A Novel Approach for Optimization Auto-Scaling in Cloud Computing Environment

Transcription

1 I.J. Modern Educaton and Computer Scence, 2015, 8, 9-16 Publshed Onlne August 2015 n MECS ( DOI: /mecs A Novel Approach for Optmzaton Auto-Scalng n Cloud Computng Envronment Khosro Mogoue Department of Computer Engneerng, Mahallat Branch, Islamc Azad Unversty, Mahallat, Iran Emal: khosro_mogoue@yahoo.com Mostafa Ghobae Aran Department of Computer Engneerng, Parand Branch, Islamc Azad Unversty, Tehran, Iran Emal: mostafaghobae@pau.ac.r Mahboubeh Shams Department of Computer Engneerng, Qom Branch, Unversty Of Technology Qom, Iran Emal: shams@qut.ac.r Abstract In recent years, applcatons of cloud servces have been ncreasngly expanded. Cloud servces, are dstrbuted nfrastructures whch develop the communcaton and servces. Auto scalng s one of the most mportant features of cloud servces whch dedcates and retakes the allocated dynamc resource n proporton to the volume of requests. Scalng tres to utlze maxmum power of the avalable resources also to use dle resources, n order to maxmze the effcency or shut down unnecessary resources to reduce the cost of runnng requests. In ths paper, we have suggested an approach based on learnng automata auto- scalng, n order to manage and optmze factors lke cost, rate of volatons of user-level agreements (SLA Volaton) as well as stablty n the presence of traffc workload. Results of smulaton show that proposed approach has been able to optmze cost and rate of SLA volaton n order to manage ther trade off. Also, t decreases number of operaton needed for scalng to ncrease stablty of system compared to the other approaches. Index Terms Cloud computng, Scalablty, Autoscalng, Learnng automata. I. INTRODUCTION Cloud computng s a new technology that has found ts place n human lfe as a basc need, and ts popularty ncreases among Internet users day by day. Cloud servce users only pay the prce based on the amount of resources they have used (Pay-per-use). On the other hand, hgh accuracy s requred to establsh trade-off between prce and effcency. Provdng hgh-qualty cloud servces at the lowest possble prce, meetng the requests and mantanng system stablty are the ultmate desre. Scalablty s one of the mportant challenges and characterstcs of cloud computng technology that can provde ths feature for cloud servces users [1, 2, 3]. One of the management technques for scalng n the cloud computng s usng a threshold approach. Threshold s determned based on factors such as the strength and speed of processng, and storage capacty. When utlzaton of resource s greater than the value of threshold, requests wll be referred to other sources and when use of resource s less than threshold, some of the unnecessary resources wll be selected and dsabled to decrease costs. Therefore, the amount of mplementaton actvtes on a resource has measured perodcally then the scalng wll done on ts bass [4]. Scalablty whch means allocaton or wthdrawal resources n proporton of requests, tres to maxmze effcency of avalable resources and usng nactve resources n order to ncrease productvty or dsablng avalable resources accordng to low volume of requests n order to decrease cost of performng requests. Autoscalng enables users to make larger or smaller nfrastructures accordng to volume of actvtes, desrable effcency and other dynamc behavors [5]. Such automaton ncreases advantages of cloud dynamc scalablty substantally and can use more resources actvely at the tme of hgh workload also t can manage cost of usng resources by dsablng unnecessary resources at tme of low requests. Effcency ndex n cloud auto- scalng mechansms s ncluded the amount of usng CPU, dsk operatons and bandwdth. Also we need accurate plan to have trade-off among these factors [6]. The Servce qualty n cloud servces s an ablty of dependent on resources and unlmted access based on the workload of the user at dfferent tmes. Exstence of these factors wll affect the cost. Because servce qualty requres usng resources wth hgher capacty, speed and power. On the other hand, utlzaton of the resources requred to pay more. Therefore, we need a mechansm to manage the trade-off between these factors. In ths paper, n addton to use prevous researches results n determnng threshold [7-11] and volume of workload [12], we have tred to offer an auto-scalng approach by learnng automata [13], n order to management trade-off between rate of SLA volaton and

