A New Service Pricing Mechanism based on Coalition Game Theory in

Transcription

1 A New Servce Prcng Mechansm based on Coalton Game Theory n Cloud Servce A New Servce Prcng Mechansm based on Coalton Game Theory n Cloud Servce 1 Luyun Xu, 2 Yunsheng Zhang *1, Frst Author, Correspondng Author Busness School, Hunan Unversty, Changsha, Hunan Provnce, Chna, , [email protected] 2 Busness School, Hunan Unversty, Changsha, Hunan Provnce, Chna, Abstract Cloud computng has great potental to transform and revolutonalze the next generaton communcaton network ndustry by makng software and hardware resources avalable to end-users as cloud servces. It s very necessary to study the servce prcng mechansm to guarantee the qualty of cloud servces. In ths paper, we present a new servce prcng mechansm based on coaltonal game theory. Frstly we buld a cloud servce model and gve a computaton method of the prce and utlty. Then a servce prcng mechansm called coalton prcng algorthm s proposed, termnals consttute coaltons n order to ncrease the throughput utlty of themselves. The smulaton results demonstrate that our mechansm can effectvely support superor cloud servce and mantan every termnal obtan enough throughput utlty. 1. Introducton Keywords: Servce Prcng; Coaltonal Games; Cloud Servce Cloud computng based on utlty computng and grd computng et al has brought sgnfcant changes to busness model and our daly lfe by transformng IT natural economy to IT commodty economy. Cloud computng has been wdely spread and appled by many companes n the IT ndustry lke Mcrosoft, Google, IBM and Amazon. Snce Erc Schmdt, the CEO of Google, frstly proposed the concept of cloud computng n 2006, the studes on cloud computng has been greatly emergng, especally the defnaton of cloud computng. For example, Vaquero et al. concluded twenty-two expert defnatons of cloud computng [1]. The most wdely accepted and refered defnaton s proposed by Unted States Natonal Insttute of Standards and Technology (NIST) Informaton Technology Laboratory: Cloud computng s a model for enablng convenent, on-demand network access to a shared pool of confgurable computng resources (e.g., networks, servers, storage, applcatons, andservces) that can be rapdly provsoned and released wth nmal management effort or servce provder nteracton. [2] As a busness model of cloud computng, as fgure 1 shows, cloud servces am to moblse all knds of network resources to provde customers wth on-demand servces lke water and electrct [3]. Therefore, cloud servces have many charactertcs lke self-servce, convenence, flexbly and so on. Accordng to the resource types, cloud servces could be categorzed nto three classes, IaaS (Infrastructure as a Servce), PaaS (Platform as Servce) and SaaS (Software as a Servce). So far more and more companes are enterng the cloud computng ndustry by provdng dfferent resource types of cloud servces. Some provders even could smultaneously provde mutl-resource types, for example, Google provdes both PaaS and SaaS. Wth more and more governments and enterprses nvestng n cloud computng ndustry, the problems lke technology maturty and safety rsk of cloud computng have been gradually solved. The research focus s swtchng to the cost of cloud servces, prce transparency and user satsfacton. Therefore, as an effectve tool to gude and optmze customer demands, the prce of cloud servces should be pad more attenton n order to satsfy users demand and reduce dle computng resources [4]. Internatonal Journal of Advancements n Computng Technology(IJACT) Volume5,Number5,March 2013 do: /jact.vol5.ssue5.9 75

