Tactics-Based Remote Execution

Transcription

1 Tactics-Based Remte Executi Raesh Krisha Bala Caregie Mell Uiversity 1 Itrducti Remte executi ca trasfrm the puiest mbile device it a cmputig giat. This wuld eable resurceitesive applicatis such as atural laguage traslati, speech recgiti, face recgiti, ad augmeted reality t be ru tiy hadheld, wearable r bdy-implated platfrms. Nearby cmpute servers, cected thrugh a lwlatecy wireless LAN, ca prvide the CPU cycles, memry, ad eergy eeded fr such applicatis. Ufrtuately, tw ayig facts clud this rsy future. First, the partitiig f a iteractive applicati it lcal ad remte cmpets that achieves gd applicati perfrmace is highly applicati-specific ad platfrmspecific. Sice mbile hardware evlves rapidly, this partitiig chages the timescale f mths rather tha years. Icrrect partitiig ca result i sluggish ad itlerable iteractive respse. Hece, a tight ad gig cuplig betwee applicati develpers ad hardware platfrm develpers appears ievitable. Secd, matters are made wrse by the fact that mbile evirmets exhibit highly variable resurce availability. Badwidth, eergy ad availability f cmpute servers ca chage the timescale f miutes r hurs, as a user mves t differet lcatis. Re-partitiig a applicati fr chaged peratig cditis at this timescale is therefre essetial. These csideratis suggest that a autmated apprach t partitiig applicatis fr remte executi is ecessary. Hwever, partitiig a applicati autmatically may result i bad perfrmace if the itrisic ature f the applicati is t factred it the partitiig decisi. Ather factr that cluds this future is that develpig mbile applicatis is especially difficult because they have t be adaptive [14, 21, 38]. The resurce cstraits f mbile devices, the ucertaity f wireless cmmuicati quality, the ccer fr battery life, ad the lwered trust typical f mbile evirmets all cmbie t cmplicate the desig f mbile applicatis. Oly thrugh dyamic adaptati i respse t varyig rutime cditis ca applicatis prvide a satisfactry user experiece. Ufrtuately, the cmplexity f writig ad debuggig adaptive cde adds t the applicati sftware develpmet time. The key questis that this thesis is addressig ca be expressed as fllws: 1. Ca autmated dyamic re-partitiig (t respd effectively t chages i mbile evirmets) be recciled with the eed t explit applicati-specific kwledge (t achieve gd applicati perfrmace)? 2. Is it pssible fr applicati develpers t easily develp adaptive applicatis? Fr this questi, I will be ccetratig the prblem f mdifyig existig applicatis t make them adaptive. The key isight that allws bth these prblems t be slved is that the kwledge abut a applicati relevat t remte executi ca be captured i cmpact declarative frm that is very small relative t cde size. Mre specifically, the full rage f meaigful partitis f a applicati ca be described i a cmpact exteral descripti called remte executi tactics r ust tactics fr brevity. Thus, the tactics fr a applicati cstitute the limited ad ctrlled expsure f applicati-specific kwledge ecessary fr makig effective partitiig ad placemet decisis fr that applicati i a mbile cmputig evirmet. 2 Thesis Statemet The full rage f meaigful partitis f a applicati ca be described i a cmpact exteral descripti called remte executi tactics r ust tactics fr brevity. This eables the develpmet f pwerful remte executi systems that ca autmatically at rutime, chse remte executi partitis, frm the list f tactics, that prvide gd applicati perfrmace. Fially, the use f tactics is csistet with sud sftware egieerig priciples that ca decrease the time eeded t develp mbile applicatis fr ew hardware platfrms. 1

2 2.1 Why are Tactics Useful? There have bee a umber f remte executi systems such as Abacus [2], Cig [19] ad Cdr [5]. They perfrm well i evirmets where resurce availability des t chage betwee the time the system decides hw t remtely execute a applicati ad whe it perfrms the remte executi. Hwever, this assumpti cmes uder fire i mbile evirmets. These evirmets are characterized by highly variable resurce cditis that chage the rder f secds [10, 14, 37]. T achieve the gd applicati perfrmace, a remte executi system shuld pick gd strategies fr remte executi fr ay particular resurce cditi. But hw d we determie what that gd strategy is? Previus slutis t tackle this prblem have falle betwee tw extremes. At e extreme, we have a methd kw as static partitiig. I this methd, the applicati is maually mdified t use remte executi ad the precise methd f remte executi alg with the exact servers t be used is hard-cded it the applicati. This methd is cceptually easy t uderstad ad relatively easy t implemet. Hwever, this static methd is extremely ieffective i mbile evirmets where resurces chage dyamically. At the ther extreme, we have full dyamic partitiig. I this methd, a rutime system is built that aalyzes the curret evirmet ad determies the curret resurce availability. The rutime the tests every pssible way f remtely executig the applicati ad picks the sluti that achieves best perfrmace. This methd is highly effective but extremely difficult t implemet. Buildig such a rutime is hard ad searchig all pssible remte executi pssibilities is frequetly itractable i practice. The perfect sluti is t build a system achieves the perfrmace f full dyamic partitiig with the ease f static partitiig. Ufrtuatelt, a geeral sluti that achieve this seems t be impssible due t the ifiite ways i which applicatis ca be partitied. Hwever, I claim that it is pssible t achieve this sluti fr hadheld devices because f the fllwig claim: Fr a large umber f useful mbile applicatis, the umber f useful ways f splittig the applicatis fr remte executi is small. These useful ways f splittig the applicati are called the tactics f the applicati. Tactics are specified by the applicati develper ad are high level descriptis f meaigful mdule-level partitis f a applicati. Give this ifrmati, it is pssible t build a remte executi system that adapts applicati perfrmace by pickig the apprpriate partitis accrdig t the curret resurce availability i the evirmet. I my thesis, I will validate the claim by shwig that it is pssible t specify the tactics f a large umber f useful mbile applicatis. Tactics als allw the remte executi system t use extra resurces i the evirmet t autmatically imprve applicati perfrmace. This use f tactics is explaied further i Secti Need fr Adaptive Remte Executi Systems This wrk is e f the first t lk at develpig a remte executi system that ca autmatically adapt applicati behaviur depedig the resurces available i a mbile evirmet. It builds up the wrk f the Odyssey [29] ad Spectra [13] prects. Hwever, why is such a system useful? Give the expetial icrease i cmputatial pwer (as predicted by Mre s Law), why ca t mbile devices ust execute all their applicatis lcally ad at full quality? Why d we eed systems that execute applicatis remte servers ad / r chage their fidelity [29] 1 i rder t achieve acceptable perfrmace? After all, des t Mre s Law mea that adaptive rutime systems will becme bslete i 5 years time whe mbile devices becme much mre pwerful? The aswer is ad there are three reass fr this. First, the mbile device market is drive by the eed t make devices that are smaller ad lighter tha the previus geerati i rder t satisfy the demads f the csumer. Satisfyig these requiremets usually requires sacrificig resurces (like disk space) i rder t meet the space ad weight criteria. Secd, battery perfrmace has t fllwed Mre s Law ad has remaied fairly cstat ver the years. As such, whe the devices becme smaller, the device maufacturers will have t use slwer less pwerful but mre pwer-efficiet CPUs ad smaller memries i rder t achieve decet battery lifetimes. Third, wireless badwidth has t bee icreasig at the same rate as wired badwidth. Fr example, wireless badwidth has icreased frm 2Mb/s t 11Mb/s with 54Mb/s frthcmig. Hwever, wired badwidth t the desktp has icreased frm 1 Fidelity refers t a applicati specific metric f quality. Fr example, speech recgiti has higher fidelity whe usig a large vcabulary rather tha a small vcabulary. Fidelity rages frm 0 t 1, with 1 beig the best quality ad 0 the wrst. 2