2 10 A Novel Approach for Optmzaton Auto-Scalng n Cloud Computng Envronment cost of scalablty to preserve stablty n system. Learnng automata s able to select the best response among many receved responses from envronment. Usng learnng automata causes to have a better management n trade-off between cost and SLA volaton also enable us to have smultaneously mnmum cost, reducton n SLA volaton and system stablty (reducton n scalablty operatons). The rest of paper s organzed as follows: second part s dedcated to related works. In thrd part, we wll explan our proposed approach and n forth part we wll evaluate t. Fnally the last part s dedcated to concluson and future work. II. RELATED WORKS Varous researches have been accomplshed n the feld of auto scalng. The am of some of them s to offer a scalablty approach for a specal applcaton lke server of web and others have done for optmzng the mechansm of scalablty. In ths part we are gong to consder some of the prevous researches about autoscalng. M. Humphrey et al [6], consdered auto scalng n clouds n order to dedcaton fast resources wth mnmum cost for group of ndependent works. Results showed effcency of the approach n fast resource dedcaton but t s not recommended for stuaton of unequal prorty and f there s needed a specal level of productvty. Menasce et al [14], presented a structure for schedulng tasks wth deadlnes n a heterogeneous envronment. In ths proect, a workng set (DAG), s receved as nput, and t s assumed that there are avalable a varety of servces for any of sub-obs. Decson makng on the tmng of the sub-obs s done by consderng ts desred performance and access cost. Tmng process s done by usng genetc algorthm. N.Chohan et al [15], offered sample stablzaton mechansm for acceleratng the mplementaton of tasks n the context of cloud, to reduce ther expenses. Overall, ths research [16], has been done wth the am of provdng a cost-effectve structure for cloud servces (proftablty for cloud servce provder) wthn the Servce Level Agreement (SLA). Note that from the perspectve of the customer, cost comparson s done between costs of mplementaton n the context of cloud and the mplementaton cost n the normal stuaton. Roy Nlaba et al [17], have proposed an algorthm whch does not use reacton approach n order to autoscalng web resources. Rather, they have suggested a predctor soluton by applyng the concepts proposed by Sharf et al. [18.19], whch can be used by systems that show a mxed behavor (contnuous dynamc or dscrete) and t has a large set wth controllng lmts. The base of approach s controllng prncples of volume n the future that called advanced optmzaton. Optmal stuaton s calculated through repetton n a determned perod of tme consderng current and future lmts. Treu C.Chue et al [20], offered an approach to autoscale web applcatons n a vrtual cloud envronment. The most mportant ssue n web applcatons s nablty to desgn or even to predct the number of actve users. The mechansm ncludes a frst-end load balancer, and a sub-system (to keep recordng) and an ntellgent control system (scalng ndex). Load balancer s utlzng for trackng and balance for users requests n order to access to web servces on the web servers n cloud VMs. Scalng s done accordng to number of actve users. Results show that algorthm s able to manage hgh traffc meetng factors of cost and good effcency. Jng Q Yang et al [21], offered cloud archtecture for the purpose of auto-scalng resources based on antcpated workload. Ths method conssts of two sub dvson pre-scaled and real tme scalng. Also, the lnear regresson model has been used to predct traffc scalng s classfed n three ways: self-healng scalng, scalng of source level and scalng of vrtual machne level. The man dea n self-healng scalng s that two VMs wll be able to operate overlappng. The purpose of antcpatng the volume of work s estmaton the number of servce requests n a perod of tme. Devaton management between estmatng traffc and real amount of traffc s the man ssue n ths approach. III. PROPOSED APPROACH An effcent Scalable mechansm s able to meet the desred qualty of servce, also optmal cost for users, On the other hand, load balancer usng approprate dstrbuton n system samples wll be able to reduce the frequency of the system needed to be scaled up as much as possble to preserve stablty of system. As mentoned before, trade-off between cost and SLA volaton makes dffcult to acheve such mechansm because havng hgh qualty servces s requred more expenses. On the other hand, cost mnmzaton could face us wth SLA Volaton. We use our learnng automata n our proposed approach to manage and trade-off between cost and rate of SLA volaton. Followng n ths secton frstly we descrbe needed scalablty framework to mplement proposed approach then brefly ntroduce the automata and fnally we present our proposed algorthm. A. Scalablty Framework Fg.1. Scalablty Framework Determnng and usng an effcent scalng approach requres to create and utlze a sutable framework

