Send Orders for Reprns o reprns@benhamscence.ae The Open Cybernecs & Sysemcs Journal, 2015, 9, 639-647 639 Open Access The Vrual Machne Resource Allocaon based on Servce Feaures n Cloud Compung Envronmen Lu Yan and Guo Chenxan College of Informaon Engneerng, Zhongzhou Unversy, Zhengzhou, 450044, P.R. Chna Absrac: Ths paper presens he vrual machne resource allocaon algorhm based on busness characerscs under a cloud compung envronmen. In hs algorhm, s defned o descrbe he user servce characersc me delay facor and prce facor and oher parameers, resource scheduler compung daa cener accordng o he user on he busness characerscs of he descrpon and he saus of cloud calculaed he cos of he busness ndex, and accordng o he cos ndex dsrbuon of cloud compung resources. Frs nroduced he sysem model of he algorhm, and hen he model s esablshed accordng o he correspondng problem of he sysem model, and hen pu forward he correspondng resource allocaon scheme, fnally, he performance of he algorhm s verfed by smulaon. The smulaon resuls show ha he proposed algorhm n cloud compung plaform o mprove he ulzaon rae of resources a he same me, sgnfcanly reduce he compuaonal characerscs of dfferen busness users of cloud use cos, mprove he cloud user servce experence. Keywords: Busness characerscs, cloud compung, resource allocao vrual machne. 1. INTRODUCTION Wh he developmen of cloud compung, more and more users wll busness mgraon o he cloud servce provders of cloud compung fla Sao a vrual machne runnng n he cloud compung plaform, become he cloud user. Cloud servce provders accordng o he specfc needs of he cloud user busness dsrbuon of s correspondng vrual machne confguraon and quany. Usng he curren resource allocaon mehod can mprove he rae of cloud resources beer, and ncrease he cloud servce provder revenue. However, from he cloud user's pon of vew, hese mehods can no brng much benef o he cloud user, or even reduce he user experence, from a long-erm pon of vew, he long-erm ncome of a cloud servce provder wll also have a negave mpac. As he busness characerscs of dfferen cloud of dfferen users, o he vrual machne resource prce, delay and resource deploymen and oher requremens also each are no dencal. So we n he process vrual machne resource allocao allocaon s necessary for vrual machne resources accordng o he characerscs of he cloud user servce, hereby reducng he cos of low cloud users, and mprove he cloud user servce experence, and hen realzes maxmum cloud servce provders of long-erm ncome [1]. In hs paper we from he cloud user perspecve ssued, pu forward he vrual machne allocaon algorhm based on a cloud compung busness characerscs, n order o mprove he ulzaon rae of he plaform n cloud compung resources a he same Address correspondence o hs auhor a he College of Informaon Engneerng, Zhongzhou Unversy, Zhengzhou, Hena 450044, P.R. Chna; Tel: 13803770071; E-mal: luyan0407@126.com me, reduce he cloud user cos, mprove he cloud user servce experence. 2. THE SYSTEM MODEL Consder a compung plaform consss of dsrbued n a pluraly of dfferen geographc locaon daa cener o form clouds, each daa cener capacy, delay performance and vrual machne resource prces are no he same. Cloud servce provders accordng o he user's requremens for he allocaon of cloud resources correspondng vrual machne, operaon of cloud users of vrual machne n he daa cener [2]. In order o undersand, we do he followng defnons: Defnon one: a resource scheduler, runnng sae of compung daa cener accordng o he user's requremens and he cloud cloud vrual machne resource allocaon