Partner selection of cloud computing federation based on Markov chains

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1 COMPUER MODELLING & NEW ECHNOLOGIES (12B) Abstract Partner selecton of cloud computng federaton based on Markov chans Lang Hong 1,2, Changyuan Gao 1* 1 School of Management, Harbn Unversty of Scence and echnology, Harbn , Helongjang, Chna 2 School of Management, Helongjang Unversty of Scence and echnology, Harbn , Helongjang, Chna Receved 1 June 2014, Cloud computng s currently a hot n the feld of nformaton technology, and cloud computng allance s an mportant drecton. In order to make the league better development of cloud computng and solve the problem of selectng partners of cloud computng federaton. hs paper bulds the structure of cloud computng federaton system and evaluaton ndcators system of federaton partner selecton, and proposes a dynamc comprehensve evaluaton model based on Markov Chan and Analytc Herarchy Process, further to get exact values of evaluaton for dfferent evaluatng targets n dfferent tme perods. Based on the assessment analyss and smulaton results there are a reasonable selecton coalton partners. he example demonstrates the feasblty and ratonalty of the evaluatng method and bulds n ths paper, whch would gude partner selecton of cloud computng federaton. Keywords: cloud computng federaton, partner selecton, Markov chan, analytc herarchy process 1 Introducton Cloud computng, as a new I delvery models, s a major achevement of modern nformaton technology. And t has been recognzed as the most worth watng technologcal revolutons. At present, whether cloud computng servce or the users, have begun to pay attenton to cloud computng can brng about tremendous busness value. In 2013, cloud computng ndustry have reached 100 bllon RMB, t wll rapdly development n the future. Wth the technology of cloud computng research deeply and appled gradually mature, accordng to largescale, vrtualzaton and cloud computng technology of elastc scalablty, on-demand servce, low cost [1], appeared to ntegrate cloud computng hardware and software nfrastructure. Network nfrastructure, transports Uygur, termnal equpment manufacturers and market promoton of busness and computng clouds of dfferent subjects of commercal servces n the ndustral chan of resources, to provde servces to the needs of users [2]. he development of the cloud computng ndustry allance s the nevtable choce to meet ths demand. Cloud computng s guded by the demand of the market, as the bass for cooperaton wth the cloud computng ndustry value chan allance, specfcally refers to the value chan to enterprses, research nsttutes, ndustry assocatons and between users, n order to realze the whole value chan value maxmzaton, and constantly mprove the strength and level of compettveness and the members of ther own, by contract the ways of complementary advantages, beneft sharng, rsk sharng loose network of tssue [3]. At present, scholars research on allance partner choce has made some achevements, the choce of the method of evaluaton and optmzaton studes are presented wth AHP (Analytc Herarchy Process, referred to as AHP), fuzzy comprehensve evaluaton method, data envelopment analyss (Data Envelopment Analyss, referred to as DEA), lnear weghted method, advantages and dsadvantages of dstance soluton method (echnque for Order Preference by Smlarty to an Ideal Soluton, OPSIS) and the organc combnaton of the above varous methods. However, these methods have shortcomngs n common s the need for hstorcal data, a larger number of [4]; two s only for a moment by the evaluaton ndex of statc state evaluaton and analyss of [5], for the dynamc ndex for evaluatng the accuracy s not hgh. But cloud computng has ts own characterstcs: cloud computng s an emergng ndustry, cloud computng n the varous partners runnng tme are shorter, not the exstence of the state of development of decades of hstorcal data. In addton, the allance partners were assocated wth cloud computng technology, I ndustry related, has the characterstcs of fast speed of development, change quckly, the development of the state wll change wth tme and the dynamc changes of [6]. herefore, the above method s not well suted to cloud computng allance partner selecton. o sum up, accordng to the characterstcs of dynamc, cloud computng development present stuaton and the partner's uncertanty, ths paper puts forward the dynamc * Correspondng author s e-mal: 590

