Journal of Engineering Science and Technology Review 10 (5) (2017) Research Article

Transcription

1 Jestr Journal of Engneerng Scence and Technology Revew 0 (5) (07) 4-49 Research Artcle Study on Fault Tolerance Method n Cloud Platfor based on Worload Consoldaton Model of Vrtual Machne Zhxn L,,*, Le Lu and Zeyu Tong 3 JOURNAL OF Engneerng Scence and Technology Revew College of Coputer Scence and Technology, Jln Unversty, Changchun 300, Chna School of Coputer Technology and Engneerng,Changchun Insttute Of Technology, Changchun 300, Chna 3 Departent of Appled Matheatcs and Statstcs, Johns Hopns Unversty, Baltore, 8, Unted States wwwestrorg Receved Aprl 07; Accepted 7 October 07 Abstract The fault tolerance ethod of vrtual achnes (VM) guarantees relablty to the servce capablty of cloud platfors VM worloads are dynac and uncertan, and thus, they affect the relablty and tas processng capablty of entre cloud platfors In ths study, a fault tolerance ethod based on the VM worload consoldaton odel was proposed to solve probles concernng the relablty and tas processng capablty of cloud platfors caused by VM worloads, thus provng the relablty of VMs and overall perforance of cloud platfors Frst, the ethod was analyzed on the bass of the dstnct relatonshp of VM worload and VM relablty and tas processng capablty Then, the worload state of VM was predcted and analyzed by lnear regresson usng VM worload ontorng data, and the VM worload consoldaton algorth was constructed based on expected worload constrant and optzaton of fault tolerance te Fnally, the fault tolerance ethod based on the VM worload consoldaton odel was copared wth the Rado ethod and the Max ethod Research results deonstrate the potental of the proposed ethod to prove VM relablty n cloud platfors by 0% and 47% copared wth those for the Rado and Max ethods, respectvely In the sae worload phase, the tas copleton rate of the proposed ethod ncreased sgnfcantly (5% and 30%, and % and 30%), and the percentages were hgher than those for the Rado and Max ethods, respectvely Moreover, the proposed ethod shortened tas response te Ths study concludes that the worload consoldaton of VMs can ncrease the relablty and tas processng capablty of VMs Ths proposed ethod can provde technologcal support to the fault tolerance of VMs n cloud platfors Keywords: cloud coputng, vrtual achne, worload consoldaton, fault tolerance Introducton Wth the rapd developent of cloud coputng technology, an ncreasng nuber of enterprses has started to offer VM servces n cloud platfors [] The relablty of VMs could drectly nfluence cloud platfor applcaton servces, such as e-als, databases, web applcatons, and so on [] Subsequently, the fault tolerance ethod of VMs can effectvely prevent falures n cloud platfors Fault tolerance can also prevent the prolferaton of error logs fro one VM syste to the next; consequently, cloud-based servces that are contnuously offered to the outsde world are also proved, thus ncreasng the relablty of the entre cloud platfor However, cobnng the fault tolerance of VM worloads s necesstated pror the deployent of VM fault tolerance n cloud platfors The worload state of VMs n cloud platfors s dynac and uncertan [3,4] Therefore, understandng how fault tolerance s deployed on the bass of the cobned relatonshps of VM worload and VM relablty and tas processng capablty s peratve The fault tolerance ethod of VMs n cloud platfors was developed under the aboveentoned crcustance At *E-al address: lzx@alslueducn ISSN: Eastern Macedona and Thrace Insttute of Technology All rghts reserved do:0503/estr0505 present, the exstng fault tolerance ethod selects possbltes of VM errors accordng to the physcal state of a server where a VM s located However, even f t attepts to solve VM relablty probles, the exstng ethod often fals to establsh the optzaton of VM fault tolerance te and effectve consoldaton of VM worload state on fault tolerance The optzaton of VM fault tolerance te was studed by usng tolerance ethods and odels [5] to tolerate faults upon the assupton of possble errors However, wthout the allocaton and consoldaton of VM worloads, the approach frequently caused fault tolerance