3 10Mb/s t 100Mb/s ad w 1000Mb/s i the same perid f time. Hwever, the desktp market is t cstraied by battery lifetimes, by space ad weight cstraits r by badwidth cstraits. As such, desktp machies will still be fllwig Mre s Law ad the gap betwee the cmputatial capabilities f the desktp machie ad the mbile device will remai large eve i the freseeable future. These gaps i resurce availability betwee desktp machies ad mbile devices is e f the reass why adaptive rutime systems are ecessary. May applicatis are develped primarily fr the desktp market. As such, they require the cmputatial ad badwidth capabilities f the desktp machie. Mbile users watig t use such applicatis their mbile devices will require a remte executi system that ca ru these applicatis remte servers. It culd be said that remte executi systems are uecessary because applicati writers are develpig applicatis specifically fr mbile devices. Hwever, these applicatis ted t be scaled dw versis f desktp applicatis. They usually are t as pwerful as the desktp versis. A remte executi system wuld allw the use f these mre pwerful desktp applicatis mbile devices that wuld therwise t be able t adequately ru these applicatis. Fially, there are a large class f cmputatially itesive iteractive applicatis that are imprtat fr mbile users. These iclude laguage traslati, speech recgiti ad augmeted reality applicatis. Hwever, these applicatis require cmputatial pwer that usually exceeds a wearable mbile device s capabilities. Remtely executig these applicatis will allw them t be used these wearable mbile devices. All these factrs strgly idicate that creatig a pwerful adaptive rutime system fr mbile devices is a valuable edeavur. Hwever, the challege with such systems is t build them such that they are able t quickly react t resurce chages i the highly variable mbile evirmet. I claim that tactics allws me t build such a system. Furthermre, a key difficulty with usig existig remte executi systems is that mdifyig applicatis t use them teds t be prhibitively expesive. The secd part f my claim is that tactics allws me t use sftware egieerig techiques t reduce the time eeded t develp adaptive mbile applicatis. 2.3 Reducig Adaptive Mbile Applicati Develpmet Time The prliferati f task-specific mbile ad wearable devices with shrt lifetimes places severe stress the develpmet ad maiteace f adaptive mbile applicatis. A critical factr limitig the cmmercial success f such a device is the sftware develpmet time eeded t create useful applicatis fr it. The lger this develpmet time, the shrter the useful life f the device i the marketplace. Slw sftware develpmet ca make the device bslete by the time it emerges as a prduct. Busiess pprtuities are measured i mths rather tha years i this fast-paced field. Develpig mbile cmputig applicatis is especially difficult because they have t be adaptive [14, 16, 21, 38]. The resurce cstraits f mbile devices, the ucertaity f wireless cmmuicati quality, the ccer fr battery life, ad the lwered trust typical f mbile evirmets all cmbie t cmplicate the desig f mbile applicatis. Oly thrugh dyamic adaptati i respse t varyig rutime cditis ca applicatis prvide a satisfactry user experiece. Ufrtuately, the cmplexity f writig ad debuggig adaptive cde adds t the applicati sftware develpmet time. Hw ca we reduce the sftware develpmet csts f adaptive mbile applicatis? This is a fudametal questi that I address i this thesis. My prpsed sluti is based three bservatis that are derived frm first-had experiece with buildig adaptive mbile applicatis. First, mst applicatis fr mbile devices ca be created by mdifyig existig applicatis rather tha writig ew applicatis frm scratch. Secd, the mdificatis fr adaptati typically affect ly a small fracti f ttal applicati cde size. Much f the cmplexity f implemetig adaptati lies i uderstadig the base cde well eugh t be cfidet f the chages t make. Third, the chages fr adaptati ca be factred ut clealy ad expressed i a platfrm-eutral maer. My apprach ca be summarized as fllws: 3

