Employng Rlvanc Fdback to mbd Contnt and Srvc Importanc nto th Slcton Procss of Compost Cloud Srvcs Dmosthns Kyrazs, Nkolaos Doulams, Gorg Kousours, Andras nychtas, arnos Thmstoclous, Vasslos C. Vscouks Unvrsty of Praus, Dpartmnt of Dgtal Systms Karaol & Dmtrou 80, 85 34 Praus, Grc Natonal Tchncal Unvrsty of Athns Iroon Polytchnou 9, Athns, Grc dmos@unp.gr, ndoulam@cs.ntua.gr, gkousou@mal.ntua.gr, amny@mal.ntua.gr, mthmst@unp.gr, v.vscouks@cs.ntua.gr Abstract. Cloud computng s ssntally changng th ay srvcs ar bult, provdd and consumd. As a paradgm buldng on a st of combnd tchnologs, t nabls srvc provson through th commodtzaton of IT assts and on-dmand usag pattrns. In th mrgng ra of th Futur Intrnt, clouds am at facltatng applcatons that mov aay from th monolthc approach nto an Intrnt-scal on, thus xplotng nformaton, ndvdual offrngs and nfrastructurs as compost srvcs. In ths papr prsnt an approach for slctng th srvcs that comprs th compost ons n ordr to mt th nd-to-nd Qualty of Srvc QoS rurmnts. Th approach s nhancd th a rlvanc fdback mchansm that provds addtonal nformaton th rspct to th mportanc of th contnt and th srvc. Th lattr s prformd n an automatd ay, allong for usr prfrncs to b consdrd durng th srvc slcton procss. W also dmonstrat th opraton of th mplmntd approach and valuat ts ffctvnss usng a ral-orld scnaro, basd on a computr vson applcaton. Kyords: cloud computng; ualty of srvc; compost srvcs; rlvanc fdback Introducton As th cloud srvc modl maturs and bcoms ubutous, srvc-basd applcatons ar amongst th frst bng dployd n such platforms. In th maorty of th cass, ths applcatons mov aay from th monolthc approach toards a paradgm that mphaszs modular dsgn, gvng rs to th dr adopton of th cloud srvc modl. Snc ths applcatons consst of applcaton srvc componnts, adfa, p., 0. Sprngr-Vrlag Brln Hdlbrg 0
thy ar consdrd to b compost srvcs oftn rfrrd to as orkflos vn though th trm orkflo ncluds also th orchstraton of th srvcs []. Ths applcaton srvc componnts provd spcfc functonalty, contrbutng to th ovrall applcaton s on, and may b offrd by dffrnt provdrs. Thr has to b notd that ths dstrbutd paradgm s also appld across th cloud srvc stack snc bsds srvc componnts, nfrastructur.g. ntorkng or storag rsourcs may b offrd as a srvc thn th ovrall applcatons. A rprsntatv xampl rfrs to a vdo rndrng applcaton hch conssts of srvc componnts.. shadrs complaton, txturs complaton, rndrng but could also rur th provson of a storag srvc to for th output vdo fl. Thus, Futur Intrnt applcatons offrd through cloud nvronmnts oftn mpos th nd to ngag mor than on provdrs that offr th corrspondng srvc. orovr, on has to consdr that th nd for dpndablty as on of th man Futur Intrnt Archtctur Dsgn Prncpls [] s fundamntal for th usr nds, hl t has bn notd that focus of cloud computng s also th usr xprnc [3]. In ths contxt, srvc slcton should account for both th spcfc rurmnts th rspct to th Qualty of Srvc QoS and th usrs vson rfrrd as Qualty of Exprnc - QoE. In th cas of compost srvcs, addtonal challngs ars gvn that: th usrs st thr rurmnts for th ovrall nd-to-nd QoS and not for spcfc srvc typs, th usrs may hav spcfc prfrncs accordng to thr xprnc for ndvdual srvcs and provdrs, dlvry of th compost srvc may b provdd across a fdraton of provdrs. To addrss ths challngs, nd to st n mchansms to stmat, sarch and thn rtrv for th most sutabl slcton of th most avalabl srvc componnts of hch an applcaton / compost srvc conssts to dlvr an ovrall ualty of a srvc across a fdraton of provdrs consdrng at th sam tm th usrs xprnc and prfrncs. A QoS-aar srvc slcton approach that ncorporats a rlvanc fdback mchansm