Scentfc Programmng Volume 2016, Artcle ID 8239239, 9 pages http://dx.do.org/10.1155/2016/8239239 Research Artcle Adaptve Cost-Based Task Schedulng n Cloud Envronment Mohammed A. S. Mosleh, 1 G. Radhaman, 1 Mohamed A. G. Hazber, 2 and Syed Hamd Hasan 3 1 School of IT & Scence, Dr. GR Damodaran College of Scence, Combatore, Inda 2 Internatonal School of Software Engneerng, Wuhan Unversty, Wuhan, Chna 3 Informaton Systems Department, Kng Abdulazz Unversty, Jeddah, Saud Araba Correspondence should be addressed to Mohammed A. S. Mosleh; mohammed.mosleh@grd.edu.n Receved 22 June 2016; Revsed 19 September 2016; Accepted 20 October 2016 Academc Edtor: Frank De Boer Copyrght 2016 Mohammed A. S. Mosleh et al. Ths s an open access artcle dstrbuted under the Creatve Commons Attrbuton Lcense, whch permts unrestrcted use, dstrbuton, and reproducton n any medum, provded the orgnal work s properly cted. Task executon n cloud computng requres obtanng stored data from remote data centers. Though ths storage process reduces thememoryconstrantsoftheuser scomputer,thetmedeadlnesaserousconcern.inthspaper,adaptvecost-basedtask Schedulng (ACTS) s proposed to provde data access to the vrtual machnes (VMs) wthn the deadlne wthout ncreasng the cost. ACTS consders the data access completon tme for selectng the cost effectve path to access the data. To allocate data access paths, the data access completon tme s computed by consderng the mean and varance of the network servce tme and the arrval rate of network nput/output requests. Then the task prorty s assgned to the removed tasks based data access tme. Fnally, the cost of data paths are analyzed and allocated based on the task prorty. Mnmum cost path s allocated to the low prorty tasks and fast access path are allocated to hgh prorty tasks as to meet the tme deadlne. Thus effcent task schedulng can be acheved by usng ACTS. The expermental results conducted n terms of executon tme, computaton cost, communcaton cost, bandwdth, and CPU utlzaton prove that the proposed algorthm provdes better performance than the state-of-the-art methods. 1. Introducton Cloud computng s a promsng technology that provdes effcent servces to the customers n a dstant vrtual platform on a pay-per-use model. The defnton for cloud computng gven by NIST [1] s as follows: cloud computng s a model for enablng ubqutous, convenent, on-demand network access to shared computng resources whch can be provsoned and provded wth mnmal nteracton. Cloud computng provdes dfferent types of servces such as nfrastructure, software, and platform to the requested users wth a specfc prcefortheservces.cloudservcesusethenternetandthe central remote servers to mantan the data and applcatons. Cloud computng allows consumers and busnesses to use applcatons wthout nstallaton and access ther personal flesatanycomputerwthnternetaccess.thsapproach mproves the computng processes such as data storage and processng. Cloud s deployed n dfferent models: publc cloud, prvate clouds, hybrd cloud, communty cloud, and dstrbuted cloud are some examples. Servce orented archtecture s the basc prncple of the cloud computng whch consders everythng on the cloud as a servce [2]. Infrastructure-as-a-servce (IaaS) s the servce of provdng the physcal machnes (PM) or vrtual machnes (VM) to the user for processng resources, data parttonng, scalng, securty, and backup processes. Platformas-a-servce (PaaS) provdes the vendors wth the platforms for development of applcatons ncludng databases, web servers, and developmental tools. Software-as-a-servce (SaaS) provdes servces for the e-mals, vrtual desktops, communcaton processes, and gamng applcatons. The servces are normally pad servces whose prce s fxed by the servce provders based on the usage level of the customers. The prce of the cloud servces s very less compared to the other nstalled servces. In cloud computng, the tasks are performed n the physcal machnes (PMs) or the VMs as per the task requrements. The data requred for the executon of the tasks and servces arestoredatmultpledstantstoragelocatonscalledthedata centers whch are also used wth specfc cost [3]. When the
2 Scentfc Programmng tasks are performed n the processng machnes, the requred data are requested and obtaned from the data centers. The data from the data centers has to reach the VM wthn the partcular tme whch s always the access completon tme. The problem wth ths process s that the data s accessed through certan paths whch are bound by the computaton and storage costs. So t s possble that ether one of the two stuatons arses: n order to obtan the data n tme, the cost hastobesacrfcedor,nordertoreducethecost,thedelay n data access has to be accepted. Ths problem reduces the overall schedulng performance. In order to overcome the data access problem, an adaptve cost-based task schedulng (ACTS) s proposed n ths paper so that the data s obtaned at the requred tme wthout delay and through affordable cost paths. The proposed approach estmates the completon tme for accessng the data [13] that