Power Consumption Optimization Strategy of Cloud Workflow. Scheduling Based on SLA

Transcription

1 Power Consumpton Optmzaton Strategy of Cloud Workflow Schedulng Based on SLA YONGHONG LUO, SHUREN ZHOU School of Computer and Communcaton Engneerng Changsha Unversty of Scence and Technology 960, 2nd Secton, Wanal South RD, Changsha, Hunan CHINA Abstract: -Cloud computng, as a new model of servce provson n dstrbuted computng envronment, faces the great challenge of energy consumpton because of ts large demand for computng resources. Choosng mproper schedulng method to execute cloud workflow tends to result n the waste of power consumpton. In order to lower the hgher power consumpton for cloud workflow executng, we propose a power consumpton optmzaton algorthm for cloud workflow schedulng based on SLA (Servce Level Agreement), whch can reduce power consumpton whle meetng the performance-based constrants of tme and cost. The algorthm frst searches for all feasble schedulng solutons of cloud workflow applcaton wth crtcal path, then the optmal schedulng soluton can be found out through calculatng total power consumpton for each feasble schedulng soluton. The expermental results show that compared wth tradtonal workflow schedulng algorthms based on QoS, the optmzaton algorthm proposed n ths paper not only meets the constrants of tme and cost defned n SLA, but also reduces the average power consumpton by around 0%. Key-Words: - Cloud computng, Cloud workflow, SLA, Crtcal path, Schedulng solutons, Power consumpton optmzaton Introducton Due to ntegratng a large number of computng resources and storage resources n cloud data center, cloud computng system needs to solve varous problems to mplement a hgh effectve, low-cost and safe dstrbuted computng platform []. The hgh power consumpton [2, 3] s one of the most serous problems for cloud computng system. Accordng to statstcs, the power consumpton of cloud data centers [4] has rsen by 56 percent from 2005 to 200, and n 200 accounted to be between. and.5 percent of the global electrcty use [5]. There exsts power consumpton waste caused by mproper schedulng method n addton to the necessary power consumpton for executng user tasks n cloud computng system [6]. Cloud computng system usually contans a lot of computers wth dfferent performance whch may need dfferent response tme and power consumpton to execute the same tasks. Wth regard to power consumpton, the msmatched schedulng soluton [7] usually spends hgher power consumpton to fnsh user requred task whch can be executed wth lower power consumpton. Therefore, how to realze cloud computng system wth low power consumpton through schedulng resources approprately have been wdely concerned. Cloud workflow [8, 9] s a new applcaton mode for workflow management system n cloud E-ISSN: Volume 3, 204

2 computng envronment, whch can provde optmzaton solutons for cloud computng system to reduce operaton cost and mprove the qualty of cloud servces [0]. The schedulng of cloud workflow whch s the same as that of grd workflow [] s the problem of mappng each task to a sutable resource and of orderng the tasks on each resource to satsfy some performance crteron [2,3]. The schedulng algorthm of workflow can be dvded nto two categores: schedulng algorthms based on best-effort servce and schedulng algorthms based on OoS constrants [4]. Yu et al. [5] proposed economy-based methods to handle large-scale grd workflow schedulng under deadlne constrants, budget allocaton, and QoS. Dogan and Özgüner [6] developed a matchng and schedulng algorthm for both the executon tme and the falure probablty that can trade off them to get an optmal selecton. Morett et al. [7] suggested all of the pars to mprove usablty, performance, and effcency of a campus grd. At present, there are a few works addressng cloud workflow schedulng. Juve n lterature [8] compared the performance of runnng some scentfc workflows on the NCSA s Abe cluster, aganst the Amazon EC2. Both use Pegasus [9] as the workflow management system to execute the workflows. Prodan et al. [20] proposed a b-crtera schedulng algorthm that follows a dfferent approach to the optmzaton problem of two arbtrary ndependent crtera, e.g. executon tme and cost. RC2 algorthm [2] for schedulng tasks n hybrd cloud was proposed by Lee and Zomaya to acheve relable completon. An ntal schedule s frst calculated based on prvate cloud (or locally owned resources) to mnmze cloud resource usage. In 20, Bttencourt and Madera proposed the HCOC algorthm [22] to schedule cloud workflows wthn deadlne whle mnmzng compute cost. In addton to task executon tme and compute cost that are used n the technques descrbed so far, the PBTS algorthm proposed by Byun et al. [23] begns to consder other aspects n the cloud. So far, there are few works solvng energy-aware cloud workflow schedulng. Regardng the exstng energy consumpton models [24, 25, 26], they all consder only two levels of energy consumpton n a machne correspondng to ts dle and full-load states. These models, however, do not properly reflect the current energy-aware mult-core archtectures. Authors n lterature [27] proposed an energy-aware heurstc schedulng for data-ntensve workflows n vrtualzed datacenters, whch ntroduces a novel heurstc called Mnmal Data-Accessng Energy Path for schedulng data-ntensve workflows amng to reduce the energy consumpton of ntensve data accessng. Pareto-based mult-obectve workflow schedulng algorthm was proposed n lterature [28, 29], whch captures the real behavor of energy consumpton n heterogeneous parallel systems based on emprcal models. We can know from the aforementoned schedulng algorthms of workflow applcatons that regardless of tme optmzaton algorthms, cost optmzaton algorthms schedulng or energy-aware schedulng algorthms for workflow applcatons, all of them have not effectvely solved the power consumpton optmzaton problem faced n the cloud computng envronment, whch lkely result n power waste phenomenon as msmatch schedulng of cloud workflows. So, we propose a power consumpton optmzaton algorthm of cloud workflow schedulng based on servce level agreement whch tres to match each task of workflow applcaton to the reasonable servce provded by server n cloud computng system. The key of optmzaton algorthm proposed n ths paper s to fnd out all feasble schedulng solutons for canddate cloud workflow applcatons. 2 Cloud workflow model 2. DAG model of cloud workflow The DAG (Drected Acyclc Graph) s a well-known model for descrbng workflow applcatons n E-ISSN: Volume 3, 204

3 dfferent computng envronments. So, a cloud workflow applcaton also can be represented by the DAG G=(T,E) ( as shown n fg.), where T s a set of tasks t ( =,2,,n), and E s a set of edges e, (t t ) that descrbe the dependences between tasks. In gven DAG model for a cloud workflow, f t a T and e,a E for all t T, then the task t a s called an entry task of the cloud workflow; f t z T and e z, E for all t T, then the task t z s called a ext task of the cloud workflow. In order to better understand the DAG model, we always add two dummy tasks of t entry and t ext to the begnnng and end of the cloud workflow, respectvely. tentry t t 5 t 4 t 3 t 2 Fg. DAG model of a cloud workflow The servce provders offer several servces wth dfferent QoS for each task of every cloud workflow. We assume that each task t of cloud workflow can be executed by k servces wth dfferent QoS attrbutes, S = {s,, s,2,, s,k }. There are many QoS attrbutes for servces n cloud computng system, ncludng executon tme, cost, relablty, power energy effcency, and so on. In ths paper, we consder the most mportant three factors: executon tme, cost and power energy effcency for our schedulng model. ET(t, s, ) and