Proceedns of the 2015 Internatonal Conference on Industral Enneern and Oeratons Manaeent Duba, Unted Arab Erates (UAE), March 3 5, 2015 Resource Manaeent For Worflows In Cloud Coutn Envronent Based On Grou Technoloy Aroach Saleh Shahd-Pasha Deartent of Industral Enneern Shoal Unversty Aol, Iran Mladshahd@al.co Ehsan Teyouran Deartent of Industral Enneern Mazandaran Unversty of Scence and Technoloy Babol, Iran ehsan.teyoran@al.co Vahd Kayvanfar Deartent of Industral Enneern Arabr Unversty of Technoloy Tehran, Iran v.ayvanfar@aut.ac.r Abstract Data transferrn and load balancn are soe of factors whch ae the resource allocaton roble ore challenn n cloud coutn (CC) envronent. So due to the lac of exact soluton for ths roble, ths aer resents a new atheatcal odel based on rou technoloy to allocate the vrtual achnes (VMs) to worflows wth the a of controlln the nuber of data transferrn and server load varaton sultaneously. Grou technoloy s a resource allocaton technque n ndustral envronents wth roven ablty to otze soe easures (e.. art oveents, resource utlzaton) also a enetc alorth () s desned to fnd near otal soluton for larer robles wth no otu soluton. Zahra Booyav Deartent of Industral Enneern Unversty of Scence and Culture Tehran, Iran sh.booyav@al.co Koorosh Mashhad Alzaheh Deartent of Industral Enneern Sadjad Unversty of Technoloy Mashhad, Iran oorosh.farvahar@al.co. Accelerate technoloy deloyent; v. Accelerate busness nnovaton; v. Accelerate return on nvestent (ROI). Infrastructure as a Servce (IaaS), Platfor as a Servce (PaaS) and Software as a Servce (SaaS) are an servces of CC that are avalable to users based on ay as you o odel to execute ther coutatonal tass [1, 2]. F. 1 ndcates the layers of servce n CC. SPs and users usually have dfferent oals whch the ost ortant ones are user satsfacton n cloud qualty of servces (QoS) and ncreent of resource utlzaton rate (fro the SPs ersectve). The effcency of the CC envronent can be roved f the oals le these be consdered n resource allocaton stratees [3]. Keywords-Cloud coutn; Grou technoloy; Vrtual achne; Worflow; Matheatcal odel; Genetc alorth I. INTRODUCTION Today, advances n nforaton technoloy have ncreased the deand for coutn ower reatly; users need to do ther coutatonal tass wthout the need for exensve hardware and software at any te or lace. Cloud coutn (CC) s a new coutatonal technoloy tres to delver coutn as a servce slar to tradtonal ublc utlty servces (.e. water, electrcty, as, and telehony). It s obtaned fro develoent of several technoloes ncludn: hardware, nternet technoloes, dstrbuted coutn and systes anaeent. Ths odel brns benefts to both consuers (eole and coanes that do not have ther own nfrastructure) and servce rovders (SPs) that encourae the to nvest n ths technoloy. Soe of the entoned benefts are lsted below [1]:. Reduce catal costs;. Reduce IT anaeent efforts; Fure 1. Servce layers of cloud coutn There are any dfferent nds of coutn tass. Soe of the are very sle (e.. sendn a coent on socal networs) but the ajorty of tass n cloud systes consst of ultle councated subtass (e.. scentfc worflows). An onlne hotel boon s llustrated as a worflow n F. 2. 1544
