Application of VCG in Replica Placement Strategy of Cloud Storage

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1 Internatonal Journal of Grd and Dstrbuted Computng, pp Applcaton of VCG n Replca Placement Strategy of Cloud Storage Wang Hongxa Computer Department, Bejng Youth Poltcs College, Bejng, Chna whx4617@163.com Abstract Wth the rapd development of the cloud computng and bg data, many data center network are growng more large scale, and the number of servers s ncreasng at an exponental rate. In ths paper, VCG (Vckrey-Clarke-Groves) mechansm was ntroduced nto replca placement. Mappng from replca placement model to VCG mechansm was establshed and sutable payment functon was desgned. Data storage strategy s bult on the dstrbuted fle system. In order to ensure the relablty of data storage GFS and HDFS are further both referred to the concept of replca of data block. Ths paper generally ntroduced replca strategy, and mentoned that the storage locaton of data replca s a key pont of the research on replca technology currently. Ths paper manly appled one cloud platform smulaton tool---the CloudSm to smulate the algorthm. The performance of the algorthm was analyzed n the CloudSm platform and compared t wth other related algorthms. The smulaton experments were done on the replca placement strategy of the data block, whch shows that replca placement strategy can also acheve the establshed goal. Keywords: VCG; replca placement strategy; cloud storage 1. Introducton Cloud Storage s developed on the bass of Cloud Computng whch s used to store and manage the mass data n network. Cloud Storage ntegrates a large number of heterogeneous devces to provde external storage and busness access etc. through the applcaton of software. Cloud Storage ntegrates of Grd Computng, Dstrbuted Systems, Vrtualzaton, Load Balancng and other tradtonal network technology. It has the propertes of hgh relablty, hgh scalablty, hgh effcency and low cost etc. The focus researches are data securty, performance optmzaton, resources schedulng strategy, and servce payment model n cloud storage system currently. Wth the rapd development of nformaton technology and the hgh speed grows of global data [1], confrontng PB level storage requrements, tradtonal fle system has some dffcultes n capacty and performance expanson. Cloud storage s wdely used by many companes because of ts advantages such as expandablty, cost-effectve, hgh fault-tolerance [2-4]. As the bass of cloud storage, dstrbuted fle system acheves promoton of system avalablty and fault-tolerance by ntroducng data replcaton technology. However, replca brngs about a lot of problems such as bandwdth and storage consumpton, unexpected data nconsstency etc. [5, 6] Data storage strategy s bult on the dstrbuted fle system (DFS). In order to ensure the relablty of data storage further, GFS (Google Fle System) and HDFS (Hadoop Dstrbuted Fle System) are both referred to the concept of replca technology of data block. It s pad attenton that the storage locaton of data replca s a key pont of the research on replca technology currently. Ths paper brefly ntroduces the storage mechansm when place a sngle replca, and then Multple-replca-to-multple-servce placement strategy s drawn out. Followng that, ISSN: IJGDC Copyrght c 2016 SERSC