2 A New Servce Prcng Mechansm based on Coalton Game Theory n Cloud Servce Fgure 1. The archtecture of cloud servces In recent years, the studes of cloud servce prcng schemes could be generally devded nto two drectons, the fxed prcng strateges[5][6] and the dynamc prcng strateges[7][8][9]. The fxed prcng strateges have been wdely adopted by many companes who provde cloud servces, but ths type of prcng schemes s not optmal for several problems lke nflexblty, unfarness and network congeston. Hence there are some scholars proposng dynamc prcng strateges to overcome the above shortcomngs. As there s no an effectve unfed cloud servce prcng system, the exstng dynamc prcng schemes utlze dfferent theores such as revenue management theory, fnancal opton theory n order to optmze allocaton of resources and ensure qualty of servce. Game theory as a wdely used strategc decson-makng theory, has been utlzed n many felds such as economcs, computer scence, poltcs and bology. It studes strategc behavors among ratonal decson-makers mathematcal analyss and predcton [10]. Game theory usually nvolves several basc concepts: player, acton, nformaton, strategy, utlty functon and equlbrum. Game theory can be devded nto cooperatve game and non-cooperatve game. Accordng to the exstng papers, game theory has been wdely used n prcng. Therefore, n ths paper we employ coaltonal game to study prcng scheme for cloud servces on the bass of effectveness, ndvdual ratonalty, coalton ratonalty, group ratonalty [11]. The prmary contrbutons of ths paper can be summarzed as follows: (1) A new game model based on coaltonal game s bult for the prcng of cloud servce. (2) An effcent servce prcng mechansm whch s called coalton prcng algorthm s proposed for cloud servce competton among termnals. (3) A comprehensve set of expermental results demonstrate that the algorthm can effectvely support superor cloud servce and mantan every termnal obtan enough throughput utlty. The rest of the paper s organzed as follows. Secton 2 presents related works. In Secton 3, our game model for the prcng the cloud servce s bult. Secton 4 gves a new servce prcng algorthm. Secton 5 s expermental evaluaton. Fnally, Secton 6 concludes the paper. 2. Related work As the lmted developng tme of cloud computng and the complexty of cloud servce prcng, there s stll no an effectve unfed cloud servce prcng system. Many scholars have been workng on prcng strateges for cloud servces. In ths secton, we provde a bref dscusson on the prcng schemes n cloud servces. In practce, most of the cloud provders adopt the fxed prcng strateges for cloud servces, such as the pay-per-use method, the subscrpton prcng method and the tered prcng method[5][6]. Because ths knd of prcng mode s easy to understand, carry out and be accetped by users. However, some scholars have ponted out that the fxed prcng schemes may brng some problems such as unfarness, network congeston. On the other hand, cloud provders could not realze the maxmum revenue by employng the fxed prcng strateges. 76

3 A New Servce Prcng Mechansm based on Coalton Game Theory n Cloud Servce In order to solve the above problems, some scholars have desgned dynamc cloud prcng strateges from many aspects. In the paper[7], the authors pont out that a dynamc prcng strategy whch changes wth tme could be the optmal prcng method. They propose a prce-demand model and a dynamc prcng scheme for a cloud cache that offers queryng servces n order to acheve proft maxmzaton. In the paper[8], the authors develop nter-organzatnal economc models for cloud servce prcng when there are several cloud provders co-exstng n a market. By devsng and analyzng three prce-qos game-theoretc models and the unque pure strategy Nash equlbrum n the QoS-drven prcng models, what prces and QoS level to set for cloud provders of a gven servce type could be known n order to co-exst n the cloud market. In the paper[9], the authors desgn a cloud resources prcng model that concern the dynamc ablty of the model to guarantee Qualty of Servce and proftablty constrants. They prce the cloud resources by employng fnancal opton theory and treatng the cloud resources as underlyng assets. 3. Our game model for the prcng of cloud servce 3.1. The formaton of our game model We consder a servce prcng game model for network termnals as fgure 2 shows. There are a set of termnals and hardware computaton resources, and the termnals compete the resources for computaton. The coaltonal game model can be descrbed by four elements: players, coalton, prce and utlty as follows. (1) Players: The termnals are the players, as shown n fgure 2, T, {1,2,...,8} are the players of our game model. (2) Coalton: A non-empty subset of players s called coalton, and the players n the same coalton wll cooperate wth each other. There are four coaltons n fgure 2, and they are TC 1:{ T1, T2} TC 2 :{ T3, T4} TC 3:{ T5, T 6} TC 4 :{ T7, T 8}. (3) Prce: There are two knds of prces. One s the ask prce Pr ce( Ask ) of hardware platform resources, and the other s the bd prce Pr ce( Bd ) of termnal coaltons. (4) Utlty: The utlty s the obtaned throughput of the termnal coalton TC. It s the characterstc value of the cooperatng termnals n the same coalton, and able to reach the maxmum value under the cooperaton of each player n the coalton, no matter what actons may be taken by players out of the coalton. In other words, coalton TC obtans the maxmum utlty wthout any help of other players. Fgure 2. Termnal coalton formaton model 77