EC(t, s, ) are defned as the executon tme and the executon cost of executng task t on servce s,, respectvely. The data transfer tme of a dependency e, only depends on the amount of data to be transferred between correspondng tasks, and t s ndependent of the servces whch execute them [30]. Therefore, TT(e, ) s defned as the data transfer tme of a dependency e,, and ndependent of the selected servces for t and t. t 7 t 6 t 8 text 2.2 Crtcal path for cloud workflow A schedule of cloud workflow applcaton s defned as an assgnment of servces to the cloud workflow tasks. If SS(t ) denotes the selected servce for task t, then a schedule of cloud workflow w(t, E) can be defned as: ( ) ( ) ( ) Sched w = {SS t t T SS t = s S }, () Defnton.Servce Graph: SG=(S,D), where S={s mappng(s,t )} s a set of servces whch nclude all selected servces for each task n cloud workflow applcaton, D={e, (s,s ) (t,t )} s a set of edges between servces ( each edge descrbes a dependency between servces n Servce Graph). In ths paper, a feasble schedulng soluton for cloud workflow applcaton s represented as the correspondng servce graph. For example, cloud workflow applcaton n fgure s scheduled to form mappngs between tasks and servces as follows: t s,3, t 2 s 2,2,t 3 s 3,4, t 4 s 4,6,t 5 s 5,6, t 6 s 6,8, t 7 s 7,8, t 8 s 8,, and ts correspondng servce graph can be obtaned(as shown n fg.2). S,3 S 5,6 S 4,6 S 3,4 S 2,2 Fg.2 Servce graph of a cloud workflow applcaton Defnton 2.Crtcal path: For any cloud workflow applcaton establshed workflow model wth drected acyclc graph w(t, E), each task s matched to a sutable servce durng the schedulng. All of mappngs between servces and tasks can generate a servce graph n accordance wth w(t,e) of the cloud workflow applcaton, whch ncludes a correspondng crtcal path denoted as WCP n ths paper. Wth the WCP of cloud workflow applcaton, S 7,8 S 6,8 S 8, E-ISSN: Volume 3, 204

4 the executve tme of cloud workflow applcaton can be defned as: T = ( ET ( SS ( t )) + TT ( SS ( t ))) (2) t WCP As for the total cost of cloud workflow applcaton executon, t can be calculated as follows: n C = Cost( SS( t )) (3) = In order to meet the tme constrant of cloud workflow schedulng, we need to defne ts earlest start tme EST, earlest fnsh tme EFT and latest fnsh tme LFT for each task of cloud workflow applcaton. Due to the earlest start tme of task t at whch t can start ts computaton, EST of t can be computed as follows: EST ( tentry ) = 0 (4) ( ) EST t = max { EST ( t ) + ET ( t, SS( t )) + TT ( e ) p p p p, t p predecessors( t) (5) Accordngly, the earlest fnsh tme of each task t s the earlest tme at whch t can fnsh ts computaton, the EFT of t s computed as follows: ( ) ( ) ( ) ( ) EFT t = EST t + ET t,ss t (6) The latest fnsh tme of task t s the latest tme at whch t can fnsh ts computaton, such that the whole cloud workflow can fnsh before the user defned deadlne, D. LFT of t can be computed as follows: LFT ( t ext ) ( ) = D (7) LFT t = mn { LFT ( t ) ET ( t, SS( t )) TT ( e )} c c c c, tc successors( t) (8) Defnton 3.Crtcal parent: the crtcal parent of node t s the unscheduled parent of t that has the latest data arrval tme at t, that means t s the parent t p of t, for whch EST (t p ) + ET (t p, SS(t p )) + TT (e p, ) s maxmal. The algorthm obtanng crtcal path for cloud workflow schedulng s as follows: Input: w(t,e) Output: WCP Begn Step: add t entry, t ext and ther correspondng dependences to w; Step2: for (=;<=n;++) do Step3: Select the fastest dle servce for each task t : SS(t ); Step4: end for Step5: for (=;<=n;++) Step6: compute EST(t ) accordng to Eq. (5); Step7: end for Step8: for (=n;<=;--) Step9: compute LFT(t ) accordng to Eq. (8); Step0: end for Step: mark t entry and t ext as scheduled nodes; Step: t=t ext, WCP=null; Step2: whle (there exsts an unscheduled parent of t) do Step5: add CrtcalParent(t) to