of VMs, whch then affected the syste perforance of cloud platfors In dscussng the effects of VM worload on fault tolerance [6], the VM worload layout was optzed by easurng networ transsson speed and delay, and consequently, to save on bandwdth and ncrease effcency However, the layout neglected the pacts of CPU and eory worload, as well as fault tolerance te on fault tolerance, wth the consoldaton of VM worloads In ths study, a fault tolerance ethod s establshed accordng to the relatonshps of VM worload and VM relablty and tas processng capablty The proposed ethod not only counterbalances the pacts of VM worload on fault tolerance to soe extent, t also solves ssues concernng the optzaton of fault tolerance te The proposed ethod also proves the relablty of VMs

2 Zhxn L, Le Lu and Zeyu Tong/Journal of Engneerng Scence and Technology Revew 0 (5) (07) 4-49 n cloud platfors and the overall syste perforance of cloud platfors State of the art Past wors have reported a nuber of relablty ssues of cloud platfors caused by VM worload [7], as well as the portance of VM fault tolerance to safeguard the relablty and avalable capablty of cloud platfors (eg, EC [8], Google App Engne [9], VMware [0], Xen [], KVM [], etc) Meanwhle, ost exstng research has focused on the reactve and preventve fault tolerance ethods [3] In the reactve fault tolerance ethod, bacng up the fault tolerance s consdered a coon approach Wth respect to VM bacups, Xu et al [4] proposed the strategc plannng of odels and adaptve bacups based on a genetc algorth to shorten bacup te Cully et al [5] used bacups for entre cluster states by consderng a worload phase, and then realzed fault tolerance by recoverng the latest chec pont for a cluster after error dentfcaton Machda [6] reduced the nuber of bacup servers by optzng the layout of redundant VMs to ensure relablty (e, aster and vce nodes were baced up n ntervals for fault tolerance) However, the ethod was costly and necesstated an optzaton of fault tolerance te In the preventve fault tolerance ethod, fault tolerance s conducted followng a resource worload state analyss [7] VMs are transferred to another server to ensure noral servce operatons, and ths s carred out by studyng fault tolerance trgger ponts and the fault tolerance odel The preventve fault tolerance ethod also anly nvolves worload balancng technology [8, 9], energy savng technology [0], syste consstence technology [], and so on Zhang et al [] analyzed a runnng resource state wth the hdden Marov odel, calculated the future runnng state probablty of a syste, and conducted dynac adustents n syste resource allocaton to ncrease the effcency of VM recovery aganst falures Mallc et al [3] perfored clusterng analyss on resource states usng dfferent nuercal ranges of hstorcal resource worload ndces A cluster was desgnated to a state pont, and the short-ter resource state was predcted wth the Marov odel Bruneo et al [4] reported that VM could be restarted regularly wth a VM software recovery strategy to solve the agng falure of VM caused by worload and protect the avalablty of VM Wu et al [5] proposed a developental ethod for odelbased fault tolerance and realzed seven ddle faulttolerance-echanss n cloud platfors, thus realzng the cross-platfor characterstcs of the fault tolerance echans However, those ethods could nether protect the fault tolerance te of VMs nor guarantee effectve worload consoldaton, and they were also costly for syste operatons In addton, syste relablty decreased when the worload pressure of the cloud platfor syste was large To solve the above relablty probles, ths study proposes the reducton of VM worload by usng a VM worload consoldaton algorth that s based on the relatonshp of VM worload and VM relablty and tas processng capablty When the server worload expectaton exceeds the threshold, the VM syste adopts fault tolerance processes n antcpaton of VM errors The fault tolerance te of VM s optzed to reduce fault tolerance frequency, thereby protectng the avalablty and provng the perforance of the cloud platfor syste Ths study s presented as follows: Secton 3 ntroduces the easureent of the server-vm worload, the VM worload predcton