A Churn-prevented Bandwidth Allocation Algorithm for Dynamic Demands In IaaS Cloud

Transcription

1 A Chur-preveted Badwidth Allocatio Algorithm for Dyamic Demads I IaaS Cloud Jilei Yag, Hui Xie ad Jiayu Li Departmet of Computer Sciece ad Techology, Tsighua Uiversity, Beijig, P.R. Chia Tsighua Natioal Laboratory for Iformatio Sciece ad Techology, Beijig,Chia {yagjl12,jie-h12,lijiayu12}@mails.tsighua.edu.c ABSTRACT I curret IaaS datacters, the etwork badwidth resource is shared across teats i a best-effort maer. I this way, teats are iterfered by each other, which leads to upredictable badwidth performace ad further does harm to the applicatios deployed i data ceters. Besides, with time-varyig amout of users to support, teats choose to adapt to dyamic demads by retig differet umber of virtual machies(vms) from IaaS providers at differet service stage. Teats dyamic demad for VMs triggers the badwidth reallocatio, which produces badwidth chur ad also cotributes the upredictable performace. Those proposed strategies all put emphasis o VM s performace isolatio by reservig basic badwidth for VMs. To our best of kowledge, there is o oe that has take the teats dyamic demads for VMs ito cosideratio. I this paper, we model this badwidth allocatio as a optimizatio problem, which is subject to the basic badwidth guaratee ad badwidth chur. With the help of the defiitio of satisfactio degree for host machie, we desig a simple ad efficiet algorithm to solve this optimizatio problem by usig the iequality of arithmetic ad geometric meas. Uder some experimets, we demostrates our badwidth allocatio strategy ca provide ot oly the predictable performace but also limit badwidth chur, both of which improve performace of applicatios deployed i IaaS cloud. KEYWORDS Badwidth chur, Badwidth allocatio, Network performace, Badwidth demad 1 INTRODUCTION As a paradigm of utility computig, cloud computig has attracted may eterprises (e.g. Dropbox ad Facebook video storage) to deploy their busiess ito cloud due to its ecoomic resources. Usig multiplexig ad virtualizatio, the Ifrastructure as a Service (IaaS) providers (e.g., Amazo EC2[1]) lease the resources i the form of virtual machies (VMs), which are charged i a pay-as-you-go price model. I this way, teats are able to achieve isolated performace for CPU ad memory. However, to the best of our kowledge, curret IaaS providers do ot offer performace guaratee for etwork badwidth. Ulike the CPU ad memory, etwork badwidth i IaaS cloud is shared across differet teats i a best-effort maer, which gives rise to iterferece amog teats. The etwork performace ca be iflueced sigificatly owig to other teats dyamic demads whe there is o basic badwidth guaratee. For the public cloud, the demads of teats varies at ay time, which makes the dataceter traffic highly differet from ay other etwork traffic to a large extet. Now we explai the teats dyamic demads from two aspect. First, some eterprises, amely teats for the IaaS providers, choose to dyamically icrease(decrease) their demads for VMs whe their services eed to host more(less) users. The time-varyig demad for VMs ca adapt to the chagig amout of user, which is ISBN: SDIWC 1

2 also the chief reaso that teats(eterprises) are willig to migrate their service to the clouds. That meas there is o eed to ivest the ifrastructure for teats whe there is a rapid icrease i user cout because the ivest will become a idle asset later whe the surge of demad passes. From a smaller graularity, the traffic of VM is also chagig over the time owig to the trasformatio of commuicatio patters. Both of the two sides depict the etire dyamic demads of teats. Based o the metioed dyamic demad previously, the badwidth sharig must hadle the followig two problems: 1) No badwidth guaratee. Lack of ecessary badwidth isolatio resembled i the CPU ad memory, the etwork performace is iterfered by each other, leadig to the upredictable executio time of jobs, further icreases the risk of reveue loss for teats ad IaaS providers. 