Towards Autonomous Service Composition in A Grid Environment

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1 Towards Autonomous Servce Composton n A Grd Envronment Wllam K. Cheung +, Jmng Lu +, Kevn H. Tsang +, Raymond K. Wong ++ Department of Computer Scence + Hong Kong Baptst Unversty Hong Kong {wllam,jmng,hhtsang}@comp.hkbu.edu.hk Abstract Web servces are becomng mportant n applcatons from electronc commerce to applcaton nteroperaton. Whle numerous efforts have focused on servce composton, servce selecton among smlar servces from multple provders has not been addressed. Such ssue s more serous when servces are embraced n Grd platforms, whch are usually resource-conscous. Expermental results show that our consderatons are vald and our prelmnary soluton works well n our Globus grd network. Keywords: Autonomous servces composton, Bddng, Web servces, Grd computng 1. Introducton Web servces are becomng the promnent paradgm for electronc busness and nteroperable applcatons across heterogeneous systems. However, Web servces standards such as WSDL [1], UDDI [2], and SOAP [3] do not address the ssues of servce re-use and composton, especally dynamc composton of exstng servces from multple sources. Varous efforts on addressng ths ssue ncludng the recent ntatve of BPEL4WS [4] focus on representng compostons, whereas, the actual ssues nvolved n composng the servces, e.g., the selecton process and composton consderatons such as run-tme costs, etc., have not been consdered. Another technology that s gettng ncreasng popularty s Grd [5]. Grd s a dstrbuted envronment that enables flexble, secure, coordnated resource sharng, among dynamc collectons of ndvduals, nsttutons and resources. The beneft of embracng Web servces on Grd have been recently realzed n the Open Grd Servces Archtecture (OGSA) of Globus (GT3) the de-facto standard of School of Computer Scence & Engneerng ++ Unversty of New South Wales Australa wong@cse.unsw.edu.au Grd mddleware [6], and shown n varous projects (e.g., Geodse - MyGrd - However, Grd platform s n general more conscous regardng the utlzaton and relablty of resources, and servces composed n Grd need to be planned n an optmzed way. Along ths lne and dfferent from prevous works, ths paper attempts to nvestgate the underlyng crtera n practce, propose an ntal soluton usng a bddnglke mechansm, and fnally realze ts sgnfcance by mplementng the soluton (called BU-Grd) and runnng seres of experments. Expermental results are encouragng and further mprovements shall be obtaned from our ongong effort. 1.1 Related Works Due to the ncreasng attenton to Web servces from the research and ndustry communtes, there have been lots of recent works addressng varous ssues of Web servces (e.g., [7]). To name a few, for example, n [8], the ssue of servce composton s addressed n the context of Web components, as a way for creatng composte Web Servces by re-usng, specalzng and extendng exstng ones. McIlarth and Son [9] proposed an approach to buldng agent technology based on the noton of generc procedures and customzng user constrants. They argue that an augmented verson of the logc programmng language Golog provdes a natural formalsm for programmng Web servces. Prototypes that gude a user n composng Web servces n a sem-automatc manner have been proposed n [10,11]. The sem-automatc process s facltated by presentng matchng servces to the user at each step of a composton and flterng the possbltes by usng semantc descrptons of the servces. Whle there are numerous papers descrbng specfcatons and methods for servce composton, seldom of them have addressed the ssues of choosng servces based on ts costs and resources (whch s an mportant ssue n utlzng resources n a Grd