the begnnng of WCP; Step6: t=crtcalparent(t); Step7: end whle End. 3 Power consumpton model For the power consumpton of any cloud workflow whch s composed of n tasks durng the executon, we can buld the followng model: n PC = P( t, SS( t) T ( t, SS( t)) (9) = Where PC denotes the total power consumpton of cloud workflow executon, SS(t ) represents the selected servce for task t, P(t, SS(t )) ndcates the power whch s needed to execute task t on servce SS(t ), T(t, SS(t )) denotes the tme that s spent to execute task t on servce SS(t ). In ths paper, we assume that the task set of cloud computng system s W={w,w 2,,w m }(m>=n), and the arrval rate for each type of cloud task s denoted as λ (=,2,,m). E-ISSN: Volume 3, 204

5 Each servce can establsh a queung model of M/M/ to process a knd of user task requrement, s, means that the servce selected for task w s deployed on the server h. So, the arrval rate λ, of task w allocated to h can be represented as follows: λ = P λ (0),, Where P, represents the probablty for s, deployed on server h to execute task t. If the servce rate for servce s, to reply task t s μ,, the average response tme ART of executng t on s, can be calculated as follows: ART = µ λ,, () If the tme constrant of task w s qt, then μ, can be expressed as follows n the case of ART=qt : µ = λ + = P λ + (2) qt,,, qt The servce ntensty for server h to execute all m types of task (w, w 2,, w m ) can be expressed as follows: m λ ρ = = µ m,, =, = P λ P, λ + qt (3) The power of any servce s, at the moment of t denotes as Power, can be calculated as follows: c b,, (), () ρ Power t = P + a t (4) Where P c s constant power consumpton of servce s,, a, and b, are power parameters. Generally, dfferent servce ntensty corresponds to dfferent power parameters. Wth Eq. (3), the power of server h for s, can be computed as follows when the workload of servce s, tends to stablty: m P, λ c b, P + a, ( ),0 < λ, max( λ, ) = Power P =, λ + qt 0, λ, = 0 (5) Therefore, we assume that the start tme and the end tme for servce s, are represented as tme and tme 2,respectvely. Then, the power consumpton durng the executon of servce s, can be calculated as follows: P λ PC P a dt tme n 2 c, b,, = ( +, ( ) ) tme = P, λ + qt (6) 4 Optmzaton of power consumpton 4. Model of power optmzaton Accordng to the model of power consumpton n ths paper, power consumpton optmzaton of cloud workflow schedulng ams to reduce the total power consumpton of cloud workflow executon based on the constrants of tme and cost n Servce Level Agreement, and ts optmzaton model s represented as follows: n mn P( SS( t), t) = Tme( SS( t), t) makspan t WCP n C( SS( t), t) Cost = (7) Where P(SS(t ),t ) denotes the power consumpton whch s produced by executng task t on matched servce SS(t ), Tme(SS(t ),t ) denotes the tme s needed to execute t on selected servce SS(t ), makspan represents the total tme constrant specfed n user s Servce Level Agreement, C(SS(t ),t ) represents the requred cost that s necessary to execute task t wth allocated servce SS(t ), Cost ndcates the total cost defned n user s Servce Level Agreement. Defnton 4.Feasble Schedulng Soluton: f a schedulng soluton correspondng to a servces graph can fnsh the executon of cloud workflow w(t,e) successfully whle meetng the requred tme attrbute and cost attrbute n user s Servce Level E-ISSN: Volume 3, 204