The SPs ay face dfferent challenes when allocatn coutatonal resources to data-ntensve worflow (e.. ontae worflow) due to the lted caacty of resources and servers councaton constrants. The load balancn of syste s one of the challenes whch can drectly act on user satsfacton and resource effcency. The load ust be well dstrbuted on servers durn the worflow executon to decrease the data transfer te and deendency balance [4]. Data transfer s a bad factor whch aes syste slowdown and ncrease ower consuton. Fure 2. A sale of worflow (onlne hotel boon) A soluton for resource allocaton roble can be deal when the transferrn of data between servers s nal. In ths case, the SPs ust try, as far as ossble, to allocate the subtass of a worflow to a server to avod unnecessary transfers. But ths s not always ossble n colex cloud envronents; a server rejects the requests when t volates ts caacty lt. So, soe subtass of a worflow ust be transferred to other servers. It leads nevtably to ncrease the coleton te of tass. Alon wth these the nuber of vrtual achnes s an ortant roble that affects on syste erforance. A lare nuber of vrtual achnes can cause nterface n ther erforance and reduce the user's consent. Therefore they need to have a stratey to control all these sultaneously. Grou Technoloy (GT) s a anufacturn technque whch revents unnecessary oveents (transfers) and ncreases the roducton effcency n anufacturn systes. GT was frst roosed by Mtrofanov [5], and then develoed by Burbde [6]. The an objectve of GT s roun the slar arts and achne to ncrease the flexblty. Cellular anufacturn syste (CMS) s an alcaton of GT for lantaton of the odern anufacturn systes (e.. flexble anufacturn syste and just n te). Cell foraton (CF) s a coonent of CMS that oranzes ndeendent cells by secfyn the achne and art rous. The ultle roven abltes of GT to allocate the resources n CMS are as follow: Ablty to reduce the total executon cost and te; Ablty to rovdn a sutable anaeent on VMs; Ablty to rovn resources utlzaton; The tes entoned above as well as the structural slartes of cloud systes and CMS are the ost ortant reasons to suest GT for resource anaeent n CC. The servers, vrtual achnes and tass n CC have slar functons to cells, achne and coonent resectvely. So n ths aer, a new atheatcal odel based rou technoloy s roosed to rove the effcency of cloud by nzn the transfer between the servers, nuber of created VMs and server load varaton. Also a enetc alorth s desned to solve the lare scale robles and seed u the decson-an. The reander of the aer s structured as follows: a lterature revew on resource anaeent n cloud coutn and rou technoloy s resented n Secton 2. Proble odeln and roosed enetc alorth so as to tacle the consdern roble are ven n Secton 3 and 4, resectvely. Secton 5 s devoted to coutatonal results, and fnally concluson and future rears are suarzed n secton 6. II. RELATED WORK Many studes have been conducted n the context of resource allocaton n CC wth dfferent olces. These studes have tred to offer varous aroaches. Soe of the eloy heurstcs [7, 8, 9, 10, 11, 12] and others ve etheurstcs aroaches [13, 14]. Besde these, varous researches have been done secfcally n the context of worflow resource anaeent. An aroach resented by Varalash et al. [15] to handle the worflows wth the a of eetn the user-referred QoS araeters. Sh et al. [16] desned a resource allocaton echans for handle aret-orented worflows. The urose of ther ethod s eetn the dfferent consuers deands. A ult-objectve otzaton aroach resented by Szabo Sh et al [17] to allocate the scentfc worflows wth consderaton of transfer and executon te. The results showed that ther aroach can outerfor the current state-of-the art aroaches. Chen and Deelan [18] Proosed an aroach to allocate the resources to larescale scentfc worflows n order to reduce ae san and resource cost of worflows. They showed that the heurstcs and enetc alorths s a roer canddate to solve ths roble. A PSO based aroach for worflow anaeent roosed by Pandey et al. [19] to control the transfer cost and dstrbute the load on resources well. Lewse, we attet to fnd an exact soluton for worflow anaeent n such a