provdng addtonal nformaton durng th slcton procss th rspct to th usrs QoE and prfrncs has bn studd and valuatd through xprmnts n ths papr. Th prsntd algorthm allos for th slcton of srvcs nstancs / componnts offrd by dffrnt provdrs that compos th ovrall applcaton / compost srvc basd on th nd-to-nd QoS paramtrs posd by th nd usr. Furthrmor, th QoE conssts as an addtonal crtron thn th slcton algorthm. To nabl th lattr hav dvlopd a rlvanc fdback mchansm that concluds n an automatd ay.. thout usrs ntrvnton to th mportanc of th contnt and th srvc. Ths s n fact an on-ln larnng procss that updat and dynamcally volv th profl of a usr th rspct to th rlvant tms chosn n a dynamc mannr. Th trm mportanc rfrs hr to th usr s prfrncs basd on QoE mtrcs for spcfc srvcs thn th compost srvc, for hch th usrs do not pos xplctly QoS rurmnts - ths rurmnts rman as nd-to-nd ons at th ovrall compost srvc lvl. Th rmandr of th papr s structurd as follos. Scton prsnts rlatd ork n th fld of th QoS-aar slcton procsss and rlvanc fdback mchansm, hl Scton 3 ntroducs th prsntd approach. Th ncorporatd rlvanc fdback mchansm s addrssd n Scton 4, hl th man rsarch topc and focus of
our ork QoS-aar srvc slcton algorthm s ntroducd n Scton 5. In th last scton Scton 6, prsnt an xprmnt hch conductd n ordr to dmonstrat and valuat th opraton of th mplmntd algorthm for a ral-orld computr vson applcaton n an ndustral nvronmnt. Th prformanc of th proposd mchansm s dpctd n th rsults and th valuaton scton. Scton 7 concluds th a dscusson on futur rsarch and potntals for th currnt study. Rlatd Work anagmnt of compost srvcs n cloud nvronmnts s on of th man topcs thn th rsarch communty. Varous approachs hav bn proposd addrssng dffrnt aspcts of managmnt both of atomc and of compost srvcs. In most cass, srvc managmnt s lnkd th th provson of QoS guarants and th managmnt of SAs, hch nclud such QoS trms.g. SA volaton protcton [4]. Wthn ths scton manly focus on rsarch approachs n th ara of srvc slcton n th cas of compost srvcs, srvc confguraton and nvocaton as ll as rlvanc fdback. A far schdulng algorthm for managng compost srvcs of 3D mag rndrng as proposd n [5]. QoS-aar srvc slcton for compost srvcs has bn addrssd by dffrnt rsarchrs. Constrant Programmng has bn proposd n [6], hl Intgr Programmng and xd nar Intgr Programmng has bn usd n [7,8,9] n ordr to optmz compost srvc xcuton plans. Authors of [0] propos a orkflo ngn namly WSQoSX that bulds on top of [7] and [9] to construct a fasbl soluton basd on a backtrackng algorthm. A hurstc mthod to dntfy srvcs that mt local QoS paramtrs rsultd by th dcomposton of nd-to-nd QoS constrants usng ntgr lnar programmng s ntroducd n [,], hl follong such a dcomposton mthod and applyng fuzzy logc control to support dynamc srvc slcton th th lost cost s proposd n [3]. Authors n [4] propos hurstc algorthms to fnd nar-optmal solutons n polynomal tm by modlng th srvc slcton as a 0- knapsack problm and a mult-constrant optmal path problm. An ntrstng ork s prsntd n [5] through a rputaton-aar modl for srvc slcton that taks nto account th usrs xprnc to dvlop a QoS smlarty mtrc that rflcts th dffrncs btn th provdrs advrtsd QoS and th usrs xprnc. As rgards rlvanc fdback, svral orks hav bn proposd manly n th fld of contnt-basd mag rtrval. In partcular n [6], a hurstc mthodology s adoptd basd on th standard dvaton of th rlvant slctd faturs. Th man da of ths schm s that upon a usr s slcton, all faturs of mportanc should shar lo dvaton valus hl th nsgnfcant faturs should charactrz by larg dvaton valus. Hovr, as mntond n th conclusons of ths papr, thr s a nd for an optmal ght updatng stratgy. An approach toards ths drcton has bn ntally rportd n [7], hr th paramtrs of th ghtd Eucldan smlarty mtrc ar optmally stmatd so that th dstanc ovr all slctd rlvant mags ar mnmzd. Th gnralzd Eucldan allos ntrconncton btn