2) Badwidth chur. The badwidth reallocatio triggered by the teats time-varyig demad for VMs results i badwidth chur problem. Whe teat s demad for VMs chages, VMs will be placed o some host machies or removed. I this situatio, the allocated badwidth of VMs that still stay o host machie will fluctuate frequetly due to the VMs comig ad goig. To achieve predictable performace, may badwidth allocatio schemes have bee proposed to guaratee miimum badwidth by reservig the basic badwidth for the teats/vms. However, they pay o attetio to the badwidth chur problem caused by the reallocatio whe VMs joi or leave host machies. Both of the two facets cotribute to upredictable performace that is harmful for the teats ad should be resolved appropriately. I this paper, we model the badwidth allocatio as a optimizatio problem for satisfactio degree, which is subject to the basic badwidth guaratee ad limited badwidth chur extet. To achieve the optimizatio objective, we try to make the differet VMs o the same host machie allocated etwork badwidth as equal as possible, which ca be proved to be correct, o the basis of guarateeig basic badwidth ad cotrollig the extet of badwidth chur. Uder the experimets, we demostrates that our scheme ca guaratee the basic etwork badwidth ad efficietly cotrol the extet of badwidth chur betwee two cosecutive badwidth allocatios. The remaider of the paper is orgaized as follows. Sectio II discusses the backgroud ad related work. Sectio III describes our model ad desig to resolve the above problems. Sectio IV evaluates the badwidth allocatio algorithm by simulatios. Fially, we coclude the paper i V. 2 RELATED WORK Recetly, several mechaisms have bee proposed to achieve predictable etwork performace. I geeral, there are two aspects i those works: the first is to provide miimum badwidth guaratee, ad the other is to share the etwork badwidth resource iproportio As metioed above, Oktopus[2] ad Secodet[3] focus o predictable etwork performace with the thought of providig miimum badwidth guaratee by reservig specific badwidth for VMs. They use the static reservatios across the data ceter to guaratee the badwidth allocatio of the etire iter-vm etwork. The proposed virtual topologies i those researches ca provide strict etwork isolatio at VM level. However, they ca t realize high utilizatio for badwidth owig to eglect of the dyamic feature of dataceter traffic. GateKeeper[4] is work coservatio based o hose model ad provides miimum badwidth guaratee for VMs by shapig the traffic of VMs ad the residual badwidth is allocated i a best-effort. ISBN: SDIWC 2

3 The other existig policies provide etwork isolatio at VM level with the help of proportioal badwidth provisioig. Seawall[5] is a hypervisor-based mechaism to share etwork proportioally o cogested lik accordig to the assiged weightsof VMs that are commuicatig o that lik. As a statical multiplexig mechaism, Netshare[6] uses weighted fair queueig for badwidth allocatio across differet teats to achieve proportioal badwidth sharig. The policies preseted i the above require to support of per-vm queue i switches for rate cotrol, which meas hard to be scaled. Meawhile, teats ad VMs i the dataceter come ad go dyamically, which gives rise to a high rate of chur. Not takig ito cosideratio of the dyamic demad exacerbates etwork performace i badwidth allocatio. Guo[7] puts emphasis o the dyamic ature of dataceter traffic of VM, which ca address oe aspect of teats dyamic demad by usig a Logistic model. Nevertheless, the case of variable teats demad for VMs is igored. With a view to the basic requiremets preseted i FairCloud[8], we also address the dyamic demad for comig-ad-goig VMs o host machies. Whe VMs joi or leave host machies, the badwidth reallocatio will be triggered. Aimig to achieve good etwork performace, we set a parameter β to limit the badwidth chur extet betwee two cotiuous allocatios. Meawhile we defie satisfactio degree as a measuremet for the badwidth allocatio state of host machie. By allocatig badwidth to VMs hosted o the same machie as equal as possible, we ca make host machie achieve the best satisfactio degree. At the same time, the miimum badwidth allocatio ca be guarateed ad badwidth chur caused by cotiuous reallocatio is also hadled efficietly. 