RO-BURST: A Robust Virtualization Cost Model for Workload Consolidation over Clouds

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1 !111! 111!ttthhh IIIEEEEEEEEE///AAACCCMMM IIInnnttteeerrrnnnaaatttiiiooonnnaaalll SSSyyymmmpppoooiiiuuummm ooonnn CCCllluuuttteeerrr,,, CCClllooouuuddd aaannnddd GGGrrriiiddd CCCooommmpppuuutttiiinnnggg RO-BURST: A Robut Virtualization Cot Model for Workload Conolidation over Cloud Jianzong Wang 1, Rui Hua 1, Yifeng Zhu 2, Jiguang Wan 1, Changheng Xie 1, Yanjun Chen 1 1 School of Computer Science and Technology, Huazhong Univerity of Science and Technology, China 1 Wuhan National Laboratory for Optoelectronic, China 1 Key Laboratory of Data Storage Sytem, Minitry of Education of China 2 Department of Electrical and Computer Engineering, Univerity of Maine, USA Correponding Author: c xie@hut.edu.cn Abtract A more public cloud computing platform are emerging in the market, a great challenge for thee Infratructure a a Server (IaaS) provider i how to meaure the cot and charge the Software a a Service (SaaS) client for the cloud computing ervice. Thi problem i compounded a virtualization technology i deployed in many cloud platform to conolidate erver and improve their utilization. Thi paper tudie three different but related model for apportioning cot in a private or public cloud environment upported by virtualized data center. With given workload placement cenario and randomly elected workload, thee model etimate the cot for each workload. Through imulation and thorough comparion of the reult, we finally champion the RO-BURST model tailored for the ervice provider need, that i characterized by robutne and burtine. What i more, we import Cot Volatility Factor to enure that our model i able to adjut itelf to the market and multiform demand in power and hardware component, uch a dik and CPU, howing it compatibility and extenibility. We alo come up with a pricing trategy with repect to erver the workload employ, which generate an applicable and le placement-enitive fee for the client. Keyword-IaaS and SaaS, Cot model, Pricing trategy, Workload, and Cloud; I. INTRODUCTION A. Preface In the pat, many enterprie, epecially the mall one, had to hot their computing ervice and oftware application on infratructure they owned and maintained at high cot. Recently they tart to reort to an advanced technology for more cot-efficient and flexible IT ervice. The bringing forward of cloud make the computing power a kind of new-tyle commodity poible with the computing power of 1 trillion time per econd, enough to imulate nuclear exploion and predict climate fluctuation. In addition, virtualization help to expand hardware capacity and effectively reduce the expene through dividing an integrated erver into coniderable different virtual reource. Therefore, under the pay-a-you-go model [1], IaaS provider mut utilize the virtualization to obtain the flexibility and tability that thoe end-uer require in both private or public cloud. One of thoe preent challenge for IaaS provider i that when virtual machine and different reource demand are aigned into reource pool, how to accurately etimate the cot and place reaonable charge to SaaS client according to the demand of each workload in the competitive cloud environment. B. Example of Motivation Why i an efficient and reliable cot etimate model of great importance to both IaaS provider and SaaS client?firt of all, workload characteritic can influence the ervice charge. Two application with the ame average demand of computation reource could be charged differently. Figure 1 how the demand of dik of two different workload. Workload A hare 22% peak demand in dik, and it average demand lie in.2%. Workload B alo ha.2% average demand but the peak value only amount to 6%. We can eaily infer that they both hare imilar average demand in dik but huge difference in peak demand, which conduct diimilarity in burty demand. Such diimilarity make the number of erver that hot each workload varied. A a reult, workload A mut be aigned to more erver than workload B to meet it much higher peak demand.secondly, workload placement trategie alo impact the ervice cot. For example, we aumed that 1 workload are allocated into a reource pool with two erver. In the firt cenario, 8 workload are aigned to one erver and the remaining 2 are aigned to the other erver. In the econd cenario, 5 workload are aigned to each erver. We have ame amount of erver and workload here, but the cot of each workload under uch two placement cenario may vary ignificantly.thirdly, unallocated part of each erver reulting from the influence of burtine and workload placement trategie hould alo be taken into account when evaluate whether the cot model i reliable and robut enough. C. Contribution In thi paper, we tudy and compare three different cot etimate model. We obtain 2 workload on a company hared ervice platform. After analyzing the cot and characteritic of variou workload in different workload placement trategie, we find that the third model, named a RO-BURST, i the bet one and i deployable in real ytem. Thi model ha the following four alient feature. RO-BURST finely reflect the burty attribute of each workload over the cloud. RO-BURST take into account the unallocated reource, thi model i much le enitive to workload placement and i more robut. Such model ha trong malleability and compatibility,! ! ! !/// $$$ IIIEEEEEEEEE DDDOOOIII !///CCCCCCGGGrrriiiddd !