dffrnt fatur lmnts though that sngularts ssus ar obsrvd n ths ork. Othr orks apply Support Vctor achns SV [8] or sm-suprvsd larnng framorks [9]. Fnally, dscrmnant analyss as ntroducd n [0] and contntbasd samplng algorthms on th us of non-lnar structurs n []. Th dffrnc btn th rsarch outcoms prsntd n ths scton and our proposd approach ls on th fact that th ons prsntd hr addrss th cas of slctng srvcs and nods basd on QoS paramtrs by dalng only th th spcfc cass of mnmzng on of th paramtrs hlst furthrmor all th paramtrs hav th sam ght attrbut. In our study, th QoS paramtrs ar dalt n a combnd ay as ll, hl also ntroduc a rlvanc fdback mchansm to dntfy th usr prfrncs and affct th slcton procss snc on of th paramtrs may play a mor mportant rol durng th slcton procss. Th prsntd QoSaar slcton mchansms dvlop xcuton plans that nabl QoS optmzaton. Nvrthlss, hat s of maor mportanc n clouds spcally n fdratd ons s th potntal ntork ovrhad that affcts th nd-to-nd QoS lvl of compost srvcs.g. ncras of ovrall tm du to data mov across sts. To ths drcton ntroduc th mportanc of th contnt and th srvc n th slcton procss, hch may altr th slcton and as a rsult th xcuton plan accordngly. To som xtnt, usrs xprnc and as a rsult mportanc has bn dscussd n th ltratur, but th lmtatons of th corrspondng orks rfr both to th nonautomatd ay to obtan th QoE and to th applanc of such approachs only to srvcs and not to contnt as ll hch may b utlzd by th srvcs. 3 Compost Srvc anagmnt Ovrv As alrady mntond, th am of th ork prsntd n ths papr s to dntfy and dscrb th procsss that nd to b compltd n ordr to slct srvcs / componnts of hch a compost srvc conssts th rgard to th provdd QoS mtrcs. To achv th lattr ntroduc a slcton algorthm along th ts mplmntaton. Each compost srvc contans procsss - srvc typs - that can b xcutd from a st of srvc canddats, hch ar annotatd th QoS nformaton. Th proposd approach allos th slcton of ths canddats for ach srvc typ basd on th usr constrants and th QoS paramtrs xposd from th srvc provdrs for ach canddat. Th approach s dpctd n th follong fgur Fgur through a data flo dagram to clarfy th rurd and gnratd nformaton for ach mchansm: Th nd usr and th srvc provdr ar xtrnal ntts. Th nd usr sts constrants on spcfc QoS paramtrs.g. complton tm, avalablty, cost, tc. Th srvc provdrs offr srvcs annotatd th QoS nformaton. Ths nformaton along th th usr constrants consst as nput for th slcton algorthm. To datastors ar usd. Th frst on holds th montorng nformaton both on applcaton lvl and on nfrastructur lvl hch s xplotd by th rlvanc fdback mchansm. Th scond stor holds th outcom of th slcton procss.
Th srvc managmnt approach ncluds four 4 man procsss: a montorng mchansm namly Applcaton & Infrastructur ontorng provdng montorng nformaton for th nfrastructur.. us of rsourcs and th applcaton.. numbr of rusts to a srvc, an nactmnt srvc namly Compost Srvc Enactmnt that confgurs and nvoks th corrspondng srvcs, a rlvanc fdback mchansm namly Contnt & Srvc Importanc that provds n an automatd ay th usr prfrncs th rspct to dffrnt srvc typs basd on montorng nformaton.g. for a rspons tm QoS paramtr th ght factor of t durng th slcton procss should b dcrasd f th usr consums th srvc rgardlss of ts rspons tm, v a slcton algorthm namly QoS-aar Srvc Slcton that obtans th nformaton from th usr and th provdrs as ll as th prfrncs from th rlvanc fdback mchansm n ordr to mak th optmum srvc slcton