3 MODEL AND PROBLATION Firstly, we depict the assumptio ad model of data ceter etwork i this part, the propose the optimizatio model based satisfactio degree for hadlig the badwidth allocatio problem. 3.1 Data Ceter Network Model Based o recet works o sharig data ceter etworks, we abstract the coectios of VMs ito a hose model[9] show i Fig. 1. I this model, all VMs of teats are coected to a simple logical o-blockig switch with guarateed badwidth o each access lik, which meas that the etwork badwidth throughput of each VM ca oly be blocked by its access cotrol. Fig. 1: Hose model, where N VMs are coected to a ublocked switch Uder the help of existig advaced researches o multi-tree topologies[9][10]ad multi-path routig[11], the full bisectio badwidth has bee improved cosiderably, which makes the physical badwidth of host machies ca be fully utilized without cosiderig the bottleeck liks iside the dataceter. I this way, the problem of maagig teat etwork badwidth i the dataceter is coverted to a more tractable problem of maagig the host machie s etwork access, amely, shiftig the etwork bottleeck from etwork fabric to the edpoit liks that coect each host machie to the etwork fabric. I dataceters, VMs are placed o some host machies accordig to the demads for CPU, Memory ad Storage, usig "pay-as-you-go" ISBN: SDIWC 3

4 price model. After VMs placemet, etwork badwidth is allocated i "best-effort" way, which ca t guaratee etwork performace i curret IaaS dataceters. We cosider the IaaS data ceters cosistig of M physical machies, M={p 1, p 2 p m } ad every physical machie cotais N VMs, N={v 1, v 2,..., v }. With the help of hose model, we just eed to cotrol the badwidth access to the IaaS data ceter o each host physical machie without cosiderig the iside fabric. Uder this circumstace, we should kow the straightforward badwidth demad of each VM. The the badwidth demad of VM N ca be deoted as d, so all the badwidth demad for VMs o host machie i, i M, ca be depicted as a vector, such as D i = {d 1, d 2 d }. Now, we ca describe each VM by usig the specific badwidth demad. For the badwidth allocatio, we should firstly cosider the guaratee for the etwork performace. We choose to assig a miimum badwidth guaratee to each VM firstly as i [12]. I this way, we ca achieve a etwork badwidth guarateed performace. At the same time, the specific badwidth demad should also be assiged to each VM. With the descriptio above, each VM ca be depicted from two aspects icludig the miimum badwidth guaratee ad its badwidth demad, so the VM i ca be depicted as a two-dimesio vector, such as V i = {l i, d i }. Besides, we also eed to record the rate of badwidth allocatio iformatio for each VM. The aother parameter r i for VM iis used to keep track of the allocated badwidth. As a result, VM i ca be described as a three-tuple [l i, d i, r i ]. For the host machie, we represet server p m usig a two-tuple. First, we should figure out the total etwork badwidth of the host machie as C m. The the residual badwidth of server p m is recorded as R m after each badwidth allocatio. Asa result, the host machie ca also be depicted from two aspects icludig the total capacity ad the residual badwidth, such as p m = {C m, R m } for the host machie p m. To achieve a good etwork performace, we use a parameter S to measure the quality of etwork performace for each VM. For each VM, its badwidth demad has bee proposed ahead. Accordig to its miimum badwidth guaratee l i ad total badwidth demad d i, VM i will be placed o a proper host machie with the allocated badwidth capacity r i. The the parameter S ca be deoted