Optimized Scheduling and Resource Allocation Using Evolutionary Algorithms in Cloud Environment

Transcription

1 Receved: June, Optmzed Schedulng and Resource Allocaton Usng Evolutonary Algorthms n Cloud Envronment Anusha Bamn Antony Muthu 1* Sharmn Enoch 1 1 Noorul Islam Unversty, Inda * Correspondng author s Emal: anushabamn@gmal.com Abstract: Cloud computng s a powerful computng technology, whch render a flexble servces at anywhere to the user. One of the major ssue of cloud computng was schedulng. In ths work, a bacteral foragng optmzaton algorthm wth genetc algorthm () was combned to fnd out trustworthy schedulng problems n cloud workflow. Generally job schedulng and resource allocaton n cloud s a tedous optmzaton problem at the tme of consderng QoS requrements. Lot of exstng works under schedulng only concentrates on cost optmzaton and deadlne problems, and t gnores the mportance of relablty, avalablty and robustness. The man subscrpton of my work s to state a new optmzed approach to schedule the jobs effcently and allocate the resources n a effcent manner by ntroducng algorthm. Experments were done n PSO, Genetc, and then Genetc and was combned to generate a hybrd optmzed result, and the work was compared wth above mentoned algorthms. The algorthms were executed for 52 teratons and totally 1 runs are calculated. The sze of the job as well as vrtual machnes was vared for each teraton to calculate performance varaton. We consdered the optmzaton parameter as tme and cost, and throughput. The work s mplemented n cloudsm to create a smulated cloud envronment. Fnal result shows better performance and maxmum resource utlzaton n when compared to PSO,,. Keywords: Workflow, Schedulng, Resource allocaton, Bacteral foragng optmzaton. 1. Introducton In recent years dstrbuted envronments are playng a major role for computng. In that way cloud computng was used as a technology to use the resources based on pay and use model. Cloud servces are classfed n the terms of: Infrastructure as a Servce (IaaS), Platform as a Servce (PaaS), and Software as a Servce (SaaS) [1, 2]. In ths work we target on Infrastructure as a Servce to utlze the pool of vrtual resources. These resources are accessed on the bass of on demand. Resources consdered n cloud are RAM, network speed, bandwdth. We can use the resources n a flexble manner. But the maxmum utlzaton of resources provdes better performance. The new schedulng technques must be utlzng the resource effcently. To overcome lot of the problem n schedulng [3, 4, 5, 6, 7] a new workflow model was ntroduced n ths paper. However the smple schedulng and resource allocaton s not mportant, but the optmzaton of resource allocaton [8, 9, 1] s mportant. Hence we developed a new schedulng approach whch schedule the gven job nto number of tasks and allocate each task to resources n an optmzed manner. In our work the frst stage s to splt the job nto number of tasks, and n the second stage specfc allocaton of tasks nto resources was done. The resource allocated s consdered as vrtual machnes. Each job s allocated vrtually to needed resources. For better allocaton we combned the concept of genetc () [11, 12] and bacteral foragng optmzaton (). The ftness value s calculated for each teraton and the best ftted value was choose by consderng mnmzed tme and cost. The problem s to assgn a task to resources and regulate the tasks on resources to optmze the entre cost and utlzaton tme. The man objectve s task