4 A lightweight semi-autmatic prcess fr custmizig the API used by the applicati t iterface with the adaptive rutime system Such custmizati is targeted t the specific adaptati eeds f each applicati. Next, a tl fr autmatic geerati f cde stubs that maps the custmized API t the specific adaptati features f the uderlyig mbile cmputig platfrm will be prvided. Fially, the ru-time supprt fr mitrig resurce levels ad triggerig adaptati will be factred ut f applicatis ad it a set f peratig system extesis fr resurce adaptati. Each f these cmpets plays a imprtat rle i the verall effectiveess f the apprach. The first cmpet (semi-autmatic prcess) amrtizes the effrt f uderstadig a applicati ad extedig it fr adaptati. The secd cmpet (stub geeratr) isulates applicati cde frm frequet chages f the uderlyig mbile cmputig platfrm. The third cmpet (OS supprt) allws a clea separati f plicy ad mechaism the OS mitrs resurce levels ad triggers adaptati, but it is the idividual applicatis that decide hw t adapt. OS supprt als helps esure that the adaptatis f multiple ccurretly executig applicatis d t iterfere with each ther. This apprach cmplemets traditial sftware egieerig techiques such as cde mdularity ad separati f ccers [33]. I additi, the apprach takes it accut the ctext-sesitive ature f adaptati plicies. I ther wrds, high level attributes such as a user s lcati, physilgical state, ad cgitive lad are fte imprtat factrs i determiig hw a lw-level adaptati decisi shuld be made [41]. This implies a bridgig f system layers that is t cmm i -mbile applicatis. I this thesis, I am ccetratig slely reducig the cst f develpig adaptive applicatis. I am t tacklig ther sftware egieerig issues eve thugh, I claim that, my methds als facilitate ther ice sftware egieerig prperties (like makig sftware maitaiece easier) due t the use f mdularity ad separati f ccers. 2.4 Validati I pla t validate this thesis i the fllwig steps: Defie the sematics ad sytax f tactics. This step ivlves clearly defiig what the tactics f a applicati are ad what ifrmati they cvey. A laguage fr specifyig tactics als eeds t be develped. This laguage has t be simple eugh fr applicati writers t easily use yet be pwerful eugh t capture the full sematics f tactics. Develp a prttype remte executi system that uses tactics. This prttype shuld demstrate that it is pssible fr applicatis t specify their tactics t a remte executi system usig the develped laguage. I have already started implemetig this prttype system ad it is called Chrma. I the rest f this prpsal, I will be usig Chrma t refer t the prttype beig built t validate the effectiveess f tactics. Demstrate that tactics prvide eugh ifrmati t partiti applicatis effectively by shwig hw the sematics f tactics allws Chrma t autmatically prvide gd perfrmace fr applicatis. Shw that tactics are applicable t a wide rage f applicatis. The verificati f this step will ivlve defiig the tactics fr a large umber (abut 5-10) f useful mbile applicatis. Verify the effectiveess f the sftware egieerig methds i reducig the applicati develpmet time by quatifyig the time eeded t add the mbile applicatis used fr validatig the geerality f tactics t Chrma. There are several prblems that eed t be slved t reach this gal: Describig tactics requires sme kid f laguage. Hwever, what shuld this laguage lk like? Will a simple laguage suffice? Tactics describe the useful partitis f a applicatis. Hwever, what fuctiality des Chrma require i rder t successfully pick the right partiti t use at rutime? At the very least, Chrma will eed t kw the curret resurce availability i the system alg with the expected resurce usage f each partiti specified i the tactics. Hwever, hw shuld this ifrmati be cllected ad saved? Als, hw shuld Chrma cmbie these tw pieces f ifrmati t decide which partiti t use? 4

5 Chrma uses remte servers t execute the remte partitis specified by tactics. Hwever, hw shuld these remte servers be discvered? T be useful t the user, Chrma eeds t kw the user s prefereces. This is because the user may have reass t prefer certai partitiigs ver ther partitiigs specified i the tactics ad these prefereces culd chage dyamically. Hwever, btaiig these prefereces frm the user autmatically is a kw hard prblem [18]. Sftware such as Prism [41] perate at a higher layer tha Chrma ad attempts t capture user prefereces. Prism s gal is t achieve the task requested by the user. By exchagig ifrmati betwee Chrma ad Prism, it may be pssible fr Chrma t btai user prefereces via Prism. Prism culd ifrm Chrma abut the user s prefereces fr each f the applicatis that make up that task ad leave the task f adaptig each applicati accrdig t the curret resurce availability t Chrma. Hwever, defiig the iterface betwee Chrma ad Prism is a -trivial task. The effectiveess f tactics i a remte executi system eeds t be prperly verified. T d this, a large umber f applicatis eed t be mdified t use Chrma. Their tactics eed t be prperly specified ad the imprvemets i applicati perfrmace achievable by Chrma eeds t be quatified. Ufrtuately, addig existig applicatis t Chrma culd be time csumig as it requires makig each applicati adaptive. As such, develpig sftware egieerig methds that lwer the time eeded t add existig applicatis t Chrma is crucial t make this aspect f the verificati maageable. The remaider f this dcumet is rgaized as fllws. Secti 3 describes the prti f this wrk that has bee cmpleted, r largely cmpleted a laguage fr describig tactics ad a 1st-pass implemetati f Chrma that uses tactics. It als describes the 4-step prcess that has bee develped t make the develpmet f adaptive mbile applicatis faster. Secti 4 is my pla fr the remaider f the thesis wrk: it describes my apprach twards the prblems metied here extedig Chrma t make better use f tactics, imprvig the sftware egieerig methds, ad the verificati f bth aspects f my thesis statemet. Secti 5 describes five scearis that this thesis hpes t eable. Secti 6 is a itemized list f wrk items fr the prpsed thesis wrk. Secti 7 describes related wrk while Secti 8 utlies the expected ctributis f this wrk t the field. Fially, Secti 9 prvides a time-lie fr the thesis wrk. 3 Cmpleted Wrk 3.1 The sematics ad descripti f Tactics Assumptis The pwer f tactics lies i their ability t distill the useful ways f partitiig a applicati fr remte executi. Hwever, first a mdel f the applicatis targeted ad the kid f remte executi beig perfrmed by thse applicatis eeds t be created. I this thesis, I csider the class f cmputatially-itesive iteractive applicatis as they ctai a large umber f useful mbile applicatis. Examples iclude speech recgiti, atural laguage traslati ad augmeted reality applicatis. These are the kids f applicatis that have bee evisied as beig key mbile applicatis i the ear future [39, 46]. I explai hw I pla t select the applicatis I will be csiderig i Secti Fr this class f applicatis, I claim that carse-graied remte executi is sufficiet. These applicatis have a user i the lp ad as such ca usually sustai latecies f up t 1-2 secds. This is i ctrast t systems like Java RMI [42] that perfrm fie-graied remte executi the rder f micrsecds. I will be usig remte prcedure calls (RPC) [7] t perfrm this carse-graied remte executi. Furthermre, I assume that each RPC is fully self ctaied ad has side effects. Hwever, the use f RPCs ad the lack f side effects is t itrisic t tactics but rather simplificatis fr the purpse f this thesis. Fially, each applicati is made up f peratis. A perati is a applicati-specific ti f wrk such as traslatig a setece fr a laguage traslatr r reducig a scee fr a augmeted reality applicati. Each perati ca have its w uique set f useful partitis. As such, the cmplete set f tactics fr a applicati will ctai tactics fr each f the peratis supprted by that applicati. 5