3 A Novel Approach for Optmzaton Auto-Scalng n Cloud Computng Envronment 11 ncluded effectve factors n scalng mechansm. Therefore we offer a framework then the proposed scalng approach, wll be presented based on ths framework. Fg.1 shows the man components of scalablty framework nclude: Load Balancer, Scalng Manager, Cost Manager, Vrtualzaton Manager and Server Cluster. Load balancer: User requests, generated by the customer, are sent to the servces through admsson controller. Frst, admsson controller flters out nvald requests then remaned requests wll be offered to load balancng. As there s more than one vrtual machne on server cluster, qualty of dynamc sendng requests to the dfferent VMs needs more precse management. So we use load balancer for ths purpose. Load balancer collects nformaton related to the varous VMs condtons from Server cluster then sends requests to the sutable VM accordng to the nformaton and based on load balancer strategy. Scalng manager: scalng manager s the central component of the framework and ts responsblty s montorng and controllng the process of allocaton resources to the requests n the clusters. The decson about tme and qualty of scalng s the responsblty of scalng manager. Scalng manager controls effcency of cloud system at perodc ntervals and determnes strategy of scalablty accordng to the software context. If the effcency of system s less than the threshold value, lcense of releasng addtonal vrtual machne wll be ssued n order to reduce the cost of scalng. Also, f the effcency of system s more than threshold, management wll ssue the lcense of actvaton the vrtual machne, n order to avod addtonal SLA Volaton. Vrtualzaton manager: resources on the clusters drectly and operates the strateges offered by scalable management then adds or releases vrtual resources. Cost manager: cost manager s responsble to consder and select economcal resources among SLA provder s resources. In fact whenever system needs to move nto Scale up mode, cost manager operates optmal VM selecton (smultaneous SLA and desred cost provder) accordng to learnng automata technque. It means among the resources of SLA, nstances wth lowest cost wll be selected durng a certan number of repettons and ther probablty wll be rased. Also, f an nstance doesn t have ths stuaton, cost manager reduces ts probablty. At the end, Cost manager chooses the sample wth hghest probablty and ntroduces t to scalable management for scalng up. Also at the tme of scale down, above operatons wll be done to select the actve nstance wth the hghest cost to decrease the expenses of scalablty, only dfference s that frstly selecton has been done among actve nstances and secondly at the end, the nstance wth hghest penalty (lowest probablty) wll be selected and ntroduced to the scalablty management to make t dle (Scale down). Server Cluster: as you can see n Fg.1, server cluster conssts of a pool of VMs whch have been used based on traffc and n order to offer servces to the users to provde effcency factors. In ths paper, n order to ease the task, we have consdered a separate vrtual machne for any partcular servces. B. Learnng Automata Learnng algorthms struggle to mprove ther performance and features toward a specal am by knowng related envronmental stuatons. [22,23]. Learnng automata s knd of such algorthms whch try to change ts condtons probabltes, based upon responses come from surroundng then t shows specal reacton n any condtons. Learnng automata [24] s an abstract thng wth lmted actons. A learnng automata s performance s n a manner that t selects one of ts actons among set of ts actons and apples t to the envronment. Then the mentoned acton wll be evaluated by a random envronment and automata select ts next acton based on response of envronment. The method whch automata use t