2 Dik demand in utilization (%) Workload A Workload B cloud torage ytem. Strictly peaking, cloud torage i a kind of ervice rather than pure torage itelf. The core of cloud torage i to convert torage device into torage ervice by integrating application oftware and torage device. There are ome key advantage for cloud torage, e.g., cloud torage provider offer ditant ytem and data backup to keep the mot important data of client from being detroyed by natural diater or man-made damage. A the emergence of PaaS and SaaS, in mot circumtance, it i alway an excellent choice for people to tranfer local torage to the cloud for it implification of management, reliability and cot-effectivene Day Fig. 1. Dik demand under workload A and B which can not only be applied to dik, but alo CPU, memory, network and power, etc. Baed on the reult of RO-BURST model, the ervice for SaaS client can be appropriately priced by their IaaS provider, and uch price i more competitive than other becaue RO-BURST i able to adjut itelf to market trend. The remainder of thi paper i organized a follow. Section II preent background knowledge. Three cot model and pricing trategy are well defined in Section III. We dicu our experiment and evaluate the model and pricing trategy in Section IV. Section V introduce the related work. Concluion and decription of our future work are preented in the lat ection. II. BACKGROUND A. SaaS and IaaS Software a a Service (SaaS) provider, which are partly deployed on the bai of Infratructure a a Service (IaaS), uch a Google and Oracle, offer conumer the convenience to requet application directly over the cloud through Internet. Conumer do not need to intall or manage infratructure uch a network, erver, operating ytem and torage, and avoid high cot of locally inveted oftware and hardware. The IaaS model guarantee baic reource for the client to hot their application, which involve complicated infratructure management that could be handled by IaaS provider themelve like Amazon and Microoft. B. Cloud Storage Cloud torage i a notion developed and extended from cloud computing. By employing cluter and grid technology, and ditributed file ytem, cloud torage leverage maive torage device work together to provide the capacity and throughput required by end application. Cloud computing uer do not need to own the pace of a pecific torage device over the cloud but the data acce ervice brought by whole C. Workload Conolidation Before virtualization came into being, thouand of erver were ued together, which could generate a lot of heat and thu horten the working lifetime of thee machine, and of coure, accompanied with an increaed cot on the hardware acquiition and maintenance. In order to make IT infratructure uch a erver, network and databae better meet the current and predictable future demand of different kind of application in the cloud environment, we often chooe to implify and optimize end-to-end infratructure by bringing workload conolidation. Nowaday, there exit many tool to imulate uch conolidation, one of which i HP capacity advior [2]. The capacity advior purpoe i to find optimal olution for workload placement. It firtly travere former workload to predict upcoming peak demand, and then finihe the earch by applying genetic algorithm [3] while enuring peak demand le than the capacity of the attribute for each erver. D. Cot Etimation in Cloud Environment Among the bigget companie offering cloud computing, Amazon AWS [4] charge cutomer by the number of virtual intance an application occupie and how long it ue them: cot=pricet (t i the total running time, price i the price per VM), while Google AppEngine charge by the number of CPU cycle a cutomer application conume, and recently Microoft tarted to charge for it Azure cloud computing ervice. More and more large IT giant tart to accelerate the development and cooperation of buine model on cloud computing. Gartner Reearch predict the market cale of cloud computing will amount to $15 billion by 214, and mall and medium will pend over $1 billion on cloud computing by 214 [5]. Thi pay-a-you-go model make reource-conumption baed pricing particularly enitive to how a ytem i deigned, configured, optimized, monitored, and meaured. III. COST MODELING A. Service Framework The architecture of our preented RO-BURST framework i hown in Figure 4. Numerou workload of SaaS application firtly go through the Conolidation Engine [6] which i able to find an appropriate workload placement [7] for each workload, and then the workload will be aigned into different erver 444!111