6 3.1.2 Sematics ad Laguage fr Describig Tactics Give the assumptis i Secti a simple laguage was develped fr allwig applicati writers t specify the tactics fr their applicatis. This laguage csists f tw prtis; The first part csists f a descripti f the varius prcedures i the applicati that ca be remtely executed. These prcedures are the set f RPCs fr that applicati. The iputs ad utputs t each RPC is specified ad each RPC ca be executed either remtely r lcally ad this decisi is made at rutime. The secd part f the laguage is used t describe the hw these RPCs ca be usefully cmbied. The RPCs ca be cmbied i the fllwig ways: 1. Sequetial depedecies betwee RPCs are allwed. I.e., it ca be specified that RPC A has t be fiished befre RPC B. 2. It is pssible t specify that a grup f RPCs have depedecies betwee them ad ca executed i parallel. Fr example, it ca be stated that RPCs A, B ad C ca all be executed i parallel. Hwever, i my iitial prttype, if a grup f RPCs is specified as beig able t be executed i parallel, that grup must be fllwed by a sigle sequetial RPC. I.e., if it is stated that RPCs A, B ad C ca be executed i parallel, tha all 3 RPCs must be fllwed by the same sequetial RPC D. It is t pssible fr RPC A t be fllwed by RPC D while RPCs B ad C are fllwed by sme ther RPCs. This limitati f the prttype will be fixed if I fid real applicatis that require mre cmplicated patters f specifyig RPCs. Each f these RPC cmbiatis is a separate tactic ad fully describes e way f cmbiig RPCs t cmplete a perati. A perati ca have may tactics that may differ i their fidelity ad resurce usage. As such, at rutime, the remte executi system picks the tactic that prvides the highest fidelity while satisfyig all resurce cstraits. A frmal descripti f the sytax f this laguage used fr describig tactics is prvided i Appedix A. The data depedecies betwee RPCs ca be determied by aalyzig the iputs ad utputs f each RPC as specified i the tactics descripti (sice I assume side effects). Each f the idividual remte calls that make up a particular tactic ca be ru either lcally r ay remte server. This decisi is made at rutime. If ecessary, the applicati develper ca cstrai a particular tactic t use particular servers fr its perati (This is shw i Figure 1 fr the dict tactic). Eve thugh the tactics may differ i their resurce usage ad fidelity, each tactic is guarateed t prduce a prper result fr the give perati if the remte calls are perfrmed i the rder specified by the tactic (I assume side effects as metied i Secti 3.1.1). Sice the data depedecies ad rderig betwee remte calls is fully specified by the tactic descripti, it is pssible t autmatically parallelize the executi f these remte stages. This additial pwer f tactics is explaied further i Secti Example Descriptis Figure 1 shws the descripti f the tactics fr a example applicati. This applicati is a atural laguage traslatr called Paglss-Lite [15]. Paglss-Lite has three differet traslati egies; Glssary (glss), Dictiary (dict) ad Example-based (ebmt). Each f these traslati egies ca be executed idepedetly f each ther. Usig mre egies results i higher quality results. The utput f each egie is fed it a laguage mdeller (lm) that cmbies the varius utputs ad creates the fial traslati. The three traslati egies ad the laguage mdeller are specified as fur RPCs (server glss, server dict, server ebmt ad server lm) that ca be remtely executed. The fur RPCs ca be cmbied i seve useful ways as shw by the seve tactics (glss, dict, ebmt, glss dict, glss ebmt, dict ebmt ad, glss dict ebmt). The & detes a sequetial depedecy betwee RPCs while RPCs iside brackets ca be executed i parallel. Fr Paglss-Lite, we see that the seve RPCs are created as fllws; there are seve differet ways f usig e r mre f the three traslati egies. Each f these egies is idepedet f each ther ad hece ca be executed i parallel with ther egies. The utput f all the egies must be set t the laguage mdeller fr fial prcessig. The tactics dict, has als specified that the server dict RPC shuld be executed a specific remte machie (gs129.sp). I Figure 2, we see the descripti f the tactics fr Jaus [44], a speech recgiti prgram. Jaus perfrms speech-t-text traslati f spke phrases. Recgiti ca be perfrmed at either full r reduced fidelity. The reduced fidelity uses a smaller, mre task-specific vcabulary that limits the umber f phrases that ca be successfully recgized but requires less time t recgize a phrase. 6

7 !#"$% & ' ( *),+ -.&! #"$% & '-.& &( *),+ /01! #"$% & /01 &( *),+ 20 3#% & /&0 &( 4-.&&( 56 & &( 5 "$ & ' 7& 7 &8),+ 9 : ; :&$ < $& = 4> 20?+ 9 : ; :&$ < $& -.& = ABC5DE #FG>% & 20?+ 9 : ; :&$ < $& % &/&01 = /01>% H0?+ 9 : ; :&$ < $& &-.&%= I6 &-.&*)J>% & 20?+ 9 : ; :&$ < $& /&01%= I6 /&01*)J>% & 20?+ 9 : ; :&$ < $& -.& &/&01K= -.&56 &/&01*)J> & 20?+ 9 : ; :&$ < $& &-.& /&01=L & IM & -.&N & /&011)O>% 20P+ Paglss-Lite has fur RPCs ad seve tactics (ways f cmbiig the fur RPCs) that are listed after the DEFINE TACTIC keywrd. These seve tactics give differet ways f cmbiig the remte calls (listed after the keywrd RPC) fr this applicati. Each f these calls ca be executed lcally r at a remte server ad this is determied at rutime by Chrma Figure 1: Tactics fr Paglss-Lite 4- &Q( &. 1& '( 71. "$% 7&1 7 &*),+ 4- & -(1.& -. 1& '( 71. "$% 7&1 7 &*),+ 9: ; : $ < $ Q( &. 1&R= - &Q( &. 1&S+ 9: ; : $ < $ -(1.& -. 1&R= - & -(1.& -. 1&S+ The tactics declarati fr Jaus ctais tw remte calls (d full recgiti ad d reduced recgiti) that ca be ru either lcally r remtely. Figure 2: Tactics fr Jaus Jaus has tw remte calls that ca be executed either lcally r remtely. These tw pssible ways f executig Jaus are captured by Jaus s tactics, as shw i Figure 2. The tactic T1UWV8V X,Y,Z[1\*]5^_^[] uses the full fidelity vcabulary t d the recgiti while the tactic X,Y1`1UZY*` X,YWZ[1\*]5^ _a^[] uses the reduced fidelity vcabulary t d the recgiti. Fially, i Figure 3, we see the tactics fr Face [40]. Face is a prgram that detects huma faces i images. It is represetative f image prcessig applicatis f value t mbile users. Face ca ptetially chage its fidelity by degradig the quality f the iput image. Face ca be ru either etirely lcally r etirely remtely. I bth cases, it rus the exact same remte prcedure ad it has ther mdes f perati. It thus has ly e tactic ad this is shw i Figure 3. I all three applicati examples, we see that the descripti ca be divided it a list f RPCs fllwed by the varius useful methds f cmbiig these RPCs. These three examples als prvide evidece that the laguage is easy t use ad is able t capture the sematics f tactics. Hwever, the laguage ca ly be fully verified by defiig the tactics fr a larger umber f applicatis (explaied i Secti 4.1). 7