odel, the relablty odel, and the fault tolerance ethod Secton 4 presents the experent and the result analyss Secton 5 provdes the conclusons 3 Methodology 3 Measureent of server-vm worload VM worload s strongly dynac, and thus, easurng resource worload can effectvely dentfy the state of resources Establshng whether or not a VM s overloaded can be deterned by the resource states Therefore, choosng the approprate easureent for resource worload can enhance worload consoldaton and ncrease VM relablty Defnton : Worload phase Worload phase refers to a te nterval durng VM operaton The VM worload s relatvely stable n a sngle worload phase, and ths s expressed as l={ l, l, l +,} Ths defnton can be used to easure the change rate of VM worload n the worload phase When the VM s n runnng state, the change n worload fro phase l to phase l reflects the worload state n ths partcular worload phase Defnton : Server worload expectaton Worload expectaton refers to the average easure of server coputng resources that are occuped by the VM after vrtualzaton In cloud platfor systes, the resource pool s coposed of a seres of servers Each server s expressed by S and the server resource cluster of cloud envronent s S={S,S S n} In the ntal state, each node server S s assued to be allocated wth VMs n S ( v v ), where v represents the nuber of VMs Each server S contans ultple hardware resources, such as CPU, eory, networ, and so on The VM worload dscussed n ths study anly refers to two hardware resources: CPU and eory The proporton of CPU and eory of VM are expressed as C vcpu and C ve The CPU and eory resources of each VM are regularzed to VM worload s calculated based on CPU utlzaton λ and eory utlzaton λ vcpu ven The worload of v on node server S n the worload phase l s expressed as: W ( ) C C, l = λ vcpu vcpu + λ () ven ven The calculated worload of server S n worload phase l s the worload su for all VMs as follows: W( l ) = W ( l ) () =, In ths study, the server worload expectaton s defned as: ( ) E( W( l ) S l = ), (3) 4

3 Zhxn L, Le Lu and Zeyu Tong/Journal of Engneerng Scence and Technology Revew 0 (5) (07) 4-49 where s the nuber of VMs on server S Server worload expectaton reflects the average proporton of VM worload on server resources When S ( ) s exceedngly large, the VMs occupy relatvely ore l server resources n worload phase l and thus are overloaded An overload constrant ( Ω ) s set for the overload decson strategy S ( l ) <Ω, where Ω denotes the degree of overload Gven that the total worload of the server n worload phase l s lower than Ω, e, 0< Ω <, the VM resource allocaton attepts to reduce the degree of overload In addton, gven that the expresson clas extensve syste resource consupton for the graton fault tolerance of VMs, the syste can tolerate a certan degree of overload However, syste perforance declnes sgnfcantly when overloadng s reached at a certan pont, and ths occurrence reduces relablty 3 VM worload predcton odel The VM worload s strongly correlated wth te on the bass of the VM worload easureent; n other words, the VM worload of the prevous worload phase sgnfcantly affects those of the next phase Therefore, VM worload can be predcted by lnear regresson The telness and accuracy of VM worload predcton for the succeedng worload phases not only affect the cloud coputaton of VM relablty, they also nfluence the optzaton of VM fault tolerance te In ths study, W,( l ) denotes the ntal worload of v on server S, whle W,( l ) denotes the VM worload n worload phase l Based on the changes n the VM worload for a worload phase: W ( l ) = W ( l ) + ϖ ( l ), (4),, where ϖ ( ), l s the observed change rate of worload of v on server S n worload phase l In prevous worload phases l={ l, l, l +,}, the worload varaton set s ϖ,( l ) = { ϖ,( l), ϖ, ( l3), ϖ,( l )} VM worload s predcted by lnear regresson on the bass of VM dynacs Fro the defnton of general lnear regresson, the estaton functon s: ϖˆ ( l ) = θ + θ * l, (5), where ϖ ˆ,( l + ) s the predcted VM worload change rate of the next worload phase Fro the VM worload change rates n prevous phases, the coeffcents θ 0 and θ are obtaned by solvng the lnear regresson equaton