3 Fig. 2. lying in the reource pool. Service Framework C. Three Model Next, we introduce three cot etimate model for the IaaS provider. We refer to thee a Average-baed, Burty-baed, and RO-BURST. Firtly, we conider the Average-baed model (model 1) which only take into account the average demand of a workload on a erver. It i a model mot widely employed by IaaS provider. For a given et of workload W running a et of erver S, the model can be expreed a follow: = C dik = C dik Ū Ū (1) Ū dik Ū dik (2) Our RO-BURST ytem conit of two component, including Cot Generator and Price Generator. The Cot Generator take the report of erver uage to automatically etimate the cot and the Price Generator implement the pricing trategy baed on the reult of cot etimate to come up with a reaonable and robut price for IaaS provider. B. Parameter Explanation In thi paper, we limit our tudy to dik and CPU utilization when etimating the cot of an individual workload. However, our model can be eaily extended to conider the utilization of other component, uch a network and memory. Important parameter ued in the three different model are elaborated in Table I and Table II Complement explanation: If U (t) i 2%, it mean workload w take 2% CPU reource of erver at time intant t; if U (t) i, that mean workload w doe not ue erver at time t. We introduce a new concept, named burty utilization Û and Û dik to model the burtine of workload. We define the burtine a the difference between the peak and average utilization. For example, the burty utilization for CPU i Û = max(u (t)) mean(u (t)). To incorporate the impact of burtine of workload and unallocated erver reource of placement trategie, we dik propoe the concept of average cot (, ), burty cot (Ĉ, Ĉ dik,), and unued cot (Č, Č dik ). Our model alo involve parameter α and β a the CPU and Dik volatility factor in order to adjut our model to current incontant market trend (e.g., Thailand flood that occurred in November 211 reulted in a coniderable rie in hard dik price) We introduce the baic cot C and C dik here for the conideration of importing volatility factor. In addition, according to current market, the cot of a 1TB dik and a CPU with 42.8MHz are $128 and $175 repectively, we et the value at $1 for the convenience of calculation. The total cot of Average-baed model(model 1): w = S,w α + dik,w β (3) S where Ū and Ū dik are the average utilization of workload w on erver, and α and β are the volatility factor of CPU and dik. The key weakne of the Average-baed model i that it doe not take the impact of burtine and unallocated reource into conideration. To addre thi weakne, we propoe the Burty-baed model (model 2). Thi model ha three major tep. Firt of all, we calculate the average cot of a workload w on a erver. Next, we ue both burty demand of each workload hoted on erver and the reult of firt tep to evaluate the burty portion of the total cot of the erver. In the third tep, the unallocated cot of the erver i apportioned baed on the burty cot. At lat, we um up the reult and bring volatility factor α and β to the model. The Burty-baed model i preented below. Cot of CPU = C : Ĉ = Ĉ Č = Č C = Ū Ū (4) + Û C + Û C Ĉ (5) Ĉ (6) + Ĉ + Č (7) where, Ĉ, and Č are the average, burty, and unued cot of CPU on erver, repectively, and Û i the burty CPU utilization of workload w on erver. 444!222

4 TABLE I KEY PARAMETERS USED IN MODEL 1 Parameter Decription Remark and Equation Ū, Ū dik Mean CPU and dik phyical utilization on erver by Value range : [,1%] workload w C, C dik Baic cot of CPU and dik on erver. We et the value at $1 α, β Volatility index of CPU and dik cot, their value are actual cot/baic cot TABLE II KEY PARAMETERS USED IN MODEL 2 AND 3 in thi paper The value of baic cot i $1 in thi paper Parameter Decription Remark and Equation Û, Û dik Burty CPU and dik phyical utilization (i.e.,the difference Value range:[,1%] between peak and average utilization) of workload w running Û = max(u ) Ū, and on erver Û dik = max(u dik ) Ū dik Ǔ, Ǔ Unued CPU and dik phyical utilization of erver. Value range: [,1%] Ǔ =1% max(u ) and Ǔ dik =1% max(u dik ), dik, Ĉ, Average