of th avalabl canddats. In ths papr prsnt th rlvanc fdback mchansm and th slcton algorthm grn hghlghtd n Fgur and furthr dscrbd n Sctons 4 and 5 accordngly. Th montorng and th nactmnt srvcs dvlopd n [,3] ar usd durng our xprmntaton and for complton rasons ar ctd n Fg.. Fg.. Compost srvc managmnt ovrv 4 Contnt and Srvc Importanc t us assum that hav a st of N avalabl srvcs, dnotd as S, th =,,,N. Each srvc s charactrzd by a lst of attrbuts T S [s s s ]. For ths rason, dnot a srvc through a vctor n contrast to a scalar. Exampls of srvc attrbuts s, can b prcson, xcuton tm, avalablty, purchas cost, sz and vn srvc mtadata.g., n a TV contnt srvc provson, srvc
mtadata rfr to contnt typ, rsoluton, ovrlays, or vsual proprts of TV stram. For ach attrbut, st a ght, s, hch xprsss th dgr of mportanc concrnng th attrbut a. In ths notaton, hav omttd suprscrpt snc assgn th sam ght attrbut for all srvc typs. In convntonal srvc slcton mchansms, ual ghts ar assgnd for all attrbuts, manng that th slcton s ndpndnt from th sgnfcanc of a srvc, or n othr ords that all srvc attrbuts ually contrbut to th slcton procss. Anothr cas s to manually st th ghts s by th usrs. Ths, hovr, mpls that th usr s forcd to obtan cloud computng knoldg and tchncal dtals, hl n fact thos dtals should b mplct. An altrnatv s to mploy a rlvanc fdback stratgy, hch mplctly valuats a st of srvc slctons and thn usng an on-ln larnng stratgy, stmats th contrbuton of ach attrbut dgr of mportanc to th fnal srvc slcton procss. Thus, th optmzaton stratgy s adustd accordng to nformaton as rgards srvc rlvanc th rspct to usr s prfrncs. Fg.. Rlvanc fdback mchansm ovrv Upon a ury, a st of K srvcs ar slctd from th pool of all N avalabl srvcs by th applcaton of an optmal slcton algorthm. Th usr valuats th rtrvd srvcs and slcts a small numbr out of th K rtrvd as rlvant. Thn, th rlvanc fdback mchansm s actvatd to dynamcally updat mportanc ghts of ach srvc attrbut. Ths ay, at th follong srvc slcton procss th K rtrvd srvcs ar mor rlvant to usr s prfrncs. Th rlvanc fdback algorthm uss nformaton drvd from th montorng modul hch dynamcally calculats th currnt valus of th attrbuts of a slctd srvc. Rlvanc fdback lads to a dynamc slcton of th srvcs attrbuton, undr a transparnt and hddn to th usr ay thout mposng hm/hr to gt cloud computng or softar ngnrng knoldg. Instad, th schms ust rcv an valuaton of th systm prformanc va th srvc montorng procss, and thn th schms appls ntllgnt larnng algorthms to modfy th dcson ruls and conclud to a concrt, usr-cntrc udgmnt as rgards srvc slcton procss. To typs of rlvanc fdback schms ar xplotd n ths papr. Th frst on updats th dgr of srvc ghts mportanc xplotng only srvc obsrvatons,.. usng only th statcs of srvc montorng.. th ury vctor s not takn nto account thn th rlvanc fdback schm. Instad, th dynamc updatng s accomplshd on th actvatd srvcs statcs bng xtractd through th srvc montorng procss. Th
scond typ s an optmal mthodology that stmats n ght factors so that, aftr th adaptaton of th contnt/srvc mportanc coffcnt th n rtrval rsults to trust currnt srvc rusts as bng submttd by th usr hl smultanously rtanng th rlvant srvcs slcton at a maxmum possbl dgr. 4. Srvc Obsrvaton-basd Rlvanc Fdback In ths approach, srvc attrbuts ar slctd va th actvatd srvcs,.., th ons hch accpt th usr snc thy satsfy hs/hr nformaton nds and prfrncs. t us rcall that upon a srvc rust, K srvcs hav bn rtrvd from th systm to th usr. t us also rcall that a small numbr of thm s chosn by th usr, mplctly mplyng that ths srvcs ar thn hs/hr prfrncs. t us dnot as E T [ ` ], th =,,, ths slctd and actvatd srvcs. Vctor E s of th sam