Optimization of Resource Schedule Based on Improved Particle Swarm Algorithm in Cloud Computing Environment

Transcription

1 391 A publcaton of CHEMICAL ENGINEERING TRANSACTIONS VOL. 46, 015 Guest Edtors: Peyu Ren, Yancang L, Hupng Song Copyrght 015, AIIC Servz S.r.l., ISBN ; ISSN The Italan Assocaton of Chemcal Engneerng Onlne at OI: /CET Optmzaton of Resource Schedule Based on Improved Partcle Swarm Algorthm n Cloud Computng Envronment Zhao Hongwe *a, Shen Hongye b a school of Informaton Engneerng, Shenyang Unversty, Shenyang, Laonng, Chna b Informaton Engneerng School, Shenyang rado and televson unversty, Shenyang, Chna Zhw30@163.com In order to optmze resources schedulng of cloud computng and to mprove the utlzaton rato of the resource as well as guarantee applcaton qualty of servce, the dynamc resources schedulng algorthm basng on mproved PSO has been desgned and mplemented after the study on the Resources Schedulng system of cloud computng. Frstly, the man dea of mproved PSO s to extend Partcle Swarm Algorthm to the nteractng swarms model by cooperatve Models. Secondly, a comprehensve model has been desgned and mplemented n consderaton of the amount of resources, each jon ponts performance and current load dstrbuton. Fnally, the result of the experment ndcates that the mproved PSO can effectvely reduce the number of vrtual machne mgraton and the number of physcal nodes use, the schedulng system can mprove the effcency of dspatchng resource n the Cloud Computng system. 1. Introducton Cloud Computng s a computng model, provdng resource to users by nternet, of whch the nfrastructure cloud of these resources need not be understood, acknowledged or controlled by users. Also, Cloud Computng s a busness model that dstrbutes task to computer resource pool, so that varous applcaton systems can obtan necessary computng power, storage space and a varety of software resources accordngly. (Cheng, 009). In cloud computng system, because of the complexty and uncertanty of cloud computng, resources are dynamc changes. In order to mprove the resource utlzaton, how to schedule resource has become the core mechansm n cloud computng system. (Fan and Wang 009), Early cloud resource schedulng research manly focused on mprovng the resource utlzaton rate. The current research s manly energy savng aspects n the cloud computng system, t s manly through the vrtual machne mgraton and turn off unnecessary servers to reduce the system energy consumpton. Therefore, n Cloud Computng Center, on the one hand to mprove resource utlzaton, on the other hand, to ensure the qualty of servces and mprove the applcaton performance of the cloud applcatons. However, multple targets are conflct n each other (He and Qu, 009). As an nnovatve artfcal n ntellgence skll, Swarm ntellgence 1 fts for the soluton of complcated problems of optmzaton, whch s enlghtened by aggregated actvtes of anmals, such as brd flocks, ant colones, and fsh schools, and so on. Recently, n order to solve actual problems, a number of methods of algorthmc Swarm ntellgence have been worked out, the most effectve of whch s Partcle Swarm Optmzaton (PSO). Enlghtened by the bologcal swarmng s actvtes, such as school of fsh, n flocks of brds, and even that of manknds (Clerc, 005). PSO s an optmzaton nstrument, carred out to settle a varety of functon optmzaton problems. Beng as a skll to solve problems, the man advantage of PSO s ts hgh speed of convergence, whch exceeds that of Evolutonary Algorthms (EA) and the rest global optmzaton algorthms. Therefore, how to obtan the load nformaton of each node and how to use PSO measurement and evaluaton of the local agent to schedule resource wll be the man research drectons n cloudng system.(hayes,003) Please cte ths artcle as: Zhao H.W., Shen H.Y., 015, Optmzaton of resource schedule based on mproved partcle swarm algorthm n cloud computng envronment, Chemcal Engneerng Transactons, 46, OI: /CET

