to carry out the smulaton experment ell. The expermental results sho that the scheme has reduced the tme for cloud computng to complete the task more reasonable load dstrbuton th more reasonable resource allocaton. Key ords: CLOUD COMPUTING, FROG LEAPING ALGORITHM, RESOURCE SCHEDULING, SCHEDULING SCHEME 1. Introducton The task scale n the cloud computng envronment has been alays n a grong state recently hle the res ources als o gradually s ho heterogenety. H o e- ver, the resource management servce qualty of cloud computng drectly affects the performance ndexes of the computer s ys tem. Currently, ho to get the optmal choce from cloud computng resource schedulng strateges s alays one of the hotspots n the study of modern cloud computng. In the research lterature, foregn scholars manly put forard a combnaton of statc and dynamc algorthms for cloud computng resource schedulng. Wth the statc schedulng algorthm, on the one hand, M tasks are allocated to N resources. On the other hand, ths knd of resource schedulng based on cloud computng, to a certan extent, s often requred to prepare for the release of resource dynamc applcaton [1]. As far as ts nature s concerned, cloud computng s often the process of provdng external servces, hch often requres to embody the basc cost of resource consumpton. In fact, the statc algorthm s d ff cult to meet full requrements of cloud computng resource schedulng fundamentally, hch leads to a aste of resources hle the dynamc schedulng algorthm has relatvely more applcatons, heren cloud computng resources are alays n a multple-constraned state. Hoever, the applcatons of genetc algorthm and partcle sarm optmzaton (PSO) algorthm, to a certan extent, often need to fundamentally allo for resource schedulng features usng frog leapng algorthm n cloud envronment before further smulatng the bologcal behavor n nature. The proposed scheme by copng th the 436 Metallurgcal and Mnng Industry

2 problems exstng n the above-mentoned algorthms ultmately reflects that t has both the advantage of fast speed and a good performance. Thus ths cloud computng resource schedulng scheme s relatvely better than the orgnal one [2]. In terms of optmal cloud computng resource schedulng, a better result can be acheved n smulaton test and analyss hen the proposed strategy s employed based on mproved shuffled frog leapng algorthm [3]. 2. Analyss of cloud computng resource schedulng The so-called cloud computng system often provdes hgh qualty servce, and for maxmzng the use of resources, contnuously reduces aste. Cloud computng system s generally characterstc of he- The sze of output and nput fle s expressed by OutputSze and FleSze respectvely hle length of task by Length. The applcaton of cloud computng often needs to combne runnng cost and the task s effectvely completed n the analyss of user requrements[5]. In the smplfed computer complexty, completon tme of cloud computng users s denoted expressed by QOS. In cloud computng system, task s ndcated by Task[] hle resource j by VM[ j ]. The executon tme concernng Task[] based on VM[ j] s thus recorded as tme ( Task[] VM[ j ]). Hoever, X as cloud computng resource schedulng scheme, ts completon tme s expressed by formula (3) accordngly: Tme( X ) = max t j= 1 k =1 Tme ( Task[] VM[ j ]) (3) Where n task number s denoted by k and quantty of cloud computng resource by l. 3. Resource schedulng scheme based on mproved frog leapng algorthm n cloud envronment 3.1. Tradtonal shuffled frog leapng algorthm Tradtonal shuffled frog leapng algorthm as frst proposed to manly embody the smulaton process of frog foragng behavor and ntegrated n partcle sarm optmzaton algorthm. Wth the advantage of memetc algorthms, t has ganed de range applcaton. Ths algorthm frst generates F frog partcles th the dvson of populaton, and then combnes the ntal populaton n portfolo, as s expressed by P = { X1, X2,, X F }, heren the potental soluton to a problem s expressed th a frog[6]. For frog k, ts poston s X = { X, X,, X }. The ftness value of k k1 k 2 kn Informaton technologes terogenety, and t s most sutable for operatng on cloud computng resources n the state of resource competton [4]. In current cloud computng, resources are often combned th a vrtual method and provded for cloud computng users. Any resource s represented by VM, as s shon n formula (1): VM ={ CPU, Memory, BandWdth, MIPS,Cos t, } (1) The sze of computer memory s denoted by Memory, runnng cost by Cost,computer netork banddth by BandWdth and the number of nstructons executed by CPU by MIPS.Cloud computng users n the submsson of the task s supposed to ell prepare for of specfc descrptons, as formula (2) shos belo: Task ={ Length, PesNumber, FleSze, OutputSze, CPU, Memory, BandWdth } (2) each frog s calculated th f[ Xk ] and arranged n a descendng order n preparaton for basc permutaton applcaton before t s dstrbuted equally n M populatons. For populaton j, t s expressed by E j and ts value s calculated th the formula belo: E = { Sj+ mt ( 1) Pt 1 l N} j (4) The number of frogs n the populaton s denoted by N and F = M N. Secondly, for local search, the orst frog n the populaton s labeled as X, the locaton of hch updates n a ay expressed by formula (5)[7]. Xne = X + D D = r [ Xb X] (5) The locaton of the best frog s labeled as X b and ts random number s expressed by r. Once Xne > X, then Xne = X ; otherse, Xb = X g. After re-updatng, once Xne < X, a frog s generated at random and labeled as X. In the executon of the above process by the populaton, the number of teratons ll be n a termnated state [8]. Fnally, for the global nformaton exchange, after local search s done combnatons ll be carred out to generate ne populatons, hch are to be updated th reference to the arrangement of ther ftness value for searchng operatons tll optmal termnaton condtons are met eventually[9] Improvement on shuffled frog leapng algorthm Generally speakng, mprovement on shuffled frog leapng algorthm often requres an update polcy n analyss of the fundamentals. Hoever, the local search performed n a restrcted area tends to be confronted th relatvely slo convergence speed. Thus, optmal probabltes ll emerge once the search s Metallurgcal and Mnng Industry 437

3 stuck n a local area. The updatng of ndvdual frog s shon by formula (6): D = r c ( X X ) + W (6) B W= 1 1,max 2 2,max n,max T [ r, r,, ] (7) Informaton technologes X + D D Dmax X ne = D X + Dmax D > D T max (8) D D r s any number located n [1,1]; the range of c s beteen 1 and 2; and the maxmum uncertanty s expressed by,max. The optmzaton of partcle sarms s related to the locaton of partcles. It s amed to acheve speed update prmarly, to fnd the optmal soluton fundamentally and leads to effectve update of each frog s locaton and ts due applcaton n applyng partcle update mechansm[11]. In the longest coherent expresson, a frog s moble step s expressed by formula (9): ' ' D= RrD [ 2 + r3( xg x + rc( Xb X) + W (9) The eghtng factor s denoted by R.In terms of the best optmzng process of frog strategy, the frst step s to assume { 1, 2,, } Xg = Xg Xg X gd (10) Where n X g s mapped and mprovement on shuffled frog leapng algorthm s done. The value range s calculated th formula (11): z = x ( x x ) x (11) L u L g The mnmum value of the varable s denoted by xl hle ts maxmum value by x. U The second step s to carry out teratve processng for formula (10). After multple teratons, pont ( m chaos sequences amount to m,.e., z ) ( m = 1, 2, ) The thrd step s obtan a feasble soluton ( ) ( ) ( ) ( ) x m = ( m 1, m 2,, m x x x d ), as shon n formula (12): ( m) ( m) xg = xl + ( xu xl ) z (12) The fnal step s to base substtuton on ndvduals after chaotc mappng and to replace some ndvduals n terms of probablty P g. The calculatng process of Pg s shon n formulas (13) and (14): 0 CR > q Pg = 1 CR q (13) q = ln t/ (1 + ln t ) (14) Layout procedure The frst step n the layout procedures s to dentfy the encodng scheme, hch s represented n formula (15). X [ x, x,, x ] x = 1 2 p [1, l± 1) (15) The specal encodng ay n combnaton of the search of optmal soluton s expressed by (16). X = [[ x1],[ x2],,[ x p ]] (16) [ x ] s manly a resource sequence of x roundng don to nteger. In the summary of completon tme of the task, the basc defnton of ftness functon s represented by(17). 