2 Based on value added rade assocatons SaaS PaaS IaaS Research nsttutes Market orented COMPUER MODELLING & NEW ECHNOLOGIES (12B) comprehensve evaluaton method based on Markov chan and AHP combnaton model. Cloud computng s proposed allance archtecture, establshng the cloud computng allance partner selecton evaluaton ndex system, make full use of Markov chan "features no aftereffect", determned the comprehensve evaluaton ndex weghts wth the AHP method, through the analyss and calculaton of concrete, comprehensve evaluaton that all partners of the value, provde accurate decson-makng bass for the selecton of allance partners. 2 he Markov chan prncple 2.4 HE MEMBERSHIP FUNCION We can obtan a steady vector wth n evaluaton ndex, then the membershp matrx F s followng. F S1, S2,, S n (3) 3 he proposed algorthm 3.1 HE CONSRUCION OF EVALUAION SYSEM 2.1 HE DEFINIION 1907, the Russan mathematcan Markov found, some systems n the process of state transton, the change of state s only related wth the recent state, whch has nothng to do wth the long-term state. Namely the (n+1) the converson result of the system, only depends on the nth state, ths property s known as a non-aftereffect. Consstent wth no effect of state transton process s known as the Markov process, the whole process of a seres of Markov called Markov chan [7] HE SAE RANSIION PROBABILIY MARIX Supposed that there are n states the system s S j at tme S n 1 at tme n S1, S2,..., Sn. he state of, the system state converts to, and the transton probablty s (n) P j hs probablty s called the one step transton probablty of Markov chan. Arranged the n a matrx based on some order, whch named one step transton probablty matrx P. hen, then the one step transton probablty matrx s followng. P j (n) P11 P12 P1 n p21 p22 p 2n P=. (1) pn1 pn2 pnn he Cloud computng allance archtecture. Cloud computng s take the market demand as the gudance, loose network organzaton for cooperaton on the bass of the cloud computng ndustry value chan. he core of cloud computng Federaton consttuton s a cloud computng servce, ncludng three man categores: hardware nfrastructure, the server, storage, network resources are encapsulated nto servces provded to users, e.g. Amazon EC2, IBM Blue Cloud and so on. Platform provder put the computng envronment, envronment of development platform, etc. as a servce to the users, e.g. Wndows Azure platform provded by Mcrosoft, Google App Engne platform from Google and so on. Software and applcaton program s encapsulated nto a "servce" provded to the user by software, e.g. CRM (Customer Relatonshp Management) provded by the Salesforce.com. In addton, cloud computng allance also ncludes related research nsttutes, all knds of users, ndustry assocatons, etc. Please see Fgure 1. Implement aton Soluton Polcy makng and supervson department Operaton & mantenance Indvdual Government Cloud users ermnal equpment Enterprse System ntegrator Consultng 2.3 HE SEADY SAE VECOR Publc cloud Markov chan wll gradually n a stable state through a number of transformatons, whch s ndependent of the ntal state. here wll be the only state vector S, whch s named as the steady state vector S x1, x2,..., xn, When, satsfes 0x 1, and Equaton (2) s called steady state vector. 1 x S SP n x 1 (2) Infrastru -cture Platform Software Cloud computng servce FIGURE 1 he cloud unon archtecture dagram he Establshment of cloud computng allance partner selecton evaluaton ndex system. Establsh a scentfc evaluaton ndex system plays a 591