4 A New Servce Prcng Mechansm based on Coalton Game Theory n Cloud Servce 3.2. Our servce prcng mechansm It s assumed that there are N hardware platform resources n cloud servce system, and they are HR,,, HR. Assumed that there are n termnals n the hardware platform competton, and they 1 N consttute m termnal coaltons. There s a medum named ntermedary agent n the cloud servce system to coordnate the prcng process. The whole prcng process can be descrbed as follows. As shown n fgure 3, on the hand, the prce can be calculated by prce functon UTC ( for cloud resource provders,. In ths paper, the round-robn schedulng algorthm s used to be allocate hardware computaton resources. It means the whole throughput utlty of one hardware platform s equally dvded for the termnals runnng on t. It s assumed that the hardware platforms use a lnear prcng model for selectng prce, and the prce of each selecton s a lnear functon of the total throughput of all the termnals runnng on the correspondng hardware platform. Ths s because, the more throughput on a hardware platform, the more loads occurs. So the termnals whch select larger throughput hardware platform wll pay more prces. The lnear prcng model s benefcal to mprove the system performance. In other areas, there are many correspondng algorthms utlzng ths knd of thought [12]. So the prce whch the hardware platform HR ask for can be descrbed as: Pr ce( Ask) dthhr (1) And the utlty of termnal T that suppored by the hardware platform HR can be descrbed as: U ( T) U ( HR) Pr ce( Ask) (2) So the utlty of termnal coalton TCn :{ T1,..., T n} s formed by n THHR U ( TC ( dthhr) (3) m 1 In formula (3), THHR s the whole throughput of hardware platform of the lnear prcng model, and HR, d s the coeffcent m s the number of termnals runnng on hardware platform There s a counter on each hardware platform n order to record termnal choose HR. HR. m, and t pluses one when one Fgure 3. The servce prcng model n cloud servce 78

5 A New Servce Prcng Mechansm based on Coalton Game Theory n Cloud Servce On the other hand, all termnals consttute m termnal coaltons for bggest throughput utlty. The formaton scheme of termnal coalton s proposed on the bass of our game theory. The scheme s dvded nto two steps. The frst step s that the termnals collect the prces Pr ce( Ask ) of the hardware resources whch can support computaton. The second step s that calculatng the average prce of the potental hardware resources and bdng t as Pr ce( Bd ). So we can obtan the formula: Pr ce ( Ask) 1 Pr ce( Bd) (4) N Then the ntermedary agent compare two knds of prces. Therefore, there are three cases: (1) If Pr ce( Bd ) s greater than Pr ce( Ask ), at ths tme the end prce of the two sdes s Pr ce( Bd ), and the prcng process s over. (2) If Pr ce( Bd ) s equal to Pr ce( Ask ), at ths tme the end prce of the two sdes s Pr ce( Bd) or Pr ce( Ask ), and the prcng process s over. (3) If Pr ce( Bd ) s lower than Pr ce( Ask ), at ths tme the prcng process s not over, the termnal coaltons wll wat for some tme and calculate the average prce of the potental hardware resources agan. The prcng process enters a loop untl t s n lne wth case (1) or case (2). Obvously, the whole system wll obtan the maxmal throughput when each termnal obtans the mnmal prce and maxmal throughput utlty. So the maxmal throughput of the cloud system can be descrbed as: w w n THHR max Ucloud U k ( TC ( dthhr ) (5) k 1 k 1 1 m In formula (5), Uk( TC s the throughput prce of hardware platform k, and w s the number of the hardware platform of the cloud system. 4. Coalton prcng algorthm In ths secton, we present a coalton prcng algorthm on the bass of our coaltonal game model n secton 3. The algorthm s desgned for hardware computaton resources selected by each termnal coalton n a cloud servce system. The man dea of the coalton prcng algorthm comes from the prcng process of our servce prcng mechansm. Each termnal coalton makes the hardware platform selecton decson by maxmzng ts throughput utlty and mnmzng ts prce. Accordng to the analyss of the above secton, the algorthm can be descrbed as follows. 1Intalzaton, the prce and utlty of all the hardware platforms n the cloud are set zero. 2 Loop 3 Fnd the potental hardware platforms from HR,..., 1 HR N 4 Collect the prce Pr ce( Ask ) of the potental hardware platforms 5 Calculatng the average prce of the potental hardware platforms and take t as Pr ce( Bd ) by usng formula (4) 6 Calculatng the throughput utlty UTC ( of termnal coalton TC n 7 If Pr ce( Bd) Pr ce ( Ask), usng Pr ce( Bd ) and UTC ( 8 Else If Pr ce( Bd) Pr ce ( Ask), usng Pr ce( Bd ) or Pr ce( Ask) and UTC ( 9 Else If Pr ce( Bd) Pr ce ( Ask), wat for some tme and go to 3 N 79