for selectng next acton wll be determned accordng to the used learnng algorthm. Along the process automata learns how to select optmal acton. Envronment ncludes all nternal and external condtons whch affect automata. Generally t s possble to present envronment as a set: E {,, c} n whch s set of nputs, s set of outputs and c s envronment penaltes. Fg. 2, shows relatonshp between learnng automata and envronment. Fg.2. Relatonshp between Learnng Automata and Envronments s the number of actons automata s able to do, s output of envronment whch dffers accordng to the model and knd of envronment Varous models defned for probable envronments are dvded to 3 groups: P- Model, Q-Model and S-Model. In Q-model, envronment outputs are dscrete values of zero and one and n S- model output s always a constant value between zero and one. In ths paper, we use P-Model. In ths model mght be one of the two values of zero or one. Zero means desrable acton and the probablty of an acton ncreases whle probablty of other actons decreases. One means undesrable acton and probablty of current acton decreases and probablty of other actons ncrease so that after recevng amount of one from envronment, automata change the acton. C s the set of probablty of penaltes for actons of automata whch defnes as follow: c Pr ob[ ( ) 1 ( ) ], 1, 2,..., r (1) (n) Envronment Learnng Automata These values change over the tme. Values of c often (n)

4 12 A Novel Approach for Optmzaton Auto-Scalng n Cloud Computng Envronment are not clear and knowng them means thorough understandng of the potental envronment that s not possble n most applcatons. Learnng automata tres to know these values. In our sample envronment wll operate n tmes n any acton: New nput load (a) enters n envronment. Includes reward and penalty comes from envronment. Probablty vector s as P= {0.5, 0.5}. Accordng to automata operaton, hgher penalty n non-optmzed sample, lower the chance of ts selecton so t can assure us of selecton an optmum sample If n repetton selects a so that we have n (n +1) repetton: Receved a favorable response: p ( n 1) p ( n) a[1 p ( n)] (2) p ( n 1) (1 a) p ( n) (3) Receved an unfavorable response: p ( n 1) (1 b) p ( n) (4) b p ( n 1) (1 b) p ( n) (5) r 1 C. Proposed Algorthm Proposed algorthm ncludes 3 sub-algorthms. Frst algorthm s responsble to manage scalablty process. The process ncludes montorng and measurng the system effcency n order to perform scalablty. Second algorthm s responsble to select optmum samples then ntroducng t to the scalable management at the tme of actvaton a new sample (hgh scale). Fnally, the thrd algorthm s responsble to select sutable sample at the tme of deactvaton operatng samples (low scale) n order to savng cost of scalablty. Algorthm 1: Scalablty management Begn Whle (the system s runnng and n the begnnng of an nterval) End For (every S n the cloud), Montor u () t, If ( u () t u ), upper threshold Execute VM-level cost-aware scalng up If ( u () t u ), lower threshold Execute VM-level cost-aware scalng down Generally, the process works as an algorthm (Algorthm.1), measures effcency of actve vrtual machnes at the tme of startng any perod of tme and perodcally at the tme of workng then compares them wth hgh and low thresholds. If the measured value s hgher than threshold, algorthm calls scale up to ncrease VMs. Otherwse, f the measured value s lower than the threshold, algorthm wll call scale down to decrease VMs. If there s none of these stuatons, cloud wll stay n a stable condton. In the Scale up, frstly rate of SLA Volaton s calculated because the amount of servce requests has been ncreased more than the servce capacty. Servce capacty s specfed accordng to the MIPS processor vrtual machnes of that servce. Then requested amount of mentoned servce faced wth lack of resources, wll be calculated to meet the requests of servce addng more vrtual machnes (Algorthm.2). Algorthm2: Cost-aware scalng up at t th nterval Begn Calculate SLA Volaton Calculate shortage Request Whle (shortage Request s not empty) For (every VM n VM Lst) If (VM s sutable for shortage Request) Gve reward to VM based on ts cost // cheapest VM has most reward else Gve penalty to VM based on ts cost // expensve VM has most penalty Calculate chance(reward, penalty) Cheapest VM Select VM wth bggest chance // bggest chance s equal to cheapest VM Add Cheapest VM End When the capacty of cloud servce