2 envronment). For nstance, [12] mentoned a smple scorng servce based on the summaton of the servces' weghted scores. However, the detals of estmatng the scores and evaluatng crtera (whch are crucal n the actual mplementaton and system evaluaton, agan, especally n Grd) have been left out. Blythe et al., n [13], used lmted state nformaton (the current data storage of the dstrbuted hosts) for optmzng servces compostons for e-scence applcatons. The work closest to ours s due to Sample et al. [14] that ncorporated servces uncertanty (e.g., costs, performance, relablty) va probablstc modelng n the composton process. servces (e.g., the semantcs of the nput/output parameters) are descrbed by some machne understandable semantc Web language (e.g. OWL-S). Relatonshps and concepts of the vocabulares used to enable semantc matchng of servces are shared n a ontology repostory. A servce consumer s a clent program whch sends servce requests (e.g., n terms of desred nput/output relatonshps) to the Grd/Web servce broker whch bears the duty of selectng sutable prmtve servces, composng them as well as montorng ther executon. 1.2 Paper Organzaton The remanng of the paper s as follow. Secton 2 gves a typcal envronment for autonomous servce composton. Secton 3 descrbes n detal the overall system archtecture of BU-Grd. Secton 4 provdes n detal a bddng mechansm for servce selecton n a dynamc Grd envronment. Expermental results and the lessons learnt are found n Secton 5 and 6, respectvely. Secton 7 concludes the paper wth a number of future research drectons. Fgure 2. The system archtecture of BU-Grd. 3. BU-Grd System Archtecture Fgure 1. A typcal servce composton envronment. 2. Autonomous Servce Composton A typcal envronment for supportng Grd/Web servce composton s llustrated n Fgure 1. A collecton of servce provders expose, va the Internet, the servces they support as Web servces. The servces are regstered at a servce regstry (e.g. UDDI) for servce dscovery. The semantcs of the avalable Web The archtectural desgn of the proposed BU-Grd, to be further descrbed n the followng (also see Fgure 2 for an overvew), contans components that are common n most of the servce composton systems. In addton, t s featured by the ncorporaton of a) bddng servces and bd evaluaton components for dynamc servce selecton, as well as b) a plan base and a plan retrever for plan re-use support. Whle the focus of ths paper s to study n detal how the state nformaton can be used to form the selecton crtera and optmze the overall system utlzaton va a bddng mechansm, detals about the plannng part and ts relatonshp wth the proposed servce bddng mechansm wll also be ncluded for completeness.

3 3.1 Servce Regstraton and Indexng Semantc descrptons of Grd servces are stored at the Servce Regstry, whch may nclude: - Hgh-level servces descrptors: E.g., for e- busness applcatons, they can be company name, busness nature/categores, contact person, phone number, emal address, etc. - Low-level servces nterface descrptors: E.g., servce name, functonal descrpton, URL of the WSDL fle or Grd Servce Handle (GSH), semantcs of the nput/output parameters, etc. To support effcent access of GSHs from the Servce Regstry and effcent update of the servces state nformaton, both the hgh-level and low-level servce semantcs are ndexed. Furthermore, to extend the servce dscovery capablty to go beyond smple keyword search, dfferent doman-specfc ontologes are mantaned n Ontology Repostory to support semantc matchng. 3.2 Task Specfcaton & Servce Composton In BU-Grd, a task s represented by specfyng the requred nput 1 and desred output. To plan for the task (or to satsfy the specfcaton), a meta-level servce wll be composed on-demand usng the prmtve servces avalable n the Servce Regstry. By treatng the nput as the ntal state, the desred output as the goal, and the avalable servces as the operators, servce composton can readly be formulated as an AI plannng problem [14]. Under the Grd context, one challenge s that the plannng has to be performed n a dynamc envronment, contanng multple functonally equvalent operators (servces) but wth possbly dfferent mplementatons as well as tme-varyng resources. Besdes, servces matchmakng based on semantcs s also a non-trval task Servces Matchmakng To enable correct matchmakng between Grd servces, we need to well-defne servces compatblty. There exst at least two types of compatblty measures, namely data type compatblty as well as semantc compatblty. Eq.(1) and Eq.(2) gve two possble forms of compatblty n terms of data type and semantcs between an output of a servce and an nput of a matchng servce. a) Data Type Compatblty 1 Sometmes, a task can be fully specfed by only desred output, for example, accessng some processed e-scence data from the grd. Compatblty ( type t output, type nput 1 ) = same / upcast downcast otherwse (1) where upcast means the output has to be upcasted (e.g., from nt to float) so as to be fed nto the next nput, and smlarly for downcast. b) Semantcs Compatblty 1 equvalent (2) Compatabl tys ( semantcnput, semantcoutput) = 0.8 subclass 0 otherwse where subclass means that the output s a subclass of the nput and the need of ontology s explctly mpled Plannng Based on the servces compatblty measures defned, servce composton can be proceeded usng dfferent plannng paradgms. One example s regresson plannng whch s based on backward channg. Startng from the output of the specfed task as the ultmate goal, the planner can search the Servce Regstry for servces wth ther outputs compatble wth that of the specfed task. It s possble that the set of compatble servces can be categorzed nto several dstnct servce nterfaces, each contans a unque nput/output par. One can then use those dstnct servce nterfaces as sub-goals and contnue to search for the best plan. Sometmes, for effcency purpose, one may want to use a local search strategy by choosng one of the nterfaces and contnung the search. The selecton can be done based on a local performance estmaton of the nterfaces. See Fgure 4 for an overvew and refer to Secton 6.2 for more dscusson on dynamc plan optmzaton. As one servce nterface s n fact representng a group of functonally equvalent servces, ts performance estmaton should be characterzed by the best servce under the same nterface. So, under ths scenaro, the remanng queston s how to select the best servce under the dynamc envronment Servce Selecton Servces wth equvalent nput/output nterfaces can have dfferent mplementatons and have transent performance due to tme-varyng system load, data cached, etc. A mechansm for makng a wse choce for better Grd resource utlzaton s needed. We beleve that bddng based on a dynamc scorng scheme can be adopted for the servce selecton task, as detaled n Secton 4.