cot, burty cot, and unued cot repectively. Ĉ dik, Č, Č dik = C Ū dik = C dik Ū dik Ĉ = C Û w C w C w Total cot of workload w by uing Average-baed model Total cot of workload w by uing Burty-baed model Total cot of workload w by uing RO-BURST model Ĉ dik Č Č dik = C dik = C = C dik Û dik Ŭ Ǔ dik Cot of Dik C dik : a follow. dik = dik Ĉ dik = Ĉdik Č dik = Čdik C dik = dik, Ĉ dik dik Ū dik dik + Ĉdik Ĉ dik Ū dik (8) + Û dik C dik dik + Û dik Cdik (9) Ĉ dik (1) + Čdik (11) where, and Čdik are the average, burty, and unued cot of dik on erver, repectively, and Û dik i the maximum (peak) dik utilization of workload w on erver. The total cot of Burty-baed model(model 2): C w = C,w α + C dik,w β (12) S S The third model, named a RO-BURST model, inherit the advantage of former two propoed model. However, thi model differ from model 2 in that it ue meaurement of a given et of S erver in the hared reource pool intead of the individual erver to evaluate the average, burty and unallocated cot repectively. RO-BURST model i well defined Cot of CPU C : Ĉ = = ( S Č = C = Ĉ ( ) ( Cot of Dik C dik : Ĉ dik = dik = ( S S S ) Ū S Ū,w (13) + Û C dik,w + Û,w C S, Č ) Ĉ S, (14) Ĉ,w (15) + Ĉ + Č (16) ( S Ĉ dik ) dik ) dik S, Ū dik S, + Û dik Ū dik,w (17) C dik dik,w + Û dik,w Cdik (18) 444!333

5 Č dik = C dik = ( S dik Č dik ) + Ĉdik dik Figure 3 and 4 4 how the relationhip between the peak and average demand in CPU and dik of 2 workload repectively. We ummarize three characteritic about workload a follow: If we define a workload a one with burty demand when the peak-average ratio i higher than 5, for 1% of thee S, Ĉ dik,w (19) + Čdik (2) The total cot of RO-BURST model(model 3): C w = C,w α + C dik,w β (21) S S D. Pricing Strategy Apart from employing RO-BURST model to calculate the infratructure cot of cloud ervice, we alo provide an applicable pricing trategy baed on our RO-BURST model. There are two advantage of RO-BURST for the IaaS pricing: firtly, it robutne help to tabilize cot etimate and make the pricing neither far beyond nor much lower than actual cot. Secondly, a price baed on RO-BURST model own more competitivene for IaaS provider with it dynamic adjutment to the market trend. The equation for pricing trategy i introduced a below (θ repreent the number of erver hoted by a workload w, S mean the total number of erver in the pool (1 θ S), δ repreent profit index and we et it 12% here). Price = C w S/θ δ (22) where θ repreent the number of erver hoted by a workload w, S mean the total number of erver in the pool (1 θ S), δ repreent profit index and we et it 12% In the next ection, we will evaluate thee three model under a variety of workload in detail. IV. MODEL CHARACTERIZATION AND PRICING STRATEGY To tetify whether the three propoed model are efficient, robut and feaible enough, we collected 2 workload from one IT company hared data center, each of which i aigned to different erver in real IT application environment, including ERP, OA, Code Tracking, Web Service, et al. Thee workload have been traced for 2 day and recorded every 5 minute, o we have 5,76 record for each workload, and each trace decribe a workload reource uage. We conduct a comprehenive tudy to compare the competivene, tability and robutne in term of cot etimate of thee three model under different workload placement cenario. Unle particularly pecified, C and β are 1%. A. Workload Characteritic and C dik are et at $1, and α workload, they hare uch feature in dik uage. For example, the ratio of dik could range from hundred to ten and ha a mean value of A for CPU, 65% of the workload how burtine and the average ratio amount to For thoe workload with high dik demand, they may not have high uage in CPU (e.g., workload 2, 5, 19). However, we alo find that workload 14 and 15 have both high CPU and dik requirement, o there i no apparent relevance between dik and CPU demand. For 2% of thee workload, both peak and average dik demand are much more intenive than other, and they conduct burtine more eaily B. Influence of Burtine on Cot Etimate Without lo of generality, thi paper conider an experiment etting with 5 erver in a hared reource pool and 3 workload placement cenario. Scenario I i an equal ditributed trategy. It aign 2 workload to 5 erver, and enure that a erver conitently keep hoting 4 workload and the peak demand le than the capacity of each erver. Scenario II i a greedy ditributed trategy. A econd erver could not be aigned a workload until the firt erver i ued up. Scenario III i a balanced trategy, employing 4 erver to hot 2 workload, with each erver aigned 5 workload. Due to pace limitation, thi paper only preent the detailed reearch reult of two workload, named a C and D, in thi paper. The reult of the other trace are very imilar to the one preented. Figure 5 how their CPU demand for the cot interval(2 day). For workload C and D, they have almot the ame average CPU uage, i.e.,.2625% and.2569%. However, their peak CPU demand, vary ignificantly, with 4.