2 Receved: June, schedulng and resource allocaton must be carefully analyzed and jontly optmzed to acheve reduced tme and cost, and fnally producng the QoS parameter of better relablty. Ths work s based on the swarm ntellgence technques of bacteral foragng optmzaton () [13] and evolutonary computng concept of genetc algorthm () [14-16]. was ntroduced by the person Passno and t s nspred by the socal foragng behavor of Eschercha col. has nspred the concentraton of researchers for the reason that of ts sutablty and performance n recoverng real-world optmzaton problems occurred n numerous applcaton domans. The concept of E.col s used to solve smple optmzaton problems. Where the chemotactc step sze s adjusted for each run accordng to the current ftness of a bacterum. was nspred by Darwn s theory about Evoluton. was commonly used for natural selecton and generatng hgh qualty solutons n optmzaton. The objectve of s to solve optmzaton problem. In ths paper, we develop a cost and tme mnmzaton schedulng technque whch supports n the cloud. From the servces of cloud computng we are usng the feature of Infrastructure as a Servce (Iaas). In IaaS we consder the features of a computng resources and vrtual machne performance. To get an optmzed result n task schedulng and resource allocaton both concepts are merged. and were combned to solve problems n schedulng and resource mappng. By usng these algorthms the optmzed results were acheved wth good resource utlzaton. The rest of the paper structured as follows. Secton 2 provdes a bref lterature survey about schedulng, resource allocaton concepts and varous algorthms. Secton 3 represents the problem defnton, whch ncludes the nput, output, constrant and objectve. Secton 4 and 5 represents the standard bacteral foragng optmzaton algorthm and standard genetc algorthm. The proposed algorthm was explaned n secton 6. Smulaton and analytcal results were analyzed and plotted n secton 7. Fnally secton 8 represents the concluson of the entre work. 2. Related work Schedulng under dstrbuted systems has been studed well n the prevous decades. Varous algorthms are mplemented n schedulng to meet the QoS constrants of users. The general am of schedulng s to reduce the executon tme of jobs. More number of schedulng algorthms s proposed for dstrbuted computng [17-]. Most of the algorthms [-22] are appled for cloud schedulng based on ts sutablty. Goal of the schedulng algorthms are achevng better performance. The concept of schedulng the bag-of-task (BoT) applcaton was proposed [23] n agent based schedulng concept. In ths paper 14 schedulng concepts are executed concurrently. Based on the sze of the task tme s allocated for sharng the resources. The proposed elastc resource allocaton technque wll dynamcally allocate and reallocate the resources. The result shows that the BoT was allocated and reallocated effcently. The precedence constraned schedulng of parallel applcatons on heterogeneous computng systems (HCSs) was proposed n [5]. Ths proposes a parallel b objectve hybrd genetc algorthm to reduce the energy consumpton and ncrease makespan. The energy consumpton was mnmzed by usng a method of dynamc voltage scalng (DVS). Results prove that t domnates prevous algorthm n terms of completon tme, makespan and energy consumpton. A schedulng algorthm based on berger model [24, 25] was desgned to establsh the dual farness constrant n vrtualzed cloud. The farness of resource allocaton was judged by the applcaton of justce functon. The results showed that the user tasks and the farness were effcently executed. A Bogeography-Based Optmzaton (BBO) was proposed to sole bnary nteger problem n job schedulng through better soluton adaptaton strategy [26]. In BBO, the and ACO strateges were ncorporated to generate a new set of solutons, at each teraton; the Mann-Whtney test was conducted to evaluate performance output of BBO algorthm. Results proved that BBO performance was better than the and PSO algorthms. An Improved Genetc Algorthm (I) was proposed for job schedulng by speedng up the process of [27]. The proposed model has fve components such as preprocessng unt, job schedulers, users, and data center and data center manager. The preprocessng unt encoded the attrbutes nto users job attrbute vector, whch ncluded expected nstructon count, job deadlne and delay cost. An optmal task schedulng and resource allocaton was proposed usng Partcle Swarm Optmzaton (PSO) based ftness functon [28-29, 31]. To balance the load PSO based ftness functon was appled to reduce the make span and to maxmze the processng capacty. The results showed that the PSO based method resulted n less executon tme and cost. A Poston Balance Parallel Partcle Swarm Optmzaton (PBPPSW) [31]