correspondng o he cloud user, a daa cener resource scheduler self runs n cloud compung plaform on he. Defnon wo: daa cener saus monorng process, runnng on each daa cener, runnng sae real-me monorng daa cener, he amoun of resdual resources ncludng daa cener and vrual machne resource prce nformao saus nformaon and o he resource scheduler sends locaon daa cener. Defnon hree: daa cener saus able, daa sorage cener sae nformaon for resource scheduler module, accordng o he sae nformaon cener daa process sae monorng repors are updaed n real me, ncludng he daa cener of he resdual amoun of resources, me delay and resource prce nformaon. Accordng o he above defno he sysem archecure can be represened as n Fg. (1). 1874-110X/15 2015 Benham Open
640 The Open Cybernecs & Sysemcs Journal, 2015, Volume 9 Yan and Chenxan Fg. (1). Vrual machne sysem framework of dsrbuon maps based on busness characerscs. ( ) sad he cloud user submed o ( ) In Fg. (1), R l, n,c he resource scheduler resource reques, wheren l = l f, m, h, b represens a cloud user for vrual machne confgurao ncludng he processor f, memory m, exernal memory bandwdh h and b; n represens a number of cloud user needed for vrual machne; c says busness characerscs he cloud user ; L, P, D respecvely represens he amoun of remanng resources daa cener n repor daa cener saus monorng process o a resource scheduler, resource prce and me delay esmaon nformao where he resource prce P = P ( f, m, h,b ), ncludng processor compung resource prce f, resource n memory m, exernal sorage resource h and bandwdh resource b ; n order o smplfy he sysem model, we assume ha a cloud users wh only one ype of busness [3]. 3. MODEL CONSTRUCTION AND PROBLEM DE- SCRIPTION 3.1. Descrpon of Servce Feaures For he specfc busness, such as massvely mulplayer onlne game busness, he busness of large amoun of nernal nformaon neraco vrual machne deploymen for he use of cenralzed deploymen mode, and hghly sensve o he delay, resource prce has lle nfluence on ; and as mcro-blog socal busness, wh srong regonal characerscs, he smaller amoun of nernal busness nformaon neraco more suable for vrual machne deploymen adopng dsrbued deploymen mode, a he same me delay hs knd of busness he sensvy s low, prce sensve. The dfference of busness of cloud users leads o s busness characerscs also vary, accordng o he vrual machne deploymen requremens, me delay facor and prce facor, we can use o descrbe he busness characerscs of cloud user. c = ( s,!," ) (1) 3.2. Saus Updaes of Daa Cener On he prcng model of resource prce daa cener and cloud provder, n he sysem model, akng no accoun he equalzaon of mulple daa cener load demand, we use dynamc prcng model based on he resdual amoun of resources. Specfcally, daa cener resources prce P by he k, daa cener saus monorng process accordng o he formula calculaed. P k, =! 1 P k,o +! 2 f L k, ( ) (2) In he formula, k represens he daa cenre number, sad he curren me,! as weghng facor, "! = 1, P k,o s a reference prce of resources daa cener k, s consa f ( L k, ) s he funcon assocaed wh he resdual amoun of resources. Tme delay esmaon of daa cener renewal process: resource scheduler perodcally Xang Yun compung daa cener sendng delay es sgnal, and record he response me delay d he es sgnal, sendng perodc esng sgnal for k,, and whenever a resource scheduler receves he sae d nformaon from he daa cener wll also sen o he daa cener of an expermen he es sgnal. Delay of daa cener s obaned by he formula: I d k, D k, =! (3) I =1 where I represens he mos recen I sgnal, namely he mean response delay me delay I recenly a es sgnal o he daa cener esmaon. 3.2. Descrpon of he Problem In order o nfluence he nuve descrpon of daa cener delay and resource prce on he busness, we pu forward
