A Hybrid Approach for Web Service Selection

Transcription

1 Computatonal Engneerng Research / ISSN: Hybrd pproach for Web Servce Selecton Motaba Khezran 1, Wan M. N. Wan Kadr 2, Suham Ibrahm, and laeddn Kalantar 4 1,2 Faculty of Computer Scence and Informaton System, Unverst Teknolog Malaysa, Johor Bahru, Malaysa,4 dvanced Informatcs School, Unverst Teknolog Malaysa, Kuala Lumpur, Malaysa bstract Web servce selecton whch s specfed to evaluate and select the best canddate from dscovered Web servces became one of the most sgnfcant topc n recent research on Servce orented rchtecture (SO). Indeed, current approaches are not suffcent enough to overcome Web servce selecton problems. Due to the nature of Web servce selecton, t s mportant to lead t to Mult Crtera Decson Makng (MCDM). However, there are several MCDM methods such as HP, NP, TOPSIS and VIKOR. In ths paper we propose a hybrd approach to solve Web servce selecton problem. Frst, we apply nalytcal Herarchy Process (HP) to evaluate the weghts of crtera nstead of collect the weghts drectly from servce consumer. In the next tread, we use VIKOR (VIšekrterumsko KOmpromsno Rangrane) to dentfy and rank the approprate canddate servces. Fnally, n order to demonstrate the proposed method we have afforded an example usng four crtera of QoS and fve alternatve servces. Keywords: Web Servce, Web Servce Selecton, MCDM, HP, VIKOR. 1. Introducton Nowadays, web servces are one of the most wdely used groups of SO and servce computng. lot of organzatons and companes develop applcatons whch are accessble through Internet. Therefore, the capablty of selectng correctly and combnng nter organzatonal and varous servces at runtme on the Web s an sgnfcant ssue n the Web Servce applcatons development [1]. One of the most sgnfcant current dscussons n SO s Servce Selecton whch should evaluate dscovered servces and choose the best canddate from them. Web Servce Selecton s appears when there s a set dscovered Web Servces that can fulfll the user s requrements; one of these servces should be selected to return back to servce consumer [2]. When more than one Web Servce whch meets functonal requrements s avalable, the Web Servce Selecton uses some crtera to select the best canddate servce. It s essental that ths selecton s talored to the users preferences whle one user may have need of hgh qualty another may requre low prce []. The value of non functonal propertes n these matchng Web Servces may be dfferent, but essentally they should have mnmum requrements. The selecton crtera may have an nterdependent relatonshp together. number of methods for decson makng are addressed n Web Servce Selecton because of the complcaton that exsts durng the selecton process. [4]. Two sgnfcant tasks n the process of usng servces are selecton and rankng n whch every soluton for them s affected drectly on descrpton of servces. Durng descrbng a servce, three tems have to be consdered: behavor, functonal, and nonfunctonal. The non functonal propertes (QoS) of the servces are used as crtera for selectng servces. On one hand, the maorty of servce selecton technques apply QoS and on other hand behavor of QoS based servce selecton, let researchers to lead servce selecton problem to Mult Crtera Decson Makng (MCDM). There are some MCDM technques that ths paper presented VIKOR to solve servce selecton problem to dentfy best canddate servce [5]. The VIKOR s a method for mult crtera optmzaton of complex systems. It determnes the compromse ranknglst, the compromse soluton, and the weght stablty ntervals for preference stablty of the compromse soluton obtaned wth the ntal (gven) weghts [6]. Rankng and selectng from a set of alternatves n the presence of conflctng crtera s goal of ths method. VIKOR addresses the mult crtera rankng ndex based on the partcular measure of closeness to the deal soluton [7]. VIKOR s a useful method n Servce Selecton problem based on MCDM because t can be work on stuaton where the preference of user s not clarfed at the begnnng of selecton process. Frst of all the decson matrx s arranged based on QoS crtera and alternatve servces and the weghts of each crteron wll be gathered based on user preference then the VIKOR method wll be appled. Consequently, based on the preference of servce requester obtanable servces wll be ranked. The remander of ths paper s structured as follows: Secton 2 outlnes the related works of the Web Servce Selecton based of MCDM; n Secton the proposed approach and applyng on Servce Selecton are dscussed; Secton 4 IJCER Jan-Feb 2012 Vol. 2 Issue No Page 190

