Dynamic Service and Data Migration in the Clouds

Transcription

1 rd Aual IEEE Iteratoal Computer Software ad Applcatos Coferece Dyamc Servce ad Data Mgrato the Clouds We Hao Departmet of Computer Scece Norther Ketucky Uversty Abstract Cloud computg s a emergg computato paradgm. To support successful cloud computg, servce oreted archtecture (SOA) should play a major role. Due to the ature of wdely dstrbuted servce provders clouds, the servce performace could be mpacted whe the etwork traffc s cogested. Ths ca be a major barrer for tasks wth real-tme requremets. I clouds, ths problem ca be solved by mgratg servces to dfferet platforms such that the commucato cost ca be mmzed. I ths paper, we cosder the problem of servce selecto ad mgrato clouds. We develop a framework to facltate servce mgrato ad desg a cost model ad the decso algorthm to determe the tradeoffs o servce selecto ad mgrato. 1 Itroducto Cloud computg s a emergg computato paradgm wth the goal of freeg up users from the maagemet of hardware, software, ad data resources ad shftg these burdes to cloud servce provders [1]. Cloud computg has draw the atteto of major dustral compaes, scetfc commutes, as well as ed user groups. The Clouds provde a large pool of resources, cludg hgh power computg platforms, commo devces, storages, data ceters, ad software servces. It also provdes maagemet to these resources such that users ca access them ubqutously ad wthout currg performace problems. At the same tme, servce oreted archtecture (SOA) has bee a popular framework may applcato domas. The eablg techologes SOA allow servces to be dscovered, composed, ad executed [2][3][4][5]. Based o these techologes, servces ca be rapdly composed ad the composte servce ca be deployed to acheve the desred goal. To support successful cloud computg, SOA should play a major role wth ever creasg mportace. All the hardware, software, ad data resources ca be wrapped as servces clouds. Whe a ed-user wshes to accomplsh a certa task, a composto servce ca be employed to dscover the eeded resources ad compose them to provde the desred fuctoalty ad qualty to the ed-user. Curretly, may Web servces have bee deployed by dfferet orgazatos that are wdely dstrbuted over the Iteret. These are mostly software servces rug o fxed hardware resources. Whe composg multple I-Lg Ye ad Bhava Thurasgham Departmet of Computer Scece Uversty of Texas at Dallas {lye, bhava.thurasgham}@utdallas.edu servces for a system, t s lkely that some selected software servces are hosted at wdely dstrbuted stes. Ths brgs potetal performace problems. Sedg a servce request alog wth a large quatty of put data across the wde area etwork ca be costly. It creases the etwork traffc ad rases the potetal of uexpected delays due to etwork cogestos. Ths ca be a major barrer for applcatos that have real-tme requremets. For example, a commader may dyamcally assemble a commad ad cotrol applcato that volves a large umber of web servces, such ay data servces based o cotuous put from the remote sesors, mage processg servces, formato fuso servces, etc. to assst her/hs decso makg. Commucato amog two data processg servces may volve a large amout of data ad may result delays due to etwork cogestos. Such delays ca affect the tmeless of the decso ad cause costly cosequeces. I SOA research, there have bee some works cosderg the commucato overhead servce composto. I [5], servce selecto based o the goal of mmzg commucato cost has bee cosdered. However, f there are a lmted umber of servces to choose from, t may be dffcult to sgfcatly reduce the commucato latecy. I cloud evromet, ths problem ca be solved by cosderg servce mgrato. Oe of major advaces cloud evromet s that computg hardware resources ad ther maagemet utltes are all provded as servces. The wdely dstrbuted computg resources ca be used to host mgrated servces to potetally mmze the commucato cost. However, ot all servces ca be mgrated. Servces based o hardware resources are less flexble ad caot be mgrated (ot the cyber world). Whe the servces volve commo hardware devces, the devces, eve though o-mgratable, are lkely to be all over the place. Thus, t s possble to select oe that ca result mmzed commucato cost. Whe a servce volves specalzed hardware, the t caot be mgrated. Servces ca potetally be mgrated, but the mgrato costs ad gas have to be evaluated to esure et performace gas. I ths paper, we cosder the problem of servce selecto ad mgrato a cloud. We develop a framework to facltate servce mgrato ad desg a cost model ad the decso algorthm to determe the tradeoffs o servce selecto ad mgrato. The mportat ssues addressed ths paper clude: /09 $ IEEE DOI /COMPSAC Authorzed lcesed use lmted to: Uv of Texas at Dallas. Dowloaded o Aprl 16,2010 at 17:27:12 UTC from IEEE Xplore. Restrctos apply.