as r i ad this d i parameter is called as satisfactio degree. I other words, the satisfactio degree is used to describe the state of the badwidth allocatio for VM i, amely S i = r i (r d i d i ), which i meas that if VM i is allocated more etwork badwidth for the demad d i, the etwork performace is better ad thesatisfactio degree is higher. I our paper, we focus o achievig a good badwidth allocatio state for a host machie but ot for the idividual VM. The we defie the satisfactio degree for the host machie p m as the product of satisfactio degree of all the VM hosted o the same server p m, such as r i i=1,which is the objective fuctio defied i the followig optimizatio problem of badwidth allocatio. Takig ito cosideratio of the badwidth demad of VM, we ca t place VMs o ay host machie arbitrarily. We assume the badwidth allocatio is r i for the VM i hosted o server p m whe the VM i [l i, d i, 0] has a miimum badwidth guaratee l i ad badwidth demad d i. Obviously, the allocatio should be bouded by the total badwidth capacity of host machie p m, i.e., d i ISBN: SDIWC 4

5 i= r i C m i=1 I the meawhile, the VM should be guarateed the miimum badwidth for etwork performace, so we use the costrait r i L i to esure that each VM ca achieve predictable etwork performace, ad L i = mi{l i, d i } Correspodigly, the allocated badwidth should also ot surpass the badwidth demad d i, the r i d i However, each badwidth reallocatio ca trigger the badwidth chur problem because of lackig of limitatio for the extet of badwidth. Our allocatio strategy focuses o decreasig the badwidth chur betwee two cotiuous allocatio. We defie aother parameter β ( 0 β 1 ), which is used to cotrol the extet of badwidth chur. For example, r i is the curret badwidth allocatio ad r i is the badwidth reallocatio after a ew VM jois the same host machie. Obviously, r i r i. To cotrol the extet of badwidth chur, we should set the costrait as r i βr i, which meas that the reallocated badwidth r i will be o less tha the previous badwidth allocatio βr i. Usig the simple costrait betwee two cosecutive allocatio, the extet of badwidth chur ca be cotrolled efficietly by adjustig the parameter β ad the predictable etwork performace is also obtaied further. Now we describe a detailed scee to help uderstad the modelig problem. Whe teat wats to icrease its VM demad, the ew VM [r i, d i, 0] will joi i a host machie. The badwidth of existed VMs hosted o the same host machie will be reallocated owig to the ewly joied VM ad we record their previous badwidth allocatio as r j, j {1,2,..., }. Based o the descriptio above, we ca model the badwidth allocatio as the followig optimizatio problem: s. t. max i= r i d i i=1 (1) r i C m (2) i=1 r i L i (3) r i d i (4) r i βr i (5) Accordig to (2), the sum of the allocated badwidth should be o more tha the capacity of the host machie. If the sum is less tha the i=1 r i capacity, amely < C m, it meas that there is some residual badwidth for the host machie afterbadwidth allocatio ad all the badwidth demad of VMs is less tha the capacity of host machie. As a result, the VM i will get all its badwidth demad r i = d i ad its satisfactio degree is 1. This situatio is a special case. However, we should process the case that the total badwidth demad of all VMs is greater tha the capacity of host i=1 r i machie. As a result, = C m ad the allocated badwidth r i is ot equal to the badwidth demad, amely r i d i, which meas the total badwidth demad is beyod the capacity of host machie ad some VMs ca t obtai the whole demad badwidth. 3.2 Algorithm Desig Accordig to the model aalysis above, we ca see that the objective fuctio is to achieve the best satisfactio degree. I the modelig, the badwidth demads of differet VMs have bee poited out clearly, which meas that the parameter d i i the objective fuctio (1) is a ISBN: SDIWC 5