4 Fgure 3. Servce selecton. The broker then selects a servce mplementaton accordng to the probablty dstrbuton: B ( I ) P () = (4) B I ( ) 4.3 Estmaton of Servce Performance After the selected mplementaton fnshed the assgned job, t wll notfy the broker the result. The broker wll then return the actual servce tme A, and the estmated servce tme of the th servce mplementaton wll be updated as t+ 1 t E I = 1 α E ( I) + α A (5) ( ) ( ) where α s the updatng rate. In our experment, ts value s set to 0.8. Fgure 4. An overvew of servce composton and executon process. 4. Servce Selecton Va Bddng Here we propose a bddng-lke mechansm for the aforementoned servce selecton problem wth the hope of balancng the load among a set of Grd nodes n a vrtual organzaton. 4.1 Notatons Let I denote a partcular servce nterface, E (I) denote the estmated servce tme of the th mplementaton for the servce nterface I, B (I) denote the value sent to the broker by the th mplementaton for bddng the nterface I to be performed. 4.2 Bddng Process The broker (search engne) frst notfes each of the servce provders that host the requred servce mplementatons. Beng notfed, each servce mplementaton wll make use of the current estmated servce tme E (I) (track record) as well as the current system load (current resource) to compute a bd value as n Eq.(3) and send the bd back to the broker: 1 B ( I ) = ( 1 L ) (3) E ( I) where L s the system load of the node hostng the th servce mplementaton. Fgure 5. The sequence dagram of the bddng process. 5. Experments In order to study n detal the effectveness of the proposed bddng process on the Globus platform and the behavor at each grd node, we have set up a small grd envronment wth four grd nodes, one beng the Servce Broker and the other three beng the Servce