are requred by the VM machnes durng the partcular task executons. Then the cost of each possble path s estmated by the sum of computaton, communcaton, and storage costs [14] of the path. Usng the completon tme for data access the prorty of the tasks s assgned. The paths wth hgh cost but wth quck data access are assgned to tasks wth hgh prorty and the paths wth low cost are assgned to the low prorty tasks. Thus the data paths can be adaptvely selected to reduce theoverallcostandeffectvelydelverthedataattherequred tme. The remander of the paper s summarzed as follows: Secton 2 explans the related researches brefly and presents the analyss of schedulng schemes. Secton 3 presents the methodologes utlzed n the paper. Secton 4 provdes the expermental results and ther dscussons. Secton 5 concludes the research. 2. Related Works A cloud scheduler s a cloud-enable dstrbuted resource manager. It manages vrtual machnes on clouds to create an envronment for job executon. The FIFO scheduler n Hadoop MapReduce, far scheduler n Facebook, and capacty scheduler n Yahoo are typcal examples that serve the cloud systems wth effcent and equtable resource management, but none of these schedulers satsfes QoS (qualty of servce) constrants. Therefore, they are not applcable to soft real-tme needed applcatons and servces, whch are becomng more and more mportant and necessary n thehybrdcloudenvronment.themanobjectveofths secton s not to propose methodologes to overcome all of the current ssues n cloud task schedulng but to study and analyze some of the current methodologes and focus on fndng ther drawbacks. Sahn and Vdyarth [4] presented a cost-effectve deadlne constrant dynamc schedulng algorthm for the scentfc workflows. The workflow schedulng algorthms n thegrdandclustersareeffcentbutcouldnotbeutlzed effectvely n the cloud envronment because of the ondemand resource provsonng and pay-as-you-go prcng model. Hence the schedulng usng a dynamc cost-effectve deadlne-constraned heurstc algorthm has been utlzed to explot the features of cloud by consderng the vrtual machne performance varablty and nstance acquston delay to determne the tme schedulng. The problem wth the approach s that VM falures may adversely affect the overall workflow executon tme. Tsa et al. [5] proposed hyper-heurstc schedulng algorthm (HHSA) for provdng effectve cloud schedulng solutons. The dversty detecton and mprovement detecton operators are utlzed n ths approach to dynamcally determnethebetterlow-levelheurstcfortheeffectveschedulng.hhsacanreducethemakespanoftaskschedulngand mproves the overall schedulng performance. The drawback s that the approach has hgh overhead of connecton whch reduces the mportance of schedulng and thus reduces the overall performance. Zhu et al. [6] proposed an agent-based dynamc schedulng algorthm named ANGEL for effectve schedulng of tasks n the vrtualzed clouds. In ths approach, a bdrectonal announcement-bddng mechansm and the collaboratve process are performed to mprove the schedulng performance. To further mprove the schedulng, elastcty s consdered to dynamcally add VMs. The calculaton rules are generated to mprove the bddng process that n turn reduces the delay. The problem wth ths approach s that t reduces the performance as t does not consder the communcaton and dspatchng tmes. Zhu et al. [7] presented an evolutonary multobjectve (EMO) workflow schedulng approach to reduce the workflow schedulng problem such as cost and makespan. Due to the specfc propertes of the workflow schedulng problem, the exstng genetc operatons, such as bnary encodng, realvalued encodng, and the correspondng varaton operators are based on them n the EMO. The problem s that the approach does not consder monetary costs and tme overheads of both communcaton and storage. Zhang et al. [8] proposed a fne-graned schedulng approach called phase and resource nformaton-aware scheduler for MapReduce (PRISM) for schedulng n the MapReduce model. MapReduce has been utlzed for ts effcency n reducng the runnng tme of the data-ntensve jobs but most of the MapReduce schedulers are desgned on the bass of task-level solutons that provde suboptmal job performance. Moreover, the task-level schedulers face dffcultes n reducng the job executon tme. Hence the PRISM was developed whch dvdes tasks nto phases. Each phase wth a constant resource usage profle performs schedulng at the phase level. Thus the overall job executon tme can be reduced sgnfcantly but the problem of meetng job deadlnes n the phase level schedulng s a serous concern that requres specfed attenton. Zhu et al. [9] presented real-tme task orented energy aware (EA) schedulng called EARH for the vrtualzed clouds. The proposed approach s based on rollng-horzon (RH) optmzaton and the procedures are developed for creaton, mgraton, and cancellaton of VMs to dynamcally adjust the scale of cloud to acheve real tme deadlnes and reduce energy. The EARH approach has the drawback of the number of cycles assgned to the VMs that cannot be updated dynamcally.