wth the least square ethod Moreover, θ 0 and θ change wth hstorcal worload change rates By usng ths ethod, dynacally adustng the paraeters of the lnear regresson odel based on worloads n the latest phase s feasble Thus, the ethod can adapt to worload fluctuatons we defne θ 0 and θ n the followng way: θ θ l ϖ ( l ) l l * ϖ ( l ),, 0 = n l ( l),, = n l ( l) (6) n l * ϖ ( l ) l ϖ ( l ) (7) On the bass of worload change rate ϖ ˆ,( l + ) n the next worload phase, the predcted VM worload, l Ŵ ( l + ) can be calculated fro equaton (4) The server worload expectaton S ˆ( l ) + can be obtaned fro equaton (3) If S ˆ( l ) + < Ω, then the server s not overloaded n worload phase l + ; that s, the server s runnng relably, and worload consoldaton allocaton s conducted 33 VM relablty odel Gven that server relablty s a rando dstrbuton event of te, the faults are nfluenced by sudden worload ncrease n cloud platfor systes and errors accuulate of VMs These faults are consdered as rando faults, such as syste breadown caused by sudden ncreases n VM worload, whch occur occasonally Studyng the syste fault log [6-8] showed that server relablty copled wth Webull dstrbuton In ths study, server relablty dstrbuton was verfed by an experent based on the relablty analyss of VMs n cloud platfors Defnton 3: VM relablty refers to the probablty of VM resources to accoplsh assgned functons, and t s one of the an evaluaton ndces of relablty of VM resources n runnng state In ths study, the absence of a VM error log s consdered n VM relablty easureent; that s, R v reflects the relablty of v The Webull probablty densty dstrbuton functon of the two paraeters s: l ( ) η l f( l ) = ( ) e, η>0 (8) η η The relablty functon s: l ( ) R ( l ) = e η, (9) v where s the shape paraeter; η s the scale paraeter; and l s the worload phase VM relablty wth server worload expectaton can be expressed as: R ( l ) = R{ t l S ( l )} (0) v The VM relablty n the odel s desgnated wth a lower value than the syste threshold for fault tolerance Let: R ( l ) <Φ, () v where Φ s the threshold of syste relablty When the relablty value of v s saller than the threshold of syste default, fault tolerance occurs Choosng the approprate fault tolerance te ncreases syste relablty and reduces syste perforance loss 43

4 Zhxn L, Le Lu and Zeyu Tong/Journal of Engneerng Scence and Technology Revew 0 (5) (07) 4-49 Theore ndcates that the hgher the VM relablty s, the hgher the tas copleton rate wll be In other words, ncreasng VM relablty can protect the tas copleton rate of the cloud platfor Theore : Gven the sae context and constant tas processng capablty µ, and tas copleton rate hv of 34 Fault tolerance ethod The future runnng state of VMs s predcted by usng establshed odels on VM worload and relablty n cloud platfors In ths secton, the VM fault tolerance ethod s descrbed The syste structure of the VM fault tolerance odel s shown n Fg The Worload Manager odule s pleented for worload anageent and consoldaton, whle the Falure Detector odule s used for error detecton VMs n a sngle worload phase, f W, (l ) < W, (l + ), then Rv (l ) > Rv (l + ) Proof: Accordng to nown condtons (e, the sae context and constant tas processng capablty µ, and tas copleton rate hv of VMs n a sngle worload phase), equaton () can be used to derve: Rv (l ) = (µ, W, (l )) / ln( hv ) and Rv (l + ) = (µ, W, (l + )) / ln( hv ) Gven that W, (l ) < W, (l + ), we can deduce that Rv (l ) > Rv (l + ) Theore suggests that the VM worload n the worload phase durng the runnng state of VM resources s negatvely correlated wth VM relablty Therefore, worload consoldaton and allocaton are needed to reduce VM worload and prove VM relablty Theores and establsh the relatonshp of relablty, worload, and tas copleton rate of VMs Subsequently, worload consoldaton and allocaton are ntroduced Theore 3: Gven a tas resource request {τ q, q = r}, Fg Syste structure of the VM fault tolerance odel f ths request s accoplshed by n physcal achnes (S={S,S Sn }) or by the sae quantty of VMs 34 Basc prncple behnd the fault tolerance ethod The an prncple behnd the fault tolerance ethod s related to the analyss of relatonshps of VM worload and VM relablty