form as vctor S. In a mor gnrc form can lt th usr to assgn dffrnt dgrs of rlvanc for ach actvatd srvc E. Thn, th rlvanc fdback schm can b appld by adoptng a hurstc schm smlar to th ons appld to contnt-basd mag rtrval [6]. In partcular, th man concpt of ths hurstc s that f a partcular fatur lmnt, say th s a prfrrd attrbut by th usr, thn, th valu of ovr all slctd srvcs, =,,.., ll b consstnt and thus th standard dvaton of th rspctv fatur lmnt ovr all slctd rlvant sampls should b small [4]. In uaton, s th avrag valu of th -th fatur lmnt ovr th slctd sampls,.. / standard dvaton. On th othr hand, f th taks larg valus ovr th slctd srvcs, ths mpls that th rspctv fatur s not of usr s ntrst. Ths da smply mans that th ghts s can b stmatd as s Though th da bhnd th proposd systm s ut rasonabl, t stll rmans an ad hoc mthod. For ths rason, as mntond n th conclusons of [5], thr s a nd for an optmal updatng schm to stmat th ghts s. 4. Optmal Rlvanc Fdback constrand by Usr s Dmands and Srvc Obsrvatons Th scond approach mprovs th prformanc of th frst ad hoc mthodology. Th da hr s to optmally calculat th n ght attrbuts for a srvc takn nto
account both th actvatd srvcs,.. th vctors T [ ] ` E and th usr s dmands bng xprssd through a srvc rust vctor say T ] [ ` Q. Agan, vctor Q s of th sam form as vctors E and S. Th proposd notaton also support som attrbuts of th ury vctor Q not to b dfnd. In othr ords, ths smply lt ths attrbutons undfnd. As t has bn provn n [6], th cross corrlaton smlarty masur s a mor sutabl dstanc mtrc compard to convntonal Eucldan dstanc and varatons of t, lk th ghtd and th gnralzd on. Cross corrlaton crtron s a normalzd masur, hch xprsss ho smlar to fatur vctors ar and thus t ndcats a mtrc of thr smlarty. Furthrmor, corrlaton rmans unchangd th rspct to fatur vctor scalng and / or translaton. For xampl, addng or multplyng a constant valu to all lmnts of a fatur vctor affcts th Eucldan dstanc but not th corrlaton. Th cross corrlaton mtrc can b xprssd as: 3, N N N Q Whl ts ghtd vrson can b gvn as: 4, s Q t us no assum that out of th K bst rtrvd srvcs hav bn slctd by th usr. Thn, assumng that all th srvcs ar rlvant to th actual usrs nformaton nds, th optmal ght s s stmatd so that th follong crtron s maxmzd, 5, s s A Q Dffrntatng th uantty A th rspct to all ghts attrbuts s and thn sttng thr valus ual to zro, drv,.., / s A =0, for all ghts attrbuts s, =,,..,, hav that n n m m m m m m m n n s G s s G s A 0 E, n,,, 6 In uaton 6, G E s th nrgy of vctor E. Th prvous uaton rfrs to a lnar systm of uatons as th numbr of unknon ghts. By dvdng to uatons of th form shon n 3 on ovr th othr, for xampl th ons
corrspondng to A / sn and A / sk, n k, th follong rlaton of ghts s and s s obtand n k k sn sk n n GE k G E n k 7 Euaton 7 s a lnar systm of uatons th - unknons. Hovr, substtutng th ght rato xprssd n 6 to th systm of 5, all th non-lnar uatons ar satsfd. Ths mans that 7 s th soluton of th maxmzaton problm of 5 and thus on ght out of s a fr varabl. An xplanaton of ths s du to th proprts of A. Indd, scalng th fatur vctor has no mpact on th corrlaton. For ths rason, among all possbl solutons, slct th on n hch th -norm of th ghts s ual to on,..,, hr T [ s s s ]. Assumng thout loss of gnralty that th frst ght, s s th fr varabl thn, hav that s B, hr Bk In uaton 8, hav slctd th postv soluton snc ths lads to th maxmzaton of A. Th othr ngatv soluton rsults n th mnmzaton of th normalzd cross corrlaton. Introducng rcursv mplmntaton, th man advantag of 9 s that t can b rcursvly stmatd as mor and mor srvcs bng actvatd through th montorng modul. Ths mans that, f hav stmatd th ghts