6 kow coditio ad we should obtai the badwidth allocatio r i. The, the problem ca be coverted to the optimizatio problem that tries to obtai the maximum value of the product r i, amely max r i, where i=1 r i = C m. I the optimizatio problem, we ca see that the sum of the allocated badwidth r i is a fixed value ad we try to achieve the max value of their product. Based o the aalysis above, we ca desig our algorithm ispired by the ature of AM-GM iequality described as follows: x 1 + x x x 1 x 2 x ad that equality holds if ad oly if x 1 = x 2 = x. The coditio that the AM-GM iequality satisfies the equal case idicates that the closer the x i (i 1,2 ) is, the larger the value of their product is. The we ca coclude the followig equality: Theorem x i > 0, i {1,2,, }, i=1 x i = C. If x i is closer to each other, that s to say, the variace is smaller, i=1 x i is larger. I this sectio, we will give a detailed descriptio about our allocatio algorithm desig. I the badwidth allocatio system as i Fig. 2, the host machie has the total system iformatio, icludig its ow badwidth iformatio about its total badwidth capacity C, the residual badwidth capacity R ad the allocated badwidth iformatio of VMs o the host machie. As for the VM i o host machie, it ca be depicted as a three-tuple [l i, d i, r i ], which is recorded i badwidth allocator of host machie. Based o the aalysis above, we ca make the badwidth allocatio for each VM as eve as possible to achieve the best satisfactio degree for the host machie. However, the badwidth demad of VMs is differet ad so is the miimum badwidth guaratee, which impedes the idea that we allocate the badwidth to VMs equally. Aimig Fig. 2:Badwidth allocatio i host machie at to solve this problem, we propose a multi-stage algorithm that ca make the badwidth allocatio as equal as possible uder the restrictio above. Whe some VMs joi or leave a host machie, the badwidth reallocatio ca be triggered. At first, we should update the badwidth allocatio iformatio as follows. As for the VMs hosted o physical machie, their origial record iformatio is a three-tuple [l i,d i,r i ], the we update l i = max{βr i, l i } ad r i = l i. I the case that there are some ew VMs to joi host machie, alog with the ew VM request [l j,d j,0], all the three-tuple of VMs [l i,d i,r i ] will be sorted by the third dimesio r i. I the meawhile, we decide whether the badwidth requests ca be accepted i the allocatio usig the followig criteria: result = { accepted, i= l i < C i=1 rejected, otherwise If the request is accepted, we ca divide the case ito two kids of situatios. Firstly, whe the total badwidth demad is less tha the capacity of host machie, amely i=1 d i C, all the badwidth demad of VMs o the same host machie ca be satisfied, so r i = d i. The other case is that the capacity of the host ISBN: SDIWC 6

7 machie ca t satisfy all the badwidth demad of the VMs, the we call our badwidth allocatio algorithm.. To achieve etwork performace, the miimum badwidth should be guarateed. I other words, the miimum badwidth should be allocated firstly. Ispired by Theorem 1, we allocate etwork badwidth to VMs as equally as possible uder the premise of guarateeig etwork performace. After VMs badwidth allocatio iformatio preprocessig ad sort i a ascedig order as described above, we choose to decide how much the amout of the badwidth is allocated to VMs. I this processig, we will determie two parameters, oe is the miimum badwidth demad of the VMs that have the least badwidth guaratee value. Owig to the badwidth demad sort, we just eed to search the VMs that have the miimum badwidth guaratee i the set A, where all VMs wait to be allocated. The the value of miimum badwidth demad ca be defied as follows: MiDd = {d i l i = l 0, i A} mi For example, [ 20, 30, 20 ], [ 20, 37, 20 ], [ 20, 32, 20 ], [ 35, 40, 35 ] are the sorted badwidth demad. I the example, we ca see that the miimum badwidth guaratee is 20 ad there are three VMs meetig the coditio that they have the miimum badwidth guaratee. Amog the three VMs badwidth request [20,30,20], [20,37,20], [20,32,20], their