5 Provders. Fgure 5 shows the sequence dagram of the overall bddng process. All the Grd servces are runnng n the servce contaner provded by GT3. The BrokerServce queres the IndexServce of each Grd node to get the lst of avalable servce mplementatons. BddngServce consults MasterManagedJobFactoryServce (MMJFS) of ts own node to get the current system nformaton. Three experments have been conducted for evaluatng three dfferent vrtual organzaton scenaros on the grd platform: Experment 1 assumes that the avalable servce mplementatons (Servce A) n all the nodes are homogeneous, and all the ncomng servce requests can be served by Servce A. Experment 2 assumes that the avalable servce mplementatons are heterogeneous, ncludng Servce A, B and C. The mplementatons of Servce A and Servce C are 3 and 2 tmes less effcent than that of Servce B. Also, all the ncomng servce requests can be served by ether the mplementatons of Servce A, B or C. Experment 3 assumes that each node contans one composte servce mplementaton and one prmtve servce mplementaton needed as part of the composte servce. The servce request stream s of homogeneous type and requests the Servce Broker for the composte servce A+2B regularly. The composte servce A+2B means that t has to perform subtask A frst before two subtasks B can be performed n parallel. A composte servce request s sad to be fulflled only f all ts subtasks are fnshed. Thus, there are n fact two levels of bddng as llustrated n Fgure 6. Fgure 6. An llustraton of a mult-level bddng needed by composte servces. The nter-arrval tme for the servce request was 20 seconds throughout the experments. For performance evaluaton, nformaton lke job start tme, end tme, system load, and servce tme are collected durng the experments and the results are shown n Fgure 7-15 Gven: servce requests arrve at a 20 sec. nterval node-1 node-2 node-3 (CPU 2.6GHz) (CPU 0.65GHz) (CPU 0.7GHz) Expt. 1 (homo.) Servce A Servce A Servce A Expt. 2 (hetero.) Servce A Servce B Servce C Expt. 3 (composte) Servce A+2B, Servce B Servce A+2B, Servce B Servce A+2B, Servce B Table 1. Experment setups for performance evaluaton. Observaton 1: Whle the three experments were desgned to correspond to three dfferent vrtual organzaton scenaros on the grd platform, our proposed bddng mechansm managed to dstrbute the servce request streams to the three Servce Provders for mprovng system utlzaton, as shown n Fgure 8, 11 and 14. Observaton 2: In Experment 1, as all the servce mplementatons were homogeneous, node-1, beng the most powerful machne, naturally shouldered more jobs va the bddng mechansm, when compared wth the other two nodes. Observaton 3: By comparng Fgure 7 (Expt. 1) and Fgure 10 (Expt. 2), t s noted that the servce mplementatons of both Servce A and C beng less effcent than that of Servce B resulted n more jobs beng assgned to node-2 whch s hostng the more effcent servce mplementaton Servce B n Expt. 2, even though node-1 s the fastest machne. Ths renforces the desgn of the proposed bddng mechansm that, other than the computng power, t should (mplctly) take nto the consderaton of the effcency of the servce mplementaton and react accordngly. Observaton 4: As we moved from Experment 1 to 3, the overall load of the set of requested jobs was ncreasng (see Fgure 8, 11, 14). We observed that all the grd nodes were movng closer to be full loaded at most of the tme, whch we beleve to be an ndcator of good resource utlzaton. However, the servce tme per job fluctuated qute serously as the overall load ncreases (see Fgure 14). We beleve that the fluctuaton s caused by the tme dependency requrement of the composte servces. We are stll nvestgatng the condtons and bddng strateges for reducng the fluctuaton, and thus mprovng the servce relablty.

6 Fgure 7. Job schedules under homogeneous servces scenaro (red for node-1, blue for node-2, green for node-3). Fgure 10. Job schedules under heterogeneous servces scenaro (red for node-1, blue for node-2, green for node-3). Fgure 8. System load of each grd nodes under homogeneous servces scenaro. Fgure 11. System load of each grd nodes under heterogeneous servces scenaro. Fgure 9. Job servce tme of each grd nodes under homogeneous servces scenaro. Fgure 12. Job servce tme for each grd node under heterogeneous servces scenaro.

The effect wll be especally mportant f the executon tme per job s short and the quantty of them s huge.

Fgure 14. System load of each grd nodes under composte servces scenaro. a) node-1 (A+2B) b) node-1 (B) c) node-2 (A+2B) d) node-2 (B) e) node-3 (A+2B) f) node-3 (B) Fgure 15.

7 6. Dscusson and Future Works 6.1 Accuracy of The Provded Load Estmaton The current mplementaton of the GT3 can only provde up-to-mnute state nformaton, where we encountered some dffcultes n more fne-graned load balancng. The effect wll be especally mportant f the executon tme per job s short and the quantty of them s huge. It seems that a Grd servce for supportng on-demand real-tme system load reportng could be needed n the Grd mddleware. Fgure 13. Job schedules under composte servces scenaro (all the jobs are mxed). Fgure 14. System load of each grd nodes under composte servces scenaro. a) node-1 (A+2B) b) node-1 (B) c) node-2 (A+2B) d) node-2 (B) e) node-3 (A+2B) f) node-3 (B) Fgure 15. Job servce tme for each grd node under composte servces scenaro. 6.2 Dynamc Plan Optmzaton The next obvous step of ths work s to ntegrate the bddng mechansm one step upward to the plannng step. By assumng that each Grd servce nterface keeps a table of scores S to ndcate ts desrablty to use some other servces, where the scores can be some statstcs computed durng the bddng for servces selecton (Secton 4). Then, the setup wll be smlar to that of the PageRank algorthm [15] used by Google search engne for ndcatng Web page mportance. For example (see Fgure 4), let R denote the reward for a selected plan (can be a constant equal to, say, 1), N denote the number of the outputs of the specfed task, n denotes the current updatng servce nterface, m denotes the servce nterfaces that use the output of current servce nterface n, and α denotes the updatng rate (can be a constant equal to some value less than 1). For servce nterfaces wth ther outputs form the outputs of the specfed task (.e., the ultmate goal), t+ 1 t R Sn = ( 1 α ) Sn + α N Then, for the subsequent plannng steps, t+ 1 t t S = 1 α S + α S n ( ) n m m Such a scorng scheme mples mplctly that frequently selected (good track records) servce nterfaces wll be updated more frequently. Also, those nterfaces often appear near to the fnal output of the selected plans (brngng you faster to the goal) wll have hgher scores. Also, those nterfaces provde more outputs (more resourceful) wll have a hgher score. We are currently studyng the effectveness of such a scorng scheme. 6.3 Plan Base Performng servce composton from scratch can be a tme-consumng process for tme-crtcal applcatons. One can use a plan base for storng plans that have