upon a gvn usr s dmands, at th follong tratons, do not nd to r-calculat th ghts from scratch, but can xplot th prvous stmatd ghts to updat th currnt ons. r To stmat th ghts rcursvly, dnot as F l th follong uantts l r k r r k k Fl m0 GE k, l,,.., 9 hr rcall that s a forgttng factor. In ths uaton, dnot as k th numbr of slctd srvcs at th k-th traton of th algorthm. Thn, th ghts at th fnal r-th traton ar xprssd by r r l Fn r sn sl 0 n Fl r Hovr, F lr can b stmatd rcursvly usng only nformaton of th currnt traton stp and th prvously obtand F lr-. In partcular, hav that 0 F l k G k E G E 8
l r Fl r Fl r G E l,,..., 5 Srvc Slcton In ths scton dscrb th algorthm usd thn th srvc slcton mchansm n ordr to conclud to th componnts / canddats pr srvc typ of th compost srvc basd on th QoS paramtrs. Th man goal of th algorthm s to rsult to an optmum slcton th rgard to th QoS mtrcs st by th usr and th corrspondng ons publshd by th srvc provdrs. Th algorthm s stratgy s ntally to slct canddats n a ay that th constrants st by th usr ar mt.g. slct srvcs that mt th rustd avalablty lvl thout volatng th budgt constrant. Aftrards, th nstancs that offr hghr lvl of QoS.g. n trms of avalablty or xcuton tm ar dfnd and rplacmnts on th ntal slcton tak plac. Wthn th algorthm, th usr s prfrncs ar xprssd th th ghts s for th corrspondng paramtrs obtand by th rlvanc fdback mchansm. Ths valus xprss ho mportant ach paramtr s consdrd to b by th usr and ar xprssd through th ght factor of uaton 0. Follong, dscrb n dtal th maor stps of th algorthm along th thr sub-stps.. Charactrsaton of th provdd QoS pr srvc typ takng nto account all provdrs offrs for th spcfc srvc typ.. Calculaton of th mnmum and maxmum valus for ach on of th QoS paramtrs st by th usr for ach srvc typ of th compost srvc basd on thr srvc nstancs canddats... Computaton of th plot valus for th paramtrs basd on th mnmum and maxmum valus of thm th th us of th follong functon: F Paramn x nparamvalu axparamvalu n nparamvalu n n x xp n * In th abov functons, x s th valu of QoS paramtr for ach srvc nstanc and nparamvalu, axparamvalu ar th mnmum and maxmum valus of th paramtr as dscrbd n th prvous sub-stp..3. Calculaton of th n paramtrs valus that ll b usd furthr on basd on th aformntond functons th th us of uaton : NParam nvalu IntalParamnValu * F IntalParamnValu Paramn In th abov uaton, IntalParamnValu rfrs to th valu of th paramtr that as ntally obtand by th srvc provdrs as thr offr..4. Calculaton of th follong ConvrtdIndx that ll b usd n sul n ordr to procd th th slctons: 3 n
ConvrtdIndx y x l k NParamValu NParamValu Ths ndx s th maor crtron durng th slcton procss snc t shos for ach srvc nstanc th offrd lvl of ualty for spcfc paramtrs th rgard to th corrspondng valus of othr paramtrs. W st n th numrator th paramtrs for hch a dcras n thr valus optmzs th ovrall ualty.g. complton tm and n th dnomnator th paramtrs for hch an ncras n thr valus optmzs th ovrall ualty.. Intal srvc slcton for on paramtr.. For ach srvc typ, a canddat s slctd that mts th usr s paramtr constrant accordng to th mportanc sortng provdd by th rlvanc fdback mchansm.g. slcton basd on avalablty.. Calculaton of th ovrall paramtrs valus.g. for cost, complton tm for th compost srvc accordng to th srvcs slctd n th prvous substp. If ths xcd th usr s constrants, a slcton cannot b mad and th algorthm nds, othrs t contnus th th nxt stp. 3. Idntfcaton of a canddat for ach srvc typ. Th rason for ths stp s to dscovr th canddats that provd hghr lvl of QoS for ach srvc typ. 3.. For ach srvc typ, th canddat th th lost valu of th ConvrtdIndx s dfnd n comparson th th on slctd n Stp of th algorthm. If no nstancs ar dfnd, th srvc typ s xcludd from th rst of th algorthm xcuton snc no optmzaton can b prformd. If ths appls for all srvc typs, th algorthm nds and th ntal slcton s consdrd to b th fnal on. Othrs, t contnus th th nxt sub-stp. 3.. Slcton of th canddats for ach srvc typ th th lost valu of th ConvrtdIndx. 