2 39. Related works Ths paper focuses on the mprovement on the retreval speed of resource and the constructon of Cloud Computng system wth hgh effcency by studyng the schedulng methods and load balancng features under the Cloud Computng envronment (Gu, 007). GFS, the abbrevaton of Google Fle System (Google Fle System) launched by Google, can meet the processng data requrements wth rapd growth. Google Fle System s a scalable dstrbuted fle system, sutable for the large-scale, dstrbuted, large amount of data access applcatons, runnng on ordnary low-cost hardware devces, whle provdng resource of hgher overall performance and fault tolerance functons wth a large number of users. However, a GFS cluster always handles the Master schedulng request by a global agency to, so that the global resource agency wll become the system bottleneck under the large-scale Cloud Computng envronment wth more nodes. In cloud computng envronment, cloud resources can be dvded nto vrtual resources and physcal resources. Therefore, the schedulng of cloud resources can be dvded nto two levels, they are physcal layer and vrtual layer respectvely. The meanng of vrtual layer s submtted by user jobs to be dvded nto multple tasks, as the core of the schedulng s to the task allocaton approprate vrtual resources.the physcal layer refers to the meanng reflects the relatons between vrtual and physcal resources. At present, many research work has been carred out for cloud computng resource schedulng method, presented the cloud utlty maxmzaton model, and compared wth the tradtonal schedulng model. Objectve functon s no longer to mnmze the maxmum completon tme, but to reach the effect of usng maxmum schedulng goals. The cloud utlty maxmzaton model s relatvely smple, t cannot meet the expandng cloud computng scale. Accordng to the new features of cloud computng, proposed the adaptve management model of vrtual machne placement n cloud computng envronment. However, the model of the cloud computng s not perfect, and the optmzaton cannot reach the desred effect. Wth the development of bologcal heurstc algorthms, many scholars proposed cloud resource schedulng algorthm based on ABC(artfcal bee colony)algorthm, fsh, PSO(partcle swarm optmzaton), these bologcal heurstc algorthm has characterstcs of smple structure, strong search capablty and can quckly fnd the optmal soluton of the cloud resource schedulng, mprove the effcency of cloud resource schedulng. However, bologcal heurstc algorthm have some shortcomngs n practcal applcaton. These shortcomngs manly nclude the slow convergence speed, easly fallng nto local optma, and thus cannot obtan optmal cloud resource schedulng scheme. Therefore, we need to mprove the bologcal heurstc algorthm (Eberchart and Kennedy, 1997). In the schedulng strategy of Cloud Computng system, we need to coordnately use the dstrbuted resource. In order to mplement the load balancng and mprove resource utlzaton, how to schedule resource becomes a central mechansm n Cloud Computng system, whle the schedulng strategy depends on the load nformaton acquston and the technology processng computng node nformaton (Lu, 011). 3. Resource Schedulng Base Partcle Swarm Optmzaton 3.1 Partcle Swarm Optmzaton Partcle swarm optmzaton algorthm smulates the behavor of a brd of prey. In the partcle swarm optmzaton algorthm, each soluton s consdered to be a Partcle n space. Each one has ts own poston and speed and there s the ftness value that s determned by an optmal functon.5,6 In addton, each partcle knows best poston and current locaton so far n the entre swarm, whch usng the followng nformaton to change ther current poston: (a) The current poston; (b) The current speed; (c) The prevous best poston; (d) The best poston of the entre swarm. Partcle swarm can be thought of as a partcle n dmensonal space and t can transfer nformaton x ( x, x,... x ) accordng to certan rule. The th partcle s represented as vectors of 1 n the dmensonal space, the locaton of the th partcle n the dmenson space s X. the th partcle velocty s also a v ( v, v,..., v ) dmensonal vector, denoted as 1. In the formula (1) and (), =1,... m, m s the total number of partcles n the swarm; V s the th partcle flght velocty vector; s ndvdual best poston of partcle; s best poston n the swarm; c1, c: weght; R1,R s random valus of [0, l]; By formula (1), () the th partcle decson the next poston (Cheng and Zheng, 009). pd p