3 Receved: June, method was ntroduced wth hgh proft resource allocaton and flexble user satsfacton level was mantaned. The performance metrcs of average response tme, total proft and number of vrtual machnes were consdered for evoluton. The results showed that the PB-PPSO method acheved ncreased proft and small response tme wth a less number of vrtual machnes. A hybrd schedulng algorthm [32-34] was proposed by combnng genetc algorthm and fuzzy theory to assgn the task to vrtual machnes. Genetc algorthm was modfed to balance the load and to reduce the executon tme and cost. An agent based best ft resource allocaton scheme was proposed to ncrease the resource utlzaton [35]. The results showed that the best ft approach was better n terms of job executon tme, cost, vrtual machne allocaton and resource utlzaton. A contnuous resource allocaton strategy was presented to optmze the schedulng process n cloud [36]. The suggested resource allocaton mechansm adopted mnmal domnaton matchng to compensate the trade-off space. 3. Problem defnton Assume the cloud customer has dfferent jobs and each job s spltter nto number task and each task has dvded nto sub tasks. These tasks are allocated to resources (memory, network, CPU) as vrtual machnes. The concept of schedulng and resource allocaton has dfferent am. We need to fnd a schedule to execute a DAG workflow on Infrastructure as a Servce computng resource to mnmze the executon cost and tme. Input: The schedule s defned n the format of S=(R, A, TC, TT) n the form of set of resources (R), task to resource allocaton (A), total cost (TC) and total tme (TT). For each resource R= (r 1, r 2... r n ) dfferent vrtual machnes are allocated, and each resource has ts start tme ST r and end tme ET r. Here A represents allocaton and the allocaton consst of set of tuples n the form of at a r t= (t, r j, ST r, ET r ). A task t s scheduled to run on the resourcer j. The total cost TC and total tme TT are calculated as follows: R ( ETr TC STr ) C VM 1 TT max ET : t T r (1) (2) Output: Assgnment of tasks to resources n mnmzed tme and cost. The n number of tasks and n number of resources are allocated as {(t 1, r 1 ), (t 2, r 2 )... (t n, r n )}. Constrants: Each task must be completed wthn short tme and wthout nterrupton. One vrtual machne can complete one task at a tme. The processng tme s depends on the vrtual machne allocated. Objectve: The am s to assgn each task to matchng vrtual machne resources and sequence the tasks to mnmze the tme, cost and throughput. The challenge of job schedulng and resource allocaton was optmzed by combng wth mnmzed tme and cost, fnally relablty s acheved. 4. The standard bacteral foragng optmzaton algorthm was used to solve optmzaton problem [18]. Bacteral Foragng Optmzaton s an evolutonary method based on E.col bactera. The area havng hgh level nutrents are searched by bactera. Ths task s used for optmzaton process. By sendng sgnals ndvdual bacterum communcates wth others. Durng foragng locomoton s acheved by a set of tensle flagella. E.col bactera tumble or swm usng flagella. These are the two basc operaton of bactera performed at the tme of foragng. After consderng two prevous factors foragng decson s taken by bactera. The process, n whch a bacterum moves by takng small steps whle searchng for nutrents, s called chemotaxs. The basc dea of A s mmckng chemotactc movement of vrtual bactera n the problem search space. Foragng theory s based on the assumpton that anmals search for and obtan nutrents n a way that maxmzes ther energy ntake E per unt tme T spent foragng. Hence, they try to maxmze a functon lke E/T. Maxmzaton of such a functon provdes nutrent sources to survve and addtonal tme for other mportant actvtes (e.g., fghtng, fleeng, matng, reproducng, sleepng, or shelter buldng). Herbvores generally fnd food easly but must eat a lot of t. Carnvores generally fnd t dffcult to locate food but do not have to eat as much snce ther food s of hgh energy value. The envronment establshes the pattern of nutrents that are avalable and t places constrants on obtanng that food (e.g., small portons of food may be separated by large dstances). Durng foragng