6 A New Servce Prcng Mechansm based on Coalton Game Theory n Cloud Servce 10 Obtan the maxmal throughput utlty of the system maxu cloud by usng (5). 11 End loop for all termnal coaltons. 5. Expermental evaluaton In ths secton, we present the software smulaton experments on CloudSm[13] to test the performance of our algorthm. CloudSm s a smulaton toolkt for modelng and smulaton of cloud computng envronments consstng of both sngle and nter-networked clouds. It s developed by Grd Computng and Dstrbuted Systems Laboratory of The Unversty of Melbourne and Grdbus. The CloudSm toolkt s devded nto four layers. They are respectvely SmJava, GrdSm, CloudSm and UserCode from bottom to top. It supports for modelng and smulaton of large scale cloud computng data centers and vrtualzed server hosts wth customzable polces for provsonng host resources to vrtual machnes. It also allows user-defned polces for allocaton of hosts to vrtual machnes and polces for allocaton of host resources to vrtual machnes. The smulaton topology s shown as fgure 2, and multple termnal coaltons compete for two hardware platforms. We compare the prce performance and throughput utlty of our algorthm wth Fxed Prcng algorthm ntroduced n Secton 2. In the smulaton, there are fve cases, and the number of termnals are respectvely set as 10, 20, 30, 40, 50. In each case, thare are two hardware platforms. The expermental results show the changes of termnals behavor and the whole throughput of the cloud servce system. Fgure 4. The total prce of the cloud system 80

7 A New Servce Prcng Mechansm based on Coalton Game Theory n Cloud Servce Fgure 5. The total throughput of the cloud system Fgure 4 and fgure 5 respectvely show total prce of the fve cases and the total throughput of Fxed Prcng algorthm and our algorthm. As shown n fgure 4, the total prce respectvely drop by 10%, 15.8%, 21.4%, 27.8%, 17.5% when the number of termnals s 10, 20, 30, 40, 50. Average speakng, the total prce drop by 18.5%. As shown n fgure 5, the total throughput respectvely ncrease by 40%, 44.4%, 50%, 37.5%, 35% when the number of termnals s 10, 20, 30, 40, 50. Average speakng, the total throughput ncrease by 41.38%. Because of frequent swtchng between hardware platforms, the loads of hardware platforms are unbalanced and the total prce and the total throughput are unstable for fxed prcng algorthm. By contrast, n our algorthm, the loads are balanced on all hardware platforms, and the total throughput s hgh and stable. 6. Concluson The computaton mode of network has a sgnfcant change, that s, cloud computng based on utlty computng and grd computng s wdely deployed as knds of cloud servces. The core ssue of cloud computng network s how to run plenty of termnals on some hardware platforms. So t s necessary to desgn approprate servce prcng mechansm to handle wth cloud servces. In ths paper, we present a new servce prcng mechansm based on coaltonal game theory. Frstly we buld a cloud computng model and gve a computaton method of the prce and utlty. Then a servce prcng mechansm called coalton prcng algorthm s proposed. Termnals consttute coaltons n order to ncrease the throughput utlty of themselves. A comprehensve set of expermental results demonstrate that our algorthm can effectvely declne the whole prce and ncrease the throughputs of the cloud system. 7. Acknowledgments Ths work was supported by Natonal Natural Scence Foundaton of Chna (No ), Hunan Natural Scence Foundaton (No. 11JJB007), Program for Changjang Scholars and Innovatve Research Team n Unversty (No. IRT0916), Innovatve Research Group Hunan Natural Scence Foundaton (No. 09JJ7002). References 81