and tas processng capablty By usng a atheatcal odel, the utual relatonshps aong the above paraeters are verfed Defnton 4: Tas processng capablty of VM ( µ ) A hgh µ of VM resources n unt te reflects strong resource servce capablty In the expresson, W, (l ) s the (SP ={vvn }), where the nuber of servers requred for the VM to settle s n (n n ), then the relablty of the VM ( Rv ) s saller or equal to relablty of server ( RS ), e, Rv RS Proof: Fro secton 33, f the fault probablty of a servce to n physcal achnes obeys Webull dstrbuton F (n, Rs ), then the expectaton and varance of the fault probablty of physcal achnes n the worload phase are VM worload; Rv s the relablty of v ; and hv s the E ( F ) = (n,ηγ( + tas copleton rate of v µ s then defned as: µ, = Rv (l )*ln( / hv ) + W, (l ) If n physcal achnes (n n ) are used, and each physcal achne nvolves VMs, (n = n ), then the VMs on these physcal achnes cannot run norally (e, faults are observed on the physcal achnes) Thus, the fault probablty of VMs obeys Webull dstrbuton F (n, Rsp (t )), whch s the sae as the physcal () )) and Var ( F ) = (n,η [Γ( + ) Γ ( + )]) The tas processng capablty of VMs reflects the relatonshp between resource worload and relablty Theore : If two VMs have the sae µ, and W, (l ), where Rv and R*v denote hgh and low relabltes of dstrbuton After vrtualzaton, the atheatcal expectaton of fault probablty n the worload phase s VM ( Rv > R*v ) whle hv and h*v denote the tas E ( F ) = E (n,ηγ( + copleton rates of VM under hgh and low relabltes, then hv > h*v n Var (Y + Y+ + Yn +n ) = ( n,η [Γ( + ) Γ ( + )]) Var ( F ) = Proof: Accordng to nown condtons, f two VMs have the sae µ, and W, (l ), then the followng can be derved fro equaton (): hv = e * h v = e ( µ, W, ( l )) Rv* ( µ, W, ( l )) Rv )) The correspondng varance s: and Expectedly, the followng Var ( F ) Var ( F ), Rv RS can be obtaned: Results ndcate that the hgher the nuber of VMs on each server s, the hgher the varance wll be The varances of the VM and server are equal only when there s one VM Gven that Rv > R*v, we can deduce that hv > h*v 44

5 Zhxn L, Le Lu and Zeyu Tong/Journal of Engneerng Scence and Technology Revew 0 (5) (07) 4-49 on one server By referrng to the varance, the relablty of the VM s lower than that of the physcal achne When ultple VMs are operated on a physcal server, the hardware fault affects ore applcatons copared wth cases wheren each physcal server s responsble for a sngle tas only Hence, n the cloud platfor envronent, the relablty of the VM s lower than that of the server In other words, the lelhood to develop the functons of a servce through VM s lesser copared wth that through the server Theore 4: Gven tas resource request {τ q, q = r}, f average tas processng capablty s Ρ(spn ) when a tas s the tas processng capablty of a VM s U, n processng capablty s: r U, = µ, + unforly allocated to VMs of dfferent servers, and t s Ρ( spn ) when a tas s allocated to VMs of only a few servers To eet tas copleton rate hv and server local expectaton Ω, we derve Ρ(spn ) > Ρ(spn ) τ q, then the average worload phase for Ρ( sp ) = µ, / q =,, q tas processng s l = = r Proof: Gven that n > n and the total nuber of VMs s, the average nuber of VMs on each server s (spn = = n ) < (spn = = n ) the average tas τ q / U, If τ q s dstrbuted on = ( Rv * ln( hv ) + W, (l )) / q =,, q = VMs unforly, then the tas processng capablty s {µ,µ, }, where µ, s the tas processng capablty of = ( Rv * ln( hv ) / + S (l ) v on server S The average worload phase for the tas = r processng s!l = τ q / nµ,, where n s the total nuber Accordng to Theore 3, the relablty of usng VMs on any servers s hgher than that of usng VMs on a few servers only; that s, Rv > Rv On the bass of equaton (3), q= of VMs Then, U, ax = µ, and l!l r Therefore, Ρ(spn ) > Ρ(spn ) s proven τq Theores 3, 4 and 5 prove that allocatng a tas to dfferent VMs on servers durng worload consoldaton and tas allocaton not only ncreases the relablty of VMs, t also shortens tas processng te and proves processng effcency q =,, q When tas request τ q s consdered for a VM, the worload of ths VM ncreases The tas processng capablty s U, = Rv (l ) *ln( hv ) + W,(l ) A specfc VM worload s hgher than the average VM worload, whch s allocated by tass On the bass of equaton (3), the worload of the specfc VM s hgher or equal to the average server worload expectaton W, (l ) S (l ) To eet the tas deand, U, µ, Gven that VMs have the sae confguraton, U, {µ,,µ, } ; that s, U, ax = µ, the server worload expectaton eets S (l ) < Ω Proof: Suppose the current VM s v = v, and ts tas processng capablty s U,, then U, = µ, + 34 Optzaton of fault tolerance te Fault tolerance of VMs results n syste perforance loss Therefore, the fault tolerance te necesstates optzaton to reduce fault tolerance frequency, thereby relevng nfluences on syste perforance and protectng noral servce operatons In ths study, l ʹ represents fault tolerance te, whch s selected fro the changes n predcted worloads of the predcton odel entoned n Secton 3 If the predcted server worload expectaton Sˆ (l ʹ) s hgher than the worload expectaton of syste default (Ω), e, Sˆ (lʹ) > Ω, then Allocatng tass to a few VMs enhances VM tas processng capablty However, processng te also ncreases due to lted resources Therefore, l!l Theore 4 shows that the average dstrbuton of tass on dfferent VMs can shorten tas processng te and ncrease processng effcency Whether or not the tas s allocated to VMs on a sngle server or to VMs on dfferent servers s deterned by Theore 5 Defnton 5: The average tas processng capablty Ρ( sp ) of server S n sp = {vv } s the su of VMs then the fault tolerance of VM s pleented n l ʹ The best fault tolerance te n ths study s deterned by Theore 6 Theore 6: If the ntal worload of v on server S s W, (l ) and the average change rate of v n prevous worload phases l s w, then the fault tolerance te of on server S : v s: Ρ( sp ) = µ, / l ʹ=(Ω W, (l )- ( ) w θ0 ) / θ, = where Ω s the upper lt of worload expectaton; W, (l ) s the ntal worload; and w s the average = ( Rv *ln( hv ) + W, (l )) / = worload change rate Coeffcents θ 0 and θ are calculated fro equatons (6) and (7) The optal worload phase for VM fault tolerance te n equaton (4) s nfluenced by two factors: te and spatal changes Ths approach ples that VM fault tolerance te s related to worload changes and VM worload on a few servers To reduce fault tolerance Theore 5: Gven a tas resource request {τ q, q = r} n whch n and n servers are used, then n > n The sae quantty of (4) (3) VMs ( sp{, =n } = {vv } and sp{, =n } = {vv } ) are deployed on the server The 45

6 Zhxn L, Le Lu and Zeyu Tong/Journal of Engneerng Scence and Technology Revew 0 (5) (07) 4-49 frequency, the lowest relablty of VM s chosen for fault tolerance and 46 TB storage capactes) All server nodes are connected by ggabt optcal fber exchangers The software envronent s deployed by two servers usng Xen vrtualzaton platfor Forty VMs are ntalzed (Table ): The Red Lnux 50 operaton syste and Ngx applcaton servce progra are nstalled nto the VMs, and a dstrbuted webste (JDK6 edton) s establshed The CPU-bound obs are calculated through JMeter sulaton An analog pressure worload s launched to test the VM worload and collect data The pressure worload, whch s generated by the clent end, allows the VMs wth Ngx to operate wth full worload 343 Fault tolerance ethod based on worload consoldaton The tas resource request {τ q, q = r} s anaged by the Worload Manager odule Accordng to Theore 5, a tas s allocated to dfferent VMs on dfferent servers by the Worload_Consoldaton functon to reduce VM worload and ncrease relablty The optzaton of fault tolerance te (l ʹ) s deterned by the VM worload predcton odel Thus, the proposed ethod not only eployed fault tolerance to VMs wth errors, t also effectvely reduced fault tolerance frequency and syste cost, as well as proved syste perforance The scrpt of the fault tolerance ethod s shown n Fg Table Deployent of software envronent Xen 50 verson VM 40 CPU Core Menory G 4 Analyss of experental results Frst, the valdty of the proposed basc worload evaluaton strategy s evaluated The ean square error (MSE) s used as an evaluaton ndex of perforance predcton: t =l ( y yˆ ) MSE = (y ) t = l t t t =l t = l (5) t where yˆ t s the predcted worload; yt s the actual worload; and