2 (1) It s ecessary to cosder the frastructure support the cloud to acheve servce mgrato. The computato resources (computer platforms) the cloud eed to be able to support executo of dyamcally mgrated servces. We develop a vrtual mache evromet ad correspodg frastructure to provde such support. (2) It s also essetal to have a strog decso support to help determe whether to mgrate some servces ad where to place them. The cosderato volves the servce mgrato cost, cosstecy mateace cost, ad the commucato cost gas due to mgrato. We develop a cost model to correctly capture these costs ad help determe the tradeoffs servce selecto ad mgrato clouds. The, we use a geetc algorthm to search the decso space ad make servce selecto ad mgrato decsos based o the cost tradeoffs. The rest of the paper s orgazed as follows: the overvew of the cloud model s dscussed Secto 2. It presets all ettes the cloud model. I Secto 3, we dscuss workflow for servce composto. Secto 4 presets servce selecto ad mgrato decso process, cludg costrats o the selecto of cocrete servces ad computg platforms. Secto 5 summarzes the paper. 2 Cloud Model for Supportg Servce Mgrato We cosder the cloud evromet cosstg of geeral computg platforms ad servces. The servces ca be software ad data ad some other o-mgratable servces. The geeral computg platform () s also a servce. It s specfcally categorzed the system due to ts potetal provdg the evromet for hostg software ad data servces. Let G = {g 1 N} deote the set of N s the system. Also, let S = {s for all } deote the set of servces besdes the s. s ca be a software, data, or o-mgratable servce. G ad S are dyamc sets that ca chage over tme. We assume that each servce has a resdet (R) ad we use R(s ) to refer to the resdet platform for servce s. Each the cloud uses the Vrtual Mache (VM) techology to provde a uform executo platform so varous servces ca be mgrated dyamcally ad executed at ay s. Vrtual mache ot oly provdes desred executo platforms for varous servces, but also acheves a certa degree of securty/protecto. It prevets the servces rug o a from attackg the or terferg other web servces rug o the same. To support servce mgrato, t s also ecessary to provde some maagemet servces, cludg the servce mgrato decso (SMD) servces, the servce mgrato maagemet (SMM) servces, ad the certfcate authorty (CA) servces. To dfferetate, we call the regular web servces the applcato servces ad these servces the frastructure servces. The SMD servces provde algorthms for makg mgrato decso for applcato servces. It matas eeded formato such as the s ad other cloud resources characterstcs, overlay etwork topology, servce access patters, etc. ad uses them for decso makg. The SMM servces mata formato of all servces, cludg ther mgrated ad replcated copes to help wth dscovery of ear-by servces to facltate servce composto. It ca be vewed as a exteso of the UDDI regstry, but provdes more advaced fuctoaltes. Executg mgrated servces o platforms may pose securty rsks to the platform as well as to the servces. Thus, t s ecessary to have proper frastructure supports for mutual authetcato ad access cotrol amog s ad servces. We use dstrbuted CA servces to acheve ths goal. CA SMD SMM Applcato Servces Servces ot Cloud hosted by s Other o-platform based physcal servces, such as prtg servces, robotc servces, t Iteret Fgure 1. Servce based cloud evromet. Fgure 1 llustrates the servce based cloud evromet. I the cloud, applcato servces ad frastructure servces are hosted by s. Cloud may corporate other physcal devces wrapped as servces. Clets teract wth the cloud to compose servces for ther tasks whle the frastructure servces workg trasparetly to dscover ad select the most sutable servces ad replcate the mgratable oes f eeded to mmze the commucato cost ad satsfy real tme costrats. Clet ca be ed users, eterprse customers, or software applcatos. 