8 been executed. A smlar dea has been echoed n [13]. The archved plans (as some optons of pre-composed servces) can then be used for the constructon of new plans. The reuse of plans should be can ncrease the effcency of plan constructon. For better use of the storage resource, there can also be some related polces for deletng plans that appear obsolete. Fgure 16. Plan Generaton. 7. Concluson Ths paper focuses on servce selecton, whch s usually dsregarded by prevous works n Web servce composton. Whle Web servces embraced n Grd platforms s gettng popular, we demonstrated that servce selecton could make sgnfcant performance and resource utlzaton dfferences durng servce composton. In partcular, the servce bddng mechansm proposed here ensures the performance of the servce to be performed and also the farness to the servce provders/bdders. Although the expermental results were encouragng, we beleve that further nvestgaton on selectng servces for large scale servce composton wll encourage more Web servce usages, especally for Grd envronments where resource utlzaton and servce performance are concerned. Acknowledgement Ths work s supported by Centre for E-Transformaton Research, Hong Kong Baptst Unversty under the RGC Group Research Grant (HKBU 2/03/C). References 1. WSDL, 2. UDDI, 3. SOAP, 4. BPEL4WS, lbrary/ws-bpel/ 5. I. Foster, C. Kesselman, and S. Tuecke, "The Anatomy of the Grd: Enablng Scalable Vrtual Organzatons," Internatonal Journal of Hgh Performance Computng Applcatons, Vol. 15, pp , I. Foster, C. Kesselman, J.M. Nck and S. Tuecke, "Grd Servces for Dstrbuted System Integraton," IEEE Computer, June, IEEE Internet Computng, Specal ssue: Mddleware for Web servces, J. Yang and M. Papazoglou, Web components: A substrate for web servce reuse and composton, Advanced Informaton Systems Engneerng, Proceedngs of the 14th Internatonal Conference, CASE 2002 Toronto, Canada, May 27-31, S. McIlrath and T. Son, Adaptng golog for composton of semantc Web servces, Proceedngs of the 8th Internatonal Conference on Prncples of Knowledge Representaton and Reasonng, Evren Srn, James Hendler, and Bjan Parsa, Semautomatc composton of Web servces usng semantc descrptons, Proceedng of Web Servces: Modelng, Archtecture and Infrastructure workshop n ICEIS, Aprl, L. Chen, N.R. Shadbolt, C. Goble, F. Tao, S.J. Cox, C. Puleston, P.R. Smart, "Towards a Knowledge-based Approach to Semantc Servce Composton," 2nd Internatonal Semantc Web Conference (ISWC2003), October 2003, Florda, USA, Lecture Notes n Computer Scence, LNCS 2870, pp B. Benatallah, Q. Sheng, and M. Dumas, The Self-Serv envronment for Web servces composton, n IEEE Internet Computng, pages , 7(1), J. Blythe, E. Deelman, Y. Gl, C. Kesselman, A. Agarwal, G. Mehta, K. Vah, "The Role of Plannng n Grd Computng," Proceedngs of the 13th Internatonal Conference on Automated Plannng and Schedulng (ICAPS), June 9-13, 2003, Trento, Italy 14. N. Sample, Pedram Keyan, Go Wederhold, "Schedulng Under Uncertanty: Plannng for the Ubqutous Grd," Proceedngs of the Ffth Internatonal Conference on Coordnaton Models and Languages (Coord2002) 15. R. Motwan, S. Brn, L. Page, and T. Wnograd, "The PageRank Ctaton Rankng: Brngng Order to the Web, Stanford Dgtal Lbrares Workng Paper, 1998.

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