Scentfc Programmng 3 Table 1: Drawbacks of schedulng schemes n lterature. Author Schedulng scheme Drawbacks Sahn and Vdyarth [4] Cost-effectve deadlne constrant dynamc schedulng algorthm VM falures ncrease the workload of other VMs and affect the executon tme Tsa et al. [5] Hyper-heurstc schedulng algorthm Hgh overhead of connecton Zhu et al. [6] Agent-based schedulng algorthm n vrtualzed Nonconsderaton of communcaton and clouds (ANGEL) dspatchng tme reducng performance Zhu et al. [7] Evolutonary multobjectve (EMO) workflow Nonconsderaton of monetary costs and tme schedulng overhead does not mprove performance Zhang et al. [8] Phase and resource nformaton-aware scheduler for MapReduce (PRISM) Deadlnes are not specfed Zhu et al. [9] Energy aware rollng-horzon (EARH) optmzaton based schedulng Lack of updaton n number of VM cycles Magulur and Srkant [10] Throughput-optmal schedulng & load-balancng Utlzng queue lengths n weghts s based on algorthm assumpton Zuo et al. [11] Self-adaptve learnng partcle swarm Lack of prorty to deadlne constrant tasks optmzaton- (SLPSO-) based schedulng results n task falures Su et al. [12] Cost effcent task schedulng Does not consder the completon tme and cost (computaton cost and communcaton cost) Magulur and Srkant [10] suggested a schedulng method for job schedulng wth unknown duraton n the cloud envronment. The job szes are assumed to be unknown not onlyatarrval,butalsoatthebegnnngofservce.hence the throughput-optmal schedulng and load-balancng algorthm for a cloud data center s ntroduced, when the job szes are unknown. Ths algorthm s based on usng queue lengths for weghts n max-weght schedule nstead of the workload. Zuo et al. [11] presented self-adaptve learnng partcle swarm optmzaton- (SLPSO-) based schedulng approach for deadlne constrant task schedulng n hybrd IaaS clouds. The approach solves the problem of meetng the peak demand for preservng the qualty-of-servce constrants by usng the PSO optmzaton technque. The approach provdes better schedulng of the tasks wth maxmzng the proft of IaaS provder whle guaranteeng QoS. The problem wth ths approach s the lack of prorty determnaton whch results n falure of deadlne tasks. Schedulng tasks n a cloud computng envronment s a challengng process. In [12] Su et al. presented a cost effcent task schedulng method that can be utlzed for processng largeszeprograms.buttheperformanceoftheapproachs not suffcent as t dd not consder the completon tme and cost for schedulng. From the lterature t s found that the major ssues n the above descrbed methods are hgh cost consumpton especally for communcaton and computaton of data from cloud data centers. The nablty to meet up the deadlnes, due to the napproprate data path allocaton whle task schedulng, s another area of concern. The analyss of varous schedulng schemes s lsted as below. 2.1. Analyss of Schedulng Schemes. Generally, the effcent task schedulng concepts of the clusters and the grd are not effectve n the cloud envronment. The man reason s that n cloud computng the resource provson s on-demand and the resources are provded on the bass of pay-per-use. Hence the schedulng approach has to make use of the features of the cloud n order to effcently schedule the tasks wthout tme delay. Whle processng a task n a VM, the data are needed to be obtaned from the dstant data centers located at multple locatons. As the tasks are deadlne constrant, the data are needed to be obtaned wthn the partcular tme usng effectve schedulng approaches. However, the soluton for schedulng deadlne constrant tasks n the cloud leads to a new problem n the form of cost. The computaton and the storage resources are the basc resources n the cloud envronment that forms the cost models. Table 1 shows the varous schedulng schemes descrbed n lterature and ther drawbacks. The hgh cost problems can be reduced by effectvely selectng the mnmum cost paths based on avalablty of the data paths. The problem s that not all the tasks take the same executon tme whch means some tasks requre data qucker than the other tasks. But when usng only the mnmum cost path,thedatawouldhavetowatnqueueormghtbelostdue to queue overflow. So the cost paths are needed to be selected adaptvely for deadlne constrant tasks. These two problems are the major focus of ths research. 3. Adaptve Cost-Based Task Schedulng The proposed adaptve cost-based task schedulng (ACTS) sdscussednthssecton.theschedulngofthetasksto the VMs can be performed effectvely usng the proposed schedulng method. Ths work takes nspraton from the work of Su et al. In ther work, cost effcent task schedulng s used whch consders the overall executon tme and total monetary costs for schedulng. Though the executon tme and monetary cost are consdered ths scheme cannot be consdered as effcent due to the reason that these two factors are collaboratve factors. The executon tme s the