badwidth demads are 30, 37, 32 respectively. The the least badwidth demad is 30, amely MiDd = 30. The other parameter is the secod least badwidth guaratee if it exists, amely SecGua = 35 ad the least badwidth guaratee is 20. Accordig to the derived two parameters MiDd ad SecGua, we elicit the ext badwidth value allocated i the ext allocatio roud usig the followig rule: MiDd, If SecGua = 0 UpBad = { mi {MiDd, SecGua}, otherwise,which gives the upper-limit to the ext badwidth allocatio roud. As listed i the example, the curret allocated badwidth is their guarateed badwidth for all the VMs. If allocated cotiually, the badwidth of wait-allocated VMs that have the leastallocated badwidth ca reach to the upper-limit UpperBadwidth. To allocate badwidth equally, the residual badwidth R of host machie is firstly allocated amog the amout M of VMs that have the least allocated badwidth curretly. The the allocated badwidth i this roud is BadAlloc = mi{ R, UpperBad CurBad} M The the VMs that have the least allocated badwidth obtai badwidth from host machie ad update their badwidth allocatio as follows: r i = r i + BadAlloc The the residual badwidth of host machie is also updated R = R M BadAlloc ISBN: SDIWC 7

8 If there are somevms satisfyig that their allocated badwidth is equal to the badwidth demad, amely r i = d i, their badwidth allocatio will be fiished completely. If all the VMs have fiished badwidth allocatio or host machie has o residual badwidth, the badwidth allocatio algorithm is termiated. allocate badwidth usig Traffic Cotrol(TC) tool to limit the rate of each VM i a liux based OS. 3.3 Algorithm Aalysis From the descriptio about our algorithm, we should firstly sort the badwidth demad vector, which uses the time of O(log). I the badwidth allocatio process, the amout of the ext roud badwidth allocatio is determied by traversig VMs demads vector ad this step just costs O(1) with the help of the sort above. Based o the aalysis above, the badwidth allocatio will be executed may times, which is determied by the total amout of the differet VM s badwidth guaratee, demad ad allocated, amely 3 O(). The the total the time complexity of our badwidth allocatio algorithm is O(log) + O(3), amely O(log). The badwidth allocatio based o game-theory[?] develops a cetralized allocatio method ad a distributedoe. The first is O( 2 ) ad the rate of covergece of distributed allocatio is determied by its step-size ξ. Compared with other badwidth allocatio scheme, we ca just limit the allocatio i the host machie ad the time complexity is determied oly by the amout of the hosted VMs. I geeral, the amout of the VMs i a host machie is the order of te, which is very small for the thousads of the machies i IaaS data ceters. 4 EXPERIMENT To validate the effectiveess of our proposed scheme, we simulate a IaaS data ceter with homogeeous servers, of each equipped with etwork badwidth capacity of 1Gbps. As assumed i hose-model, the data ceter has a full-bisectio badwidth etworkeref[8] ad we Fig.3:Experimet eviromet Table I:Curret badwidth allocatio iformatio VM Guaratee(Mbps) Demad(Mbps) Allocated(Mbps) X X X X X As pictured i Fig. 3, two host machies are liked through a switch with capacity of 1 Gbps. The teat A has five VMs placed o the server 1 ad each VM commuicates with two other VMs hosted o the server 2. The curret detailed badwidth demadad allocated badwidth iformatio of teat A s VMs are listed i Table. I. As metioed i Sec. I, the badwidth i data ceter is allocated i a best-effort way. I this way the badwidth allocatio will be iterfered each other whe there are some VMs to joi the same host machie. Owig to the teats dyamic demad for VMs, a ew VM request arrives at the host machie that host all the VMs of teat A. The ew VM has a badwidth demad of 175Mbps ad its guarateed badwidth is 30Mbps. Based o the best-effort badwidth allocatio, the badwidth of all the existed VMs o the same host machie will be decreased owig to the badwidth sharig across the teats i the data ceter. As illustrated i Fig. 4(a), VMs(X1-X3) ISBN: SDIWC 8