2 Computatonal Engneerng Research / ISSN: llustrate the method by an arthmetc example; Secton 5 s concluson of ths paper n whch the future works for Web Servce Selecton are dscussed. 2. Related Works There are some Servce Selecton researches based MCDM [8], [9], [10], [11], [], [12], [1], [14]. We nvestgate these researches based on some crtera such as User preference, utomaton, Scalablty, and aggregaton functon. These crtera are addressed n prevous work [15]. Wang et al [8] present a model to select the exact web servce based on user s preference. Ths model s a fuzzy decson makng model that also wll be used by ndependent thrd-party n an expermental QoS envronment n the Internet to dstngush web servce level for web servce provders and lend a hand to requesters to make the rght selecton. In order to assst servce provders and requesters wth consderaton of ther preferences, Huang n [9],[10] attempt to propose a method based on fuzzy group decson makng wth respectng to Smlarty ggregaton Method (SM). lthough the ggregaton method n MCDM s addressed, aggregaton functon and user preference are mssed n ths research. Kerrgan [] presents a vson of Servce Selecton mechansms n the WSM that addressed the decson makng n manual, automated, and hybrd selecton methods. Ths paper dd not address such crtera as aggregaton functon user preference and scalablty n Servce Selecton mechansm. Toma et al [12] proposes a mult crtera rankng approach for web servces selecton. Frst the ontologcal models are appled on non functonal propertes (QoS) then they used to specfy the rules. These rules are evaluated by rankng method evaluates by a reasonng engne and fnally a ranked lst of servces wll be generated based on user preference. lso the scalablty s addressed n ths paper. But regardng the automaton there s no nformaton n ths research. For the selecton of a logstc servce provder (LSP) Yng et al [1] present a comprehensve methodology based on NP and VIKOR. s t s an mportant aspect to choose the best logstcs servce provder for logstcs management, t s dvded two components. NP s addressed to assure weght of crtera and n second dvson the VIKOR method s appled to solve MCDM problem. Lo et al [14] apply fuzzy TOPSIS method for solvng the servce selecton problem wth respect of user s vson. Frst for estmatng the weghts of each crtera the lngustc terms stands for trangular fuzzy numbers are exploted then the fuzzy TOPSIS s appled to resolve the MCDM problem n servce selecton. In ths paper the HP method s used to evaluate weghts of crtera and VIKOR method s appled to resolve the servce selecton problem n vew of decson makng. In the research frst the par wse comparson matrx s arranged then we apply HP to reach weghts of crtera. Moreover, the decson matrx based on QoS crtera and a set of alternatve servces s generated and fnally the VIKOR method s concerned.. Proposed pproach for Web Servce Selecton In ths secton we proposed our approach. In ths approach, as shown n Fgure 1, frst the weghtng of crtera wll be evaluated by the HP and then for decson makng we use the VIKOR method. The steps of our approach are shown n below: IJCER Jan-Feb 2012 Vol. 2 Issue No Page 191

3 Computatonal Engneerng Research / ISSN: Recognze Relaton of Crtera HP Weghts of Crtera VIKOR Decson Matrx Rankng of Servces Fgure 1 Process of the Proposed pproach.1. Weghtng of Crtera by HP HP s a process for developng a numercal score to rank each decson alternatve based on how well each alternatve meets the decson maker s crtera [16]. In ths paper, we explan brefly how to apply HP for fndng the weghs of crtera and explanng of the formulas n more detals s out of the scope of ths paper. HP s a par wse comparson method that each crteron s comparng to each other and gets the score wth respect to Table 1: Table 1. Standard Preference Table PREFERENCE LEVEL Equally preferred 1 Equally to moderately preferred 2 Moderately preferred Moderately to strongly preferred 4 Strongly preferred 5 Strongly to very strongly preferred 6 Very strongly preferred 7 Very strongly to extremely preferred 8 Extremely preferred 9 NUMERICL VLUE The crteron that has better level wll get the numercal number mentoned n the table and the other wll get the recprocal of the value. To evaluate weghts of crtera a matrx should be created; based on the defnton the sample of matrx for three crtera s shown below: IJCER Jan-Feb 2012 Vol. 2 Issue No Page 192