3 COMPUER MODELLING & NEW ECHNOLOGIES (12B) crucal role for the subsequent coalton partners to select the results of scentfc research. Accordng to the characterstcs of cloud computng allance, refers to the lterature research, survey results and expert opnon, Re.[9] concluded that the selecton of evaluaton ndcators of the cloud computng allance partner ncludes the followng fve aspects, qualty levels, economy, technology, user-frendlness and aglty [9], Please see able 1. ABLE 1 he ndex evaluaton system of the cloud computng federaton partner selecton arget layer Select the best partners (a) he frst-level ndex Qualty level (b1) Economy (b2) echncal level (b3) User-frendlness (b4) Aglty (b5) he second-level ndex Servce speed (c1) Management level (c2) he degree of safety and relablty (c3) Cost (c4) Demand Pay (c5) Versatlty (c6) Resource sharng (c7) Elastc scalablty (c8) Automated management level (c9) Convenence (c10) the acheve ablty to customzaton (c11) Renewal capacty (c12) Adaptablty (c13) 3.2 CONSRUCION OF EVALUAION SYSEM DEERMINE HE SE OF EVALUAION INDEX AND INDEX AND EVALUAION LEVEL evaluaton ndex for the cloud computng federaton partner selecton as shown n able 2. ABLE 2 Cloud Allance Partner Selecton of evaluaton ndex system and weght b b1 b2 b3 b4 b5 c (w) c c c c c c c c c c c c c CR CI()a RI()a (0.047, 0, , , 0)(0.3629, ,0.2394, , ) (0.52, 0, 0.52, 0.52, 0)(0.3629, , , , ) As can be seen from the results, the consstency of the comprehensve sort s acceptable. 4 he example analyss Assume that there n ndex for valuaton objects, represented by, respectvely. hen, the set of evaluaton ndex s expressed as. Supposed that there are C c1, c2,..., cn c ( 1,2,..., n) v ( 1,2,..., m) evaluaton levels for each evaluaton ndex, then the set of evaluaton levels s. he membershp degree s r j, we can obtan the th evaluaton result of ndex: V v1, v2,..., vm r r, r,..., r 1 2 m. (4) 3.3 SYSEM DEERMINE HE SE OF EVALUAION INDEX AND EVALUAION LEVEL he AHP s a knd of system analyss method wth combnng qualtatve analyss and quanttatve analyss. It s also a powerful tool n analyss of mult-objectve and mult crtera for complex large system. It has the characterstcs of clear thnkng, smple way and so on, wde range of applcatons, and strong system. Because ths method uses more mature, the specfc process no longer ago. By the calculaton, the weght of each We assume that the three enterprses A, B and C would be the cloud computng federaton partner selecton. Accordng to a set of evaluaton ndex system, we shall dvde the assess level nto fve levels. Namely, best, well, general, poor and the worst. he evaluaton level set of the membershp vector s [0.9, 0.7, 0.5, 0.3, and 0.1]. For the three enterprses A, B and C, ther recent eght quarters ndex scores can be assessed by the experts, such as the enterprse A, the concrete results see able 3. ABLE 3 A busness to be evaluated scorng Fact Sheet Evaluaton c c c c c c c c c c c c c In order to reflect the phased development trend of the assessed enterprses, We use the frst quarter as the startng pont, the every contnuous four quarters as an evaluaton 592

4 COMPUER MODELLING & NEW ECHNOLOGIES (12B) stage, dvdes the eght quarters nto fve stages, and computes ts phased comprehensve assessment value for each enterprse, then can compare the further development trend of three enterprses by ther smulaton results. 4.1 LISS HE SAE RANSIION MARIX, AND OBAINS HE SABLE VECOR AND HE MEMERSHIP MARIX Accordng to the data n able 3 and Equaton (1), such as the evaluaton ndex c1, we obtan ts transton probablty matrx P: 0 1/ 2 1/ P= Accordng to the Equaton (2), we obtan ts stable vector. Smlarly, we can S obtan the stable vectors of the other 12 evaluaton ndexes. Accordng to the Equaton (3), mmedately, we can obtan all three ndcators of membershp matrx F of the Enterprse A. 4.2 HE COMPREHENSIVE ASSESSMEN VECOR AND ASSESSMEN VALUE OF PARICIPAION ENERPRISES (5) NA he assessment value N of the 1st-4th quarter: C Wth the above calculaton process we can also calculate the other four stages comprehensve evaluaton value for the enterprse A, and ts overall assessment value of the eght quarters. he same method, we can also obtan the phased assessment value of the enterprse B and enterprse C. he concrete results as shown n able 4. ABLE 4 he comprehensve assessment value of three enterprses Comprehensve Evaluaton perod (quarter) Enterprses assessment value 1st- 4th 2nd- 5th 3rd- 6th 4th- 7th 5th- 8th 1st- 8th A N B C N N o compare horzontally assessment value of selected partners, f the assessment value s greater, t shows that the enterprse comprehensve ablty s stronger, and the development trend s also better. By the comparson for the assessment value of the recent 5th-8th quarter, the enterprse A s maxmum, so we should gve prorty to select t