2.1 DHT Based Dstrbuted SMM Servces I covetoal SOA evromet, formato regardg servces, cludg ther ames, terfaces, sematcs, propertes, etc., are mataed by UDDI (Uversal Descrpto Dscovery ad Itegrato). However, UDDI lacks the dstrbuted cocept ad does ot cosder multple servce replcas. Thus, t s ot sutable cloud evromet. We desg a dstrbuted SMM frastructure usg a dstrbuted hash table (DHT). SMM servces are hosted by selected s o strategc locatos to facltate fast accesses. These SMM servces form a P2PSMM rg (as show by the dotted lks Fgure 1). The P2PSMM rg s based o the oe-hop routg cocept [6] where each SMM keeps the full routg table. Sce s o the clouds are geerally relable ad wdely dstrbuted, the routg table chage frequecy s low. Thus, the routg table mateace cost s ot a major 135 Authorzed lcesed use lmted to: Uv of Texas at Dallas. Dowloaded o Aprl 16,2010 at 17:27:12 UTC from IEEE Xplore. Restrctos apply.

3 cocer. New SMM servces may be created ad exstg oes may be removed based o ther access patters. Multple hash fuctos are used for fault tolerace ad mproved access performace. Each SMM matas a GSP table ad a servce table. The GSP table cotas all the GSPs ad ther correspodg formato. The servce table s a partal table, oly cotas the servces that are hashed to the SMM based o the P2PSMMrg frastructure. The ames of the applcato servce are used as the prmary key the P2PSMM rg. However, clet search requests are geerally based o keywords. Sce keywords space ca be huge, we use the Hlbert Space Fllg Curve (HSFC) [7] techque to dex the keywords. Each servce s assocated wth a set of keywords. The keywords form a multdmesoal keyword space where servces are pots the space ad the keywords are the coordates. The HSFC algorthm coverts the multdmesoal keyword space to oe dmesoal dex space, a HSFC dex. The HSFC dex s the mapped to the P2PSMM rg to facltate effcet search. I the SMM servce table, each etry for a servce s smlar to the etry the UDDI regstry. However, more formato s eeded o SMM to support servce mgrato ad replcato. Each servce etry SMM matas the formato of all replcas as well as ther hostg s. Oe of the s s marked as the R for the servce. For each servce, the SMM matas servce ame, servce defto, servce type, servce sze, average request ad respose message szes, ad servce replca formato. Servce type specfes whether the servce s mgratable. For each servce replca, SMM matas ts replca type, hostg, guarateed servce latecy, etc. Replca type s used to dcate a master etry or a replca etry. The master etry s the oe hosted by the R. We assume that a servce replca has a lmted lfetme. A TTL (Tme To Lve) feld s defed for each servce replca. Whe the TTL expres, the servce replca s removed from the hostg ad the etry s deleted from the table. The TTL value for the master etry s set to 0, dcatg that t ever expres. It s also ecessary to mata the commucato costs amog servces. However, t s mpossble to mata such costs for all pars of servces, thus each servce stace matas the commucato costs to other servces that t frequetly teracts wth. Fgure 2 shows the formato mataed the servce table. I the table, a etry correspods to a. It