3 393 v ( t) ( v ( t 1) R c ( p x ( t 1)) d d 1 1 d d R c ( p x ( t 1))) x ( t) x ( t 1) v ( t) d d d d (1) () p p where c1, c are learnng rates,r1,r are random values, the best poston of the th partcle s d, s the best poston of the th partcle n ts neghborhood, and the constrcton factor s : 4 3. Improved partcle swarm optmzaton algorthm PSO The PSO algorthm has a good convergence and easy to acheve local optmum. The mproved partcle swarm optmzaton algorthm (PSO) s mproved by the dynamc mult-group collaboraton. Each teraton mproves the convergence rate of the algorthm, and the maxmum speed v max d can be used to select the mutaton partcles. Through the mutaton partcle flght, the dsadvantage of local optmzaton s reduced, and the dversty of the populaton s mantaned, so as to mprove the performance of the algorthm and the P { S, S,..., S },,..., accuracy. 1 M ndcates M swarms, S { X k X k X k } k 1 N s N partcle, formula s as follows. v ( t) ( v ( t 1) R c ( p x ( t 1)) k k k k d d 1 1 d d R c ( p x ( t 1)) R c ( p x ( t 1))) k k k d 3 3 d (3) (4) k k k x ( t) x ( t 1) v ( t) d d d (5) v t d max t max vd, vd vd v, v v max t max d d d (6) The pseudo code of the PSO algorthm s as follows: Set t == 0; Intalze. Source N swarms each swarm M partcles; WHILE (not the condtons of termnaton ) FOR (each swarm k) Calculate the k th swarm neghborhood, Fnd the best ftness of pont;set ths pont as FOR (each partcle) p ; Fnd n the partcle neghborhood, the pont wth; Set ths k pont as p ;the best ftness s replaced; Update velocty of partcle by (4); Update poston of partcle by (5); EN FOR EN FOR Set t := t + 1; EN WHILE

4 Benchmark Test 4.1. Test Functon and Parameters For any algorthm, any elevated performance over one class of problems s exactly pad for n performance over another class 13. To fully estmate the performance of PSOBS algorthm, rather than comng to a concluson wth bas facng the above problems, 5 bench mark functons have been employed as follows. These benchmark functons can be classfed from f1 to f5. Parameters of the test functons show n table 1 and the formulas of such functons are shown as below: Table 1: Parameters of the test functons m Intal Range populaton Sze f1 0 f 0 f3 0 f4 0 f5 0 [50,100] [15,30] [.56, 5.1] [100,300] [15,30] The sze of populaton of all algorthms tested was set as 100 n ths experment. 15 For canoncal PSOBS PSO, the learnng rates c1=c=.0, the ntal value of nerta weght ω s 0.9 and the end s 0.5. The experment runs for 100 tmes for respectve algorthm on every benchmark functon. The amount of generatons was set to be 100 for the 5 benchmark functons. f1=sphere f=rosenbrock f3=rastrgrn f4=grewank f5=ackley f1( x) x 1 ( ) 100 ( 1 ) (1 ) 1 f x x x x 3( ) ( 10cos( ) 10 1 f x x x 1 f x x 4( ) cos( ) ( ) 0exp( ) 1 f x x exp( cos x ) 0 e x 4. Smulaton results for benchmark functons The expermental results are showed n Table 1, such as the best, worst, average, and standard devaton of the functon values, and all algorthms are termnated after 100,000 functon evaluatons: From the results, we test the three PSO and PSO algorthms. Table lsts the results of experment for every algorthm on functons from f1 to f4. As shown n Table, the PSO converged rather rapdly to obvously better results than the rest of algorthms, whch s the most rapd one for searchng for good results wthn comparatvely few generatons. As llustrated n Fgure 1, PSO s worst not only on ts convergence speed, but also on ts performance on all benchmark functons. On Sphere functon, all algorthms perform very well. PSO s superor to others especally on convergence speed, whch can be seen n Fgure 1. From Table and Fgure 1, the convergence speed of PSO s much hgher than the others as to fndng good results wthn relatvely few generatons, the PSO s the fastest one. All algorthms were able to consstently fnd the mnmum to functons f1, f and f3 wthn 100 generatons. (7) (8) (9) (10) (11)