8 A New Servce Prcng Mechansm based on Coalton Game Theory n Cloud Servce [1] Vaquero L M, Rodero-Merno L, Caceres J, Lndner M, "A Break n the Clouds: Towards a Cloud Defnton", ACM SIGCOMM Computer Communcatons Revew, Vol. 39, No.1, pp. 50~55, [2] Hoefer C N, Karaganns G. "Taxonomy of cloud computng servces", In Proceedngs of IEEE GLOBECOM Workshops, pp.1345~1350, [3] Jang Jule, Le Jajn, He Feng, Wang Yan, Sun Je, "Formalzng Cloud Servce Interactons", JCIT, Vol. 7, No. 13, pp. 189~ 196, [4] Jun Tan, Zhchao Wang, "The Structure Character of Market Sale Prce n the Coordnatng Supply Chan", AISS, Vol. 5, No. 1, pp. 412 ~ 418, [5] Wenhardt C, Anandasvam A, Blau B, Borssov N, Men T, Mchalk W, Stosser J, "Cloud Computng - A Classfcaton, Busness Models, and Research Drectons", Busness Models & Informaton Systems Engneerng, Vol. 1, No. 5, pp. 391~ 399, [6] Youseff L, Butrco M, Slva D D. "Toward a unfed ontology of cloud computng", Grd Computng Envronments Workshop, Vol. 10, No. 12, pp. 1~ 10, [7] Verena Kantere, Debabrata Dash, Gregory Francos, Sofa Kyrakopoulou, and Anastasa Alamak, "Optmal Servce Prcng for a Cloud Cache", IEEE Transactons on Knowledge and Data Engneerng, Vol. 23, No. 9, pp. 1345~ 1358, [8] Ranjan Pal, Pan Hu, "Economc Models for Cloud Servce Markets", In Proceedngs of ICDCN pp. 382~ 396, [9] Bhanu Sharma, Ruppa K Thulasram, Parmala Thulasraman, Saurabh K Garg, Rajkumar Buyya, "Prcng cloud compute commotes: A novel fnancal economc model", In Proceedngs of 12th IEEE/ACM Internatonal Symposum on Cluster, Cloud and Grd Computng, pp. 451~ 457, [10] Zhang Xaofang, Zhu We, "The Study on Food Qualty Supervson usng Colluson Game Model", IJACT, Vol. 4, No. 4, pp. 283 ~ 293, [11] Xe Kun, Fu Meng-yng, "A Coaltonal Game Based Relay Selecton Algorthm for Cooperatve Communcaton", IJACT, Vol. 4, No. 11, pp. 132 ~ 140, [12] Xa-an B, Da-fang Zhang, We Lang, and Hong Qao, "Processor Coalton Algorthm for Vrtual Routers on a Multprocessor System", AISS, Vol. 4, No. 5, pp. 191 ~ 198, [13] JanPng Wang, YanL Zhu, HongYu Feng, "A Mult-Task Schedulng Method Based on Ant Colony Algorthm Combned QoS n Cloud Computng", AISS, Vol. 4, No. 11, pp. 185 ~ 192,