8 4-.& Q 7. b'q # H087& &7H0* "$'Q &( H087 &7201 1),+ 9: ; : $ < $ -. =c-.& Q 7. + Face has ly e remte call (detect face) that ca be ru either lcally r remtely. This is captured by its sigle tactic. 3.2 Chrma Overview Figure 3: Tactics fr Face The remte executi system beig built t validate this thesis is called Chrma. Chrma is based the Odyssey [29, 12] adaptive rutime system. It icrprates the eergy, CPU, badwidth ad file cache resurce adaptati capabilities f Odyssey. Like Odyssey, Chrma uses applicati-aware adaptati (adaptati that requires mdificatis t the applicati surce). The key ew fuctialities that are beig added t Chrma are: the use f tactics a remte executi system that adapts accrdig t the umber f remte servers i the evirmet a stub geeratr that makes it easier t add applicatis t Chrma itegrati with a task layer s as t better deal with user prefereces a set f tls ad methds t make addig legacy applicatis t Chrma faster 3.3 Chrma Desig I this secti, the desig f Chrma is described. Buildig Chrma required tw mai cmpets: A way f describig tactics. This has already bee described i Secti A methd fr selectig the particular tactic t use i a give situati Tactic Selecti At rutime, Chrma eeds t decide fr a particular applicati which tactic t use ad where t execute it. Fr example, if Chrma picks the tactic \WV1[Wd8d YefI_ (Figure 1) fr Paglss-Lite, it will als have t decide whether t execute the dy1x*g,y1x \,V*[Wd8d, dy1x8g,yx YefW_ ad dyx8g,y1x Vf remte calls f this tactic lcally r remtely. Chrma s gal is thus t decide a tactic pla. A tactic pla cmprises f a tactic umber (detig which tactic t use), alg with a list that specifies the server t use fr each RPC i that tactic. Chrma eumerates thrugh all pssible tactic plas ad picks the best e fr the give resurce availability. T be able t d this, first, Chrma eeds t be able t predict the resurce usage f each tactic pla. Secd, Chrma has t measure the curret resurce availability. Third, Chrma requires guidace frm the user abut hw t tradeff resurces i rder t pick the best tactic. Fr example, the user may specify that badwidth usage shuld be miimized (pssibly because f pricig issues) r that the system shuld cserve battery pwer as much as pssible. Give these three thigs, Chrma will be able t decide the best tactic pla fr the particular perati frm the user s viewpit Resurce Predicti Fr a give perati ad tactic pla, Chrma eeds t be able t predict the resurces the tactic pla will require. Fr this, I will use Narayaa s resurce demad predictrs [28]. The key idea here is that the resurce usage f a tactic pla ca be predicted frm its recet resurce usage. The demad predicti mechaisms are iitialized by ff-lie lggig. At rutime, these predictrs are updated with lie mitrig ad machie learig t imprve accuracy. I d t pla t exted this aspect f Chrma beyd what is already prvided by Narayaa s wrk. 8

9 3.3.3 Resurce Mitrig Figure 4: Chsig a Tactic Chrma uses multiple resurce measurers t determie curret resurce availability. These resurce measurers curretly measure memry usage, CPU availability, available badwidth, latecy f perati, file cache state ad battery eergy remaiig. Chrma als has mechaisms t retrieve resurce availability ifrmati frm remte servers Additial User-specific Kwledge A perati ca have may tactics, each f which has a differet resurce usage ad fidelity. At rutime, Chrma has t decide the best tactic pla t use fr a give perati ad resurce availability. Chrma ca determie the curret resurce availability ad the resurce demad f the varius tactic plas usig the tw cmpets described earlier. But t effectively match the resurce demad with the resurce availability, Chrma eeds t trade ff resurces fr fidelity. Hw t perfrm this tradeff is frequetly ctext sesitive ad thus dyamic. Fr istace, wuld the user f a laguage traslatr prefer accurate traslatis r sappy respse times? Shuld a applicati ruig a mbile device use pwer-savig mdes t preserve battery charge, r shuld it use resurces liberally i rder t cmplete the user s task befre he r she rus ff t bard their plae? That kwledge is very hard t btai at the applicati level as it is user-specific ad t applicati-specific. Chrma is prvided with these user-specific resurce tradeffs i the frm f utility fuctis. A utility fucti is a user-specific fucti that quatifies the tradeff betwee tw r mre attributes. These utility fuctis ca be either prvided directly by the applicati r by Prism. These utility fuctis will allw Chrma t ptimize the tactic selecti fr ther user specified metrics like cservig battery pwer r miimizig etwrk badwidth Selecti Prcess Figure 4 shws hw all the cmpets wrk tgether. Chrma determies the predicted resurce demad fr each tactic f the curret perati by queryig the resurce predicti cmpet. At the same time, Chrma determies the available resurces via the resurce mitrig cmpet. These resurce mitrs als query ay available remte servers t determie the resurce availability thse servers. This ifrmati is ecessary as the latecy f the tactic is determied by where each idividual remte call i that tactic is beig executed. Determiig resurce availability demad ca be a very time csumig perati. Hece, t imprve perfrmace at the cst f accuracy, the resurce mitrs perfrm these queries peridically i the backgrud ad cache the results. Chrma iterates thrugh every pssible tactic pla ad picks the best tactic pla t use fr this perati. It des this by pickig the tactic pla that maximizes the utility fucti specified by the user (As described i Secti