matas the IP address, ts resource statstcs, avalablty, ad potetal accoutg formato. Also, the logtude ad lattude coordates for each s mataed. It ca be used to calculate the geographcal dstace betwee s ad estmate the commucato cost betwee servces they host whe the commucato cost betwee the servces s ot avalable. Fgure 3 shows the formato mataed the table. Etry Felds Servce Name Servce Type Servce Defto Servce Sze Message Sze Servce Replca Iformato Etry Felds Name Resource Statstcs Physcal Locato Avalablty Servce Charge Replca Type TTL(Tme To Lve) Hostg Servce Latecy Commucato Cost Meag The ame of the servce. The type s defed accordg to access patter of the servce. For o-mgratable servce, type=0; for mgratable servce, type =1. WSDL defto of the servce. The sze of the servce(code or data) It stores the average request/respose message szes of the servce vocato. The message sze formato s stored the callee s servce etry. If the callee s a data servce, the update message sze s also stored. It dcates t s a master etry (the oe regstered by the R) or replca etry. TTL defes the vald perod of a servce replca. After the durato of TTL, the etry wll be deleted from the regstry. For master etry, ttl=0. The hostg the servce. The bouded average servce tme o the hostg. It stores the commucato latecy betwee some frequetly teractg servce pars. Fgure 2. Servce table etry descrpto. Meag IP address ad doma ame of the. Resource usage formato (CPU, memory, dsk, etwork, VM) of the. The logtude ad lattude coordates for the. The rato of the expected value of the uptme of the to the aggregate of the expected values of up ad dow tme of the. Servce charge for usg the. Fgure 3. table etry descrpto. 2.2 CA Servces ad Securty Protocols Securty s a crtcal ssue the cloud evromet, especally whe cosderg servce mgrato. A malcous servce may try to compromse the or try to perform a DoS attack by cosumg all the resources of the. Also, oe servce from a may explot the securty loopholes of aother ad compromse the prvacy ad/or tegrty of other servces from other s. O the other had, a may try to compromse the servces t hosts. Some of the securty problems ca be resolved usg VM techology o. However, may of the securty ssues stll rema. Thus, t s ecessary to buld a authetcato framework to cotrol the accesses betwee s ad 136 Authorzed lcesed use lmted to: Uv of Texas at Dallas. Dowloaded o Aprl 16,2010 at 17:27:12 UTC from IEEE Xplore. Restrctos apply.

4 servces. We use dstrbuted certfcate authortes (CAs) to provde mutual authetcatos. For a servce s, we cosder ts resdece R(s) as the ower. Whe the SMD servce decdes to mgrate a servce s to a g, t forms R(s) ad g to perform mutual authetcato. R(s) ad g sed ther certfcates to each other ad cotact ther CAs to valdate the certfcates. Durg the authetcato process, R(s) verfes the detty ad trust of g ad the prvleges for s to use g s resources are egotated. Whe the mgrato decso s cofrmed, both R(s) ad g form SMD to cotue wth the mgrato process. 