4 Scentfc Programmng tme for task completon. Ths means the executon tme ncludesthetmefromwhchthetasksareassgnedtoa VM untl the output of the tasks s obtaned. However, the tme consumed for each process n task executon vares and not all of them can be mnmzed. In ths sense, the tme taken for obtanng the data from the data centers for task executon s consderably hgher than all other process n task executon. Smlarly, the monetary cost s the combned cost of resources for computaton, communcaton, storage, data transfer, and so forth; n these processes, the costs for computaton and communcaton are normally hgher than other costs. But Su et al. consdered only the combned factors for schedulng. Hence n the proposed ACTS we focused on specfcally consderng the ndvdual processes as factors for schedulng. The major factors are data access completon tme, computaton cost, and communcaton cost. The data that are requred to be processed n the VM or the PM are stored n the dstant data centers. These data are needed to be fetched to the processng VMs from the data centers through the cost-effectve paths. The data access of each VM follows an ndependent Posson dstrbuton assocated wth the average rate of the arrval rate of the network I/O requests. The data access to the drver doman (PM) s processed on the bass of provdng the access to the frst come users whle the other users wat n the queue. The servce tme of a data access n the drver doman s represented n an arbtrary dstrbuton. The data access completon tme s consdered to be the determnaton pont n the selecton of the data paths. The completon tme for the data access s calculated by utlzng the parameters of the network nput/output requests n the physcal machnes. The mean of the servce tme network I/O requests n the PMs s gven by μ and the varance of the servce tme network I/O requests n the PMs s gven by σ. The arrval rate of the network I/O requests to the PMs s gven by λ. Then the completon tme t of a data access can beestmatedusngtheformula t= 2μ λ+λμ2 σ 2 2μ 2. (1) 2λμ The arrval rate of the network I/O requests to the PMs can be calculated by λ= λ (e) r (e) + λ (n) r (n), (2) where r (e) and r (n) are the rato of the CPU tme allocated to the exstng and new VMs. λ (e) and λ (n) arethearrvalrateof the network I/O requests of the exstng and new VMs to the PMs. The tasks are performed n the vrtual machne (VM) whch obtans the data from cloud centers through the data access paths. Each data access path contans resources for processng the requests and accessng the data and also requres storage resources for storng the accessed data. Each of the resources carres certan costs for utlzng the resources. The computaton cost ncludes the cost of resources for executon of the I/O requests for the data access and the cost for reaccessng the same data agan. It also ncludes the cost for regeneratng the datasets. The communcaton cost s the total cost of the resources utlzed for the processng of the I/O requests. It can be expressed as the product of the data set sze and the network traffc prce. The cost of the possble data access paths s analyzed n order to determne the mnmum cost path. The cost of each path can be estmated by Cost = Computaton cost + Communcaton cost. (3) The computaton cost and communcaton cost are vtal n the determnaton of the cost-effectve paths as these resources handle the I/O requests of the VMs. When the VM executes a task, for accessng the data from the data centers, the VM sends request for the access. The data centers receve the I/O requests and then provde access for the data. The proposed ACTS consders both the cost and the completon tme of data access for effcently schedulng thetasks.actsassgnsprortytothetasksbasedonthe completon tme. Tme T s chosen as a fxed tme and thecompletontmescomparedwtht to determne the prorty.thelowprortytasksarethosethathavemore completon tme and hence the path s selected as mnmum cost path to reduce the overall cost. The reason for ths approach to low prorty tasks s because these tasks can be executed n a normal tme wthout much urgency. Smlarly, thehghprortytasksarethosethatrequredatawthnthe lesscompletontmeandhencethepathsthatprovdequcker data access are selected wthout watng for the mnmum cost path. Ths may ncrease the cost but the man am s to obtan the requested data wthn the tme and hence the small varaton n the overall cost can be neglgble. After the executon of the tasks, the CPU utlzaton and the bandwdth utlzaton are estmated. Fgure 1 shows the proposed ACTS procedure. Ths work focuses on schedulng the tasks to the VMs wth mnmum cost paths to reduce the complexty n the data accessng from theclouddatacenter.thetasksareallocatedtotheunderloaded VMs based on the normal load condtons. The tasks allocatedtovmsaccessthedatafromthedstantclouddata centers. The cost that recurred for I/O processng s computed and the completon tme for data access s estmated. Then the CPU utlzaton and bandwdth utlzaton are calculated and updated for successve task executons. For example, let us consder V tasks of smple mathematcal programs wth flexble propertes of bandwdth, random access memory (RAM), and mllon nstructons per second (mps). These parameters of the cloud tasks are user defned and can be flexbly chosen. Moreover, the smulatons are made n the real-tme smulaton envronment (CloudSm) whch provdes user frendly behavor. The tasks are nonpreemptve dependent tasks. The VMs are ntated from the cloud envronment wth exstng VMs denoted as E and the newly ntated VMs are placed under N. Ths s because when there s large load, thenewvmsarentroduced.thetasksexecutethesmple mathematcal programs wth the length dfferng based on the ntated codes. The addton program of (a +b)sexecuted once for a task wth 4 bts whle t s repeated to acheve the prescrbed length n the chosen tasks.