The Vrual Machne Resource Allocaon based on Servce Feaures The Open Cybernecs & Sysemcs Journal, 2015, Volume 9 641 he concep of busness cos ndex, resource scheduler accordng o he cloud user servce cos ndex for he allocaon of resources o he correspondng busness. Cos ndex can be calculaed by he followng formula: T =! D + " P (4), k, k,, k, Among hem, T says he cloud user arrved a momen of s busness wh respec o daa cener k cos ndex,, k, P = P l says he user n he daa cener k s deployed on, k, k, a vrual machne confgured he requremens of cos, he same cloud users of s busness delay facor! and prce facor! s consan. From he ype se, on he same cloud user servce, dfferen daa ceners and dfferen me, s cos ndex are no he same. Accordng o busness characerscs of cloud users assgn vrual machne resource, mnmum cloud user cos, cos ndex mnmzaon problem s equvalen o solvng a cloud users, herefore, our objecve funcon can be descrbed as follows: mn T, =! T,k, x,k, K K k=1 s..! x,k, = n (5) k=1 x,k, l! L k,,"k = 1,2,..., K Among hem, T sad he oal cos of he cloud user,, says he cloud user servce reques arrval me, x sad, k, he vrual machne allocaon ndex, sad users of vrual machne n k daa cener s he upper deparmen number. The frs resrcon represens a number of vrual machne resources allocaed o he vrual machne scheduler oal user should be equal o he user requred; second lmng condons ndcaes ha he daa cener on k allocaed o he vrual machne resources requred of user should be less han he oal amoun of resources avalable o he daa cener k. Known by he frs lmng condons, he objecve funcon s equvalen o: K x mn T, = n,k,! T n,k, (6) k=1 In he formula, for a parcular user servce, he number of vrual machne needs n n s deermned, and he cos of ndex T user servce relave o a parcular daa cener s, k, he fxed value. So we can hrough he waer fllng algorhm o solve he opmzaon problem [4]. The seps are as follows: 1) Accordng o he cos ndex o sor he daa cener, remember he ranked se for he D T vrual machne, he nalzaon allocaon ndex: x,k, = 0 ; 2) Selec he daa cener, cos ndex T mnmum D, k, T f x l! L, hen daa cener for he user servce agency, a, k, k, vrual machne, x = x +, and more new n = n! 1 1, k,, k, and L = L! x l, f x l L k, k,, k,, k, >, hen k, DT = DT! k, excue sep 1); 3) If D T =!, ndcaes ha he curren cloud compung plaform does no have suffcen resources o provde servces, for he user allocaon falure, end; f x,k = n, and he dsrbuon end, oherwse execung sep 2); Through many mes of erave waer fllng algorhm, can fnd he bes vrual machne user I deploymen sraegy. 4. RESOURCE ALLOCATION ALGORITHM BASED ON BUSINESS CHARACTERISTICS Accordng o he sysem model and he analyss of he problem, we propose a vrual machne resource allocaon algorhm based on busness characerscs, The specfc process as shown n Fg. (2). The concree seps of he proposed vrual machne allocaon algorhm s as follows: Sep one, he user o subm a reques for a resource allocang resources R l, n,c ( ) ; Sep wo, f he user's vrual machne deploymen sraegy of s = 1, execue sep hree; f s = v, v > 1 execue sep four; ( ), hen Sep hree, cenralzed way for user o he vrual machne resource allocao he specfc mehod for: 1) Resource scheduler queres he daa cener k saus able, eleced o mee he needs of he user resources daa cener R ( l, n ), mus sasfy L k! n, l, and consues he canddae daa cener se G of user. 2) If he canddae user se daa