2.3 Vrtual Mache Evromet o s We use VM techologes [8] to support servce hostg ad servce executo o s. VM techologes support the setup of dfferet operatg systems o the same hardware wth each VM beg solated from each other. Thus, the desred executo evromets for specfc servces ca be easly set up statcally or dyamcally ad dfferet servces rug o the same are well protected from each other. The popular VM systems, VMware ad Xe, also allow vrtual maches beg created wthout excessve overhead. Sce Xe s a ope source software ad ts commads (xm) ca easly create, pause, ad shutdow VMs, we use Xe for realzg the VM evromet o s. Fgure 4 shows the basc desg of the VM evromet o s. A ca host multple VMs ad each VM ca host oe applcato or frastructure servce. To support servce mgrato ad secure servce executo, each VM also rus a set of maagemet compoets, cludg Mgrato Maager, Securty Maager, ad Resource Motor. Whe the SMD decdes a servce should mgrate, t forms the Mgrato Maager to wrap the servce ad trasfer t from a source to a destato. Servce mgrato mght pose securty rsks. Securty Maager teracts wth CAs ad performs servce valdato, authetcato, ad authorzato. Also the Securty Maager respods to authetcato requests ssued by servces from other VMs. Sce VM solates multple executo evromets ad supports the ablty to ru multple software stacks wth dfferet securty levels, we use VM to eforce fe-graed access cotrol to servces ad local resources. Servce Mgrato does ot always geerate performace ga. Mgrato decso eeds to cosder the resource usages of VMs, such as CPU, memory, ad dsk usages of VMs. The Resource Motor s used to motor the resource usages of the VM real tme. It perodcally reports the resource usage formato to the SMM to facltate mgrato decso makg. Applcato/Ifrastructure Servce Maagemet Compoets Resource Maager Mgrato Maager Fgure 4. evromet usg VM. 3 Workflow Model for Servce Composto Workflow s a commo model for web servce composto. We cosder that a workflow s composed of a seres of abstract servces ad these abstract servces ca be grouded to cocrete servces [9]. OWL-S (Web Otology Laguage for Servces) provdes flow cotrol costructs to specfy varous servce compostos a workflow, cludg Sequece, Choce, If-The-Else, Iterate, etc. e 12 Securty Maager VM-2 (rug Wdows) 30% as 3 50% as 1 as 2 as 4 Applcato/Ifrastructure Servce Maagemet Compoets Resource Maager Mgrato Maager Securty Maager VM-2 (rug Lux) Fgure 5. A workflow example. Let W deote a workflow, where W = (AS, E), AS s the set of abstract servces W, ad E s the set of drected ad labeled edges W. AS = {as 1,,,, as w }, where W s the umber of the abstract servces W. The edge represets the vocato depedecy betwee two abstract servces. The vocato probablty property s assocated wth each edge. Let pr(e j ) deote the vocato probablty for to voke as j whe s executed. If as j s a data servce, the vocato probablty wth read operato, rpr(e j ), ad the vocato probablty wth update operato, upr(e j ), propertes are also assocated wth the edge. We have pr(e j ) = rpr(e j ) + upr(e j ), where as j s a data servce. Fgure 5 shows a example workflow. We ca see that the workflow starts from as 1. It flows to as 2 wth two braches. The flow goes to as 3 ad as 4 wth 30% ad 50% probablty, respectvely. Note that the flow may stop at as 2 wth a 20% probablty. Each abstract servce ca be grouded by a set of cocrete servces. Let CS( ) = {s,j 1 j N S } deote the set of caddate cocrete servces for a abstract servce, where N S s the umber of cocrete servces for. SMD cotacts SMM to obta CS( ) for all W. Whe there are multple replcas of the same cocrete servce, the oly oe of them s selected ad placed CS( ) (we select the oe hosted by R). Our goal s to select the approprate cocrete servces for groudg the abstract servces a workflow so that the totoal servce lateces ad commucato costs betwee servces ca be mmzed ad the real-tme costrats for as Authorzed lcesed use lmted to: Uv of Texas at Dallas. Dowloaded o Aprl 16,2010 at 17:27:12 UTC from IEEE Xplore. Restrctos apply.