% and 1.15% repectively. Apparently, workload C i much more burty than workload D. For mot of workload, uch performance reult in complication of workload placement, o workload C call need for more erver and the charge of that hould be much higher. Table III preent CPU cot etimate of workload C and D calculated by three propoed model under cenario III. The cloe reult of Average-baed model fail to reflect workload burtine, wherea Burty-baed and RO- BURST model noticeably how the difference between two workload and objectively incorporate the impact of burtine and unallocated utilization. TABLE III COST OF WORKLOAD C AND D IN SCENARIO III Workload Model 1 Model 2 Model 3 Workload C $33.5 $37.5 $26.2 Workload D $32.7 $11. $ !444

6 2 18 Peak CPU Demand Average CPU Demand 5 45 Peak Dik Demand Average Dik Demand Workload C Workload D CPU demand in utilization Dik demand in utilization CPU demand in utilization (%) Workload Workload Day Fig. 3. The peak and average CPU demand of Fig. 4. The peak and average dik demand of 2 2 workload workload Fig. 5. CPU demand under workload C and D 4 35 Scenario I Scenario II Scenario III 4 35 Scenario I Scenario II Scenario III 4 35 Scenario I Scenario II Scenario III Cot in $ for 2 day Cot in $ for 2 day Cot in $ for 2 day Workload Workload Workload Fig. 6. Cot of 2 workload calculated by the Fig. 7. Cot of 2 workload calculated by the Fig. 8. Cot of 2 workload calculated by the Average-baed model Burty-baed model RO-BURST model C. Robutne in Propoed Model Figure [6, 7 and 8] how the cot of thee 2 workload reported by thee three model (The cot range wa hown in yellow). It i oberved that the cot range of 2 workload under Average-baed and Burty-baed model are coniderably wider than RO-BURST. For model 1 and 2, the maximum cot exceed minimum cot by 9% and 53%, while the percentile of RO-BURST only reache 2%, a hown in Figure 9. In other word,averagebaed and Burty-baed model are much more enitive to workload placement and thu lack robutne. In contrat, RO- BURST i a more reliable, predictable, and robut cot etimate model with le enitivity to placement deciion. D. Cot Volatility Factor Due to numerou factor contributing to hardware and power (e.g, CPU, dik, Memory and network) cot, the fluctuation in their price could be frequent. Hence, we import α and β a volatility factor in order to appropriately temper our cot etimate to better pre to market. Thailand take up 6% hare of global hard dik production. The recent (Nov. 211) flood in Thailand led to evere hortage of reource and thu reulted in huge rie to the acquiition price (e.g., mot 5GB and 1TB dik price have rien by an average of 8%). According to current market price, the cot of a 1TB dik and a CPU with 4 x 2.8 MHz are $128 and $175 repectively, o we et α 1.75 and β 1.28 here. Their cot are preented in Figure 1. On the contrary, if real-time update fail to be Cot in $ for 2 day Cot range of Model 1 Cot range of Model 2 Cot range of Model Workload Fig. 9. The difference between maximum and minimum cot of three model, i.e. the cot range of each model made, and we till ue the volatility factor a month ago with α 1.75 and β.7, the reult of cot etimate would be not objectively conducted, making IaaS provider uffer from a average lo of 23% (hown in Figure 11) E. Pricing Strategy Characterization Figure 12 and 13 how the performance of 2 workload calculated by Eq.(22) in ection III in cenario I and cenario 444!555