10 The tactic pla is the executed ad its resurce usage is lgged t refie demad predicti fr the future. This brute frce methd wrks well fr a small umber f tactics (less tha 30 fr a PDA), hwever it will becme cmputatially ifeasible whe the umber f tactics icreases. Hwever, we claim that the umber f useful tactics fr cmputatially-itesive iteractive applicatis are small eugh t allw this brute frce tactic selecti mechaism. This claim will be empirically validated as mre applicatis are itegrated it the systems. We als lkig at usig ther slvers that are less cmputatially demadig [23]. 3.4 Over-Prvisied Evirmets The discussi s far has fcused the assumpti that the evirmets we are i are mstly resurce cstraied. Hwever, evirmets such as smart rms, may be ver-prvisied. Over-prvisied evirmets are characterized as havig mre cmputig resurces tha are eeded fr rmal perati. It is highly valuable t have a system that wrks well if resurces are scarce but is able t immediately make use f ver-prvisiig if it becmes available. Tactics prvide us this ability as they allw us t autmatically use extra resurces t imprve perfrmace. This is pssible because tactics prvides the kwledge f the remte calls eeded by a give perati ad the data depedecies betwee them. Chrma ca use this kwledge t execute remte calls pprtuistically t imprve perfrmace i three differet ways: First, Chrma ca make multiple remte executi calls (fr the same perati) t remte servers ad use the fastest result. Fr example, Chrma ca execute the glssary egie f Paglss-Lite at multiple servers ad use the fastest result. Chrma kws that it ca d this safely because the descripti f the tactics makes it clear that executig the glssary egie is a stad-ale perati ad des t require ay previus results r state. Secd, Chrma ca perfrm the same perati but with differet fidelities at differet servers. Chrma ca the retur the highest fidelity result that satisfies the latecy cstraits f the applicati. Fr example, Chrma ca execute multiple istaces f the YefI_ egie f Paglss-Lite i parallel at separate servers (all with differet fidelities) ad use the highest fidelity result that has retured befre a specified amut f time. Third, Chrma ca split the wrk ecessary fr a perati amg multiple servers. It des this by decmpsig perati data it smaller chuks ad shippig each chuk t a differet remte server. Chrma uses hits frm the applicati t determie the prper methd f splittig perati data it smaller chuks. Iitial experimetati has shw that these ptimizatis ca prvide substatial perfrmace imprvemets fr applicatis. I additi, these imprvemets ca be prvided by Chrma autmatically whe extra servers becme available withut eedig t ifrm the applicati. Hwever, mre wrk eeds t be de t quatify the beefits f usig extra resurces as well as imprve Chrma s ability t autmatically t use these extra resurces i the best pssible maer fr a give applicati. Fially, I eed t develp mechaisms that will esure that these extra servers are used by multiple Chrma cliets i a fair ad distributed maer. 3.5 Itegrati with Prism Oe key bservati f this wrk is that determiig apprpriate adaptati plicies is critically depedet the ability t capture user expectatis. Capturig user expectatis is a hard prblem that is beig addressed i a layer called Prism. Prism treats user tasks as first class etities ad iteracts with ctext-aware cmpets t assess the physical ctext arud the user. It determies the mst accurate mdels f user expectatis usig stchastic techiques t crrelate the curret user ctext t past experieces. By capturig user expectatis utside f applicatis, we eable the reuse f user expectati mdels. This allws the migrati f user tasks i pervasive cmputig evirmets [41]. Prism is beig develped by ather Ph.D. studet, Ja Pedr Susa, ad I pla t iterface Chrma with Prism. Hwever, a key research questi is determiig the prper iterfaces betwee Chrma ad Prism. Hw much ifrmati eeds t be shared betwee Chrma ad Prism i rder t achieve the best pssible perfrmace frm the users perspective? I aim t explre this questi ad cme up with desig guidelies that ca be used t guide future effrts t cuple user level adaptati with peratig system / applicati level adaptati. 10

11 3.6 Reducig Develpmet Cst Much f the cst f buildig ad maitaiig adaptive applicatis cmes frm the lw-level at which adaptati ehacemets are captured. Uderstadig the required adaptati features ad implemetig them ver the APIs ffered by the uderlyig platfrm is a cstly prcess. Curretly, there is effective way t preserve such ivestmet except i the frm f embedded cde mdificatis. These are hard t maitai i the face f the fast rate f release f ew platfrms. T slve this prblem, I created a high-level declarative laguage (frmally described i Appedix B) that is used t describe the adaptati aspects f a applicati. That descripti is the cmpiled ad a cde stub is geerated. This cde stub creates a custmized API fr the applicati, which is derived frm the high-level descripti f the adaptati requiremets. This custmized API is much clser t the applicati s eeds tha a geeric lw-level adaptati API, ad thus makes it much easier t itegrate the adaptati aspects with the bulk f the applicati cde. Furthermre, applicatis i the same dmai, say vide players, are likely t have very similar adaptati requiremets. Hece, adaptati descriptis ca be reused amg such applicatis. Fr example, it wuld be easier t exted the ext vide player fr adaptati ce we ve cmpleted the first e. By havig a cmpiler-based apprach, it is pssible t amrtize the effrt f re-targetig a set f adaptive applicatis t a ew platfrm. After all, it is easier t re-target the cde geerati f a cmpiler tha t mdify each applicati maually. The hypthesis here is that it will be easier t re-target the cde geerati fr a ew platfrm, ad recmpile all the applicatis, tha re-targetig every applicati. The specific rutime targeted by my stub geeratr is Chrma. Hwever, the use f a stub geeratr allws me t ptetially use ay ther OS r adaptati middleware [2, 5, 19, 29], withut chages t the applicati surce r descripti files. I merely have t chage the stub geeratr t prduce cde targeted fr these ew rutime systems. Figure 5: Prcess fr addig adaptati t a applicati step Prcess t Reduce Develpmet Time The 4-step prcess that I have develped fr use by applicati develpers t create their applicati descriptis ad the crrespdig applicati stubs is shw i Figure 5. 11