5 the workflow ca be satsfed. Also, whe approprate, some mgratable cocrete servces ca be mgrated to approprate s to further reduce the commucato cost. 4 SMD Servce ad Decso Process SMD servces are resposble for makg servce selecto ad mgrato decsos. It takes a workflow W ad the correspodg real-tme requremets from a clet put. Based o W, SMD frst teracts wth SMM to detfy the cocrete servces for each abstract servce. The, SMD selects cocrete servces to groud the abstract servces ad determes whether to mgrate or replcate the cocrete servces o selected s. Let cp deote a cofgurato soluto to the servce selecto ad mgrato decso problem. Let sel( ) deote the dex of the cocrete servce CS( ) that s selected for groudg cp, where 1 sel( ) N S. Also, let loc(sel( deote the dex of the that hosts the cocrete servce, where 1 loc(sel( N. I other words, a cofgurato soluto cp, W s grouded to cocrete servce ) ad ) s selected to be hosted o. I the geetc algorthm, the cofgurato soluto cp ca be mapped to a dvdual drectly as show Fgure 6. sel(as 1 ) loc(sel(as 1 sel(as 2 ) loc(sel(as 2 Fgure 6. A dvdual represetg a cofgurato soluto. We use the geetc algorthm to search for the optmal cofgurato soluto,.e., to determe the best settgs of sel( ), 1 sel( ) N S, ad loc(sel(, 1 loc(sel( N. I the followg subsectos, we defe the costrats ad the ftess fucto for the geetc algorthm. 4.1 Costrats o the Selecto of Cocrete Servces ad s For each, f t s o-mgratable, the has to be R( ) ). If t s mgratable, the ether a exstg or a ew ca be choose to host ). Due to the real-tme requremets, t s ecessary to have admsso cotrols by both the s ad the servces. For a gve cofgurato soluto cp, t s ecessary to check whether the cofgurato would volate the admsso costrats. The admsso costrat verfcato procedure s show Fgure 7. The algorthm takes ) ad puts. It frst vokes search_smm fucto to look up the servce table o SMM. If servce ) s already o, the search_smm fucto returs the average servce tme guarateed by gloc sel( (. Next, SMM checks whether ca admt W. If so, the sg s retured. Otherwse, the algorthm returs 0. If servce ) s ot o, the ) eeds to mgrate to. So, the algorthm checks f the has eough resources to create a ew VM. If yes, the computes ad returs the guarateed average servce tme for ) o. Otherwse, the algorthm returs 0. Note that whe the retur value s 0, t meas the admsso costrats are volated ad the cofgurato soluto should be rejected from the populato. If the cofgurato soluto s vald (postve retur value), the the retured value s the guarateed servce respose latecy, whch s used the ftess value computato. Iput: W, ), Beg sg = search_smm( ), ) If sg 0 the If ca admt W the Retur sg; Else Retur 0; Else (eed to create a ew servce stace) If has suffcet resources to create a ew VM the Retur servce_latecy( ), ) Else Retur 0; Edf Ed Fgure 7. Admsso costrat verfcato procedure. 4.2 Ftess Fucto The ftess of a soluto cp, ft(cp), s defed by (1)T(cp), the total respose lateces of the selected cocrete servces ) executg o the selected ; (2)C(cp), the total commucato costs betwee the cocrete servces ) o ; (3)M(cp), the mgrato cost for all ), ) s ot o yet ad eeds to be mgrated. (4)U(cp), the cosstecy mateace cost for all ), f ) s a data servce ad eeds to be mgrated. We have ft(cp) = T(cp) + C(cp) + M(cp) + U(cp). 138 Authorzed lcesed use lmted to: Uv of Texas at Dallas. Dowloaded o Aprl 16,2010 at 17:27:12 UTC from IEEE Xplore. Restrctos apply.