3.3. Calculaton of th dffrncs n th valus of th paramtrs btn th ntal srvc slcton from Stp and th rplacmnt on from stp 3.. 4. Craton of a lst th th bst canddats for ach srvc typ n ordr to fnd possbl rplacmnts 4.. For ach dffrnc that has bn calculatd n Stp 3, th ConvrtdIndx s r-calculatd. Bascally, Stp of th algorthm s r-xcutd consdrng as ntal valus for th srvc nstancs th aformntond dffrncs and th rplacmnts ar mad basd on thr dffrncs. 4.. Th srvcs th th lost n ConvrtdIndx ar slctd. 4.3. Basd on th n slcton, th ovrall paramtrs valus ar r-calculatd. If th usr s QoS constrants ar mt, th n srvcs ar th ons dntfd n th prvous sub-stp, othrs thy ar xcludd as canddats. 4.4. Th algorthm s loopd and contnus from Stp 3 for all srvc typs. 4
6 Evaluaton Th xprmnt usd to valdat our approach as prformd for a ral-orld compost srvc that has bn dvlopd n th framork of th SCOVIS EU-fundd proct [7]. Th computr vson applcaton conssts of to srvcs Obct Idntfcaton and Procss Rcognton [8]. For ths applcaton, us as nput thr 3 ral-orld datasts vdos rcordd n NISSAN Ibrca automobl constructon ndustry. Thy captur complx ndustral procsss hch hav as a goal th assmbly of a car n th factory. Th rcordd frams dpct mtal sparks, cars upmnt racks, and orkrs prformng th assmbly as ll as robotc movmnts and frs. Bsds ths applcaton-orntd srvcs, th compost srvc of our xprmnt ncluds an nfrastructur-lvl srvc to dpct th applcablty of th algorthm along th cloud modl stack. Ths srvc rfrs to a storag srvc that s usd to stor th output of th procss rcognton srvc as ll as th output of th obct dntfcaton srvc provdng also nput for th procss rcognton srvc. W hav usd as a cloud nfrastructur for our xprmnt th on dvlopd n th framork of IROS EU-fundd proct [9]. Th nfrastructur consstd of four sts actng as provdrs, hch offr all th thr srvcs dscrbd n th prvous paragraphs. In sul, publshd dffrnt SA offrs to dmonstrat th dffrnt QoS offrs from th provdrs sd for th offrd srvcs. W hav slctd as rprsntatv paramtrs Cost dnotd as C, Prformanc dnotd as P and rfrrng to th accuracy for th obct dntfcaton and procss rcognton srvcs and th avalablty of th storag srvc, and Tm dnotd as T and rfrrng to th xcuton tm of th applcaton srvcs and th storag tm for th storag srvc. Th corrspondng offrs for ach provdr ar prsntd n th follong tabl. Tabl. Publshd QoS paramtrs Obct Idntfcaton Procss Rcognton Srvc Storag Srvc Srvc C P T C P T C P T Prov. # 77.07 90.9 38.07 45.89 9. 5. 3.47 93.9 0.93 Prov. # 93. 98.9 37. 36.04 99.03 67.93 8. 94.09 0.3 Prov. #3 73.33 8.4 484. 33.8 97.85 7.33.3 7.4 0.34 Prov. #4 89.03 9.7 336.48 45.46 85.9 50.54 9.8 88.77 0.3 Prov. #5 9.7 89.9 343. 37.08 83. 64.88 3.7 65.53 0. Prov. #6 85. 90.3 33.98 35.77 93.99 68.85 3.3 97.3 0.46 Prov. #7 6.03 86.07 459.3 4.0 93.9 68.63 9.83 9.9 0.43 Prov. #8 79.0 89.8 350.3 9.99 89.4 8.49 8. 9.03 0.99 For th sak of th xprmnt, st th follong nd-to-nd QoS rurmnts from th usr sd for th complt compost srvc: Cost: thrshold at 50 account unts, Prformanc: at last 90%, Tm: thrshold to 400 tm unts. W hav also utlzd th nformaton from th rlvanc fdback mchansm to conclud on th QoS paramtrs that ar of mportanc for th usr and thus procd th th srvc slcton accordngly. Th follong tabl Tabl provds nformaton on th usr s slctons from dffrnt provdrs obtand by SA montorng data.