12 1. The adaptati expert cllabrates with a dmai expert t prduce a applicati descripti that captures the ifrmati ecessary fr the applicati t be adaptive. Fr istace, the descripti fr XAim ctais the adaptive variables relevat t adaptive vide playig: frame rate, ecdig, frame quality, height, ad width. This descripti is platfrm-idepedet, ad ca be reused fr ther applicatis that prvide adaptive vide playig capabilities. I.e., it applies equally t XAim ad t MediaPlayer, t Liux ad t Widws. 2. A stub geeratr cmpiles the applicati descripti it a set f stubs that iterface betwee the applicati ad the uderlyig rutime supprt. 3. The applicati is mdified t ivke the fuctis prvided by the stub layer. This step is maual, ad must be de fr each applicati. Hwever, these chages are small ad lcalized as demstrated i ur case studies, ad this fact makes it easy t preserve the adaptati ehacemets i ew releases f the applicatis, as described i Secti The applicati surce cde ad stub are cmpiled, ad liked tgether t frm the applicati biary. Whe executed, this biary ivkes the rutime supprt layer t make adaptive decisis. 4 Plaed Wrk I have successfully built the first versi f Chrma, that uses tactics t perfrm remte executi f applicatis. Chrma als autmatically uses extra remte servers t imprve applicati perfrmace. The perfrmace achieved by this 1st-pass implemetati f Chrma are detailed i my MbiSys 2003 paper [4]. Hwever, I still eed t implemet the fllwig cmpets t fully validate the claims f my thesis statemet. 4.1 Validati / Mdificati f Tactics Laguage thrugh Multiple Applicati Case Studies I pla t verify the laguage used fr specifyig tactics by usig it t describe the tactics fr a large umber f applicatis. These applicatis will be chse t represet a brad rage f applicatis that might be useful fr mbile users. Based the experiece gaied i describig these applicatis, the tactics specificati laguage will be exteded ad/r mdified as ecessary Selecti f Applicatis Fr the validati f the geerality f tactics t be sufficiet, it is imprtat t chse a large rage f applicatis that adequately map the space f cmputatially-itesive iteractive applicatis. Curretly, I have used 3 research applicatis that were all develped at Caregie Mell Uiversity (CMU). Sice they were all frm the same istituti, usig them ale may t be sufficiet t verify the geerality f tactics. Fr example, all these applicatis were sigle-threaded ad as such, the applicability f tactics t threaded applicatis is uclear. Als, it is pssible that the applicatis develped at CMU were particularly suited t the use f tactics ad are t represetative f cmputatially-itesive iteractive applicatis beig develped elsewhere. Hece, t effectively verify the geerality f tactics, I pla t use varius -academic pe-surce applicatis like mplayer (a vide player). If pssible, I will als use research applicatis develped at ther uiversities. The applicatis chse shuld exhibit differet prgrammig styles like the use f threads etc. This diverse selecti f applicatis ad prgrammig styles will help demstrate the applicability f tactics t a large set f cmputatiallyitesive iteractive applicatis. I hpe t ed up with at least 5 t 10 differet applicatis cmprisig a gd mix f discrete ad ctiuus applicatis frm bth academic ad -academic surces exhibitig a rage f prgrammig styles. 4.2 Supprt fr Ctiuus Applicatis Curretly, Chrma ly supprts discrete applicatis. These are applicatis that have a clearly defied discrete ti f wrk. Ctiuus applicatis, hwever, are differet. They basically ust stream data frm a server t a cliet. There is t real discrete f wrk. Hwever, ctiuus applicatis like mvie players, are clearly a imprtat class f applicatis fr mbile users. As such, I pla t add supprt fr these ctiuus applicatis. This will ivlve defiig hw the tactics fr a ctiuus applicati shuld be defied. Oce this is de, supprt fr ctiuus applicatis will eed t be added t Chrma. 12

13 4.3 Middleware Layer t Iterface with Existig Service Discver Prtcls Chrma uses servers t remtely execute applicati cde. Hwever, first, Chrma has t fid thse servers. A umber f service discvery prtcls (SDPs) have bee develped by varius researchers. These iclude JINI [45], UPP [26], ad BlueTth prximity detecti [1]. Each f these prtcls were created t wrk i a differet evirmet. Hwever, they all ca be used t discver remte servers i the evirmet. Fr my thesis, I will t be develpig ay ew SDP. Hwever, I will be creatig a middleware layer that iterfaces with existig SDPs ad prvides a cmm iterface t Chrma. This satisfies my gal f havig a cmm view f service discvery frm Chrma s perspective. 4.4 Usig Extra Servers i Overprvisied Evirmets i a Fair Maer Each Chrma cliet is able t use extra servers i a verprvisied evirmet t imprve applicati perfrmace. Hwever, if Chrma cliets autmatically use all extra servers available, they will ed up cmpetig with each ther ad affectig their perfrmace. A cetralized scheme fr determiig which extra servers ca be used by which Chrma cliet is t feasible i a mbile evirmet. Hece, I will be develpig distributed algrithms that Chrma cliets ca use t esure that each cliet is usig the extra servers i a glbally fair maer. 4.5 Optimizatis Pssible With Usig Extra Server Resurces Curretly I have ly lked at three perfrmace ptimizatis pssible by usig extra server resurces. I pla t ivestigate ther methds f usig these resurces that ca als be used i a cmpletely autmatic fashi ad quatify the perfrmace beefits f these methds. 4.6 Itegrati with Prism Fr Chrma t decide hw t adapt ay particular applicati, it eeds t kw what the user s prefereces regardig that applicati are. I rder t btai these prefereces, e f the gals is t itegrate Chrma with Prism. Prism wrks at the task layer ad keeps track f user prefereces. As such, btaiig this ifrmati frm Prism wuld be very beeficial t Chrma. Hwever, desigig the iterface betwee Chrma ad Prism is trivial. Hw shuld ifrmati be exchaged betwee Chrma ad Prism? What ifrmati is exchaged betwee Chrma ad Prism? Hw des Prism ifrm Chrma f chages i user prefereces? Ca Prism btai ifrmati frm Chrma regardig varius applicatis? If yes, hw will this be de? These are sme f the questis that eed t be addressed befre Prism ca successfully be itegrated with Chrma. 4.7 Makig the Develpmet f Adaptive Mbile Applicatis Easier The prelimiary 4-step prcess shws prmise i makig the develpmet f adaptive mbile applicatis frm legacy applicatis easier. Prelimiary results f this 4-step prcess cupled with sme case studies are i a CMU Tech Reprt [3]. Hwever, mre wrk still eeds t be de i the fllwig areas: Supprt fr Ctiuus Applicatis Curretly, Chrma des t supprt ctiuus applicatis. As such, the mechaisms desiged t reduce the time t add applicatis t Chrma may t wrk fr ctiuus applicatis. Oce Chrma supprts ctiuus applicatis, the effectiveess f the curret mechaisms i supprtig ctiuus applicatis will becme kw. The mechaisms will the be mdified ad/r exteded t supprt ctiuus applicatis Creati f Mechaisms t Ease Server-side Cde Develpmet The mechaisms t reduce the time eeded t develp adaptive mbile applicatis curretly ly wrk fr the cliet half f that applicati. Hwever, ay applicati ca be remtely executed requires a server cmpet. Curretly, the server cmpet has t be writte frm scratch. This is extremely time csumig ad uscalable i the lg ru. I pla t wrk develpig mechaism which will reduce the time eeded t develp the server cmpet fr mbile applicatis. 13