1 Ftness( X ) = b Tme( X ) Tme( X t ) 1+ Tme( X ) Tme( X t ) (17) xt s used to denote a resource schedulng scheme that spends the longest tme on completng the task hle x b t the shortest[12] Soluton steps The soluton steps comprse n detal the follong. Frstly, assume that the number of the ntal populaton s N pop and the populaton s memeplex th ts sze as Nm at the same tme. Then the teratons of memeplex s Iter [13]. Thrdly, n encodng frog ndvduals, update each sub-populaton X based on formula (17). Fourthly, shuffle the populatons and calculate the ftness value of frogs to acheve chaos optmzaton of frogs X g n a descendng order. Ffthly, hen the termnaton condtons are met, the optmzng process gradually ends th selectng the optmal cloud computng resource schedulng scheme n combnaton th the optmal seres. 4. Smulaton experment 4.1. Experment envronment The smulaton experment s carred out on a computer th Wndo XP operatng system and 2GB memory, and embodes Cloudsm smulaton softare to prepare for ts applcaton. The analyss s done n terms of ISFLA smulaton results and n combnaton th the comparson beteen SFLA and genetc algorthm [14] Comparson of convergence speed before and after mprovement The sze of resources s set as 20 and the cloud computng systems amounts to The soluton process of optmal schedulng scheme s shon n Fgure Metallurgcal and Mnng Industry

4 Informaton technologes Fgure 1. A comparson beteen teratons of optmal soluton by chaos frog leapng algorthm before and after mprovement In the comparson, t can be seen that teratons of optmal soluton by chaos frog leapng algorthm after mprovement embodes the update polcy and apples the dea of updatng partcles, durng hch process the nerta of the last update s extended and the smulaton nformaton of the optmal ndvdual s ntroduced. Furthermore, convergence speed of the algorthm contnues to accelerate. In the use of chaos optmzaton strategy for reference, drect optmzaton of optmal ndvdual chaotc perturbatons s realzed and the populaton s reduced, hch leads to the mprovement of the speed of problem-solvng n terms of resource schedulng Result analyss In the analyss of small scale tasks, the performance of ths algorthm, compared th that of others, s often combned th the model of cloud computng system. In the allocaton of resource nodes, ths algorthm has done ell n reasonable allocaton of tas ks, applcaton of res ource s chedulng tll optmal schedulng scheme s obtaned. The Fgure 2.Task number change of the task tme s manly shon n Fgure 2. Once there are feer tasks, the user ll encounter a smaller probablty of both resource competton and conflct. For large-scale tasks, hen for example, the total sze of resources s 50, the comparson among dfferent algorthms s dsplayed n Fgure 3. In applcaton of these algorthms, hen the number of tasks ncreases, there are both a more ntense competton and an ncreased conflct probablty. Durng the ncrease of task completon tme, the mproved shuffled frog leapng algorthm has a great advantage. Ths advantage, to a certan extent, can be used to speed up the convergence rate. In the practcal analyss, the local optmal soluton can be avoded effectvely th the chaos optmzaton strategy ntroduced n tme. The effect of resource nodes on task completon tme s shon n Fgure 4. Fgure 3.Comparson of large-scale task completon tme among dfferent algorthms Fgure 4. Changes n relatonshp beteen resource nodes and task completon tme Metallurgcal and Mnng Industry 439