cener G s null, represens he curren cloud compung plaform no enough re- sources were allocaed o he user I, resource scheduler refused o user reques, sep fve; oherwse, he calculaon of canddae daa cener cos ndex T,,, k! G relave cloud k users I collecon of G n each daa cener; 3) Selec he canddae daa cener G collecon cos n- dex and he lowes n T,k, daa cener k for user deparmen cloud vrual machne; 4) Remanng resources sae of L k, on he seleced daa cener are updaed, execue sep fve; Sep four, adopng dsrbued deploymen of vrual machne as he user, he specfc mehod for:
642 The Open Cybernecs & Sysemcs Journal, 2015, Volume 9 Yan and Chenxan Fg. (2). Flow char of algorhm. 1) Resource scheduler queres he daa cener saus able, R l, n o mee he user selec he resource requremen ( ) daa cener he surplus resources, L k, mus sasfy k,! n l, where k, L s n sad he number of user needed for vrual machne, l says he cloud user desred vrual machne confgurao v says he user requremens wll be vrual machne are deployed n v daa cener, he daa cener of a canddae daa cener user n he se G ; G 2) If he canddae daa cener G collecon of daa cener < v number, represens he curren daa are no adequae o mee he cloud cener user resource requremens, resource scheduler refused o user requess for resources, sep fve; oherwse, he calculaon of he canddae se each daa cener n T k! G ;, k,, G phase o he cloud user cos of ndex 3) In he canddae se G conssng of a collecon of daa cener cos ndex T, k,, k mnmum v daa cener s seleced n G n he G collecon of he daa cener are user vrual machne deploymen. 4) The remanng resource sae L k, on he seleced daa cener are updaed, sep fve; Sep fve, he vrual machne assgnmen complee, he resource scheduler o he cloud user I feedback resuls. Compared wh he waer fllng algorhm sandards, he above algorhm consderng he sraegy requres he user o busness o he vrual machne deployme wh some per-
The Vrual Machne Resource Allocaon based on Servce Feaures The Open Cybernecs & Sysemcs Journal, 2015, Volume 9 643 formance loss, bu sll sgnfcanly reduces he user's cloud compung cos, especally o requre he use of cenralzed vrual machne deploymen sraegy users, he beer performance [5]. Table 1. The smulaon parameers. Parameers Value 5. SIMULATION AND PERFORMANCE ANALYSIS 5.1. Esablshmen of he Smulaon Model In pracce, he daa cener wh delay and prce resource ulzaon rae, herefore, we frs defne he ulzaon daa cener resources!, momen daa cener k resources ulzaon rae calculaon formula s as follows:! k, = µ 1 f k, + µ 2 m k, + µ 3 h k, + µ 4 b k, µ 1 f k + µ 2 m k + µ 3 h k + µ 4 b k (7) In he formula, µ represens he correspondng o he daa cener resources nfluence facor, delay of 0 < µ < 1,! µ = 1 ; he denomnaor ndcaes he oal amoun of resources daa cener k, he denomnaor represens he use of a correspondng amoun of resources n he me. Accordng o he ulzaon rae of daa cener resources, we defne he relaonshp beween delay and resource ulzaon rae n he smulaon model are as follows: # 1 & D k, = D k,0 exp%! e( $ % 1!" k, '( In he formula, D reference delay daa cener k, accordng o he formula, when he resource ulzaon rae s k,0 less han 70%, wh he resource ulzaon rae ncreases slowly wh ncreasng, when he resource ulzaon rae s hgher han 70%, wh he ncrease of resource ulzaon rae ncreased rapdly [6]. Smlarly, we defne he rae relaonshp by he prce of resource and resource daa cener n he smulaon model are as follows: 1 P k, = P 1!" k,o + P k,o (9) k, Among hem, P sad he benchmark prce of resources k, o daa cener k, can be seen from he above equao wh he ncrease of resource ulzao resource prce daa cener also gradually ncreases. 