2 Internatonal Journal of Grd and Dstrbuted Computng ths paper presents the constructon and the mplementaton of the mechansms domnant strategy equlbrum stuaton whch purpose s to gude the mechansm to acheve domnant strategy equlbrum. VCG (Vckrey-Clarke-Groves) mechansm [7, 8] was ntroduced nto replca placement strategy n ths paper. Mappng from replca placement model to VCG mechansm was establshed and sutable payment functon was desgned. The cloud smulaton platform---cloudsm was used to test some propertes and analyze the algorthm performances. 2. Desgnaton of Mechansm and Descrpton of Core Algorthm Generally, the default number of data block n cloud storage system s three, but t can also be set by users accordng to the practcal stuaton s (0s 3 or s 3). Ths paper deems that one of the replcas s placed randomly. And then the other two replcas are placed on dfferent racks n accordance wth the prncple of mnmum cost of the system n the study. Based on ths storage thnkng ths paper makes some relevant deductons of formulas and theores on the bass of data storage strategy and then provdes replca placement strategy based on VCG mechansm. Based on the mathematcal mode, the unt data storng cost of data storage nodes s quantfed as c, the unt data value of data object nodes s v, whch are both assumed to be M unt. If data blocks are dvded nto several parts whch are all 64M a node s storng cost for every sngle replca s 64c and a replca s own value s 64v. In order to seek clear and convenent statements n ths paper, a sngle replca s storng cost as c s consdered whch type s t(c); and ts own value s v whch type s t(v). 2.1 Sngle-Replca-to-Multple-Servce Mechansm When multple storage nodes compete wth each other for a sngle replca, the form of aucton changes nto the tradton model of a sngle tem for a sngle user whch s also the basc form of the ntal study of VCG mechansm called the second-prce aucton. Its theoretcal dervaton s gven below based on a general data storage strategy. (1) Allocaton functon and allocaton rule Assume that there are data storage nodes n the system, for any set of quotaton collecton b = (b 1, b 2, b 3,, b n ), the fnal allocaton result can be expressed as: 1 s saved o () b (1) 0 s not saved Mnmzng the total cost of system s stll a target when replcas are stored. Storage nodes whch satsfy b <v are selected as canddate nodes. Assume that the number of canddate nodes s n. n canddate nodes can be allocated as follows: VCG o ( b) arg mn C ( b, o) oo arg mn ( b o) oo arg mn oo( b1, b2,..., bn ) From Formula (2) the concluson can be drawn: the mechansm wll select the mnmum-cost node when a sngle replca was stored. The nversely proportonal relatonshp of nodes between cost and performance shows that the placement node s consdered to be the top-qualty one n the system. (2) Payoff functon and payoff rule Those storage nodes whch satsfy wll be pad as follows: (2) 28 Copyrght c 2016 SERSC

3 Internatonal Journal of Grd and Dstrbuted Computng VCG VCG p ( b) mn C ( b, o ) C ( b, o ) j j - j j j j mn b o ( b ) b o ( b) j j j - j j j mn( b, b,..., b, b,..., b ) mn ( b, b,..., b ) n j 1 2 n From Formula (3), t can be concluded after smple dervaton that p = mn (b 1, b 2,, b -1, b +1,,b n ) for b=mn (b 1, b 2,, b n ); That s when the node s quotaton s the mnmum one among all nodes, the mechansm pays the quotaton whch s the mnmum one among all nodes but. Also known as Second-Prce Aucton Thought; If b mn (b 1, b 2,, b n ) whch means the node s quotaton s not the mnmum one among all nodes, after a smple dervaton a concluson can be drawn that: mn (b 1, b 2,, b -1, b +1,,b n ) = mn j (b 1, b 2,, b n ) Therefore p =0. If the node s quotaton s not the mnmum one, the mnmum quotaton of other nodes s the same wth that of all nodes wth the presence of node. At ths moment the payoff for node s 0. Accordng to the above analyss, Formula (3) can be smplfed as follows: bk s saved p () b (4) 0 s not saved In ths formula s the mnmum node s quotaton among all nodes except node, b k = mn (b 1, b 2,, b -1, b +1,, b n ). Combne the dstrbuton functon Formula (1) and the payoff functon Formula (4), the proft that the data storage node obtans can be calculated: u ( b) p ( b) c o ( b) bk c s saved (5) 0 s not saved In the formula (5), b k s the mnmum node s quotaton among all nodes except node. Then b k s the second lower quotaton when node exsts. If the node s quotaton s true, there must be b k >c. And the proft of storage replca of the node s postve; f node s quotaton s false, then b k >b c the proft s u = (b k - b ) - (c - b ), so t cannot be guaranteed to be postve. (3)Smplfed model of the mechansm Through the ntroducton of the core algorthm of the mechansm the smplfed mode of Sngle-replca-to-multple-servce mechansm s gven as below: t 1(b 1) (3) Replca t(v) ( ) VCG Deputy t (b ) t n(b n) Server 1 Server Data storage node Server n Fgure 1. The smplfed model of Sngle-Replca-to-Multple-Servce Mechansm Fgure 1 shows a smple bddng process of mechansm. All storage nodes of cloud report ther own types to the mechansm agency. The mechansm wll choose the mnmum nodes quotaton as the replca placement node, and return the allocaton result and payoff result to the storage nodes. All storage nodes of cloud wll compare profts Copyrght c 2016 SERSC 29