Scentfc Programmng 5 Tasks Request for data access I/O request processng Vrtual machne (VM) Cloud data centers Computaton of data access tme Estmaton of computaton and communcaton cost Data access acceptance Executon of task Data access through cost-effectve path Estmaton of CPU utlzaton and bandwdth utlzaton Output Fgure 1: Adaptve cost-based task schedulng. Now let us take task v wth m resources avalable. Intally thetasksarecheckedforpossbleexecuton.allthevmsare runnng n parallel and are unrelated and each VM runs on ts own resources. There s no sharng of ts own resources by other VMs. We schedule nonpreemptve dependent tasks to the VMs. For each task V, the arrval rate λ j and T(V,m j ) are calculated. Then the costs C comp and C communcaton are computed for each data path d usng (2), (7), and (9). The computaton cost n equaton (7) s estmated as the sum of all costs ncurred for runnng a task V on a VM m of a provder p (8)whlethecommuncatoncost(9)stheproductofcostfor data requred and the nbound network traffc prces. Based on the completon tme, the tasks prorty s assgned. Then based on T(V,m j ) andcost,thepathsaresorted.thenthe paths are allocated to each task and then the underloaded VMs are loaded wth the tasks whch access the data from the cloud data center at the deadlne tme. Then the CPU utlzaton (11) and bandwdth utlzaton (12) are calculated for determnng the effcency of the system. Ths schedulng proceduressortednthefollowngalgorthm. Algorthm 1 (adaptve cost-based task schedulng). Input:numberoftasks,VMs Output:taskschedulng Begn Deploy the set of physcal machnes. E = set of exstng VMs present n the cloud computng system. N = set of new VMs to be created. Set of tasks V={V 1, V 2,...,V }. Set of resources M={m 1,m 2,...,m n }. For each task V, Arrval rate λ j to PM j usng (1) λ j = λ (e) r (e) E + λ (n) r (n), (4) N //where r (e) and r (n) are the rato of the CPU tme allocated to the exstng and new VMs. λ (e) and λ (n) arethearrvalrateofthenetworki/o requests of the exstng and new VMs to the PMs. Compute completon tme of data access T(V,m j ) usng (2) T(V,m j )= 2μ j λ j +λ j μ 2 j σ2 j 2μ 2 j 2λ jμ j, (5) //where μ j s the mean servce tme of network I/O requests n m j, σ j s the varance of the servce tme dstrbuton, and λ j sthearrvalrateof network I/O requests to m j End for Compute cost of each possble data path d usng (3) Cost = Computaton cost + Communcaton cost, C d =C comp +C communcaton. (6)
6 Scentfc Programmng Computaton cost C comp = V V mn M m j (C task (V,p,m j )), (7) where the cost of runnng a task V on provder p wth VM m j s defned as RT m j,p V C p m j, RT m j,p V DL a { C task (V,p,m j )=, RT m j,p V > DL a { {, m j M, (8) //where set of tasks s gven by V and p s the servce provder. DL a sthetmetodeadlneofv. RT m j,p V s the runtme of a task V. C p m j s the cost of runnng an VM on p for one tme unt. Communcaton cost can be computed as C communcaton =D a NW n p, (9) //where D a s the GB requred for task V and NW n p s the nbound network traffc prces per GB of the provder p. Select mnmum cost path C dmn. Assgn prorty to tasks V. If (Prorty of V = low && T(V,m j ) T) Data path =C dmn. (10) Else f (Prorty of V = hgh && T(V,m j )<T) // T safxedtmewthwhchthedataaccess completon tme of the tasks s compared to determne the prorty; analyze data paths C d whch satsfes the tme to deadlne DL a for tasks V ; data path = C dt [C dt =C dmn ]; // path has faster data access to satsfy tme to deadlne even wthout mnmum cost End f Assgn tasks to VMs. Estmaton of CPU utlzaton CPU = cl MIPS CPU MIPS 1000 cl ms, (11) // where CPU s the CPU utlzaton; cl MIPS s the calculated cloudlet s MIPS length; CPU MIPS s the MIPS raton of the CPU; cl ms s the cloudlet s duraton n mllseconds when executed on a CPU wth a MIPS ratng of CPU MIPS Estmaton of bandwdth utlzaton BW u = τ V 100 BW V ψ V, (12) // where BW u s the bandwdth utlzaton; BW V s the allotted bandwdth quota; τ V s the amount of data transferred durng the lfe of VM; ψ V s the duraton whchsthevmlfetmeandtsequaltothevm release tme to the VM creaton tme. Update VM characterstcs for next teraton. End. 3.1. Descrpton. The tasks V, the number of VMs, and VM resources m are ntalzed.. The set of exstng VMs E and the set of newly