l s the worload phase (total nuber of predctons) 4 Worload predcton analyss In ths experent, 0 low-worload VMs and 0 hghworload VMs are used to evaluate worload predcton The length of the worload phase s 50 The worload expectatons of the low-worload server and the hghworload server are shown n Fg 3 and Fg 4, and the MSE values are 0 and 009, respectvely Accordng to experental results, the MSE of the predcton error s controlled n the acceptable range and reflects the valdty of the establshed odel In addton, the MSE s ncreased gradually wth the contnuous growth of l, whch ndcates Fg Fault tolerance ethod based on VM worload consoldaton 4 Result analyss and dscusson The valdty of the proposed ethod s verfed fro these three aspects: () evaluaton and analyss of worload predcton, () analyss of VM relablty based on worload consoldaton, and (3) coparson of dfferent allocaton strateges of tas resource requests n the experent to verfy the valdty of the proposed fault tolerance ethod The three ethods are copared n the followng experents: () Rando: a tas resource request s randoly allocated to a VM; () Max: concentrated allocaton of a tas resource request to further allocate the tas of a few VMs to the axu degree; and (3) Worload_Consoldaton (proposed ethod): the tas s averaged and allocated to the dfferent VMs of dfferent servers n proper order that approprate worload phases ncreases predcton accuracy When the worload s sgnfcantly changed, the predcton accuracy s lowered to soe extent, but no nfluence s establshed for worload consoldaton 4 Deployent of experental envronent Two Daylght A840-G0 servers are used for the servce platfor (CPU: AMD 6376, 6 core 3 GHz 4; eory: 56 G and Ggabt LAN; ds array: Daylght DS800-G5 Fg 3 Actual and predcted worload expectatons of low-worload servers 46

7 Zhxn L, Le Lu and Zeyu Tong/Journal of Engneerng Scence and Technology Revew 0 (5) (07) 4-49 n ters of VM relablty and VM tas copleton rate The nuber of VMs, quanttes of requred obs, and worload phases are shown n Table Fg 4 Actual and predcted worload expectatons of hgh-worload servers 4 Relablty analyss of VMs n worload condtons In ths experent, 0 low-worload VMs and 0 hghworload VMs are used to evaluate VM relablty The experental results are shown n Fg 5 When the VM worload s lower than that of the low-worload VMs, no accuulaton errors are establshed n ters of observaton te In addton, VM relablty s nearly 00% In hghworload condtons, the accuulaton probablty of VM error logs n the entre cloud platfor s lowest (approxately 38%) n ters of observaton te The experental results reflect that the VM fault s related to the ncrease of cloud coputng syste worload Fg 6 Relablty of VMs based on worload consoldaton Table Node specfcatons, requested obs, worload phases S ( v v ) { τ, q q = r } Worload phase ( l ) S = {0} r = {40,80,60,30} {00,50,00,50} S = {0} r = {40,80,60,30} {00,50,00,50} Table 3 Relablty of VMs n the three ethods Worload phase ethod Rando Max Consoldaton Relablty(00) 00% 00% 00% Relablty(50) 00% 00% 96% Relablty(00) 00% 895% 734% Relablty(50) 85% 65% 355% Fg 5 Relablty of hgh-worload and low-worload VMs 43 Relablty analyss of the fault tolerance ethod based on worload consoldaton The thresholds of server worloads are set to 09, 08 and 07 Then, the VM worloads are consoldated and the VM fault tolerance te s optzed, as depcted by Theore 6 The experental results are shown n Fg 6 The relablty of VM s ncreased by worload consoldaton Accordngly, VM relablty s negatvely correlated wth the server worload threshold Therefore, the fault tolerance ethod based on worload consoldaton can ncrease VM relablty 44 Coparatve analyss of fault tolerance ethod based on worload consoldaton The relablty of worload expectaton threshold of dfferent servers was prevously verfed In ths secton, the Rando, Max, and Worload_ Consoldaton ethods are copared The relablty of VMs n worload phases s analyzed usng the above three ethods (Table 3) A relatvely long runnng te of loaded VMs results n a relatvely low relablty At worload phase 50, the accuulaton error probabltes of Worload_Consoldaton, Rando, and Max, are 85%, 65%, and 355%, respectvely