7 3 25 Dik cot CPU cot Total cot 3 25 Total cot when Dik Volatility Index = 1.28 Total cot when Dik Volatility Index=.7 Difference of cot Cot in $ for 2 day Cot in $ for 2 day Workload Workload Fig. 1. CPU, dik and total cot of 2 workload calculated by model 3 in cenario I when α = 1.75 and β = 1.28 Fig. 11. CPU, dik and total cot of 2 workload calculated by model 3 in cenario I when α = 1.75 and β =.7 III repectively (the introduction of cenario I and cenario III i preented in ection 4). The price that IaaS provider charge SaaS client which are depicted by blue line are almot the ame between cenario I and cenario III. The cot of infratructure that SaaS application occupy, which are depicted by red line, however, are apparently diimilar due to different workload placement. Scenario III, a better placement choice with fewer erver and le reource occupied ave more money for IaaS provider. A a reult, IaaS provider which employ a more favorable placement can earn an additional profit than thoe do not. V. RELATED WORK Recently, there are many reearche on the cot of application over the cloud by dicuing and evaluating workload conolidation in virtual environment. Ref. [8] provide a general approach, which i able to etimate the compenation (i.e., additional reource requirement) incurred by an application tranition from real hardware to a virtual machine. To achieve ervice level objective, a dynamic AutoControl ytem that involve an online model etimator and multi-input, multioutput (MIMO) reource controller i raied in Ref. [9]. In Ref. [1], it come up with an approach to determine the beneficial time frame for VM reaignment, aiming to ave energy conumption to maximize the poible profit by reducing the complexity of reource and workload management in the data center. In addition, it i alo important to determine what kind of application placement hould be ued for enhancing reliability and efficiency of cloud computing ytem, which necearily bring a reduction in cot for IaaS provider. By comparing centralized and ditributed deciion making, Ref. [11] preent a ditributed capacity agent manager to make ervice provider proce and aign their reource more quickly and accurately. Concerning workload characterization in the cloud, Ref. [12] ue data trace in the real data center to realize the prediction of workload hoted on erver by offering a prediction model. The focu on their work lie in workload characterization baed on each erver and aim to predict workload change for co-cluter. Our paper, however, preent workload characteritic baed on workload itelf in ection IV, and mainly concentrate on the generality of a workload burt attribute and it coniderable influence on cot etimate. Amazon.com Inc. Elatic Compute Cloud, or EC2, charge client by fixed-ize virtual machine requeted per hour. They omit the variation among different workload, which could generate a wate of fixed machine ize and thu reult in more infratructure cot for IaaS provider. Another Uagebaed pricing trategy baed on client actual uage, which i applied by many companie, uch a Cico [1]; alo fail to take into account the burt attribute of workload. However, our RO-BURST i a more robut cot etimate model compared with Uage-baed charge model, for our le enitivity to workload placement and burt conideration. Cloet in pirit to our work i HP lab [13]. Their work alo put forward a model with robutne, while our RO- BURST not only conider robutne, but alo involve iteration thu making our etimate more reliable. In addition, our model ha extenibility for requirement of Dik, CPU, memory and etc. Moreover, RO-BUST adjut itelf to the market trend by importing volatility factor, baed on which the IaaS provider could offer competitive pricing trategy for their SaaS client. VI. CONCLUSION AND FUTURE WORK We firtly raie the iue that an important tak for IaaS provider i to eek a refined cot model to etimate the cot of the infratructure conumed by SaaS application. Then we preent three different and omewhat connected cot model. We experiment 2 workload in the hared pool and conider the impact made by burty and unallocated reource, aiming to find a robut model with le enitivity to conolidation placement. Feature of workload performance and cot etimate calculated by each model are elaborated and com- 444!666