4 Internatonal Journal of Grd and Dstrbuted Computng wth each other whch depend on those results n order to determne whch knd of quotaton can make the maxmum profts. After several rounds of teratons all nodes wll choose the optmal quotatons, and the mechansm reaches the state of equlbrum at ths moment. In the state of equlbrum the mechansm wll return the fnal allocaton result and payoff result to the data storage nodes of cloud and the data object nodes of the clents. 2.2 Multple-Replca-to-Multple-Servce Mechansm The large data s usually dvded nto several data blocks and then they are stored. The backup of each data block wll produce a large number of replcas. In Secton A. the storage strategy of a sngle replca s ntroduced. Based on the prevous secton ths secton wll ntroduce how to store multple replcas n the cloud system. A data object node s selected and assumed that ts storage data s dvded nto data blocks, and then 3s correspondng replcas need to be placed. Assume that the system places one group of replcas randomly, and then the other two groups wll be placed on dfferent racks n accordance wth the prncple whch can acheve the lowest cost. In order to ensure data relablty to the greatest extent, those replcas of data blocks wll be placed on dfferent nodes respectvely. In ths secton, how to select data storage nodes as the placement ponts of replcas s the object of study. Assume that the data object node owns the type t j ( v j, s j, PRI j ). (1) Allocaton functon and allocaton rule Assume that data replcas are equvalent when they are on the same data object nodes. In other words, the data value of each replcas s v, and any replca s type s t(v); assume that there are data storage nodes on the cloud, the sngle-replca storage of the node s c, and ts type s t (c ). Assume that storage nodes satsfy the condton c <v. Referrng Formula (2) n Secton A. t can be drawn that the allocaton of canddate nodes made by mechansm s: VCG o ( b) arg mn C ( b, o) arg mn ( b o) arg mn oo oo oo b For the realzaton of Formula (6), n canddate nodes are arranged accordng to quotatons from small to large. Select the nodes whch are the frst lowest quotatons as placement nodes, and the set of replca placement s S. Therefore, Formula (6) can be smplfed as: 1 S o () b (7) 0 S (2) Payoff functon and payoff rule Referrng to Formula (3), the payoff functon of the mechansm can be descrbed as follows: VCG VCG p ( b) mn C j ( bj, o- ) C j ( bj, o ) j j (8) mn b o ( b ) b o ( b) j b j j js js, j j j - j j j b In ths formula, S s the set of replca placement when the node s ncluded; S - s the set of replca placement when the node s not ncluded. (6) 30 Copyrght c 2016 SERSC