4 Receved: June, there can be rsks due to predators, the prey may be moble so t must be chased and the physologcal characterstcs of the forager constran ts capabltes and ultmate success. Bacteral Foragng Algorthm s explaned by followng steps. Chemotaxs Swarmng Reproducton and Elmnatonal-Dspersal The chemotactc step was descrbed by the equaton n (3) Where Δ() n s a n-dmensonal randomly generated vector wth elements wthn the followng nterval: [-1,1]. After that, each bacterum θ (j,k,l) modfes ts poston as mentoned n Eq. (4), where C() s the stepsze for search drecton φ(). Eq. (4) represents the swm of a bacterum j, k, l j, k, l C 1 (4) 5. The standard genetc algorthm s an evolutonary algorthm ntroduced by Holland. mmcs the process of natural evoluton. It generates the soluton to optmzaton problem usng nhertance, crossover, selecton and mutaton. Produce ntal populaton Evaluate ftness functon Produce new populaton (usng mutaton and crossover) The search n the genetc algorthm starts wth an ntal populaton. Each ndvdual s evaluated by ts ftness functon. Accordng to ftness value unftted populatons are elmnated. Indvduals are manpulated usng genetc operators. Totally three operators are used n genetc algorthm. At frst the producton operator s used to create copes of best ftted populaton. The low ftness values populatons are elmnated. The second one s the crossover operator. Ths makes swappng of ndvdual elements. Thrd one s the mutaton operator. Applcaton of ths operator s used for random search. 6. The proposed hybrd algorthm The objectve of hybrd algorthm was get the mnmum functon F(φ), φεr, where φ represents the poston of the bactera. F(φ) represents an attractant repellant profle and φ represents the poston of the bactera. The nutrents for the bactera s located as, F <, F =, F > specfes the presence and absence of nutrents. The natural area for the bactera s represented as, H(j, k, l) = {φ x (j, k, l) x = 1,2,, N} (5) Eq. (5) shows the parameters of N bactera at j th chemotactc step, k th reproducton step and l th elmnaton dspersal. Then C(x, j, k, l) represent the cost of bactera at th poston. φ x (, j, k)εr n (6) φ x = ( + 1, j, k) = φ x (, j, k) + C(x)φ() (7) The value of C() > s the step sze for each tumble. Here N s s lfetme length of the bactera calculated usng chemotactc steps. Algorthm: Hybrd Algorthm Step 1: Intalze the nput parameters N, N c, N re, N s, C() N: No. of bactera N c : Chemotactc step N re : Reproducton step N s : No. of steps C(): Sze of the step taken Step 2: Calculate the elmnaton dspersal step usng j=j+1 Step 3: Calculate reproducton step usng k=k+1 Step 4: Calculate chemotaxs loop usng l=l+1 For =1, 2,,N calculate ftness functon FT (, j, k, l) Let FT= FT (, j, k, l). Save ths value to fnd better cost. Tumble: generate a random vector on [-1, 1] Move: When j 1, k, l j, k, lc n Compute FT(, j+1, k, l) Perform swm Else, Go to next bacterum (,1) Step 5: If j<n c, go to step 3. Bacterum lfe s not over.

5 Receved: June, Start Intalze the nput parameters S, N s, N c, N rs, N ed, P ed, C() Do maxmum number of teratons for elmnaton, reproducton, chemotaxs Stoppng condton satsfed Output optmal soluton Bactera s dvded nto two sets based on hybrd probablty P Evaluate bactera object functon value Perform swm Evaluate bactera object functon value Select bactera based on ftness Perform reproducton Perform crossover Perform elmnaton dspersal Perform mutate Have all bactera been updated? Have all bactera been updated? Fnd the optmal best soluton Fgure.1 Flowchart for

6 Throuhhput (%) Cost ($) tme (ms) Receved: June, Step 6: Calculate Reproducton step Nc+1 FT = FT(, j, k, l) j=1 analyss the executon tme was reduced upto 23.67% and the cost was reduced upto 13.5%. Step 7: Elmnaton dspersal: Elmnate and dsperse bacterum wth probablty P ed. If l<n ed then go to step 2, Otherwse end. 7. Smulaton and analytcal results For smulaton we used CloudSm [3] as a tool for testng and analyzng new algorthms for creaton and allocaton of vrtual machnes to Cloudlets for executon. Ths CloudSm s used to create a smulaton envronment for cloud. In the proposed work we consdered two physcal machnes (PM) and we created 4 vrtual machnes (VM1, VM2, VM3, VM4) from 2PM for testng. Here cloudlets are the jobs or tasks n the smulaton envronment; each cloudlet s assgned to ndvdual vrtual machne. We are submttng 1 types of jobs as workflow model to the physcal machne. The gven jobs are dvded nto number of -5 tasks. Totally 52 teratons are calculated for each run. And 1 tmes the workflow s executed under dfferent loads to get dfferent executon results. Followng table 1 shows the smulaton parameters consdered under our setup. The output parameters we consdered over fve algorthms are executon tme, cost and throughput. Our result gves the reduced tme and cost as well as ncreased throughput for varous workloads. Compared to PSO, and the hybrd produced better result. The work mentoned n [24] compared wth our result. Fg.2 shows the result of total executon tme of jobs. Increases n number of nstances drectly affect the executon tme adversely. But, the proper vrtual machne placement and sharng the jobs durng the demand stuaton reduces the tme consumpton effectvely. The proper VM selecton and the pror load consumpton are the major requrements for mnmum executon tme. The total cost s defned as the cost of resource and the amount of total tme perod the resource used. The results of cost versus varous jobs are shown n Fg.3. More amounts of jobs completed n a mnmal duraton are called as throughput. Hgher throughput gves the better result. If the throughput ncreases resource utlzaton also ncreases. Throughput result s shown n Fg.4. Resource utlzaton s defned n Fg.5 shows that, the number of allotted resources dvded by the total number of avalable resources. When the number of jobs vared resource utlzaton also vared. From the Table 1. Smulaton parameters Resource Parameter Quantty Number of PM 2 Number of VMs 4 Number of job 1-1 Number of task -5 Number of teraton 52 Number of executons Number of jobs Fgure. 2 Total tme Number of jobs Fgure. 3 Cost Number of jobs Fgure. 4 Throughput PSO PSO B FO PSO