5.2. The Resuls of Smulaon Analyss Consder a dsrbued cloud compung plaform consss of fve dfferen geographc locaon daa s formed n he cener of each daa cener, all have he same capacy, bu s reference resources prce and reference delay are no he same. In each me slo, he busness needs a number of vrual machne and he correspondng busness characerscs of randomly generaed, execued n a daa cener busness leave wh a ceran probably, he specfc smulaon parameers as shown n Table 1. (8) The number of daa cener 5 Daa cener compung ably Daa cener memory capacy The bandwdh of daa cener 8 3000MIPS 16GBye 100Mbps Leave he probably daa cener operaons 0.02 Busness average requred number of vrual machne 3 The number of smulaon me slo 288 Fgs. (3) and (4), respecvely, ndcaes ha he resource prce fve daa cener (Fg. 3) and delay (Fg. 4) n he 100h me slo o slo changes among he 200h. In he smulaon model, he load s proporonal o he prce of resources and me delay and daa ceners,.e. he larger he load, he hgher he prce of resources, and he delay s large. Fve daa cener benchmark prce and he reference delay are no he same, from he daa cener I o V, he benchmark prce lower reference delay ncreased. Fg. (5) shows he fve daa ceners n he 100h o 200h me slo resource ulzaon curves, as can be seen from he graph, resource allocaon algorhm s proposed n hs paper, fve daa cener load dfference, he proposed prce model algorhm wh balanced load effec. Accordng o he smulaon model and smulao he average load of fve daa cener s 60%, whle he load n Fg. (5) daa cener s beween 40%-70% flucuao demonsrae ha he proposed algorhm can acheve beer load balancng daa cener beween he arge [7]. In Fg. (6), we gve he relaon graph of rae and delay, resource prce daa cener ulzaon III resources, as can be seen from he graph of me delay and resource prces and resource ulzaon rae s proporonal o, and has a hgh correlao conssen wh he smulaon model heory, llusraes he correcness of smulaon [8]. We wll delay facor! more han 0.7 busness as delay sensve raffc, he prce facor! more han 0.7 busness as prce sensve busness, were sascal dfferen vrual machne deploymen sraegy of delay sensve raffc delay and resource cos and prce sensve busness accumulaed value, Fg. (7). Among hem, he frs ac of dsrbued vrual machne deploymen delay and prce dfferen characerscs under he busness sraegy, can be seen from he fgure, for delay sensve raffc, he cumulave delay value s far less han he prce sensve busness Fg. (7a), hs s because he busness offce delay sensve busness requremens of smaller physcal and me delay, and prce sensve busness o busness processng delay s no a src requremen; on he conrary, he me delay sensve servce resource cos accumulaon s greaer han he prce sensve servce Fg. (7b), hs s because he prce sensve busness requremens of resource prce as low as possble, whle he prce of delay sensve busness o resource s no src; Fg. (7c) and (7d) cenralzed vrual machne deploymen delay
644 The Open Cybernecs & Sysemcs Journal, 2015, Volume 9 Yan and Chenxan Fg. (3). Resource prce curves of fve daa cener. Fg. (4). Tme delay curve of fve daa cener. Fg. (5). The ulzaon rae of daa cener resources.
The Vrual Machne Resource Allocaon based on Servce Feaures The Open Cybernecs & Sysemcs Journal, 2015, Volume 9 645 Fg. (6). Dagram of he relaonshp beween resources ulzaon and rae,me delay n daa cener III(DCIII). Fg. (7a). Tme delay of dfferen servce under dsrbued VM deploymen sraeges. Fg. (7b). Resource cos of dfferen servce under dsrbued VM deploymen sraeges.