created VMs N are assgned. For each task, the data access completon tme s calculated as T(V,m j ). Smlarly the computaton cost and communcaton cost arealsocalculatednordertoestmatethecostofeach data path. Usng the completon tme and computaton cost, and communcaton cost of each path, the schedulng s performed. The tasks are assgned prortes based on the completon tme. The hgh prorty tasks whch have less completon tme are allocated fast data access paths C dt that satsfy the tme to deadlne wthout prortzng the cost. But for the low prorty tasks whch have hgh completon tme the mnmum cost paths C dmn are allocated. Then the tasks are executed and the utlzaton of CPU and bandwdth are calculated. 4. Expermental Results The experments are conducted to evaluate the performance oftheproposedadaptvecost-basedtaskschedulngand theresultsaretabulated.thecosteffcenttaskschedulng s presented n [12] utlzed n ths work for performance comparson wthout consderng the cost and completon tme of the data access and compared wth the proposed ACTS consderng the cost and data access completon tme. The experments are carred out usng the CloudSm [15] tool. The classes of the CloudSm smulator have been extended (overrdden) to utlze the newly wrtten algorthm. The smulator CloudSm opens the possblty of evaluatng the hypothess pror to software development n an envronment whch can reproduce tests. Specfcally, n case of cloud computng where the access to the nfrastructure ncurs payments n real currency, a smulaton-based approach allows cloud customers to test ther servces n repeatable and controllable envronment. Addtonally t allows tunng the performance bottlenecks before the deployment on real clouds. The effcency of the approaches s compared n terms of computaton cost, communcaton cost, executon tme, CPU utlzaton, and bandwdth. The numbers of tasks and VMs consdered are flexble to user requrements whch mean the user provdes memory, mps, and bandwdth values whch are randomly utlzed n the VM. The approprate determnaton of the characterstcs of the VM and the tasks s hghly recommended for obtanng
Scentfc Programmng 7 3500 1200 3000 1000 Computaton cost (prce) 2500 2000 1500 1000 Communcaton cost (prce) 800 600 400 500 200 0 10 20 30 40 50 Number of tasks Cost effcent task schedulng Adaptve cost-based task schedulng 0 10 20 30 40 50 Number of tasks Cost effcent task schedulng Adaptve cost-based task schedulng Fgure 2: Comparson of computaton cost. Fgure 3: Comparson of communcaton cost. desred performance evaluaton results. The VM characterstcs are as follows: ram (256, 312, 712, and 856) bytes; mps (330,370,and400);bandwdth(700,750,800,and900)bts per second (bps). Lkewse, the I/O ntensve tasks are taken as follows: length (4, 8, 11, 5, 3, 9, and 10); memory (256, 312, 378, 280, 436, 553, and 375) bytes. An I/O ntensve task performs the functon of readng the nput/output data and wrtes them onto the fles. These values are user provded values and suppose f the number of VMs s 10 then the combnaton of ram, mps, and bandwdth s chosen randomly. For example, n case of the ram for 10 VMs, the one possble set of values wouldbe256,312,712,856,256,312,712,856,256,and312, respectvely. 4.1. Computaton Cost. Computaton cost s the cost that s requred for utlzng the resources for computaton of the I/O requests for the data access. It can be computed usng (7). Fgure 2 shows the comparson of the exstng cost effcent task schedulng wthout consderng the completon tmeandthecostwththeproposedadaptvecost-basedtask schedulng (ACTS) wth consderng the completon tme and the cost n terms of the computaton cost. In the xaxs, the number of tasks s taken whle along the y-axs the computaton cost (prce) s taken. When the number of tasks s 50, the cost effcent task schedulng has computaton cost of 2890 but the proposed ACTS has 2534.8. Thus the proposed ACTS provdes better schedulng wth mnmal computaton cost. 4.2. Communcaton Cost. Communcaton cost s the cost that s requred for utlzng the resources for I/O requests and responses between the data center and the VM for the data access. It can be calculated usng (9). Fgure 3 shows the comparson of the exstng cost effcent task schedulng wthout consderng the completon tmeandthecostwththeproposedadaptvecost-based task schedulng (ACTS) wth consderng the completon tme and the cost n terms of the communcaton cost. In the x-axs, the tasks are taken whle along the y-axs the communcaton cost (prce) s taken. When the number of tasks s 50, the exstng cost effcent task schedulng has communcaton cost of 1100 but the proposed adaptve costbasedtaskschedulnghas946.6.thsshowsthattheproposed ACTS consumes less cost than the exstng scheme. 