way that the nuber of transfers wll be nzed alon wth far dstrbuton of loads. Moreover, our aroach deternes the otal nuber of vrtual achnes n the syste whch has not been studed before. Furtherore, varous studes have been conducted on GT. They focused on roosn an effcent atheatcal odel as well as heurstc aroaches. a ult-objectve atheatcal odel to create achne and art rous and also snle-objectve atheatcal odel to nze the ntra-cell and nter-cell oveents resented by Chan et al. [20]. Nar and Narendran [21] offered an alorth to secfy achne rous and art fales relyn on roducton sequence data. A enetc alorth was roosed to solve a CFP consdern several objectves 1545
ncludn oveents cost, cell load varaton and excetonal eleents (EEs) by Zhao and Wu [22]. Mahdav et al. [23] resented a unfed atheatcal odel to solve the CFP and the cell layout roble (CLP) sultaneously nzn bactracn aanst the forward oveents. Tavaol et al. [24] resented an nteer-lnear roran to nze the nter-cell oveent and achne costs. They develoed a sulated annealn (SA) for solvn t. A two-hase soluton aroach has been studed by Mahdav et al. [25]. They consder ultfunctonal achnes and develoed a atheatcal odel to rou the achnes wth the a of nzn dsslarty of achnes n cells. Tavaol et al. [26] solved a dynac cell foraton roble wht, SA, and tabu search (TS) and stated that wth the roveent of enetc oerators the robablty to acheve the otal soluton be ncreased. Accordn to the studes revewed, the an objectves of GT are as follow: Man coonents to achnes and achnes to cells Mnzn nuber of nter/ntra-cell oves Irovn resource utlzaton throuh cell load balancn Increasn the flexblty of syste The above entoned objectves can be nterreted n CC too, then consdern the n a resource anaeent fraewor could rove the erforance of cloud envronent. So, accordn to surveys conducted n the lterature; the ost contrbutons of our wor are as follow: Introduce a new resource anaeent aroach (GT aroach) for worflow anaeent. Develo a atheatcal odel (exact soluton) to control the nuber of transfers, the load of CC syste and deterne otal nuber of VMs sultaneously for sall robles. Desn a enetc alorth for larer robles wth no otu solutons. III. PROBLEM MODELING In ths research, a CC envronent s consdered wth K servers and M vrtual achne tyes whch are avalable durn the te erod. Each server and achne tye has a certan caacty ncludn: CPU, storae caacty and eory whch are nown and constant durn the te erod. There are also j worflows wth re-defned deand, rocessn te and QoS (e.. CPU, storae caacty and eory).the VMs and tass ust be dstrbuted aon K serves and M VM tyes reardn ther caacty so that the transfers between servers and the load varance of servers are nzed. The otal nuber of each VM tye (N ) s obtaned by odel. A resource allocaton roble n CC envronent s resented n F.3. Fure 3. A sle case of resource allocaton n cc A. Notatons. The notatons used n roble forulaton are as follow: B. Indces j tas, j = 1,, t; subtas, = 1,, s; vrtual achne tye, = 1,, ; server, =1,, c; C. Inut araeters Wj Worload on achne tye by tas j n server ; Aj Averae ntra-server rocessn te for tas j n server ; tj rocessn te for subtas of tas j on achne ; aj 1 f subtas of tas j wll requre achne tye,0 otherwse; L The axu aount of QOS that achne tye can rovde; C The axu aount of QOS that server can rovde; The total te whch VM s avalable; T D. Decson varables: X j 1 f subtas of tas j s done on achne tye n server,0 otherwse; Y j 1 f subtas of tas j assned to server,0 otherwse; N Nuber of achne tye assned to server ; 1546