6 The servce tme of ) o s obtaed the admsso costrats verfcato step. We add the servce tmes of all selected cocrete servces o the selected s together to obta T(cp). Let k(g m,g ) deote the estmated commucato latecy betwee s g m ad g. I the SMM, the commucato lateces betwee some frequetly teractg servce pars are stored. If the commucato latecy betwee some servce par s ot avalable, the we ca use the logtude ad lattude coordates stored the table to estmate the commucato latecy betwee the servces. I [9], expermet studes have bee coducted to derve the correlato betwee geographcal dstace ad commucato latecy ad the estmato fucto s k(g m,g )= *d(g m,g ) 0.735, where d(g m,g ) s the geographcal dstace betwee g m ad g. Let psz be the packet sze. If sm, sel( ) vokes s, sel( ) (whch ca be determed by traversg W), the SMD checks wth the SMM to obta the average request message sze, msq( s, sel( ) ), ad the average respose message sze, msr( s, sel( ) ). Note that the message szes are stored the callee s servce etry. SMD also checks the workflow W to get the access frequecy formato, pr(em ). The commucato cost betwee the cocrete servces sm, sel( ad s, sel( s pr ( em ) ( msq( s, sel( as / psz + msr( s, sel( as / psz ) k( g ( (, g ( ( ) loc sel as loc sel m as C(cp) s obtaed by addg the commucato costs of all teractg servce pars together. Now we compute M(cp). Let M( ) ) be the mgrato cost for ). If already hosts ), the M( ) ) = 0. Otherwse, SMD checks wth the SMM to obta the sze of servce ), sze( as. We have M ( as = sze( s, sel ( as ) / psz k( g ( (, R( s, ( as ) loc sel as sel M(cp) s obtaed by addg the mgrato costs of all servces together. For a data servce, t s ecessary to mata cosstecy amog all ts replcas. Here, we cosder a smplfed model ad assume that the resdece (R) for the data servce s the prmary copy of the data. We oly cosder the cost for propagatg the updates from a replca to the prmary copy. If software servce sm, sel( ) vokes data servce s, sel( ) ad updates ts data, the the updates o s, sel( ) eed to be propagated to R( s, sel( ) ). Let U( s, sel( ) ) be the cosstecy mateace cost for s, sel( ). If s, sel( ) s already hosted by R( s, sel( ) ), the U( s, sel( ) ) = 0. Otherwse, SMD checks wth the SMM to obta the average sze of the update messages, msu( s, sel( as. Also the access frequecy pr(e m ) ad the update rato upr(e m ) (the percetage of the requests beg the update requests) ca be obtaed from the workflow W. we have U( s, sel( as = ( pr( em) upr( em msu( s, sel( as / psz k( g ( (, R( s, ( as ) loc sel as sel m, e W m U(cp) ca be obtaed by addg the cosstecy mateace costs of all data servces together. 5 Cocluso We have dscussed the problem of servce selecto ad mgrato a cloud. We have developed a framework to facltate servce mgrato ad desged a cost aalyss model to determe the tradeoffs o servce selecto ad mgrato. The geetc algorthm s used to fd the optmal or ear-optmal servce mgrato decsos. 6 Refereces [1] Bra Hayes, Cloud computg, Commucatos of the ACM, Volume 51, Issue 7, July [2] C. Atkso, P. Bosta, O. Hummel ad D. Stoll. A Practcal Approach to Web Servce Dscovery ad Retreval. I ICWS2007, [3] B. Beatallah, M. Hacd, A. Leger, C. Rey ad F. Touma. O automatg Web servces dscovery. I VLDB joural, V. 14, [4] I-Lg Ye, Tog Gao, Hu Ma, A geetc algorthm based QoS aalyss tool for recofgurable servce oreted systems, Advaces Mache Learg Applcato Software Egeerg, edted by Du Zhag ad Jeff Tsa, IDEA Group Publshg, 2006, pp [5] Lagzhao Zeg, Boualem Beatallah, Ae H.H. Ngu, Marlo Dumas, Jayat Kalagaam, ad Hery Chag, QoS-Aware Mddleware for Web Servces Composto, IEEE Trasactos o Software Egeerg, Vol. 30, No.5, May [6] A. Gupta, B. Lskov, R. Rodrgues, Oe hop lookups for peer-to-peer overlays, HOTOS'03: Proceedgs of the 9th coferece o Hot Topcs Operatg Systems, 2003, Hawa, USA. [7] C. Schmdt, M. Parashar, A Peer-to-Peer Approach to Web Servce Dscovery, World Wde Web, Volume 7, Issue 2, Jue [8] A. Rbere, "Usg vrtualzato to mprove durablty ad portablty of dustral applcatos", the 6th IEEE Iteratoal Coferece o Idustral Iformatcs, July 2008, Daejeo, Korea. [9] T. Gao, H. Moussa, I. Ye, F. Basta, J. Jeg, Servce Composto for Real-Tme Assurace, COMPSAC Authorzed lcesed use lmted to: Uv of Texas at Dallas. Dowloaded o Aprl 16,2010 at 17:27:12 UTC from IEEE Xplore. Restrctos apply.