5 395 After comparng PSO wth PSO algorthms, an obvous result can be seen that the performance of PSO s sgnfcantly superor to others on contnuous unmodal functons f1 ~ f5. From the rank values llustrated n Table, the order of each performance among the above algorthms s PSO > PSO Table. Results comparson of dfferent optmal algorthms for 0 30 PSO PSO Sphere 1.5E-18.89E E E-18.4E-04.30E E E-09 Rosenbrock.3E E-0.34E E-01.34E E E E+0 Quadrc 3.3E-05.35E E-04.3E E+0.4E E+0.96E+01 Sum of dfferent powers 1.35E-0 3.8E-04.35E-03.1E E E+03.45E E+01 Fgure 1: The medan convergence results of 0 unmodal contnuous functons.(a) Sphere functon. (b) Rosenbrock functon. (c) Quadrc functon. (d) Sum of dfferent powers.

6 Conclusons In ths paper, a knd of recourse schedule method n Cloud Computng system s proposed n ths paper, followed by the analyss of the specfc model and technology related to Cloud Computng system, and then a resource dstrbuton method base on PSO has been gven out. PSO s a mult-objectve optmzaton algorthm whch consders resource utlzaton, cloud applcaton servce qualty and performance. Fnally, the result of the experment ndcates that the schedulng system can mprove the effcency of dspatchng resource and the utlzaton rato n the Cloud Computng envronment. Acknowledgments The authors are supported fnancally by Internatonal cooperaton project(project No.S01ZR0191) and the Natural Scence Foundaton of Laonng Provnce (Project No ) and the Socal Scence Foundaton of Laonng Provnce (Project No.L14ASH001) and ths work s supported n part by the Internatonal S&T Cooperaton Program of Chna (ISTCP) under Grant 011FA and Program for New Century Excellent Talents n Unversty of Mnstry of Educaton of Chna under Grant NCET Reference Cheng K., Zheng W.M., 009, Cloud Computng: system nstance and Research Journal of Software (05): Cheng Y.M., Jang M.Y. and Yuan.F., 009, A Novel Clusterng Algorthms based on Improved Artfcal Fsh Swarm Algorthm. Sxth Internatonal Conference on Fuzzy Systems and Knowledge scovery. IEEE Press (3); Clerc M., 005, Bnary Partcle Swarm Optmzers: Toolbox, ervatons, and Mathematcal Insghts, Avalable: Eberchart R.C. and Kennedy J., 1997, A new optmzer usng par-tcle swarm theory, Proceedng of the 6th Internatonal Sympo-sum on Mcromachne and Human Scence, M.orgo, L. M. Gambardella, Ant colony system: A cooperatve learnng approach to the travelng salesman problem. IEEE Transactons on Evolutonary Computaton, pp Fan Y.J., Wang., Sun M.M., 007, Improved Artfcal Fsh Swarm Algorthm. Journal of Chongqng Normal Unversty (Natural Scence Edton), 4(3):4-6. Gu L.M., 007, Mcro-Computer Informaton 4, 0. Hayes B., 009, Cloud Computng Communcatons of the ACM. Vol. 51, p He X.., Qu L.., 009, Artfcal Fsh Swarm Clusterng Algorthm. Applcaton Research of Computers, 6(10): L.M. and Sh H.H., 003, A Herarchcal Load Balancng Schedulng Model Based on Rules Computer Scence 30, 16. Lu J., 011, Improved Artfcal Fsh Swarm Algorthm and Its Applcatons n Functon Optmzaton, Journal of Shjazhuang Insttute of Ralway Technology, 10(3):33-36.