W A j j s D j. t. X j j (1) T W j N. N E. Matheatcal odel forulaton. The objectve functons of roosed odel that ven n (3) s coosed of three crtera (F 1, F 2 and F 3 ) accordn to (4-6). (2) F= α F 1 + β F 2 + γf 3 (3) 1 s c 2 j j F W A j s t c F Y 1 Y (4) (5) 2 j 1 j j c 3 N (6) F Whch α, β and γ are the user-defned references. The servers load varaton s ven n (4), where W j s obtaned fro (1) and les worload on achne by tas j n server. A j s obtaned fro (2) and coutes the averae rocessn te for tas j n server. (5) ndcates the nuber of tass transferrn between servers. Also, the nuber of vrtual achne s dentfed n (6). Now, the odel forulaton can be resented as follown: Mn F F1F2 F (9) 3 s.t X a. Y X j, j,, j j j N, j,, c X j 1 s t j D. t. X L. N j j j (10) (11), j,, (12), j,, (13), (14) L. N C Y j, X j 0,1, j,, (15) N 0, nteer, (16) The objectve functon n (7) s coosed of three ters resented n (4 6) wth dentcal references. Constrants (8) shows the relaton between two varables (X j and Y j ) and state that tas of tas j s erfored on VM n server (X j =1) f (Y j =1) and (a j =1) sultaneously. Constrants (9) and (10) ensure that each subtas s assned just to one VM n one server that requred VMs are exstent n t. Constrants (11) and (12) uarantees that vrtual achne and server's caacty are not exceeded. Constrants (13) and (14) secfy the decson varables. IV. THE PROPOSED GENETIC ALGORITHM After runnn the odel wth the Lno 9 software t was found that the roosed odel can solve robles u to 5 5 3 (tass VM tye servers) szes. So t s necessary to use aroxate soluton ethods for larer robles. Genetc alorth () s a heurstc search technque whch roosed by Holland [27] for achevn otal or near-otal solutons n colcated robles. Ths alorth uses fro bolocal technques le crossover, utaton and natural selecton to create better solutons. was roosed n dfferent felds such as desnn of artfcal ntellence roras (ae-layn rora) by Baley [28] and sulaton boloy rocesses by Rosenber [29]. Here, a enetc alorth s offered for solvn the studyn odel accordn to Tavaol et al. [26]. The factors ntended for desnn the are descrbed n follown. A. Chroosoe structure Chroosoe structure (F.4) whch coosed of three enes (.e. X, Y, and N) s descrbed below: Two ens [X] and [Y] are (subtas)-by-j (tas) atrces related to assnent of subtas to VMs and servers and ther eleents are ntalzed fro 1 to and 1 to resectvely. Also atrx [N] s related to the nuber of VMs n each server. The eleents of atrx [N] are ostve nteer values. Worth notn that atrx [X] s constant and obtaned by a j. B. Generaton of ntal oulaton For eneratn an ntal oulaton, frst atrx [N] ntalzed randoly consdern the caacty constrants of servers. Then atrx [Y] s ntalzed randoly wth reard to the ossblty of tas executon. C. Ftness value The ftness value s a crteron to easure the qualty of a chroosoe. In ths aer t s calculated by (17) and (18). Ftness functon f F Fure 4. Chroosoe w f (17) F (18) Where W s a user-defned weht, F s the value of objectve functon and f s noralzed value of F. 1547
D. Selecton stratees A ood selecton stratey should be able to select the best chroosoes as for creatn new eneraton. n ths study the ftness of chroosoes n current eneraton are frst noralzed by (19) and then the chroosoes, whch ther noralzed ftness are less or equal to zero, are selected as a atn ool. Ftness X (19) Z Where Z s noralzed ftness of chroosoe and X s the ftness of th eneraton, also as and are ean and standard devaton of ftness values for eneraton. E. Oerators Due to the atrx structure of the chroosoes the oerators should be adated wth the, so the odfed oerators are as follows: Dstrcted crossover- A bloc of arent's enes (Y or N) s selected and then all the selected eleents are relaced toether (n crossover) and chaned (n