4 Computatonal Engneerng Research / ISSN: Fgure 2: Matrx for evaluatng weghts of crtera In the followng the steps of HP are descrbed: Step1: sum all the values n each column. Step2: The values n each column are dvded by the correspondng column sums. Step: Convert fractons to decmals and fnd the average of each row. Ths sum s correspondng to weght of the crteron of the row..2. Decson Makng by VIKOR In ths secton we focus on how to apply VIKOR on Servce Selecton. VIKOR method s sutable for the system whch the preferences of crtera are not clear at the begnnng of system and t can compromse the result durng the process of system. To propose the method on servce selecton we suppose that there are m alternatve servces,,... wth respect of n crtera whch are used for evaluatng the decson matrx: 1, 2 m The steps of VIKOR method on servce selecton are as follows: Step 1. s the scales of each crteron are not equvalent the decson matrx should be normalzed, whle the scales of Response tme and Cost are dfferent. For ths purpose VIKOR method uses lnear normalzaton. In VIKOR method once the scale of crtera wll be changed the result s stable because the lnear normalzaton. In Eq. (1), (2) the normalzaton formulas are shown: )1( nd n S w 1 Where 1,2,,..., m and 1,2, n * ( * * ( R ) Max w *,,..., are the elements of the decson matrx (alternatve respect to crtera ). are best and worst elements n crtera respectvely and represents the weghts of crtera (relatve mportance). ) )2( IJCER Jan-Feb 2012 Vol. 2 Issue No Page 19

5 Computatonal Engneerng Research / ISSN: Step 2. Compute the ndex values. These ndex values are defned as: )( Where nd S R Mn S, S Mn R, R Max S Max R )4( )5( In the formula, v s ntroduced as a weght for the strategy of the maorty of crtera (or the maxmum group utlty ), whereas 1- v s the weght of the ndvdual regret. The value of v les n the range of 0_1 and these strateges can be compromsed by v=0.5. Step. The results are three rankng lsts. By sortng the values S, R, and Q n decreasng order. Step 4. Propose as a compromse soluton the alternatve whch s the best ranked by the measure Q (mnmum) f the followng two condtons are satsfed: C1. cceptable advantage: Where 2 s the alternatve wth second place n the rankng lst by Q;. M s the number of alternatve servces. C2. cceptable stablty n decson makng: The alternatve should also be the best ranked by S or/and R. set of compromse solutons s proposed as follow, f one of the condtons s not satsfed: lternatves or lternatves 1, 1 2,, and 2 f only the C2 s not satsfed, M M f the C1 s not satsfed; s determned by the below relaton for maxmum M. Q M 1 Q DQ The servce whch has mnmum value of Q s the most excellent alternatve. The core rankng result s the compromse rankng lst of alternatve servces, and the compromse soluton wth the advantage rate [6]. 4. Illustratve Example In ths part, an example s concerned to llustrate the VIKOR method and how to apply t on servce selecton. We assume that there are fve alternatve servces wth respect to four crtera. These crtera are most popular crtera based on QoS and these are: Response Tme, Securty, relablty, and Cost. The relatonshp between the crtera and alternatves can be seen n Fgure. : IJCER Jan-Feb 2012 Vol. 2 Issue No Page 194

6 Computatonal Engneerng Research / ISSN: Goal Response Tme Securty Relablty Cost S1 S2 S S4 S5 Fgure. Relatonshp between Crtera and Servces There are some solutons to evaluate the weghts of crtera: 1) from the feedback of servce requester whom used the servce before [15]; t calls Trust & Reputaton method 2) t can be evaluate by some decson makng method that n ths research we apply HP method. We do par wse comparng between crtera and the below matrx s the result of the comparson wth respect to Table 1. Table 2. Par wse Comparng Matrx R.T Securty Relablty Cost R.T Securty Relablty 1/ 1/2 1 1 Cost 1/ Based on the above table we apply steps1- n secton.1 and the weghts of crtera are as follow: W.7; W 0.15; W 0.28; W In the second step we must apply VIKOR as a MCDM method. We assume that based on above example the data of fve alternatves wth respect of four QoS crtera s gathered and the decson matrx s prepared based on the example: Table. Decson Matrx wth reverence to the QoS Crtera Crtera lternatves Response Tme Securty Relablty Cost Rght now the necessary feeds for the VIKOR method s ready, so n the followng based on gven example, we show how VIKOR method can resolve a MCDM problem n area of Servce Selecton: Frst S and R wll be computed, but as some of crtera are negatve (Response Tme, Cost) and some are postve (Securty, Relablty) the calculatng and comparng these crtera together s complex effort, thus the decson matrx should be normalzed, and the normalzed matrx can be seen n Table 4: IJCER Jan-Feb 2012 Vol. 2 Issue No Page 195