s more than ssued requests of users, scalable management wll ssue allowance for scale down to shut down addtonal VMs n order to save costs of scalng (Algorthm.3). Our crtera for determnng the on removal of vrtual machnes s that servers stll have the ablty to respond to users requests after deletng the vrtual machne. Algorthm 3: Cost-aware scalng down at t th nterval Begn Calculate extra Request Whle (extra Request s not empty) For (every VM n S ), If (VM can be removed) Gve penalty to VM based on ts cost // expensve VM has most penalty Else Gve reward to VM based on ts cost // cheapest VM has most reward Calculate chance(reward, penalty) Expensve VM Select VM wth bggest chance // bggest chance s equal to expensve VM Remove Expensve VM End V. PERFORMANCE EVALUATION We are gong to evaluate our proposed approach based on 3 crtera ncluded cost, SLA volaton and number of scalng. We have used Cloudsm [25] to smulate the proposed approach. We wll use 4 knds of VMs (Table 1) based on Amazon company VMs n smulaton tests. Regardng varety n the exsted servces n cloud, we have performed four varous knds of servces and we have not focused

5 A Novel Approach for Optmzaton Auto-Scalng n Cloud Computng Envronment 13 on a specal servce or program so that servces are ndependent of program. The servce combnes all heterogeneous applcatons such as HPC, Web and more. The workload of the system has been modeled based on the normal dstrbuton to make t closer to the real world. Table1. VM s Informaton VM type MIPS Core RAM Prce (cent) (CPU) (MB) Mcro Small 1, , Extra 2, , Large Hgh- CPU Medum 2, Scalng s operated n a 24-hour perod (288 * 5-mnute ntervals) on four dfferent servces. We consdered low threshold value of 0.2 and hgh threshold value as 0.8 n the smulaton whch have been used for comparng effcency of actve VMs n system. Also, we use the probablty P of an ntal value of 0.5 and the probablty P ntal value 0.5 n order to determne the level of chance, and penalty for samples of cloud. We have tred n selecton perods of tme to prevent from operatonal overhead and scarcty so that we have consdered an average perod of tme. We have compared our proposed approach to the two approaches, cost-aware scalng [26] and random scalng vrtual machnes n the cloud computng envronment to evaluate our approach. Cost-aware polcy s a smple and non-learnng method whch decreasng cost of servces s ts scalng prorty. Also n the random polcy, there s no management on vrtual machnes expenses. In other words, n the Scale down and Scale up operatons, addng or decreasng VMs s done randomly. We evaluate our proposed approach based on 3 crtera ncluded cost, the number of scalng (addng or deletng VMs) and rate of SLA volaton. Cost: The estmated cost of the cloud servce s based on the amount of workng hours. The users (cloud user), dependng on the speed, power and capacty of demanded resource (CPU, memory, or dsk...) and also the perod of acquston (mnmum of acquston tme s one hour) of resource should pay the cost. Naturally, our costs wll be lower when we use the resource wth lower speed and capacty, n shorter tme. Perhaps ths atttude optmzes your costs, but t should be noted that other factors of qualty of servce, wll be affected. Therefore, n order to acheve hgh qualty servce, we have to bear ncreasng costs. The number of scalng (system stablty): One of the ssues that mpact largely the dynamc scalng s the number of removed or added vrtual machnes. Ths frstly causes to accelerate respondng n the computng envronment and on the other hand the number of these processes plays an mportant role n estmaton of provder s expenses. So, lower number of scalng means lower cost and hgher speed n responses. Fnally we wll have a stable system wth mnmum cost. SLA Volaton: SLA Volaton occurs when a provder fals to meet predefned crtera (Servce Level Obectves (SLO)) n the SLA for the users. The number of all mssed deadlnes, lack of MIPS guarantee agreement, not guaranteed bandwdth agreement, the number of requests reected due to lack of supply at peak tmes and so on, are examples of volaton of the SLA. We consder three scenaros to evaluate the proposed approach. In the frst scenaro we nvestgate three scalng polces n a cloud envronment based on the assumptons (Table1) n terms of costs. In the second scenaro we compare our scalng mechansm n terms of system stablty (frequency of scalng) n a smulated envronment