5 When nodes are ncreasng, the completon tme ll be decreasng contnuously th resource competton also n a gradual eakenng of the state. On the contrary, once total resources exceed a certan node, ths magntude ll gradually decrease and by ncreasng the number of nodes of cloud computng resources, the effcency of problem solvng s mproved contnuously n cloud computng task schedulng. For a comparatve analyss of load balance beteen resource nodes, tasks are, for example, allocated th resource nodes, as shon n Fgure 5. Fgure 5.Resource load dstrbuton of dfferent algorthms Resource schedulng based on mproved SFLA tends to have a relatvely strong processng capablty n far utlzaton of resources. 5. Conclusons In general, ratonal utlzaton of resources n cloud envronment s alays one of the current concerns n the cloud computng area. Ths artcle through the analyss of the solvng process of shuffled frog leapng algorthm solves the problem of slo solvng speed and provdes an effectve soluton to the problem of beng trapped n local optma. In the applcaton of mproved shuffled frog leapng algorthm n resource schedulng, the results of the smulaton test sho that the optmal scheme for cloud computng resource schedulng can be obtaned th the realzaton of soluton fndng for large-scale tasks. Acknoledgements Ths ork as supported by ( Natonal Natural Scence Foundaton of Chna, NSFC) References 1. Luo J.P., L X., Chen M.R. (2012) Guaranteed QoS resource schedulng based on mproved shuffled frog leapng algorthm n cloud envronment. Computer Engneerng and Applcaton, 48(29), p.p Klemm M.,Hauesen J.,Ivanova G. et al.(2009) Informaton technologes Independent component analyss: comparson of algorthms for the nvestgaton of surface electrcal bran actvty. Medcal and Bologcal Engneerng and Computng: Journal of the Internatonal Federaton for Medcal and Bologcal Engneerng, 47(4), p.p Zhang M. (2015) Improved shuffled frog leapng algorthm n resource schedulng of cloud computng. Computer Applcatons and Softare, No.4, p.p Takeda A.,Sanuk N.,Kuneda E. et al.(2009) Stereotactc body radotherapy for prmary lung cancer at a dose of 50 Gy total n fve fractons to the perphery of the plannng target volume calculated usng a superposton algorthm. Internatonal Journal of Radaton Oncology, Bology, Physcs, 73(2), p.p Nng F.F., Wang J.X.(2015) Resource schedulng based on estmaton of dstrbuton shuffled frog leapng algorthm n cloud envronment. Mcrocomputer Applcatons, 31(7), p.p.59-61, Mlov A.D., Samolova R.I., Tsvetkov Y.D. et al.(2009) Structure of self-aggregated alamethcn n epc membranes detected by pulsed electron-electron double resonance and electron spn echo envelope modulaton spectroscopes. Bophyscal Journal, 96(8), p.p Lu W.J., Zhang M.H., Guo W.Y. (2011) Cloud computng resource schedule strategy based on MPSO algorthm. Computer Engneerng, 37(11), p.p.43-44, Ln W.W., Q D.Y. (2012) Survey of resource schedulng n cloud computng. Computer Scence,39(10), p.p MarkakV.E.,AsvestasP.A.,MatsopoulosG.K. et al.(2009) An teratve pont correspondence algorthm for automatc mage regstraton: an applcaton to dental subtracton radography. Computer Methods and Programs n Bomedcne, 93(1), p.p Hao L., Cu G., Qu M.C., Zhang K.(2014) A cost constraned resource schedulng optmzaton algorthm for reducton of energy consumpton n cloud computng. Chnese Hgh Technology Letters, 24(5), p.p Lm G.H., Huber G. (2009) The tethered nfntesmal tor and spheres algorthm: a versatle calculator for axsymmetrc problems n equlbrum membrane mechancs. Bophyscal Journal, 96(6), p.p Zhang H.W., We B., Wang J.D., He J.J.(2014) Cloud resource schedulng method based on estmaton of dstrbuton shuffled frog leapng 440 Metallurgcal and Mnng Industry

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