Therefore, VM relablty s proved by the proposed worload consoldaton algorth, the rates of whch are 0% and 47% hgher than those by the Rado and Max ethods, respectvely The tas copleton rates of the three ethods n the regulated worload phase are shown n Fg 7 A coparson of tas copleton rates for dfferent ob quanttes ( r = {40,80,60,30} ) n worload phase l =00 s shown n Fg 7(a), and the tas copleton rates n worload phase l ={50, 00, 50} are shown n Fg 7(b), Fg 7(c), and Fg 7(d) As shown by Fg 7, Worload_Consoldaton obtaned a hgher tas copleton rate than the two other ethods At l =50 and r = {60,30}, the tas copleton rates of Worload_Consoldaton s 5% and 30% hgher than that of Rado and % and 30% hgher than that of Max 47

8 Zhxn L, Le Lu and Zeyu Tong/Journal of Engneerng Scence and Technology Revew 0 (5) (07) 4-49 (a) (b) (c) Fg 7 Coparson of tas copleton rates 45 Valdty of fault tolerance ethod Ths experent s conducted to verfy the valdty fault tolerance ethod based on the worload consoldaton odel, and ultately, to prove servce capablty durng syste operatons In the experent, JMeter sulates worloads to test the ob response te of the VM platfor n hgh-worload and low-worload states by usng the fault tolerance ethod based on worload consoldaton As shown by Fg 8, the ob response te n low-worload condtons s approxately 0 s, then ncreases to approxately 5 s n hgh-worload condtons At worload phases 5 and 40, the ob response tes are ncreased, whch ply a syste slowdown Ths phenoenon deonstrates that the VM syste consues excessve resources for the fault tolerance echans n contnuous runnng state, and ssues are further aggravated by the contnuous deteroraton of overall syste perforance The upper lt of server worload expectaton s set to 08 throughout the fault tolerance At worload phase 5, fault frequency s reduced and ob response te s shortened, thus ncreasng syste servce capablty In ths experent, the predcton accuracy of the VM worload and the relablty of VMs based on the worload consoldaton odel have been verfed Three ethods for resource request allocaton are copared The experental results have clearly deonstrated that Worload_Consoldaton not only proves the relablty and servce capablty of the VM platfor syste, t also reduces the fault tolerance frequency of the VM platfor wth relatvely low syste cost The proposed ethod can very well protect the servce capablty of the VM platfor syste Fg 8 Job response te 5 Conclusons (d) The VMs n cloud platfors experence overload over te and encounter fault tolerance To protect the relablty of VMs, a fault tolerance ethod based on the worload consoldaton odel of VMs n Xen cloud platfor was proposed The ethod was used to ncrease VM relablty n cloud platfors and prove the resource effcency and servce capablty of VMs The followng conclusons could be drawn: () Worload consoldaton n cloud platfors can well optze the relablty and tas processng capablty of VMs The hgher the VM worload s, the lower the relablty of VMs and the longer the tas processng te wll be Therefore, VMs worload can change the relablty and tas processng capablty of VMs n cloud platfors 48

9 Zhxn L, Le Lu and Zeyu Tong/Journal of Engneerng Scence and Technology Revew 0 (5) (07) 4-49 () A VM worload consoldaton odel s establshed based on the relatonshps of VM worload and VM relablty and tas processng capablty The odel can solve the relablty probles of cloud platfors caused by VM worload and accurately ontor resources Worload consoldaton and allocaton prevent the overuse of VMs n cloud platfors (3) The fault tolerance ethod based on the VM worload consoldaton odel reduces fault tolerance frequency n cloud platfors, optzes fault tolerance te, and shortens ob response te, thus further ncreasng the relablty and tas copleton rate of VM platfors The proposed ethod perfors fault tolerance on the bass of the worload consoldaton of VMs n cloud platfors The ethod can be appled to dynac envronents such as VMs n cloud platfors, and t can prove the overall perforance of cloud platfors Consequently, the ethod can provde convenent and accurate technologcal support to the fault tolerance 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