4.3. Executon Tme. The executon tme s the tme requred to process a task n a VM. The executon tme s estmated as the product of number of cycles for executng per nstructon, tme per cycle, and the number of nstructons. Fgure 4 shows the comparson of the exstng cost effcent task schedulng wthout consderng the completon tmeandthecostwththeproposedadaptvecost-basedtask schedulng (ACTS) wth consderng the completon tme and the cost n terms of the executon tme. In the x-axs, the tasks are taken whle along the y-axs the executon tme n mllseconds (ms) s taken. When the number of tasks s 50, the exstng cost effcent task schedulng has executon tme of 4.978 ms but the proposed ACTS has 2.56 ms. Ths shows thattheproposedactsreducesthetmetakenfortheoverall process. 4.4. CPU Utlzaton. CPU utlzaton refers to the usage of processng resources or the amount of work handled by a CPU. CPU utlzaton vares dependng on the amount and type of managed computng tasks. It s estmated usng (11).
8 Scentfc Programmng 6 600 5 500 Executon tme (ms) 4 3 2 Bandwdth (bps) 400 300 200 1 100 0 10 20 30 40 50 Number of tasks Cost effcent task schedulng Adaptve cost-based task schedulng Fgure 4: Comparson of executon tme. 0 10 20 30 40 50 Number of tasks Cost effcent task schedulng Adaptve cost-based task schedulng Fgure 6: Comparson of bandwdth utlzaton. CPU utlzaton (%) 14 12 10 8 6 4 2 0 10 20 30 40 50 Number of tasks Cost effcent task schedulng Adaptve cost-based task schedulng Fgure 5: Comparson of CPU utlzaton. Fgure 5 shows the comparson of the exstng cost effcent task schedulng wth the proposed adaptve cost-based task schedulng (ACTS) n terms of the CPU utlzaton. In the x-axs, the number of tasks s taken whle along the yaxs the CPU utlzaton n % s taken. When the number of tasks s 50, the exstng cost effcent task schedulng has CPU utlzaton of 13.14% but the proposed ACTS has 11.345%. Ths shows that the proposed ACTS has less CPU utlzaton. 4.5. Bandwdth Utlzaton. Bandwdth s the amount of data that can be transmtted n a fxed amount of tme. It s gven n bts per second (bps). It s estmated usng (12). Fgure 6 shows the comparson of the exstng cost effcent task schedulng wth the proposed Adaptve costbasedtaskschedulng(acts)ntermsofthebandwdth.in the x-axs, the number of tasks s taken whle along the y-axs the bandwdth n bps s taken. When the number of tasks s 50, the exstng cost effcent task schedulng has bandwdth of 240.98 bps but the proposed ACTS has 34.123 bps. Thus from the expermental results t s clear that the proposed Adaptve cost-based task schedulng (ACTS) whch consders the completon tme and computaton cost and communcaton cost s effcent compared to the exstng cost effcent task schedulng. 5. Concluson Schedulng tasks n cloud computng wth reduced delay and effectve cost management are a challengng task. Hence n ths paper, adaptve cost-based task schedulng (ACTS) s proposed consderng the data access completon tme and the cost for data access. By consderng these two factors, the data can be fetched from the data centers effectvely and the schedulng performance can be mproved. The approach focuses on provdng data access for executng each task wth mantaned costs. Expermental results also show that the proposed adaptve cost-based task schedulng provdes better performance n terms of executon tme, computaton cost, communcaton cost, and bandwdth and CPU utlzaton when compared wth exstng cost-effcent task schedulng approach. In ths paper, the task schedulng s performed for the already determned task demands and t s qute challengng to schedule tasks wth undetermned demands. Ths could be performed by utlzng effcent resource provsonng technques n the future. The cost for regeneraton of datasets s not computed n ACTS but t s not effcent for excepton cases whch should be consdered n the future researches.
