A Novel Resource Pricing Mechanism based on Multi-Player Gaming Model in Cloud Environments

Transcription

1 1574 JOURNAL OF SOFTWARE, VOL. 9, NO. 6, JUNE 2014 A Novel Resource Prcg Mechasm based o Mult-Player Gamg Model Cloud Evromets Tea Zhag, Peg Xao School of Computer ad Commucato, Hua Isttute of Egeerg, Xagta, , Cha Emal: zta1974@126.com Abstract Recetly, cloud computg has become a promsg etwork platform for o-trval applcatos. The key feature of cloud computg s o-demad resource provso by utlty paradgm. Therefore, resource prcg mechasm plays a mportat role for realzg o-demad resource provso. Ufortuately, exstg resource prcg mechasm wll result may egatve effects o system performace, such as low-effcet egotato, extra commucato costs ad etc. I ths paper, we preset a ovel resource prcg mechasm, whch s based o multplayer gamg model ad capable of realzg batch resource allocato a effcet maer. I ths mechasm, we troduce a set of vrtual resource brokers whch s resposble for fgurg most ratoale resource prces elastc cloud evromet. To vestgate the effectveess ad performace of our prcg mechasm, extesve expermets are coducted, ad we compare ts performace wth varous approaches, clude marketbased approaches ad aucto mechasms. The results show that our prcg mechasm s more effectve to realzg batch resource allocato, especally whe the system workload s tesve. I addto, our mechasm ca sgfcatly reduce the egotato costs comparg wth extg approaches. Idex Terms cloud computg, prcg mechasm, vrtual resources, computg ecoomy, cooperatve game I. INTRODUCTION Cloud computg has emerged as a promsg techology ad t has bee creasgly adopted may areas cludg scece, egeerg, ad commercal busess, due to ts heret flexblty, scalablty ad cost-effectveess [1, 2]. Clouds are prmarly motvated by the cocepto of utlty computg, whch users have to pay resource provders for executg ther applcatos. Whle the pay-per-use prcg model s very appealg for both servce provders ad cosumers, coflctg objectves betwee the two partes hder ts effectve applcato [3, 4]. I other words, the servce provder ams to accommodate as may requests as possble wth amg to maxmzg ther profts, whch evtably coflct wth cosumer s performace requremets. I the past few years, there have bee plety of studes explotg market prcg mechasm for dstrbuted resource allocato, ad the well-kow dstrbuted systems [5, 6, 7, 8, 9, 10]. esde ths systems, may ecoomc-based polces ad schedulg algorthms have also be wdely studed, clude Resource Aucto [11, 12], FrstPrce [13], FrstProft [14], ad Proportoal-Share [15]. I typcal dstrbuted systems, ecoomc-based model s has bee prove to be effectve for resource allocato, however, t also rase other problems that caot be gored [7, 8, 9]. Frstly, ecoomc models brg about extra commucatoal ad computatoal overhead to applcatos [8, 16]; Secodly, whe the system s presece of hgh-ed applcatos that requre co-allocatg multple resources across stes, the prce egotato process s ofte low-effcet [6, 9, 17]. Curretly, may exstg cloud systems adopted fxed prcg mechasm whose advatages are easy mplemetato ad low mata cost [17]. Ufortuately, fxed prcg mechasm wll lead to may egatve effects o system performace wth the creasg of system scale, such as low resource utlzato [17, 18; 19], load ubalacg [20], udesrable QoS satsfacto [21, 22]. To address the above ssues, ths work we preset a cloud resource prcg model wth amg at overcomg the shortcomgs of exstg prce mechasms terms of effcecy ad faress. I our prcg model, vrtual resource cofgurato ad provso are defed as a two-phrase gamg procedure, whch cooperatve gamg model s appled to optmze the resource beefts ad o-cooperatve gamg model s used to balace the user s costs ad provder s beefts. The rest of ths paper s orgazed as followg: Secto 2 presets the related work; I secto 3, the gamg models are preseted wth problem descrpto; ; I Secto 4 the solutos of game models are preseted theoretcally; I secto 5, expermets are coducted to exame the effectveess of the proposed approach. Fally, Secto 6 cocludes the paper wth a bref dscusso of the future work. II. RELATED WORK I December 2009, AWS lauched Spot Istaces (SI) mechasm [23], whch users are charged at a hgher rate whe the provder expereces overload. Its orgal objectve s to ecourage users to shft ther flexble workload from provder's peak hours to off-peak hours 2014 ACADEMY PULISHER do: /jsw