7 Resource utlzaton (%) Resource utlzaton (%) Number of jobs Receved: June, J9 J7 J5 J3 J1 5 1 Resource Utlzaton (%) Fgure. 5 Resource utlzaton Tme (s) Fgure. 6 Vrtual machne Tme (s) Fgure. 7 Vrtual machne 2 B FO PSO CPU RAM Band wdth CPU RAM Band wdth The maxmum utlzaton of resources s mportant n cloud. Because better resource utlzaton gves better performance. Our test results produce maxmum utlzaton of resources compared to prevous works. The above Fgs. 6 and 7 show the sample results of resource utlzaton under vrtual machne 1, vrtual machne 2. These two vrtual machnes are placed under sngle physcal machne. CPU, RAM and bandwdth are the resources we consdered for allocaton and utlzaton. Above fgure shows that approxmately the resources are utlzed over 91.53%. 8. Concluson and future work In ths work, we addressed the job schedulng and resource allocaton problem, whch s to schedule and allocate vrtual resources to acheve hgh resource utlzaton to meet user s needs wth mnmum utlzaton parameters. The proposed work provded the soluton to the schedulng and resource allocaton problems usng optmzed hybrd algorthms. Varatons n genetc algorthm have been nvestgated and mplemented for learnng and mprove the speed of convergence. Ths work proposed a novel hybrd approach consstng of Genetc Algorthm () and Bacteral Foragng Optmzaton Algorthm (A) also the performance of schedulng and resource allocaton was tested wth varyng executon steps. These proposed expermental problems were tested wth CloudSm tool. Compared to PSO,, algorthms the hybrd demonstrated the better schedulng results wthn better cost savng scheme. Our smulaton result shows that the resources are hghly utlzed wth optmzed performance result. We can apply ths algorthm to set a real cloud envronment. One of the extenson of ths work plans to execute the defned varous workflow models wth ths same algorthm n smulated envronment. It would be nterestng to nvestgate the performance of schedulng workflows or allocatng resources to support varous workflow models. Second plan s to extend the work by automatc resource allocaton system usng self learnng algorthm. Whch means each problem and ther solutons are learned by the automatc decson system. If the same job appears agan wthout any executon the resources are automatcally allocated. References [1] P. Mell and T. Grance, The NIST Defnton of Cloud Computng, Natonal Insttute of Standards and Technology Specal Publcaton, SP 8-145, p.7, 11. [2] D. Ran and R.K. Ranjan, A Comparatve Study of SaaS, PaaS, and IaaS n Cloud Computng, Internatonal Journal of Advanced Research n Computer Scence and Software Engneerng, Vol.4, No.6, pp , 14. [3] F. Zhang, J. Cao, K. L, S.U. Khan, and K. Hwang, Mult-objectve schedulng of many tasks n cloud platforms, Elsever-Future Generaton Computer Systems, Vol. 37, pp. 39 3, 14.

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