wth the characterstcs mentoned above, to the Cost-aware and random methods and fnally n the last scenaro we compare all three methods n equal surroundng and stuatons n term of SLA volaton. A. Frst Scenaro In the frst scenaro, we dscuss cost of proposed approach aganst two other approaches. Cost s one mportant factor for user. The user s tryng to run hs/her request wth the lowest possble cost. In Fg. 3, results of smulaton have been offered accordng to the cost of scalng for all three compared approaches for 4 exsted servces n 24 hours. As you can see random approach has the worst result n scalng 4 mentoned servces regardng to ts greedy nature of performance as ths approach selects ts choce on the bass of current needs wthout regardng cost parameters whenever t needs to perform the scalng. Cost- aware non-automata approach has better results than the random approach n response to all requests. However t s obvous cost-aware approach based on learnng automata can decrease costs of scalng substantally. Our proposed approach can be more successful n decreasng scalng expenses n consdered servces usng reward and penalty technque. Fg.3. Comparng Cost of Dfferent Approaches Fg.4. Comparng Overall Cost of Dfferent Approaches

6 14 A Novel Approach for Optmzaton Auto-Scalng n Cloud Computng Envronment In Fg.4, we have compared general cost of applyng three approaches. Accordng to the results of the comparson, our proposed approach has been able to mprove scalng costs up to 85% compared to random approach and 20 percent compared to cost-aware approach, B. Second Scenaro In the second scenaro, we evaluate the number of scalng for proposed approach aganst the other two approaches descrbed above. Prevously stated VMs deleton or addton s done based upon volaton of threshold values. The number of scalng whch ndcates the system s balanced or not, also can mpose operatonal overhead and cost to system. So that sutable management for the crtera and choosng sutable threshold values can help the scalng approach to access hghest response speed and lowest cost resulted decreasng rate of SLA volaton. In Fg.5, the results of smulaton have been represented for three approaches compared to 4 per day based on the number of scalng for each approach n the cloud. Accordng to the results, the number of scalablty s changng contnually n random approach n response to the varous requests therefore system doesn t have sutable stablty. On the other hand these values are approxmately equal n our approach and Cost-aware approach so we can say that system stablty has been partly preserved. attrbutes wll be provded such as //resource avalablty, hgh throughput and low respond tme. In fact, when we wll be able to keep the qualty of the servce at the optmal rate, the rate of SLA Volaton for proposed approach wll reman low; otherwse the amount wll be ncreased. Increase of SLA Volaton, ndcates that qualty servce desred by user has not been met. In Fg.7, the results of the comparng values of SLA Volaton have been shown for three compared approaches for 4 servces n the cloud durng 24 hours. It s obvous on the results of smulaton the worst performance n SLA volaton s n cost- aware approach. To reduce SLA volaton, we should use nstances wth hgher speed and power, and we should pay more for such servces. Costaware approach has been encountered problems n SLA volaton because lowest cost s ts preference n selectng an operatonal sample to respond any knd of requests. Random approach n compare to other approaches has been able to mprove ts SLA volaton because of payng hgh costs. Therefore none of two approaches (Costaware and random) have been able to manage trade-off between cost and rate of SLA volaton. Whle our proposed approach used learnng automata has been able to manage the trade-off. It means respondng the requests of user wth the lowest rate of SLA volaton and the mnmum possble cost. Fg.7. Comparng rates SLA Volaton of Scalng Approaches Fg.5. Comparng of the Scalng Number Dfferent Approaches Fg. 6 shows general results of smulaton the number of scalng n three scalng approaches. Accordngly, our proposed approach and cost-aware method have almost the same performance and better than random approach. So the system s more stable than random samplng. In the Fg.8, the overall results of the smulaton of three scalng approaches have been shown. Accordngly, n general our proposed