2 JOURNAL OF SOFTWARE, VOL. 9, NO. 6, JUNE wth moetary cetves. After proflg SI mechasm for about two years, Wee et al. foud three terestg observatos about SI [24]: (1) SI prce s 52.3% cheaper tha the stadard prce of equvalet stace type o average. Therefore users ca acheve ths cost savgs by smply swtchg to SI stead of shttg ther workload to cheaper perods; (2) Addtoal cost savgs from shftg workload to cheaper hours s merely 3.7% o average. (3) Above two observatos have ot bee chaged over tme. ased o the above observatos, Wee cocludes that AWS SI does ot provde large eough moetary cetve for users to shft ther workloads. I [17], the authors preseted a dyamc prcg scheme whch takes efforts o mprovg the effcecy of batch resource tradg federated cloud evromets. I ther scheme, the whole cloud system s cosdered as a uformed resource market where resource supply ad demad ca be balaced by usg macro-ecoomc equvalece theory. Ufortuately, the scheme reles o market self to automatcally obtag equvalet prce, therefore t s low-effcet comparg wth the opeg feature of cloud platform. I [25], the authors proposed a dyamc secod-prced aucto mechasm to solve the allocato problem of computato capacty the evromet of cloud computg. Durg the aucto procedure, t assumes that resource prcg wll be creased sgfcatly whe the system workload s peak state. Such a assumpto s valdatg for those systems whose resource quatty s costat durg a log tme terval. Eve so, ther works proofed that secod-prced aucto mechasm ca esure reasoable proft for cloud provders. I [26], the authors preseted a three-ter cloud structure, whch cossts of frastructure vedors, servce provders ad cosumers. I such a framework, dfferet cloud servces are composted for servg user s request, whle the prces of cloud servces are decded by takg the depedecy of dfferet user budget costrat. So, the ma objectve of ther approach s creasg the QoS satsfactos of cloud users especally for those wth lmted budgets, whle t gores the profts of cloud provders. I [21], the authors proposed a decetralzed ecoomc approach, whch a set of agets were troduced to teract wth the uderlyg frastructures o behave of user applcatos. To meet the SLA performace ad avalablty goals, a ecoomc ftess s calculated by metrcs cludg performace costrats, curret workload, ad resource utlzato whe allocatg resources to a applcato. I [22], the authors proposed a herarchcal game model to aalyze the decsos of resource provders whe cloud resources are shared by both teral users ad publc users. The game model s composed of two terrelated cooperatve games: (1) The low-level game model s to descrbe the reveue sharg process betwee varous cloud provders, ad ts game soluto ca be fgured out by stochastc lear programmg techque; (2) The upper-level game model formulate the coaltoal process whe a group of provders cotrbute ther resources to a commo pool, ad ts aalytcal soluto s preseted by Markov Cha techque. III. FRAMEWORK AND DEFINITIONS The framework of resource maagemet elastc clouds s show Fgure 1. I such a framework, sgle cloud provders ca serve as depedet cloud system whe t ca provde suffcet resource for user applcato; f the resource capablty of sgle cloud provder s ot eough to satsfy the requremets of some large-scale applcatos, t ca federate together ad provde servce for users. As show Fgure 1, each cloud provder are depedet some tmes meawhle keepg coected whe eeded. Ths work model s very smlar to the Producto roker model ormal busess area [27]. Motvated by ths, we troduce the cocepto of Vrtual Resource roker (VR) to descrbe the workg of dvdual cloud provders wth amg at aalyzg the resource provso ad cofgurato cloud platform. Fgure 1. Framework of Federated Elastc Clouds Let the set of cloud provders be as {S 1,S 2,,S }, ther resource quatty s oted as {v 1,v 2,,v }. The set of VR be ote as { 1, 2,, } ad ther resource quatty s oted as {c 1,c 2,,c }. The user applcato s cosst of a set of tasks otg as {t 1,t 2,,t m }, each beg characterzed as <r, d >, where r s the resource requremet ad d s the deadle costrat. The utlty S fucto of cloud provder S s oted as U = p v, dcatg the profts of S whe t sell ts resource wth prce p. The utlty fucto of VR s oted as μ U = p c p c, where p s retal prce decded by, μ s the resource utlzato of. Therefore, U dcatg the profts of whe t sell ts resource to users wth prce p after t obtaed resources from cloud provders wth prce p. The global utlty fucto of the 2014 ACADEMY PULISHER

3 1576 JOURNAL OF SOFTWARE, VOL. 9, NO. 6, JUNE 2014 G S federated cloud system s oted as U ( U + U ) =, k = 1 whch s summato profts of cloud provders ad vrtual resource brokers. The utlty fucto of user applcato T m U = r p, where r j s resource s oted as ( j ) j= 1 quatty that task t obtaed from. It s clear that the profts of each VR wll be dfferet after a perod of tme, sce they use dfferet retal prces. Therefore, we categorze them to three set by ther profts. The set of VR wth postve profts s oted as χ + ( p) = { U > 0, [ 1... ] } ; that wth egatve profts s oted as χ ( p) = { U < 0, [ 1... ] } ; that wth zero profts s oted χ 0 ( p) = { U = 0, [ 1... ] }. From the perspectve of resource cofgurato, the profts of ay VR s decded by c ad p ; from the perspectve of resource provso, t s affected by retal prce p ad ts resource utlzato μ. Therefore, for ay dvdual 0 χ ( p), we say t s balace state uder codto 0 < p, c >. If χ ( p), the we say the whole resource tradg system s balace state uder codto < p, < c1, c2..., c >>. Combg Fgure 1 ad the above deftos, we ca see that there are three classes of partcpats: cloud provders, VRs, ad resource cosumers. The cloud provders ad VRs cooperate wth each other, sce they both am at maxmzg resources utlzato ad resource profts. O the other had, the relatoshp betwee the VRs ad the resource cosumers s o-cooperatve, as the clets hope to mmze ther costs, whch would evtably lower dow the beefts of cloud provders. IV. GAMING MODELS AND ANALYSIS A. Cooperatve Gamg Model As metoed Secto 3, the gamg model betwee cloud provders ad VRs s cooperatve, ad the former eeds to decde a optmal resource prce p, whle the latter eeds to decde the optmal resource cofgurato oted as {c 1,c 2,,c }. Therefore, the soluto of ths cooperatve game ca be oted as < p, < c1, c2,, c >>. Sce all the resource owed by cloud provders are brokered by VRs, t satsfes = 1v = = 1c. Whe the whole system s ot balacg state, we ca have G U = ( μ ) 1 p c =. Whe the system s balacg state, accordg to the deftos Secto 2, we kow that 0 k = 1 U =, therefore U G S = U k 1 = p v = = 1. That s sayg, Whe the whole resource tradg system s balace state, the global profts U G s depedet wth p ; otherwse, U G s decded oly by p. Assumg that the system s balacg state ad the curret codto s < p, < c1,, c >>, we ote the VRs profts as < U1, U2,, U >. Whe the resource tradg system s balacg state, satsfes U = μ p c p c = 0, that s μ p p= 0. Assume that the VRs resource cofguratos are chaged as < c1+δ c1,, c +Δc >, ad ther profts are oted as ' ' ' < U 1, U 2,, U > uder codto < p+δ p,( c1 +Δ c1,, c +Δ c) >. So, we obta that ' U = ( c +Δc) [ μp ( p+δ p) ] = ( c +Δc) Δ p. As the total resource s costat, we kow that ( c ) 1 +Δ c = c 1 v = = = = 1. Sce the system s balacg state, we kow that G S S U = ( U ) 1 + U = U 1 p v = = = = 1. Combg the above equatos, uder codto < p+δ p,( c1+δ c1,, c +Δ c) >, the global proft U G ' satsfes G' S' ' U = ( U ) 1 + U = = ( p+δp) v [ ( )] 1 + Δp c 1 c = +Δ = (1) = ( p+δp) v 1 Δp v = = 1 G = p v 1 = U = y Equato (1), we kow that f the resource tradg system s balacg state uder codto < p, < c1,, c >>, ay chage of < p, < c1,, c >> wll ot chage the global profts U G. As the prcg polcy of VRs ad cloud provders wll ot mprove the global beefts U G whe the resource tradg system s balacg state. Therefore, the Nash equvalet soluto of cooperatve gamg s the codto < p, < c1,, c >> whch ca make the resource tradg system balacg state. Whe the system s ot balacg state uder codto < p, < c1,, c >>, we eed to fd a feasble approach to mprove U G. It ca be doe by the followg steps: (S1) We pck out ay k that belog to χ + ( p) or χ ( p) ;(S2)Chage the resource prce p as p ', whch satsfes p' = uk pk p' p. Meawhle, we keep <c 1,c 2,,c > uchaged. Therefore, uder codto < p', < c1,, c >>, we have the followg coclusos: a) The system s stll ot balacg state; b) U G s uchaged; c) k s balacg state. (S3). Accordg to the secod cocluso Step 2, we kow that h satsfyg h χ + ( p') or h χ ( p'). Assumg h χ + ( p'), let the resource cofgurato of h beg adjusted from c h to ch +Δ δ, ad the resource cofgurato of k beg chaged from c k to c k Δ δ, where Δ δ > 0. We have kow that the above chagg of h s resource cofgurato wll crease U G. At the same tme, we also kow that the above chagg of k s resource cofgurato wll ot affect U G, because k s ow balacg state. Therefore, uder codto < p',( c1,, cl +Δδ,, ck Δ δ,, c) >, we crease the global proft U G ACADEMY PULISHER

4 JOURNAL OF SOFTWARE, VOL. 9, NO. 6, JUNE No-cooperatve Gamg Model Accordg to the deftos of Secto 3, we use < M, < p1, p2, p >> to descrbe the gamg soluto betwee VRs ad user applcatos, where M s the resource tradg matrx. Accordg to defto 2, the profts of s affected by p, μ, p, ad c. Durg the cooperatve gamg procedure, we have decded p ad c. At the same tme, μ s ot adjustable sce we ca oly obta t by statstcs. Therefore, the prcg polcy ca be oted as p (μ ), whch meas that VRs adjusts ther retal prce p by observg ts resource utlzato μ. Therefore, the key pot of solvg the o-cooperatve gamg model s fgure out the prcg fucto of VRs. Let p (μ ) be the prcg fucto of. Accordg to defto 2, we have U = c μ p ( μ ) p c. Let du / dμ = 0 we have the equato μ dp( μ)/ dμ + p( μ) = 0, ad U s maxmzed f ths equato s solvable. y solve ths equato, we ca have followg geeral soluto as p( μ) = K,1 K,2 μ, where K,1 ad K,2 are postve costat. If all { 1, 2,, } are depedet, the codto of obtag maxmzed profts s that the prcg fucto s versely proportoal to resource utlzato. We ca have multple prcg fucto sce K,1 ad K,2 ca be ay postve costat. For example, assumg that the m max prcg bouds of s [ p, p ], the ts prcg fucto ca be defed as max max m p( μ ) = p ( p p ) μ. We ca easly kow that max max m have maxmal profts whe μ = p /2( p p ). max m If p = 2 p ad p = 0.5p, the whe μ = 2/3 the have maxmal profts. Therefore, oce decdes ts prcg fucto, t ca obta ts optmal resource * utlzato by μ = K /(2 K ). Durg the rug tme, f,1,2 * t observed that μ < μ t ca low dow ts retal prce, otherwse creases ts prce. If μ = μ *, the the curret p s optmal for maxmzg profts. V. EXPERIMENTS AND PERFORMANCE EVALUATION I the expermets, we use CloudSm [28] to costruct a smulatve cloud platform, whch cossts of twelve hgh-performace clusters as uderlyg resources. The topology ad settg of dvdual clusters are deprved from the grd test-bed DAS-2. The expermetal workload (tasks stream) s geerated by usg Lubl- Fetelso model, whch s derved from the workload logs of real supercomputers. I the expermet, we maly cocetrate o the effects of resource tradg o applcato s executo tme. To aalyze the performace, our hybrd gamg model (HGM) s compared wth other four resource tradg model, cludg Commodty Market Model (CMM) [29], Double Aucto Model (DAM) [25], Vckery Aucto Model (VAM) [28],atch Aucto Model (AM) [30]. To exame the efforts of resource requremets o performace, we elarge the workload s resource requremet β tmes. The expermets are coducted four tme, wth creasg β from 1.0 to 2.5. The results are show Fgure 2. (a) β= 1.0 (b) β= 1.5 (c) β= ACADEMY PULISHER