646 The Open Cybernecs & Sysemcs Journal, 2015, Volume 9 Yan and Chenxan Fg. (7c). Tme delay of dfferen servce under cenralzed VM deploymen sraeges. Fg. (7d). Resource cos of dfferen servce under cenralzed VM deploymen sraeges. Fg. (7). Cos and me delay curve of he dfferen characerscs busness under dfferen vrual machne deploymen sraegys. and resource prce busness sraeges under he dfferen characerscs of he cumulave curve, he resul s smlar o Fgs. (7a) and (7b), ha no maer how he user vrual machne deploymen sraegy, he proposed algorhm can be calculaed usng he correspondng cos reduce users of cloud. The cenralzed vrual machne deploymen sraegy users, dfferences beween dfferen ypes of raffc delay and resource prces more obvous, he resuls show ha he proposed algorhm has beer o adop cenralzed vrual machne deploymen sraegy users reurn. In general, he Fg. (7) shows he vrual machne resource allocaon algorhm based on busness characerscs can be allocaed resources for users accordng o busness characerscs of users, hus reducng he cos of he user [9, 10]. CONCLUSION From he above-menoned cone resource allocaon algorhm based on busness characerscs manly nroduces a cloud compung envronmen proposed, hs algorhm can accordng o he characerscs of user servce, o allocae approprae resources for users, hus reducng he user cos, load balance and can acheve a cloud compung plaform. Ths chaper frs nroduces he sysem model of he algorhm, and hen he model s esablshed accordng o he correspondng problem of he sysem model, and hen pu forward he correspondng resource allocaon scheme, fnally, he performance of he algorhm s verfed by smulaon. The smulaon resuls show ha he proposed algorhm can accordng o he characerscs of user servce for
The Vrual Machne Resource Allocaon based on Servce Feaures The Open Cybernecs & Sysemcs Journal, 2015, Volume 9 647 a parcular resource allocaon o users, sgnfcanly reduce he cos o he user, and mplemen a dsrbued cloud daa cener compung plaform under dfferen load balancng. CONFLICT OF INTEREST The auhors confrm ha hs arcle conen has no conflc of neres. ACKNOWLEDGEMENTS Ths work was fnancally suppored by he Laonng Docor Sar-up Foundaon (20111041). REFERENCES [1] L. Xu W. Chen and Z. Wang, A sraegy of dynamc resource schedulng n cloud daa cener, In: Proc. of IEEE Cluser Workshops, pp. 120-127, Sep. 2012. [2] V. Jalapar, and G. D. Nguye Cloud resource allocaon games, Dep of Compuer Scence, Tech. Rep, Dec. 2010. [3] Kuln Che C-RAN Whe Paper, verson 2.5, Chna Moble Research Insue, Oc. 2011. [4] L. Guangje, Z. Senje and Y. Xueb Archecure of GPP based, scalable, large-scale C-RAN BBU pool, In: Proc. of IEEE GC Workshops, Dec. 2012, pp. 267-272. [5] Z. B. Zhu, P. Gupa, and Q. Wang, Vrual base saon pool: owards a wreless nework cloud for rado access neworks, In: Proc. of he 8 h ACM Inernaonal Conference on Compung Froners Aprl 2011, p. 34,. [6] G. Bhanage, I. Seskar, and R. Mahndra, Vrual basesaon: archecure for an open shared WMAX framework, In: Proc. of he 2 nd ACM SIGCOMM Workshop on Vrualzed Infrasrucure Sysems and Archecures, Sep. 2010, pp. 1-8. [7] S. Namba, T. Warab and, S. Kaneko, BBU-RRH swchng schemes for cenralzed RAN, In: Proc. of IEEE CHINACOM, Aug. 2012, pp. 762-766. [8] S. Bhaumk, S. P. Chandrabose, and M. K. Jaaprolu, CloudIQ: a framework for processng base saons n a daa cener, In: Proc. of he 18 h Annual Inernaonal Conference on Moble Compung and Neworkng, Aug. 2012, pp. 125-136. [9] X. F. Tao, Y. Z. Hou, and K. D. Wang, GPP-based sof base saon desgnng and opmzao Journal of Compuer Scence and Technology, vol. 28, no. 3, pp. 420-428, 2013. [10] Y. Che X. L and F. Che Overvew and analyss of cloud compung research and applcao In: Proc. of IEEE ICEE, May 2011, pp. 1-4. Receved: Aprl 02, 2015 Revsed: May 23, 2015 Acceped: June 06, 2015 Yan and Chenxan; Lcensee Benham Open. Ths s an open access arcle lcensed under he erms of he Creave Commons Arbuon Non-Commercal Lcense (hp://creavecommons.org/- lcenses/by-nc/3.0/) whch perms unresrced, non-commercal use, dsrbuon and reproducon n any medum, provded he work s properly ced.