6 Agreement, we call t a feasble schedulng soluton. We assume that Sch k s a feasble schedulng soluton for cloud workflow w(t,e) that can satsfy the tme attrbute and cost attrbute n user s Servce Level Agreement, Ω(Sch) s the set of all feasble schedulng soluton for cloud workflow w(t,e). For any cloud workflow applcaton, we suppose there should be at least one feasble schedulng soluton, and let Ω(Sch) =L where L represents the number of feasble schedulng solutons. The power consumpton whch s needed to execute a feasble schedulng soluton can be estmated as follows: PC ( FSS ) = PC,, FSS = ( S, D ) (8) k k k k s, Sk 4.2 Algorthm of power consumpton optmzaton In order to solve the problem of power waste caused by the mproper schedulng of cloud workflow, the algorthm dea of power consumpton optmzaton for cloud workflow schedulng s as follows: Frstly, algorthm needs to search for all feasble schedulng solutons among the correspondng servce graphs of cloud workflow schedulng. For a schedulng soluton of cloud workflow applcaton, f ts executon tme and executon cost obtaned by evaluaton are all less than the tme constrant and cost constrant n SLA, we can mark the soluton as a feasble schedulng soluton. Then, the power consumpton for each feasble schedulng soluton can be computed accordng to Eq.(8). Fnally, we select the schedulng soluton wth the mnmum power consumpton as the optmal schedulng soluton. The detaled algorthm of power consumpton optmzaton for cloud workflow schedulng (PCOA) s as follows: Input: DAG of cloud workflow, SG Output: optmal schedulng soluton of cloud workflow wth the mnmum power consumpton Begn Step :K=0; Step2 :for each SG of cloud workflow do Step3 : Fnd out the crtcal path for the SG ; Step4 : f ( Tme( mappng( s, t)) makspan and n = s WCP C( s, t ) Cost ) then Step5 : add SG to FSS k (feasble schedule soluton set); Step6 : k++; Step7 : end f Step8 :end for Step9 :compute the power consumpton of FSS 0 accordng to Eq.(6); Step0:mn_PC=PC(FSS 0 ); Step:for (=; <k; ++) do Step2: compute the power consumpton of FSS accordng to Eq.(8); Step3: f (mn_pc>pc(fss )) do Step4: mn_pc= PC(FSS ); Step5: set FSS as the optmal schedule soluton of cloud workflow; Step6: end f Step7:end for End. 5 Expermental results and analyss To evaluate the effectveness of power consumpton optmzaton strategy for cloud workflow schedulng proposed n ths paper, we employed three workflow schedulng methods ncludng Loss and Gan, Deadlne-MDP and PCOA to carry out four scentfc workflow applcatons, such as Montage, Epgenomcs, MRI and e-proten n smulated cloud computng envronment CloudSm, and compared the tme, cost and power consumpton for them after Loss and Gan, Deadlne-MDP and PCOA fnshed the executon of Montage, Epgenomcs, MRI and e-proten. In experment, we assgned a tme constrant (Montage:400s, Epgenomcs:400s, MRI:350s, e-proten:350s) and a cost constrant (Montage:35$, Epgenomcs:35$, MRI:30$, e-proten:30$) for each cloud workflow applcaton, whch are represented as makspan and cost, E-ISSN: Volume 3, 204

7 respectvely. Moreover, we also provded ten servces for each type of task, whch are deployed on dfferent servers. All of ten servces need to spend dfferent tme and cost to process the same task. Generally speakng, a faster servce costs more power than a slower one. Envronment parameters nvolved n ths experment and ther values are shown n table. After completng ten tmes executon for each scentfc workflow applcaton, the average tme, cost and power consumed by three dfferent schedulng methods are shown n fg.3, fg.4 and fg.5, respectvely. Fg.3 shows that under the constrants of tme and cost n SLA, Loss and Gan method performs each workflow applcaton wth the least tme, and Deadlne-MDP spends the most tme to fnsh the executon of every workflow applcaton, whle the run tme for PCOA s greater than that of Loss and Gan, and less than that of Deadlne-MDP. So, we should adopt Loss and Gan method to schedule varous workflow applcatons f the target of schedulng cloud workflow s to mnmze the completon tme. Table.Parameters settng of the smulated envronment Parameter Settng Descrpton Number of servers n h 25 smulated cloud envronment Fg.3 Comparson of average tme for performng cloud workflow Fg.4 ndcates that wth the constrants of tme and cost n SLA, Loss and Gan method needs to spend the hghest cost to execute each of