9 get less badwidth allocatio tha their basic badwidth guaratee, so they are ot guarateed basic badwidth i reallocatio with best-effort way, while our desiged algorithm achieves a guarateed etwork performace as show i Fig. 4(b). As a cotrast i Fig. 5, our multi-stage allocatio algorithm ca lower the badwidth chur extet effectively from 15% to 5% by usig a tuable parameter β ad all of VMs achieve their etwork performace. Fig.4:Badwidth Allocatio I this paper, we use the hose model to aalysis the badwidth allocatio problem i IaaS data ceter. By defiig the satisfactio degree as the measuremet of the badwidth allocatio state, we model the badwidth allocatio problem as a optimizatio problem, whose objective is to achieve the best satisfactio degree uder some restrictios. To achieve a ideal etwork performace, we ot oly guaratee the miimum badwidth but also limit the badwidth chur extet with the parameter β betwee two cotiuous badwidth allocatio. We desiged a multi-stage allocatio algorithm based o the AM-GM iequality to hadle the badwidth allocatio i host machie. As show i our experimet, each VM achieve guarateed etwork performace successfully ad the badwidth chur of each VM is also decreased from 15% to 5%. I our model, we must kow the explicit badwidth demad of VMs or teats head. With the help of the work [13], we ca predict some badwidth with dyamic demad. As metioed above, our allocatio algorithm has a ufair for the VMs with large badwidth demad. I the future, we are goig to make a limitatio o the upper limit of badwidth allocatio to realize the fairess across differet teats ad VMs. REFERENCES Fig.5:Extet of badwidth chur caused by dyamic demad I our experimet, we ca see that VMs that have less badwidth demad have a higher satisfactio degree tha those with more badwidth demad. That meas our badwidth allocatio has a preferece for the VMs with little badwidth demad. 5 CONLUSION AND FUTURE WORK [1] Amazo elastic compute cloud. [Olie]. Available: [2] H. Ballai, P. Costa, T. Karagiais, ad A. Rowstro, Towards predictable dataceter etworks, i proc. of ACM SIGCOMM,2011 [3] C. Guo, G. Lu, H. J. Wag, S. Yag, C. Kog, P. Su, W. Wu, ad Y. Zhag, Secodet: a data ceter etwork virtualizatio architechture with badwidth guaratees, i proc. of ACM CoNext, [4] H. Rodrigues, J. R. Satos, Y. Turer, P. Soares, ad D. Guedes, Gatekeeper: supportig badwidth guaratees for multi-teat dataceter etworks, i proc. of 3rd workshop o I/O virtualizatio. USENIX,2011. [5] A. Shieh, S. Kadula, A. Greeberg, C. Kim, ad B. Saha, Sharig the data ceter erwork, i proc. of USENIX NSDI, ISBN: SDIWC 9

10 [6] T. Lam ad G. Varghese, Netshare: virtualizig badwidth withi the cloud, UCSD, Tech. Rep., [7] J. Guo, F. Liu, X. Huag, J. C. Lui, M. Hu, Q. Gao, ad H. Ji, O efficiet badwidth allocatio for traffic variability i dataceters, i proc. of IEEE INFOCOM, [8] L. Popa, G. Kumar, M. Chowdhury, A. Krishamurthy, S. Ratasamy, ad I. Stoica, Faircloud: Sharig the etwork i cloud computig, i proc. of ACM SIGCOMM, [9] A. Greeberg, J. R.Hamilto, N. Jai, S. Kadula, ad C. Kim, Vl2: A scalable ad flexible data ceter etwork, i proc. of ACM SIGCOMM,2009. [10] C. Guo, G. Lu, D. Li, H. Wu, X. Z. Y. Shi, C. Tia, Y. Zhag, ad S. Lu, Bcube: A high performace, server-cetric etwork architecture for modular data ceters, i proc. of ACM SIGCOMM, [11] C. Raiciu, S. Barre, C. Plutke, A. Greehalgh, D. Wischik, ad M. Hadley, Improvig dataceter performace ad robustess with multipth tcp, i proc. of ACM SIGCOMM, [12] J. Guo, F. Liu, D. Zeg, J. C. Lui, ad H. Ji, A cooperative game based allocatio for sharig data ceter etworks, i proc. of IEEE INFOCOM, [13] M. Youg, The Techical Writer s Hadbook. Mill Valley, CA: Uiversity Sciece, [14] J. Schad, J. Dittrich, ad J.-A. Q.. e Ruiz, Rutime measuremets i the cloud: observig,aalyzig, ad reducig variace, i proc. of VLDB, ISBN: SDIWC 10