5 Internatonal Journal of Grd and Dstrbuted Computng Arrange the quotatons n ascendng order. For S, that s b b s, Formula (8) can be converted to: VCG p ( b) b b j j js ' js ( b... b b... b ) s1 ( b... b b... b ) s bs 1 For S, that s b >b s, the equaton Formula (8) s rght, and p =0 at ths moment. Therefore, the payoff functon n the formula (8) can be smplfed as: bs 1 S p () b 0 S In ths formula, b s+1 s the s+1 node prce n an ascendng order. The profts that storage nodes obtan s: u ( b) p ( b) c o ( b) bs 1 c S 0 S From Formula (10) the concluson can be drawn: If the node s quotaton s true there must be b k >c. And the proft of storage replca of the node s postve at ths tme; But f node s quotaton s false then b s+1 >b c, the proft s that u ( bs 1 b ) ( c b ), and so t cannot be guaranteed to be postve; If the node tself costs too much the false quotaton wll lead to negatve proft. (3)Smplfed model of Mechansm The smplfed model of multple-replca-to-multple-servce Mechansm s gven below as follows: t 1(b 1) (9) (10) Data node Replca 1 Replca n t(v) VCG Deputy t s(b s) t s+1(b s+1) t n(b n) Server 1 Server s Server s+1 Ascen -dng Order Server n Fgure 2. The Smplfed Model of Multple-Replca-to-Multple-Servce Mechansm Fgure 2 s a smplfed model whch shows that multple replcas are placed n the cloud and t shows one transacton case n the game of the mechansm. In the transacton all storage nodes wll choose ther optmal quotatons accordng to the allocaton results and payment results returned from the mechansm. Mechansm s fnal transacton wll be conducted n the equlbrum stuaton where all nodes choose ther optmal quotatons. And the mechansm wll return the fnal results back to the data storage nodes on the cloud and the data object nodes on clents. What should be pad attenton to s that these replcas are all belong to the same data object node n the research, so the user pays every replca p =b s+1 and thus the user need to pay totally p= p =s b s+1 for ths replca placement. Copyrght c 2016 SERSC 31

6 Internatonal Journal of Grd and Dstrbuted Computng 3. Analyss of the Mechansm s Performance Because the research on replca placement strategy s derved on the bass of general data storage strategy and ts essence s a specal smplfed form of data storage strategy. So the replca placement strategy stll satsfes the conclusons drawn from above. Ths secton gves smple analyss of the multple replcas to multple servces mechansm s performance n order to apply obtaned conclusons better to the replca placement strategy. Concluson 1: Real quotaton of the storage node s ts domnant strategy and the mechansm meets the feature of ncentve compatblty. Proof: The node rases ts quotaton whch means b c. If c meets the nequalty c b and the node doesn t get a replca successfully when the node gves a real s quotaton, t stll can t get a replca after rasng the offer because of the nequaltyb c b s, so the profts are all zero. If c meets the nequalty c b and the s node gets a replca successfully when the node gves a real quotaton as well as b meets the nequalty c b b after rasng the offer, t stll can obtan a replca and the proft s keeps unchanged as a result of the formula u ' u bs 1 c. The node gets a replca successfully when t gves a real quotaton and c meets the nequalty c b s, but t can t get a replca after rasng the offer because of c b b s. So the proft u meets the ' nequalty u u b c, the node loses ts proft whch t could orgnally get. 0 s1 The node reduces ts quotaton, namely that b c. If c meets the nequalty c b and s the node gets a replca successfully when the node gves a real quotaton, t stll can get a replca after reducng the offer because of the nequalty b c b, so the profts keeps s unchanged as a result of the formula. If c meets the nequalty c b s and the node can t get a replca successfully when the node gves a real quotaton as well as b meets the nequalty c b b s after rasng the offer, t stll can obtan a replca and the profts are all zero. If the node can t get a replca successfully when t gves a real quotaton and c meets the nequalty c b s, but t can get a replca after reducng because of c bs b. So ' the proft u meets the formula u bs c at ths tme. But because of c b, u s s negatve. Therefore, the node s actual proft s negatve despte ts opportunty to obtan a replca. In concluson, the node cannot rase ts proft by gvng a false quotaton no matter what the case t s n. Consderng that all nodes are conservatve, they wll all choose the true quotatons as ther optmal quotatons n the end. Therefore, the real quotaton of the storage node s ts domnant strategy, and the mechansm meets the ncentve compatblty. Concluson 2: Mechansm meets ex-post ndvdual ratonalty for storage nodes. Proof: For any set of quotaton collectonb- ( b1, b2,..., b 1, b 1,..., bn ), storage nodes all have the domnant strategy b * c. If the node gets a replca under the domnant equlbrum stuaton u whch equals b s+1 -c s postve and the proft as well. If the node s able to get a replca u equals zero. So the profts of the nodes are non-negatve under domnant strategy, and the mechansm meets ex-post ndvdual ratonalty for each storage node. 32 Copyrght c 2016 SERSC