5 1578 JOURNAL OF SOFTWARE, VOL. 9, NO. 6, JUNE 2014 (d) β= 2.5 Fgure 2. Comparso of Resposve Tme The expermetal results show that resource requremets affect ot oly the resource egotato tme but also the executo tme. Whe the resource requremets s low level (β=1.0), the performace of VAM a DAM s sgfcatly hgher that other polces, ad ther egotato tme s about 10.2% ad 8.7% of the total completg tme. As to HGM ad AM, the egotato tme s about 2.9% ad 3.1% of the total completg tme. Wth the creasg of β, the egotato tmes of both VAM ad DAM crease as well as ther proportos to the total completg tme. For example, whe β = 2.5, egotato tme of VAM s about 3.82 tmes of the case β = 1.0, ad ts proporto s creased to 21.3%. AM s a batch resource tradg model, ad t s very effectve to allocate multple resources to applcato. However, t has to take may rouds to complete the aucto procedure, especally whe the resource requremets are very large. Therefore, whe β = 1.0 ts performace s almost equal to HGM; however, wth creasg of β, the tme costs o aucto procedure become domator, whch makes AM s performace reduced. Comparg wth aucto model, CMM s effectve to reduce commucato-related costs. However, our expermetal results dcate that ts egotato tme creases sgfcatly whe β creases to hgh level. y examg the logs, we foud that re-egotato occurs more frequetly tha before, that s, the CMM s prcg polcy ca ot effcetly fsh the tradg for all resource requremets. For example, whe β = 2.5 about 43% tasks eed to egotato 2 tmes to obta the requred resources, ad about 7% tasks eed to do t at the thrd egotato. y the descrpto of Secto 3, we ca see that HGM s egotato costs maly come from the selecto of sutable VRs. As all VRs decde ther retal prce depedetly accordg to ther resource utlzatos, therefore, t ca avods workload cocetrato whch s very mportat for reducg the costs of re-egotato. efore HGM s balacg state, those VR wth postve profts wll crease ther resource cofgurato. y ths mechasm, they ca mprove the capablty of servg mult-resource requremets. Therefore, we ca cosder HGM as the combato of AM ad CMM. ased o the above expermetal results, our cocluso s that HGM s effectve to reduce the egotato tme, whch tur reduce the applcato executo tme. Secodly, we all vestgate the HGM s performace uder costrats to costs ad deadle. ecause of the deadle costrat, we ca ot compare the polces drectly. As a result, we select four typcal resource matchg algorthms to tegrate those prcg mechasms, cludg Roud-Rob (RR),Capabltybased Radom (CR),Optmal Mss Rate (OMR) ad Herarchcal Gamg Selecto (HGS). The expermets coducted four tmes, each wth dfferet λ parameter whch s used to defe the arrval terval of tasks. The results are show Fgure 3. Fgure 3. Resource costs wth dfferet polces ad λ parameter As show Fgure 3, the performace of RR s the best f we oly cosder the resource costs. However, RR wll result hgh deadle mss rate whe λ creases. y our result logs, the deadle mss rate s about 61.22% whe λ=10. y our expermet settg, f deadle occurs the user wll ot pay ay cost for resource provders. That s the reaso that the resource cost of RR s the lowest. Amog the left polces, HGS ca obta lowest resource costs ad ts chages are very stable for dfferet λ parameters. We otce that the rejecto rate of HGS s very hgh, whch s sgfcatly dfferet from HGM. For example, whe λ=10 the rejecto rate of HGM s oly about 6.17%. y the gamg polcy, we kow that the prcg fucto HGM s versely proportoal to the resource utlzato. ecause of ths prcg polcy, HGM s capably of mata a low rejecto rate. As show the expermetal results, whe usg the same prcg mechasm, CR ad OMR perform very smlar terms of resource costs ad rejecto