workflow applcatons and Deadlne-MDP only spends the lowest cost to perform every workflow applcaton, whle the average cost for PCOA to fnsh the executon of all workflow applcatons s slghtly lower than that of Loss and Gan method. Therefore, we should select Deadlne-MDP method f the schedulng ams to mnmze the cost of cloud workflow executon. λ [5,20] μ, [4,5] Average arrval rate for task t (=,2, ) Servng rate for server h to execute task t P [50w,80w] Idle power of server h P, [200w,600w] Executve power of server h processng task t makspan [50s-400s] Tme constrant n user s SLA cost [0$-50$] Cost constrant n user s SLA Fg.4 Comparson of average cost for performng cloud workflow We can know from fg.5 that under the constrants of tme and cost n SLA, Deadlne-MDP, Loss and Gan spend almost the same power to complete the executon of each workflow applcaton, whle the average power consumpton produced by PCOA s the optmal among the three schedulng E-ISSN: Volume 3, 204

8 methods of Loss and Gan, Deadlne-MDP and PCOA. The reason why PCOA can reduce the power consumpton of cloud workflow applcaton executon s that PCOA can select the optmal schedulng soluton for each cloud workflow applcaton through usng crtcal path. The expermental results show that compared wth tradtonal workflow schedulng algorthms based on QoS, the optmzaton algorthm proposed n ths paper not only meets the constrants of tme and cost defned n SLA, but also reduces the average power consumpton by around 0%. So, we should employ PCOA to dspatch all cloud workflow applcatons n order to reduce the power consumpton of cloud workflow executon caused by mproper schedulng algorthm n cloud computng envronment. ssue sn t mplemented n the vrtual cloud envronment. So, we wll further nvestgate the power consumpton optmzaton of cloud workflow schedulng based on the vrtual machnes allocaton n the future, and carry out experments n real vrtualzaton cloud platform so as to ensure the correctness and effectveness of research result. Acknowledgements Ths work was supported by Natonal Natural Scence Foundaton of Chna (Grant no ) and Scentfc Research Fund of Hunan Provncal Educaton Department (Grant no. 3C003). Fg.5 Comparson of average power consumpton for performng cloud workflow 6 Conclusons Ths paper studed the power consumpton optmzaton of cloud workflow schedulng as energy waste ssues n the cloud computng envronment have become ncreasngly promnent. Through analyzng the power computng model for cloud workflow applcaton executon, we have proposed a power consumpton algorthm of cloud workflow schedulng under the constrants of tme and cost n SLA. Smulated experments demonstrate that ths optmzaton method s fully effectve and feasble. But the power optmzaton References: [] Doroshn, A. V., Ner, F, Open research ssues on Nonlnear Dynamcs, Dynamcal Systems and Processes, WSEAS Transactons on Systems, Vol.3, 204, n press. [2] Azzouz, M., Ner, F., An ntroducton to the specal ssue on advanced control of energy systems, WSEAS Transactons on Power Systems, Vol.8, No.3, 203, p. 03. [3] Ymng Tan, Guosun Zeng, We Wang, Polcy of Energy Optmal Management for Cloud Computng Platform wth Stochastc Tasks, Journal of software, Vol.23, No.2, 202, pp (In Chnese) [4] Armbrust M, Fox A, Grffth R, Joseph A D, A vew of cloud computng, Communcatons of the ACM, Vol.53, No.4, 200, pp [5] Karthkeyan, P., Ner, F, Open research ssues on Deregulated Electrcty Market: Investgaton and Soluton Methodologes, WSEAS Transactons on Systems, Vol.3, 204, n press. [6] Cufudean, C., Ner, F, Open research ssues on Mult-Models for Complex Technologcal Systems, WSEAS Transactons on Systems, Vol.3, 204, n press. [7] Chuang Ln, Yuan Tan, Mn Yao, Green Network and Green Evaluaton: Mechansm, Modelng and Evaluaton, Chnese Journal of E-ISSN: Volume 3, 204

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