7 Internatonal Journal of Grd and Dstrbuted Computng 4. The Implementaton of the Mechansms Domnant Equlbrum Stuaton In the secton2 the concluson have been drawn that the storage node s real quotaton s ts domnant strategy through analyss. But the storage node sn t aware of that at the begnnng. So a realstc transacton step must be desgned to gude the selfsh and conservatve storage nodes n the system to make an optmal choce. Only n that way the mechansm can be acheved. Because the mechansm meets the ncentve compatblty characterstc, the domnant stuaton whch the mechansm fnally reaches also acheves the goal of the desgner. Referrng to the smplfed model of Fgure 1 and Fgure 2, desgned steps to acheve the mechansm are as follows. (1) Data agency nodes report the replca type t j ( v j, s j, PRI j ) to the mechansm, and then the mechansm sorts the nodes n descendng order accordng to PRI, and puts the nodes under the same prorty n descendng order accordng to v. The mechansm deals wth the data object nodes n sequence. (2) The data storage nodes determne ther own quotaton collecton B ( b 1, b 2,..., bk ) and ntalze ther proft vector U ( u 1, u 2,..., uk ) 0. (3) The data storage nodes select quotatons whch meet bt B, and report the types t ( bt ) to the mechansm. The mechansm chooses the nodes whch meets the bt vj nequalty as canddate nodes and puts these nodes n ascendng order accordng to the quotaton. (4) The mechansm chooses the frst s nodes as replca placement nodes and pays them p whch equals to b s+1. (5) Each data storage node calculates the proft of ths transacton accordng to ths u p c o, and then updates the proft vector (...,,...) formula t t t U u t ; (6) Repeat steps 2 ~ 5 untl all storage nodes fnsh vector updatng. * (7) Each storage node selects the quotaton b correspondng to max(u ) as the fnal * * * * quotaton so the quotaton set s b ( b1, b2,..., b n ). Follow the steps 3 ~ to complete ths transacton and then update the type t (b ). (8) Sort up all the data object nodes accordng to value and repeat steps 2 ~ 7 untl all replcas are placed. The pseudo-codes whch can acheve the mechansm s domnant strategy equlbrum stuaton are as below Fgure 3: Copyrght c 2016 SERSC 33

8 Internatonal Journal of Grd and Dstrbuted Computng Fgure 3. Domnant Equlbrum Stuaton Implementaton Algorthm based on the Multple Replcas to Multple Servces Mechansm 5. The Expermental Smulaton of Replca Placement Strategy The experment n ths secton follows the parameters settng and the experment envronment when the data storage strategy s smulated. We select eght replcas of the same user as the research objects and the sze of each replca s 64M. To ensure the relablty and avalablty of the data, only one replca of the same user s data s placed on each storage node. (a) The Dstrbuton of the Cloud Task to the Nodes and the Vrtual Machne (b) The Cost of each Node and the Total Cost Fgure 4. Smulaton Results of Multple-Replca-to-Multple-User Mechansm Fgure 4 shows a part of output results of the smulaton on replca placement strategy. The output results of the CloudSm platform dvdes nto two parts: one s the allocaton status of cloud tasks and the other s the cost of each node and the total system cost. From the outputs of these two parts, researchers can know the poston where the replca was placed n the system, and make the cost consumed by each node and the total system cost for ths reason clear. 34 Copyrght c 2016 SERSC