method, compared to other methods, has been able to optmze rate of SLA volaton for all servces requested by user durng the smulaton. The optmal results of our approach are outcome of successful management the trade-off between cost and rate of SLA Volaton. Fg.6. Comparng of Overall Scalng Number of Dfferent Approaches C. Thrd Scenaro In the thrd scenaro, we try to the evaluate crtera of SLA volaton rate for the proposed approach compared to the other two approaches descrbed above. The rate of SLA volaton wll be the least, when qualtatve Fg.8. Comparng Overall SLA Volaton of Dfferent Approaches VI. CONCLUSION AND FUTURE WORK Cloud servces are dstrbuted nfrastructure whch

7 A Novel Approach for Optmzaton Auto-Scalng n Cloud Computng Envronment 15 develops servces and communcatons. Scalng as one of the most mportant features of cloud computng, tres to allocate and pay back resources based on the requrements. Generally, we expect an effcent scalng mechansm wll guarantee the qualty of cloud servces by the mnmum cost. Important factors that have examned n the proposed approach are: the rate of SLA Volaton, the cost of scalng and frequency of scalng. Usng optmzed vrtual resources causes to decrease cost of a cloud servce. On the other hand, effcency mprovement wll be provded by usng approprate resources fxed by requests whch cause to have hgher costs. The frequency of scalng s a crteron to measure stablty of system at the tme or respondng to the requests specfcally at the peak of traffc. Qualty assurance of a cloud servce s dependent to meet the user servce level agreement (SLA). In concluson, we have trade-off between performance measures n our approach. Based on the evaluaton results, the proposed scalng approach whch has been offered based on learnng automata can manage the trade-off between factors of SLA Volaton and costs. The man obectve of our Scalable orented approach s management between two key benchmark ncludng rate of SLA Volaton, and cost. It s proposed as future work, to consder automatc scalng based on the automata can wth respect to the deadlnes. It should be noted that the deadlne s applcable only for works n whch the performance means effcency not ther catastrophc results. You can also use automatcally scalng based on automata for Data ntensve applcatons. REFERENCES [1] Anandh, R. and K. Chtra. "A challenge n mprovng the consstency of transactons n cloud databasesscalablty." Internatonal Journal of Computer Applcatons 52, no. 2 (2012): [2] Affe Fereydoon, Mostafa Ghobae Aran and Mahboubeh Shams "EDLT: An Extended DLT to Enhance Load Balancng n Cloud Computng." Internatonal Journal of Computer Applcatons 108(7):6-11, December [3] Md. Imran Alam, Manusha Pandey, Sddharth S Rautaray,"A Comprehensve Survey on Cloud Computng", IJITCS, vol.7, no.2, pp.68-79, DOI: /tcs [4] Vaquero, Lus M., Lus Rodero-Merno and Rakumar Buyya. "Dynamcally scalng applcatons n the cloud." ACM SIGCOMM Computer Communcaton Revew 41, no. 1 (2011): [5] Mao, Mng, Je L and Marty Humphrey. "Cloud autoscalng wth deadlne and budget constrants." In Grd Computng (GRID), th IEEE/ACM Internatonal Conference on, pp IEEE, [6] Mao.Mng and Marty Humphrey. "Auto-scalng to mnmze cost and meet applcaton deadlnes n cloud workflows." In Proceedngs of 2011 Internatonal Conference for Hgh Performance Computng, Networkng, Storage and Analyss, p. 49. ACM, [7] Monreh Fallah, Mostafa Ghobae Aran and Mehrdad Maeen. "NASLA: Novel Auto Scalng Approach based on Learnng Automata for Web Applcaton n Cloud Computng Envronment." Internatonal Journal of Computer Applcatons 113(2):18-23, March [8] Monreh Fallah, Mostafa Ghobae Aran "ASTAW: Auto-Scalng Threshold-based Approach for Web Applcaton n Cloud Computng Envronment." Internatonal Journal of u- and e- Servce, Scence and Technology (IJUNESST),Vol.8, No.3, pp , [9] Hasan Masum Z., Edgar Magana, Alexander Clemm, Lew Tucker and Sree Lakshm D. Gudredd. "Integrated and autonomc cloud resource scalng." InNetwork Operatons and Management Symposum (NOMS) 2012 IEEE pp [10] Han Ru, L Guo, Moustafa M. Ghanem and YkeGuo. "Lghtweght resource scalng for cloud applcatons." In Cluster Cloud and Grd Computng (CCGrd), th IEEE/ACM Internatonal Symposum on, pp [11] Maurer Mchael, Ivona Brandc and RzosSakellarou. "Enactng SLA s n clouds usng rules." In Euro-Par 2011 Parallel Processng, pp Sprnger Berln Hedelberg, [12] Dutrelh Xaver, Ncolas Rverre, Aurélen