as a coalton partner. In addton, accordng to the smulaton of the known data, as shown n Fgure 2 and Fgure 3, we can see ntutvely that the enterprse A s better than the other two enterprses n the development trend, and llustrate further that the accuracy of partner selecton s good by the proposed method. For example, the 1st phase (the 1st-4th quarter) of the enterprse A, based on the vector W, we obtan the comprehensve evaluaton vector C: dentfed membershp matrx F and weghted F A = C= W F (6) he value of comprehensve assessment st-4th 2nd-5th 3rd-6th 4th-7th 5th-8th 6th-9th 7th-10th Evaluaton perod( quarter) FIGURE 2 he smulaton trend development status of the alternatve enterprse he value of comprehensve assessment Enterprse A Enterprse B Enterprse C 1st-4th 2nd-5th 3rd-6th 4th-7th 5th-8th 1st-8th Evaluaton perod( quarter) Enterprse A Enterprse B Enterprse C FIGURE 3 he phased development state hstogram of the alternatve enterprses 593

5 COMPUER MODELLING & NEW ECHNOLOGIES (12B) Concluson Accordng to the developng staton of cloud computng federaton, ths paper bult the structure of cloud computng federaton system, and proposed the general range and types of federaton partners. hs study bult evaluaton ndcators system of federaton partner selecton accordng to the characterstcs of cloud computng federaton. Proposed a dynamc comprehensve evaluaton model based on Markov Chan and Analytc Herarchy Process accordng to the combnaton of both of the them, further to get concrete values of evaluaton of the targets, and ths model s more exact than other methods whch can only get the range the evaluated targets belong to. And the example demonstrated the feasblty and ratonalty of the evaluatng method bult n ths paper. So far, there has been lttle research nto partner selecton of References cloud computng federaton, there more, the evaluatng method proposed n ths paper would gude partner selecton of cloud computng federaton effectvely, and t could evaluate the further development of partners contnuously, reduce the rsk of cooperaton, and provde the decson-makng bass for scentfc development of cloud computng federaton. Acknowledgments hs work was supported by the Natonal Natural Scence Foundaton of Chna (Grant No ; No ), Natural Scence Foundaton of Helongjang Provnce of Chna (Grant No. G201301) and the Planned Constructon of Phlosophy and Socal Scences Innovaton team n Hgher Schools funded projects of Helongjang Provnce of Chna (Grant No. D201203). [1] Aljabre A 2012 Cloud Computng for Increased Busness Value Internatonal Journal of Busness and Socal Scence 3(1) [2] Garrson G, Km S, Wadefeld R L 2012 Success factors for deployng cloud computng Communcatons of the ACM 55(9) 62-8 [3] Roehwerger B, Bretgand D, Levy E, Gals A, Nagn K, Llorente I 2009 he Reservor model and archtecture for open federated cloud computng IBM Journal of Research and Development 53(1) 1-11 [4] Sueyosh, Shang J, Chang W C 2009 A decson support framework for nternal audt prortzaton n a rental car company: A combned use between DEA and AHP European journal of Operatonal Research 199(1) [5] Zhang J 2000 Fuzzy analytcal herarchy process Fuzzy Systems and Mathematcs 14(2) 80-8 [6] Zhang Q, Cheng L, Boutaba R 2010 Cloud computng: state-of-theart and research challenges he Brazlan Computer Socety 1(1) 7-18 [7] Sarukka R R 2000 Lnk Predcton and Path Analyss Usng Markov Chans Proc of the 9th Internatonal World Wde Web Conference 33(1-6) [8] Cook N, Mlojcc D, alwar V 2012 Cloud management he Brazlan Computer Socety 3 (3) [9] Wrt A 2009 Cloud Computng-A Classfcaton, Busness Models, and Research Drectons Busness & Informaton Systems Engneerng 5(5) Authors Lang Hong, , Chna. Current poston, grades: PhD student n Harbn Unversty of Scence and echnology, Chna. Unversty studes: master s degree n Publc Admnstraton from Harbn Insttute of echnology, Chna n Scentfc nterest: strategc allance and nformaton technology. Changyuan Gao, , Chna. Current poston, grades: professor and doctoral tutor at Harbn Unversty of Scence and echnology, Chna. Unversty studes: PhD n Management from Harbn Engneerng Unversty, Chna n Scentfc nterest: decson support system and the hgh-tech ndustral cluster. 594

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