8 3 25 Price for SaaS Client Cot for IaaS Provider Proft for IaaS Provider 3 25 Price for SaaS Client Cot for IaaS Provider Proft for IaaS Provider Cot in $ for 2 day Cot in $ for 2 day Workload Workload Fig. 12. The charge and cot for 2 workload under Scenario I Fig. 13. The charge and cot for 2 workload under Scenario III parion are made in thi paper. Eventually, baed on hared reource pool rather than reource within a erver, we propoe a RO-BURST model that enjoy propertie of reliability and generality. To take into conideration the market impact, we alo import volatility factor of key hardware component, uch a dik and CPU, into our model. At lat, we offer IaaS provider a pricing trategy to better charge the SaaS for the infratructure rent. Complexity and multiformity are major iue for thouand of application hoted in data center. Our introduced model, RO-BURST, baed on conideration of burtine and hared reource pool, aim to help IaaS provider to evaluate the cot of numerou kind of workload with trong robutne. RO- BURST i a model that congregate robutne - le enitive to the workload placement, and calability - can be applied to all kind of hardware reource demand, uch a dik, CPU, memory, and network. In addition, baed on our model, IaaS provider are able to et up reliable charge for their client with the imported volatility factor, which take into conideration the volatility of market price of hardware component. We alo demontrate that, the better workload placement cenario our RO-BUST ytem employ, the more cot-effective and profitable the IaaS and SaaS will achieve by uing the pricing trategy propoed in thi work. Our future work include: improving our RO-BURST model, making the volatility factor α,β automatically updated rather than manually regulated; extending our model to other realm, i.e., telecommunication, power plant and hydro-electric tation, etc.; finding a better expreion of burtine in workload intead of difference between peak and average value. We alo eek to propoe a more ophiticated and refined pricing trategy baed on our RO-BURST model to help IaaS provider and SaaS client achieve win-win. ACKNOWLEDGEMENT We thank the anonymou reviewer of CCGRID for their feedback on previou verion of thi paper. Thi Project upported by the National Baic Reearch Program (973) of China (No. 211CB3233), the National Natural Science Foundation of China (No ), the Natural Science Foundation of Hubei province (NO. 21CDB165), the HUST Fund under Grant (No.211QN53 and 211QN32), the Fundamental Reearch Fund for the Central Univeritie and National Science Foundation (CNS #111732, EAR 12789, IIS #91663, CCF #937988, CCF #737583, CCF #621493). REFERENCES [1] Cico white paper, Managing the Real Cot of On-Demand Enterprie Cloud Service with Chargeback Model. [2] HP Capacity Advior Verion 4. Uer Guide. [3] J. H. Holland, Adaptation in Natural and Artificial Sytem. Ann Arbor: Univerity of Michigan Pre, [4] Amazon web ervice. [5] Jitendra Kumar, Vernon, and Sandeep Kumar, The Economic Perpective of Cloud Computing. UACEE International Journal of Advance in Computer Network and Security,Page [6] D. Gmach, J. Rolia, and L.Cherkaova, Reource and Virtualization Cot up in the Cloud: Model and Deign Choice. Proc. of the International Conference on Dependable Sytem and Network, (DSN 211),Hong Kong, China, June 27-3, 211. [7] L.Cherkaova and J. Rolia, R-Opu: A Compoite Framework for Application Performability and QoS in Shared Reource Pool. in Proc. of the Int. Conf. on DependableSytem and Network (DSN),Philadelphia, USA, 26. [8] Timothy Wood, Ludmila Cherkaova, Kivanc Ozonat, and Prahant Shenoy, Predicting Application Reource Requirement in Virtual Environment. HP Laboratorie, Technical Report. [9] Pradeep Padala, Kai-Yuan Hou,Xiaoyun Zhu, and Mutafa Uyal, Automated Control of Multiple Virtualized Reource. EuroSy 29,Nuremberg, Germany, April 1-3. [1] Thoma Setzer and Alexander Stage, Deciion upport for virtual machine reaignment in enterprie data center. IEEE/IFIP Network Operation and Management Sympoium, NOMS,28,7-11 April, Salvador, Bahia, Brazil. [11] Trieu C. Chieu and Hoi Chan, Dynamic Reource Allocation via Ditributed Deciion in Cloud Environment. IEEE International Conference on e-buine Engineering, ICEBE 29, Macau, China, October. [12] Arijit Khan, Xifeng Yan,Shu Tao, and Niko Aneroui, Workload Characterization and Prediction in the Cloud: A Multiple Time Serie Approach. Technical report [13] D. Gmach, J. Rolia, and L. Cherkaova, Chargeback Model for Reource Pool in the Cloud. Proc. of the 11th IFIP/IEEE Sympoium on Integrated Management (IM 211),Dublin, Ireland, May 23-27, !777