9 Internatonal Journal of Grd and Dstrbuted Computng Table 1. The Correspondng Profts of all Nodes n Dfferent Prce Ranges based on the Mult-Replca Mechansm BID NODE [0,9.6) [9.6,10.24) [10.24,12.8) [12.8,15.36) [15.36,20] # # # # # # # # # # # # # # # Table 1 records the correspondng profts of all nodes n dfferent prce ranges. In order to ensure clarty and facltate the record expermental data, we deal wth all nodes one by one n sequence durng ths experment untl they select ther optmal quotatons. Fgure 5. The Correspondng Profts of all Nodes under Dfferent Quotatons based on the Mult-Replca Mechansm All nodes are the ntal quotatons before the aucton starts n the experment. After the researched node selectng ts optmal quotaton, the node s quotaton stops changng. So the quotatons of the researched node before the optmal quotatons are all the best quotatons and quotatons behnd the optmal one are all ntal offers. From Fgure 5 we can see that each node corresponds to dfferent profts n dfferent prce ranges. The red dots n the fgure represent the true quotatons of the nodes. We can Copyrght c 2016 SERSC 35

10 Internatonal Journal of Grd and Dstrbuted Computng see that all nodes are under the maxmum profts at the true quotaton. And the profts at fake quotatons are not more than the ones at the real offers. The profts at the true quotaton of the nodes # 9, # 12, # 13 n the graph are all zero that s because they can t successfully obtan replcas. They can get a storage chance when they report a lower prce, but t can be seen from the fgure that ther profts are negatve at ths moment so the relevant nodes wll not gve a fake quotaton n order to fght for the storage opportunty. (a) Fgure 6. The Cost and the Proft of each Node at the True Quotaton based on Multple-Replca Mechansm Fgure 6(a) shows the payments, the costs and the profts of all nodes. Fgure 6(b) shows the payment, the cost and the proft of each node after puttng the nodes n ascendng order accordng to the cost. From Fgure 6(a) we can see that the profts of the nodes whch successfully place the replcas are all greater than zero and the nodes whch are faled to do that can t get the payment so ther costs and profts are all zero. As what the Fgure 6(b) shows after puttng the nodes n ascendng order accordng to the cost, we can see that the system pays the same for all the nodes whch place replcas. The dfference s that the cost of each node s not the same, so dfferent profts are obtaned. The lower the node s cost s the hgher ts proft s. 6. The Performance of the Algorthm Compared wth Others Ths secton compares the bult-n algorthm of the CloudSm platform (Round Robn, RR), the shortest response tme algorthm (MIN-TIME) wth the algorthm proposed n ths paper based on the replca placement strategy. Because ths secton s famlar to some people so the some parts are omtted. Only some necessary explanaton about the results of the experment s gven out. (b) 36 Copyrght c 2016 SERSC

11 Internatonal Journal of Grd and Dstrbuted Computng Fgure 7. The Total Cost under Dfferent Algorthms based on Mult-Replcas Mechansm We can see from Fgure 7 that compared wth the bult-n R-R algorthm of the CloudSm platform ths algorthm helps the total cost whch the system consumes falls by almost half. Fgure 8. System Response Tme under Dfferent Algorthms based on Mult-Replcas Mechansm What can be concluded from Fgure 8 s that the system response tme of the algorthm proposed n ths paper s shorter than the R-R algorthm too. But t s a lttle longer than the shortest response tme algorthm. Ths s because ths algorthm ams to mnmze the total system cost rather than the shortest response tme n the ntal desgn. Copyrght c 2016 SERSC 37

12 Internatonal Journal of Grd and Dstrbuted Computng Fgure 9. The Response Tme of each Node under Dfferent Algorthms based on Mult-Replcas Mechansm From Fgure 9 we can see that the response tme of each node based on the R-R algorthm s obvously too long, whle the response tme based on ths algorthm and MIN-TIME algorthm s bascally the same. The object that Fgure 9 studed s multple replcas of a sngle user, so each storage node only owns one replca. When the object of the study expands to multple replcas of multple users, there are replcas of multple users placed on each storage node, n whch case the response tme of each node n the cloud system can approxmate to Fgure 10. Fgure 10. The Response Tme of each Node under Dfferent Algorthms based on Sngle-User Mechansm Summary For further consderaton of the relablty and the avalablty of storng data, ths paper further gves data block replca placement strategy. At the frst place, the paper brefly ntroduces the storage mechansm when place a sngle replca, and then Multple-replcato-multple-servce placement strategy s drawn out. Followng that, ths paper presents the constructon and the mplementaton of the mechansm s domnant strategy equlbrum stuaton whch purpose s to gude the mechansm to acheve domnant strategy equlbrum. At last, t makes the performance analyss on replca placement 38 Copyrght c 2016 SERSC