Moreau Jacques Malenfant and Iss Truck. "From data center resource allocaton to control theory and back." In Cloud Computng (CLOUD) 2010 IEEE 3rd Internatonal Conference on pp [13] Narendra, Kumpat S. and Mandayam AL Thathachar. "Learnng automata: an ntroducton." Courer Corporaton, [14] D. Menasc and E. Casalccho "A Framework for Resource Allocaton n Grd Computng." In Proc. of the 12th Annual Internatonal Symposum on Modelng, Analyss, and Smulaton of Computer and Telecommuncatons Systems, pp , [15] N. Chohan, C. Castllo, M. Spretzer, M. Stender, A. Tantaw and C. Krntz See Spot Run: Usng Spot Instances for MapReduce Workflows. In 2nd USENIX Workshop on Hot Topcs n Cloud Computng, HotCloud2010. Boston, MA. June [16] Y. Yazr, C. Matthews, R. Farahbod, S. Nevlle, etc "Dynamc Resource Allocaton n Computng Clouds usng Dstrbuted Multple Crtera Decson Analyss." In 3rd Internatonal Conference on Cloud Computng, Mam, Florda, USA, [17] Ro.Nlaba, AbhshekDubey, and AnruddhaGokhale. "Effcent autoscalng n the cloud usng predctve models for workload forecastng." In Cloud Computng (CLOUD), 2011 IEEE Internatonal Conference on, pp IEEE, [18] S. Abdelwahed, J. Ba, R. Su, and N. Kandasamy, "On the applcaton of predctve control technques for adaptve performance management of computng systems", Network and Servce Management, IEEE Transactons on, vol. 6, no. 4, pp , dec [19] S. Abdelwahed, N. Kandasamy, and S. Neema, "A controlbased framework for self-managng dstrbuted computng systems," n WOSS 04: Proceedngs of the 1st ACM SIGSOFT workshop on Self-managed systems. New York, NY, USA: ACM, 2004, pp [20] Cheu, Treu C., Aay Mohndra, Alexe A. Karve, and Alla Segal. "Dynamc scalng of web applcatons n a vrtualzed cloud computng envronment." In e-busness Engneerng, ICEBE'09. IEEE Internatonal Conference on, pp IEEE, [21] Yang, Jngq, Chuanchang Lu, Yanle Shang, Bo Cheng, Zexang Mao, Chunhong Lu, LshaNu, and Junlang Chen. "A cost-aware auto-scalng approach usng the workload predcton n servce clouds." Informaton Systems Fronters 16, no. 1 (2014): 7-18.

8 16 A Novel Approach for Optmzaton Auto-Scalng n Cloud Computng Envronment [22] K. Narendra and M. A. L. Thathachar, "Learnng Automata: An Introducton", Prentce Hall, Englewood Clffs, New Jersey, [23] K. Nam and A. S. Poznyak, "Learnng Automata: Theory and Applcaton", Tarrytown, NY: Elsever Scence Ltd., [24] Behnaz Seyed Taher, Mostafa Ghobae Aran and Mehrdad Maeen. "ACCFLA: Access Control n Cloud Federaton usng Learnng Automata." Internatonal Journal of Computer Applcatons 107(6):30-40, December [25] Calheros, R. N., Ranan, R., Beloglazov, A., De Rose, C. A., & Buyya, R. (2011). "CloudSm: a toolkt for modelng and smulaton of cloud computng envronments and evaluaton of resource provsonng algorthms." Software: Practce and Experence, 41(1), [26] Yang, Jngq, Chuanchang Lu, Yanle Shang, Bo Cheng, ZexangMao, Chunhong Lu, LshaNu, and Junlang Chen. "A cost-aware auto-scalng approach usng the workload predcton n servce clouds." Informaton Systems Fronters 16, no. 1 (2014): Mostafa Ghobae Aran receved the B.S.C degree n Software Engneerng from IAU Kashan, Iran n 2009, and M.S.C degree from Azad Unversty of Tehran, Iran n 2011, respectvely. He s Currently a PhD Canddate n Islamc Azad Unversty, Scence and Research Branch, Tehran, Iran. Hs research nterests nclude Grd Computng, Cloud Computng, Pervasve Computng, Dstrbuted Systems and Software Development. Mahboubeh Shams s Assocate Prof. Qom Unversty of Technology. She's receved the B.S.C degree n Mathematcs from Isfahan Unversty, Iran n 2003, and receved the M.S.C degree from Azad Unversty of Isfahan, Iran n 2006, and also receved the PhD degree n Software Engneerng from UTM n Her research nterests nclude Image Processng, Desgn and Implementaton of Persan/Arabc OCR, Desgn and Implementaton of Bometrc Authentcaton System, Desgn and Test of Relable Software, Databases, Software Engneerng, UML, ERD, DFD, ERP. Authors Profles Khosro mogoue receved the B.S.C degree n Software Engneerng from Unversty Azad Arak, Iran n 2009, and M.S.C degree from Azad Unversty of mahallat, Iran n 2014, respectvely. Her research nterests nclude Cloud Computng, Dstrbuted Systems and Cloud Scalablty technology. How to cte ths paper: Khosro Mogoue, Mostafa Ghobae Aran, Mahboubeh Shams,"A Novel Approach for Optmzaton Auto-Scalng n Cloud Computng Envronment", IJMECS, vol.7, no.8, pp.9-16, 2015.DOI: /mecs