14 4.8 Validati Metrics fr Validati Successfully evaluatig this thesis first requires idetifyig metrics that ca be used fr validati. Sme ptetial metrics iclude respse time, latecy ad average lad per server. Hwever, mre wrk eeds t be de t clearly idetify the metrics used fr verifyig each part f this thesis Geerality f Tactics I eed t shw that tactics are a geeral eugh ccept t be useful fr mbile cmputig applicatis. T validate this, I pla t defie the tactics fr a large umber (abut 5 t 10) f useful mbile applicatis. These applicatis will iclude bth discrete ad ctiuus applicatis Perfrmace f Chrma The effectiveess f Chrma will be demstrated by shwig that Chrma ca prvide gd perfrmace fr a large umber f applicatis (abut 5 t 10). I already have tetative perfrmace figures fr Chrma fr three mbile applicatis. Havig, mre applicatis are eeded t verify that Chrma ca prvide gd perfrmace fr a large umber f applicatis. This perfrmace study shuld als verify Chrma s ability t autmatically use extra resurces t imprve applicati perfrmace Reducig the Time fr Develpig Adaptive Mbile Applicatis A sizable prti f my thesis will ivlve develpig sftware egieerig methds fr reducig the develpmet time f adaptive mbile applicatis. Verify the effectiveess f the methds will t be easy. My curret idea icludes measurig the time eeded t add applicatis t Chrma usig the develped methds versus the time eeded t add the same applicati t Chrma withut the develped methds. I als pla t measure the time eeded by ther peple t use my methds t add ew applicatis t Chrma. 4.9 Ope Prblems Autmatic Methds f Abstractig Applicati Tactics Curretly, the applicati writer specifies the tactics f his applicati usig a simple declarative laguage. Autmatically determiig the tactics f a applicati, withut ay applicati writer help, is a kw hard prblem. Hwever, it is a pe prblem as t whether it wuld be pssible t build a system that starts frm a small set f tactics specified by the applicati writer ad figures ut ay extra tactics required by mitrig the executi f the applicati. 5 Mtivatig Scearis This thesis will help t eable the fllwig scearis: The scearis are listed alg with sme f the techlgy ecessary t realize each sceari. 5.1 Sceari 1 Tim is travelig i Eurpe with his ipaq ad he wats his ipaq t perfrm laguage traslati fr him. The traslati shuld always retur i 1 secd. If there are remte servers available, Chrma will use the ipaq t perfrm the traslati. Whe Chrma discvers the presece f remte servers, it will seamlessly use them t d the traslatis. Usig remte servers will result i better traslatis as the remte servers have access t larger vcabulary files. Chrma esures that the latecy cstrait f 1 secd is still satisfied. Tim is uaware f the decisi makig f Chrma. This sceari requires Chrma t be able t d the fllwig thigs: discver the presece f remte servers be able t adapt discrete applicatis seamlessly trasfer executi f a prgram frm the lcal machie t a remte server 14

15 be able t d all this while satisfyig user-specified latecy gals 5.2 Sceari 2 Jim wats t watch a vide his hadheld device. Hwever, the vide itself is t big t stre the hadheld device. Hece, it eeds t be streamed i real time t the device. Chrma autmatically sets up a cecti t a vide server ad starts streamig the vide demad t Jim s hadheld. Hwever, i the middle f the vide, the badwidth frm Jim s hadheld t the vide server drps. Chrma, autmatically des tw thigs. First, it starts usig a lwer quality vide stream frm the server t esure that Jim s vide is t iterrupted. Secd, Chrma decides if it wuld be better t switch Jim t ather vide server that has better badwidth. If Chrma decides t switch Jim t ather vide server, it will have t esure that the vide feed that Jim receives is t iterrupted i ayway. Jim may tice that Chrma is adaptig the quality f the vide stream but the quality that Jim receives shuld be acceptable t him ad he shuld t have t itervee i ay way. This sceari requires Chrma t be able t d the fllwig thigs: discver the presece f remte servers be able t adapt ctiuus applicatis be able t maage multiple vide streams at a time seamlessly trasfer frm e stream t ather be able t d all this such that the user des t see ay gap i the vide stream 5.3 Sceari 3 Jh is usig his palm sized device t perfrm speech recgiti. His palm sized device has sigificat cmputatial capabilities ad must deped remte servers t achieve decet perfrmace. Hwever, Jh is experiecig a high stadard deviati i his latecy as the remte server curretly beig used is als beig used by ther devices. Chrma tices Jh is i a smartrm ad that there are a umber f remte servers available. Each f the servers is beig used t d differet tasks. Hwever, Chrma decides t parallelize the speech recgiti by sedig the same recgiti task t multiple remte servers. The recgiti that returs the fastest is give t Jh. By usig multiple servers, Chrma maximises the prbability that Jh s recgiti will be perfrmed as fast as pssible as it is quite likely that e f the servers beig used will be uladed at the time the request is made. This sceari requires Chrma t be able t d the fllwig thigs: discver the presece f remte servers be able t use mre tha e server at a time make itelliget decisis abut hw t use extra servers autmatically parallelize applicatis such that the same perati ca be de at multiple servers use itelliget resurce maagemet plicies such that the extra servers are beig used i a glbally fair way 5.4 Sceari 4 Je is a ew hire at the cmpay. His maager prclaims him a adaptati expert ad wats him t develp a adaptive vide player fr hadheld devices ruig Liux ad X. Nt beig a vide expert, Je cllabrates with dmai experts i rder t uderstad hw t make vide players adaptive. As a result, Je realizes that vide playig adaptati is based image quality ad frame rate. Je decides t use the XAim applicati as the base. He mdifies XAim t adust its image quality ad frame rate t the available resurces. This is a tedius ad iterative prcess as Je has t make extesive chages t the XAim surce. 15