utaton). Sultaneous dstrcted crossover- A bloc of arent's enes Y and N s selected sultaneously and ther eleents are relaced toether (n crossover) or chaned (n utaton). The crossover and utaton oerators wth robablty of 0.7 and 0.4 are aled resectvely. F. Rearn An nfeasble chroosoe wll be created when the caacty of VMs and servers exceed the ertted lt so, enes should be chaned so that surlus caacty be odfed. The rear rocedure s resented n F. 5. Rearn Servers rear 1 If the servers have been chaned, then 2 Whle equed caacty of achne s less than total caacty n all servers 3 Do Rearrane coonents based on ther ossblty of don the on achnes; 4 End VMs rear 5 Else If the achne secton have been chaned, then 6 Whle equed caacty s less total caacty of servers than zero 7 Do Rearrane the achnes; 8 End 9 End Fure 5. VM and Server Rearn G. Ston crtera The rocess of stos when t reaches to axu nuber of eneratons or current te exceeds the secfc te. The seudo-code of the roosed s ven n F.5 Genetc alorth 1 Intalze araeters K,G; 3 Generate K feasble chroosoes X, X j,, X for the frst oulaton; 4 Evaluate the solutons; 5 For 1 to G,, Do 6 Select two chroosoes X, X j as arents throuh the ven selecton stratey; 7 Procedure crossover oerator; 8 Procedure utaton oerator; 9 Procedure rearn; 10 Evaluate the solutons; 11 Relace the new oulaton X +1, X j +1,, X +1 ; 12 End Fure 6. Pseudo-code of roosed V. COMPUTATIONAL RESULTS To rove the valdty of our roosed aroaches, we coare the atheatcal odel and wth Frst-coe, frst-served () alorth. Wth alorth, tass are assned the resources n the order they request the. several sall and lare exales are randoly desned wth t (tass VM tye servers) deotons. Detals of VMs resented n Table I n accordance wth Aazon EC2. The axu nuber of tass and servers for sall and lare robles are (t=5, =5, =3) and (t=20, =15 =5) resectvely. Table I VMs nforaton fro Aazon EC2 VM tye VM1 VM2 VM3 Meory (GB) 15 17.1 7 CPU (EC2 unts) 8 6.5 20 Storae (GB) 1690 420 1690 Exales are solved as otu (Matheatcal odel) wth Lno 9 software and near otu ( and ) wth MATLAB.R2010 roran lanuae on a PC Pentu (R) Dual-core CPU wth 2.50 GHz and 2GB RAM, usn Wn 7 as oeratn syste. The results are resented n Table II. Table II Coutatonal results for sall and lare szed robles Proble Lno Lno And Sall-szed robles S.S.P _01 148.36 148.36 242.23 S.S.P _02 152.92 152.92 237.15 S.S.P _03 152.98 152.98 250.01 S.S.P _04 148.18 148.18 245.57 S.S.P _05 154.35 154.35 243.6 S.S.P _06 151.66 151.66 244.42 Ga (%) Lno And And 2.1 63.27 63.27 2.3 55.08 55.08 1.4 63.42 63.42 1.2 65.72 65.72 1.7 57.82 57.82 2.5 61.16 61.16 1548
S.S.P _07 148.96 148.96 230 1.5 54.40 54.40 S.S.P _08 152.42 152.42 241.12 1.7 58.19 58.19 S.S.P _09 148.13 148.13 221.54 1.4 49.55 49.55 S.S.P _10 152.45 152.45 222.36 3.6 45.85 45.85 Av. 151.04 151.04 237.8 1.94 57.45 57.45 lare-szed robles L.S.P_01 907.02 895.28 1162 1.3 27.89 29.79 L.S.P_02 920.84 877.42 1157 4.7 27.99 31.86 L.S.P_03 924.30 902.16 1167 2.3 30.15 29.35 L.S.P_04 909.39 898.09 1204 1.2 33.05 34.06 L.S.P_05 910.04 890.48 1173 2.1 30.54 31.72 L.S.P_06 910.85 877.28 1197 3.6 31.08 36.44 L.S.P_07 921.22 897.69 1201 2.5 30.26 33.78 L.S.P_08 900.91 876.94 1159 2.6 32.19 32.16 L.S.P_09 906.53 879.96 1163 2.9 29.72 32.16 L.S.P_10 928.39 899.05 1213 3.1 28.93 32.62 Av. 913.494 889.435 1179.6 2.63 30.18 32.39 Table II s related to the coarson of Lno, and for sall and lare szed robles. Colun 1resents the roble IDs (S.S.Ps and L.S.Ps).the otu solutons obtaned by Lno are resented n colun 2. It s worth entonn that for lare szed robles (whch cannot be solved otally n reasonable te) colun 2 resents the objectve bound of lno obtaned n 2 