rate. However, ther deadle mss rates are very dfferet. I a whole, OMR s more effectve to provde deadle guaratee tha other polces especally whe λ=0.05 ad λ=0.10. However, whe we creasg λ from 0.1 to 1.0, the rejecto rate of OMR+AM creases about 2 tmes, ad the deadle mss rate creases about 2.5 tmes. Such a result happes o OMR+CMM. That s, OMR s effectve to reduce deadle mss rate whe there s o cost costrat; whe cost costrat should be cosdered, OMR s oly sutable for those applcatos wth uform workloads. ased o ths expermet, we ca see that HGM s effectve to mata low rejecto rate ad obta better 2014 ACADEMY PULISHER

6 JOURNAL OF SOFTWARE, VOL. 9, NO. 6, JUNE tradeoffs betwee resource costs ad cloud provder s profts. VI. CONCLUSION To address the ssue of resource prcg mechasm cloud evromets, a hybrd gamg based prcg model s proposed to overcome the demerts of exstg prce mechasms terms of effcecy ad faress. I the proposed prcg model, vrtual resource cofgurato ad provso are descrbed as a two-phrase gamg procedure, whch cooperatve gamg model s appled to optmze the resource beefts ad ocooperatve gamg model s used to balace the user s costs ad provder s beefts. The valdty ad soluto of the proposed prce model are preseted theoretcally, ad the expermetal results dcate that the hybrd gamg model ca sgfcatly mprove the prce egotato effcecy whe a budle of resources are egotated cocurretly, whch tur reduce the applcato executo latecy that caused by covetoal prce egotato mechasms. I addto, t also outperforms other prcg mechasm terms of user s QoS requremets, such as resource cost ad deadle guaratee, especally whe the system workload s very tesve. I the future, we pla to corporate resource reservato mechasm to our HGM framework, ad desg some elastc reservato mechasm to mprove the QoS performace. ACKNOWLEDGMENT Ths work s supported by the Provcal Scece & Techology pla project of Hua (No.2012GK3075), the Projects Supported by Scetfc Research Fud of Hua Provcal Educato Departmet (13015). Also, t s a project supported by Hua Provcal Natural Scece Foudato of Cha (No. 13JJ9022). REFERENCES [1] J. Ju, J. Wu, J. Fu, Z. L, J. Zhag. A survey o cloud storage, Joural of Computers, vol.6, o.8, pp , [2]. XU, N. Wag, C. L. A cloud computg frastructure o heterogeeous computg resources, Joural of Computers, Vol.6, No.8, pp , [3] J. Wu, Q. She, T. Wag, J. Zhu, J. Zhag. Recet advaces cloud securty. Joural of Computers, vol.6, o.10, pp , [4] L. Rodero-Mero, E. Caro, A. 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7 1580 JOURNAL OF SOFTWARE, VOL. 9, NO. 6, JUNE 2014 [25] W. L, Y. L ad H. We, Dyamc aucto mechasm for cloud resource allocato, IEEE Iteratoal Symposum o Cluster, Cloud ad Grd Computg, pp , [26] Y. C. Lee, C. Wag, A. Y. Zomaya ad. Zhou, Proftdrve schedulg for cloud servces wth data access awareess, Joural of Parallel ad Dstrbuted Computg, Vol. 72, No. 4, pp , [27] M. Osbore, A. Rubste, A. A Course Game Theory, The MIT Press, Cambrdge UK, [28] R. N. Calheros, R. Raja, A. eloglazov et al., CloudSm: a toolkt for modelg ad smulato of cloud computg evromets ad evaluato of resource provsog algorthms, Software-Practce & Experece, vol. 41, o. 1, pp , Ja, [29] R. uyya, Ecoomc-based dstrbuted resource maagemet ad schedulg for grd computg, Australa: Computer Scece ad Software Egeerg, Moash Uversty, [30] H. Zhao ad X. L. L, Effcet grd task-budle allocato usg bargag based self-adaptve aucto, Proceedgs of IEEE Iteratoal Symposum o Cluster, Cloud ad Grd Computg, pp. 4-11, Zhag Tea was bor He receved hs master degree Xagta Uversty at Curret he s a lecturer of Hua Isttute of Egeerg. Hs research drectos cludes etwork computg, cloud Computg, dstrbuted resource maagemet. Peg Xao was bor He receved hs master degree Xame Uversy 2004 ad Ph.D degree CSU at Now, he works Hua Isttute of Egeerg. Hs research terests clude cloud computg, parallel ad dstrbuted systems, etwork computg, dstrbuted tellgece ACADEMY PULISHER