Scentfc Programmng 9 Moreover, the load-balancng problems are also needed to be resolved for provdng effcent cloud computng servces whch would be our future scope of research. Competng Interests The authors declare that there s no conflct of nterests regardng the publcaton of ths paper. [15] R. N. Calheros, R. Ranjan, A. Beloglazov, C. A. F. De Rose, and R. Buyya, CloudSm: a toolkt for modelng and smulaton of cloud computng envronments and evaluaton of resource provsonng algorthms, Software Practce and Experence, vol. 41, no. 1, pp. 23 50, 2011. References [1] P. Mell and T. Grance, The NIST defnton of cloud computng, Natonal Insttute of Standards and Technology, vol. 53, no. 6, p. 50, 2009. [2] Q.Zhang,L.Cheng,andR.Boutaba, Cloudcomputng:stateof-the-art and research challenges, Journal of Internet Servces and Applcatons,vol.1,no.1,pp.7 18,2010. [3] K. Nanath and R. Plla, A model for cost-beneft analyss of cloud computng, Journal of Internatonal Technology and Informaton Management,vol.22,no.3,artcle6,2013. [4] J. Sahn and D. Vdyarth, A cost-effectve deadlne-constraned dynamc schedulng algorthm for scentfc workflows n a cloud envronment, IEEE Transactons on Cloud Computng, 2015. [5] C.W.Tsa,W.C.Huang,M.H.Chang,M.C.Chang,andC.S. Yang, A hyper-heurstc schedulng algorthm for cloud, IEEE Transactons on Cloud Computng, vol.2,no.2,pp.236 250, 2014. [6]X.Zhu,C.Chen,L.T.Yang,andY.Xang, ANGEL:agentbased schedulng for real-tme tasks n vrtualzed clouds, IEEE Transactons on Computers,vol.64,no.12,pp.3389 3403,2015. [7]Z.Zhu,G.Zhang,M.L,andX.Lu, Evolutonarymultobjectve workflow schedulng n cloud, IEEE Transactons on Parallel and Dstrbuted Systems, vol.27,no.5,pp.1344 1357, 2016. [8]Q.Zhang,M.F.Zhan,Y.Yang,R.Boutaba,andB.Wong, PRISM: fne-graned resource-aware schedulng for MapReduce, IEEE Transactons on Cloud Computng,vol.3,no.2,pp. 182 194, 2015. [9] X.Zhu,L.T.Yang,H.Chen,J.Wang,S.Yn,andX.Lu, Realtme tasks orented energy-aware schedulng n vrtualzed clouds, IEEE Transactons on Cloud Computng, vol. 2, no. 2, pp. 168 180, 2014. [10] S. T. Magulur and R. Srkant, Schedulng jobs wth unknown duraton n clouds, IEEE/ACM Transactons on Networkng,vol. 22, no. 6, pp. 1938 1951, 2014. [11] X. Zuo, G. Zhang, and W. Tan, Self-adaptve learnng psobased deadlne constraned task schedulng for hybrd aas cloud, IEEE Transactons on Automaton Scence and Engneerng,vol.11,no.2,pp.564 573,2014. [12] S.Su,J.L,Q.Huang,X.Huang,K.Shuang,andJ.Wang, Costeffcent task schedulng for executng large programs n the cloud, Parallel Computng, vol. 39, no. 4-5, pp.177 188, 2013. [13] J.-W. Ln, C.-H. Chen, and C.-Y. Ln, Integratng QoS awareness wth vrtualzaton n cloud computng systems for delaysenstve applcatons, Future Generaton Computer Systems, vol. 37, pp. 478 487, 2014. [14] D. Yuan, Y. Yang, X. Lu et al., A hghly practcal approach toward achevng mnmum data sets storage cost n the cloud, IEEE Transactons on Parallel and Dstrbuted Systems, vol.24, no. 6, pp. 1234 1244, 2013.
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