hours. Coluns 3 and 4 resent the solutons of and n 10 ndeendent runs. Also the dfferences between and Lno n dfferent runte are resented n coluns 5-7. F. 7 and F.8 show the averae noralzed objectve values whch obtaned by Lno, and for the sall and lare szed robles. As t can be seen the Lno and Have better erforance coared to. 250 200 150 100 50 1500 1000 500 0 0 Fure 7: Perforance analyss of and Lno wth sall robles Lno Lno (Obj bound) Fure 8: Perforance analyss of and Lno wth for lare robles VI. CONCLUSION Data transferrn and load balancn are snfcant challenes whch ust be consdered n worflow anaeent. For ths urose, ths aer resented a new atheatcal odel based on rou technoloy to handle these Challenes. Grou technoloy s a resource allocaton technque n ndustral envronents. The objectves of odel are controlln the data transferrn and server load varaton as well as deternn the otal nuber of vrtual achnes sultaneously. Also a enetc alorth has been roosed to solve the lare scale robles that no otu soluton exsts for the. REFERENCES [1] R. Buyya, J. Brober, A. Goscns, Cloud coutn rncles and arads. Hoboen, New Jersey, 2011. [2] S. Selvaran, G.S. Sadhasva, Iroved cost-based alorth for tas scheduln n cloud coutn, 2010 IEEE Internatonal Conference on Coutatonal Intellence and Coutn Research. Dec. 2010,. 1-5, do: 10.1109/ICCIC.2010.5705847. [3] Y. Fan, F. Wan, G. Junwe, A tas scheduln alorth based on load balancn n cloud coutn, Lecture Notes n Couter Scence, vol.6318,oct.2010,. 271-277. do: 10.1007/978-3-642-16515-3_34. [4] W. Chen, E. Deelan, R. Saellarou, Ibalance Otzaton n Scentfc Worflows, Proceedns of the 27th nternatonal ACM conference on Internatonal conference on suercoutn, June. 2013, 461-462. do: 10.1145/2464996.2467270. [5] S.P. Mtrofanov, The scentfc rncles of rou technoloy, Natonal Lendn Lbrary Translaton. Boston Sa, Yor's,1966. [6] J.L. Burbde, Producton flow analyss, Producton Enneer, Vol.50, May.1971,.139-152, do: 10.1049/te:19710022. [7] D. Eru, G. Kou, Y. Pen, S. Yon, S. Yu, The analytc herarchy rocess: tas scheduln and resource allocaton n cloud coutn envronent, The Journal of Suercoutn, Vol.64, June 2013,. 835-848.do: 10.1007/s11227-011-0625-1. [8] Xu, C. Zhao, E. Hu, B. Hu, Job scheduln alorth based on Berer odel n cloud envronent, Advances n Enneern Software, Vol.42, July 2011,. 419-425, do: htt://dx.do.or/10.1016/j.advensoft.2011.03.007. [9] G. We, A.V. Vaslaos, Y. Zhen, N. Xon, A ae-theoretc ethod of far resource allocaton for cloud coutn servces, The Journal of Suercoutn, Vol.54, Noveber 2010,. 252-269,do: 10.1007/s11227-009-0318-1. [10] L. L, An otstc dfferentated servce job scheduln syste for cloud coutn servce users and rovders, Proc.Thrd Internatonal Conference.Multeda and Ubqutous Enneern (MUE '09), IEEE Press, June 2009,. 295-299, do: 10.1109/MUE.2009.58. [11] K. Lu, H. Jn, J. Chen, X. Lu, D. Yuan, Y. Yan, A corosedte-cost scheduln alorth n swn DeW-C for nstance-ntensve cost-constraned worflows on a cloud coutn latfor, Internatonal Journal of Hh Perforance Coutn Alcatons, Vol.24, May 2010,.445-456, do: 10.1177/1094342010369114. [12] Belolazov, J. Abawajy, B. Buyya, Enery-aware resource allocaton heurstcs for effcent anaeent of data centers for cloud coutn,, Future eneraton couter., Vol.28, May 2012,.755-768.do: htt://dx.do.or/10.1016/j.future.2011.04.017. [13] N. Netjnda, B. Srnaovaul, T. Achalaul, Cost otzaton n cloud rovsonn usn artcle swar otzaton, Proc. 2012 9 th Internatonal Conference on. Electrcal Enneern/Electroncs, Couter, Telecouncatons and Inforaton Technoloy (ECTI- CON), May 2012,.1-4, do: 10.1109/ECTICon.2012.6254298. 1549
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