Accordng to ths valus and uaton 0, th ght valus of th paramtrs ar th follong: Cost = 0.5, Prformanc = 0.78, Avalablty = 0.07. Tabl. Usr s Slctons Normalzd ontorng Data Obct Idntfcaton Srvc Procss Rcognton Srvc Storag Srvc Prov. # 7 5 Prov. # 7 33 5 Prov. #3 3 8 Prov. #4 5 4 6 Prov. #5 5 3 Prov. #6 5 0 Prov. #7 9 0 5 Prov. #8 0 3 Basd on th algorthm s xcuton, hl th ntal slcton as Prov. # for th Obct Idntfcaton Srvc, Prov. #3 for th Procss Rcognton Srvc and Prov. #7 for th Storag Srvc, basd on th n valus of th ConvrtdIndx for th n ghts gvn th rlvanc fdback outcoms, rplacmnts ar suggstd concludng to th follong slctd srvcs: Obct Idntfcaton Srvc: Prov. #4 Procss Rcognton Srvc: Prov. # Storag Srvc: Prov. #3. W obsrv that du to th nput obtand by th rlvanc fdback mchansm th rspct to th usr prfrncs, th algorthm concludd to a dffrnt slcton of srvcs. For th Obct Idntfcaton Srvc, Srv. Provdr #4 as slctd nstad of Srv. Provdr #. Ths as don du to th mportanc of th Prformanc QoS paramtr n comparson to th Cost and Avalablty paramtrs. Bsds, for th Storag Srvc, Srv. Provdr #3 as slctd nstad of Srv. Provdr #7 don n ordr to mt th nd-to-nd Cost constrant for th compost srvc. In sul prsnt th valuaton of th proposd approach th rspct to th usr s QoE. To compar th prformanc of th proposd rlvanc fdback srvc slcton algorthm, us obctv mtrcs, hch ar usd for prformanc valuaton of databas managmnt archtcturs. In partcular, our valuaton s basd on th calculaton of th prcson-rcall curv, hch has bn usd n txt-basd nformaton rtrval systms [30]. Th follong fgur Fgur 5 prsnts th prcsonrcall curv usng thr vrsons as rgards th srvc slcton algorthms. Th frst to mploy rlvanc fdback mchansms, hl th last appls srvc slcton thout rlvanc f. As obsrvd among th to proposd fdback mchansms, th constrand on outprforms prcson for all rcall valus among th othr to schms. In partcular, th proposd constrand rlvanc fdback algorthm gvs th maxmum prcson.g. satsfacton of usr s prfrncs at a gvn rcall.g. rtrval of rlvant srvcs out of a spcfc thrshold. Th orst cas occurs n cas that no rlvanc fdback s dploymnt. Ths s xpctd snc n that cas all th srvc attrbuts ar consdrd of th sam mportanc. In all schms, also
obsrv that th prcson accuracy drops as rcall ncrass. It s ut rasonabl snc as mor rlvant srvcs ar rtrvd by th systm, th probablty of rcallng rrlvant srvcs among th rlvant ons also ncrass droppng th prcson accuracy. Th proposd constrand rlvanc fdback algorthm s tratvly mplmntd at ach obsrvaton cycl. Th follong fgur prsnts th ffct of th numbr of obsrvatons on th prcson-rcall prformanc. As s obsrvd, as th numbr of obsrvatons ncrass prcson also ncrass at th sam rcall valu. Hovr, th mprovmnt rat dcrass, manng that byond a crtan thrshold of th numbr of tratons no furthr mprovmnt s obsrvd conclud to a saturaton. Fg. 3. Prcson-Rcall Curv for Constrand and for slcton Wthout any rlvanc fdback for dffrnt numbr of obsrvatons 7 Conclusons Cloud nvronmnts hav not yt adoptd an ffctv schm that ll facltat ndto-nd QoS provsonng for Intrnt-scal compost applcatons [3]. Accountng for th partcularts of clouds and such applcatons, n ths papr hav ntroducd a mchansm for srvc slcton th rgard to ualty of srvc nformaton hch s xpctd to ncras th ffort to provd cloud nvronmnts th a dynamc QoS capablty. Th am of our ork s to conclud to th slctd srvcs of hch a compost srvc conssts, basd on th QoS rurmnts from th usrs and th publshd QoS paramtrs from th srvc provdrs. orovr, hav nhancd th proposd algorthm th mtrcs xprssng th usr prfrncs. Ths mtrcs ar dfnd by a rlvanc fdback mchansm rflctng th contnt and srvc mportanc thn th slcton procss snc for ach paramtr th mchansm proposs a ght factor that s takn nto consdraton durng th slcton procss. As a rsult th offrd srvcs / canddats ar prortzd accordng not only to thr QoS paramtrs but also to th ght factor of thm, hch affcts th slcton procss. Th fdback mchansm dosn t rur usr ntrvnton snc th rurd nformaton s obtand from montorng data. Th proposd approach nabls th adopton of dffrnt busnss modls snc t allos for th valuaton of altrnatv stratgs.g. dffrnt QoS paramtrs bng publshd by th provdr and montorng any changs / volatons that may occur,
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