13 Internatonal Journal of Grd and Dstrbuted Computng mechansm whch proves that the mechansm maxmzes the benefts and meets the desgner s objectves at the same tme. Namely t meets the ncentve compatblty. Ths paper manly apples the CloudSm software, one cloud platform smulaton tool, to smulate the algorthm. At the frst place, the paper brefly ntroduces the development background of the CloudSm and gves a concse descrpton of ts archtecture and core classes. Furthermore, t also expounds the envronment confguraton and the mplementaton of the program flow of the CloudSm n the practcal applcaton. Before the algorthm smulaton, the paper sets the expermental parameters, and then uses the CloudSm platform to analyze the performance of the algorthm and compares t wth other related algorthms. At last, t does smulaton experments on the replca placement strategy of the data block, whch shows that replca placement strategy can also acheve the establshed goal. Acknowledgements Ths work s supported by Bejng Hgher Educaton Young Elte Teacher Project (No. YETP1729); Bejng Educaton Commsson Socal Scence Project (No. SM ); and Educaton & Teachng Project of Bejng Youth Poltcs College (No. ZY201501); and Bejng Federaton of Socal Scence Crcles of Young Talent Project (No. 2015SKL027) References [1] R N Calheros, R Ranjan, A Beloglazov, et al. CloudSm: a toolkt for Modelng and Smulaton of Cloud Computng Envronments and Evaluaton of Resource Provsonng Algorthms[J]. Software: Practce and Experence, vol. 41, no. 1, (2011), pp [2] R N Calheros, R Ranjan, C A F De Rose, et al. Cloudsm: A Novel Framework for Modelng and Smulaton of Cloud Computng Infrastructures and Servces[J]. arxv preprnt arxv: , (2009). [3] W H Bell, D G, C. Mllar A P, et al. Optorsm: A grd smulator for studyng dynamc data replcaton strateges[j]. Internatonal Journal of Hgh Performance Computng Applcatons, vol. 17, no. 4, (2003), pp [4] B D Lee, J B. Wessman Dynamc Replca Management n the Servce Grd[C]//Hgh Performance Dstrbuted Computng, Proceedngs. 10th IEEE Internatonal Symposum on. IEEE, (2001), pp [5] L Anderegg, S Edenbenz. Ad hoc-vcg: a truthful and cost-effcent routng protocol for moble ad hoc networks wth selfsh agents[c],//proceedngs of the 9th annual nternatonal conference on Moble computng and networkng. ACM, (2003), pp [6] R L Grossman. The case for Cloud Computng[J]. IT professonal, vol. 11, no. 2, (2009), pp [7] D Brodsky, M J Feeley, N C. Hutchnson, Topology Senstve Replca Selecton[C]//Relable Dstrbuted Systems, SRDS'06. 25th IEEE Symposum on. IEEE, (2006), pp [8] Hershberger, S. Sur, Vckrey prces and shortest paths: What s an edge worth?[c], //Foundatons of Computer Scence, Proceedngs. 42nd IEEE Symposum on. IEEE, (2001), pp Author Wang HongXa. Wang HongXa(1979-) was born n XnJang, Chna. She receved her Ph.D degree from the Unversty of Scence and Technology, Bejng, Chna. She obtaned her B.Sc degree n computer scence n 2002 and M.Sc degree n computer applcaton n 2005 from Laonng Shhua Unversty, Fushun, Chna. Now, she s a vce-professor at the Bejng Youth Poltcs College. Her current research nterests nclude nformaton management, mage processng, multmeda technology and network communcaton. Copyrght c 2016 SERSC 39

14 Internatonal Journal of Grd and Dstrbuted Computng 40 Copyrght c 2016 SERSC

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