7 Computatonal Engneerng Research / ISSN: Crtera lternatves Table 4. Normalzed Decson Matrx Response Tme Securty Relablty Cost Rght now, the data n matrx are normalzed and t means that there s no dfferent between the type of crtera and all data are n same scale. In ths stuaton there s possblty of comparng data together. fter normalzaton, S and R can be calculated based on Eq. (1), Eq. (2): S 0.29 & R Followed by the ndex values s computed but before that S, S, R, and R be supposed to calculate by Eq. (4) and Eq. (5). S s the mnmum value and S s the maxmum value n table S also R and R are mnmum and maxmum value n table R. S 0.049, S R 0.049, R 0.70 & t ths tme based on the above matter the can be accessble. s the ndex value for rankng the alternatves; t can be calculated based on Eq. (). Below the and are shown: Q 0.92 & Q sorted Step4. In ths part we check whether the C1 and C2 are satsfed? For ths pont, frst we calculate the DQ then use the Eq. (6): The C1 s satsfed and has best stuaton n S and R so condton C2 also s satsfed. t ths tme we can confrm that servce alternatve s the best opton wth respect to crtera of QoS and weght of them. The fnal rankng lst s shown below: IJCER Jan-Feb 2012 Vol. 2 Issue No Page 196

8 Computatonal Engneerng Research / ISSN: Concluson In ths paper, frst we have studed the most promnent related researches and we propose a hybrd approach to support Web servce selecton. In our prevous work [5], however, we have collected the weghts of crtera drectly from servce requester. The evaluaton of relatve mportance of weghts of crtera has not been consdered. In ths paper, although, the data related to weghts of crtera are gathered from user, we address HP method for evaluaton weghtng of crtera nstead of usng the preference of servce consumer wthout evaluaton. Subsequently, the VIKOR method s addressed as a MCDM method to tackle servce selecton problem. Fnally, the VIKOR method s appled step by step to overcome servce selecton problem n the vew of MCDM. Moreover, n order to demonstrate how normalzaton of proposed method works and how t can be appled on servce selecton, we provde an example usng QoS crtera and some other alternatve servces. The result shows that our approach can select the best and most related canddates. cknowledgment Ths research s supported by Mnstry of Hgher Educaton (MOHE) Malaysa and RUG at Unverst Teknolog Malaysa (UTM) by Vot No. 00H68. References 1. Tabatabae, S.G.H., W.M.N.W. Kadr, and S. Ibrahm. comparatve evaluaton of state-of-the-art approaches for web servce composton Slema, Malta: Inst. of Elec. and Elec. Eng. Computer Socety. 2. Pan, Z. and J. Bak, QOS ENHNCED FRMEWORK ND TRUST MODEL FOR EFFECTIVE WEB SERVICES SELECTION. Journal of Web Engneerng, (4): p Kerrgan, M. Web servce selecton mechansms n the web servce executon envronment (WSM). 2006: CM. 4. Mansh, G., S. Raendra, and M. Shrkant, Web Servce Selecton Based on nalytcal Network Process pproach, n Proceedngs of the 2008 IEEE sa-pacfc Servces Computng Conference. 2008, IEEE Computer Socety. 5. Khezran, M., et al., Servce Selecton based on VIKOR method. Internatonal Journal of Research and Revews n Computer Scence, (5). 6. Oprcovc, S. and G.-H. Tzeng, Compromse soluton by MCDM methods: comparatve analyss of VIKOR and TOPSIS. European Journal of Operatonal Research, (Compendex): p Oprcovc, S., Multcrtera optmzaton of cvl engneerng systems. Faculty of Cvl Engneerng, Belgrade, (1): p Wang, P., et al. fuzzy model for selecton of QoS-aware web servces. n IEEE Internatonal Conference on e-busness Engneerng, ICEBE 2006, October 24, October 26, Shangha, Chna: Inst. of Elec. and Elec. Eng. Computer Socety. 9. Huang, C.L., et al., pplyng Sem-Order Preference Model n Content-Based Servce Dscovery. Internatonal Journal of Electronc Busness, (1): p Huang, C.-L., K.-M. Chao, and C.-C. Lo. moderated fuzzy matchmakng for web servces. n Ffth Internatonal Conference on Computer and Informaton Technology, CIT 2005, September 21, September 2, Shangha, Chna: Insttute of Electrcal and Electroncs Engneers Computer Socety. 11. Mohammady, P. and. md, Integrated Fuzzy VIKOR and Fuzzy HP Model for Suppler Selecton n an gle and Modular Vrtual Enterprse pplcaton of FMCDM on Servce Companes. The Journal of Mathematcs and Computer Scence, (1): p Toma, I., et al., mult-crtera servce rankng approach based on non-functonal propertes rules evaluaton. Servce- Orented Computng ICSOC 2007, 2007: p Yng, L. and Z. Zhguang. Selecton of Logstcs Servce Provder Based on nalytc Network Process and VIKOR lgorthm. 2008: IEEE. IJCER Jan-Feb 2012 Vol. 2 Issue No Page 197

9 Computatonal Engneerng Research / ISSN: Lo, C.-C., et al. Servce selecton based on fuzzy TOPSIS method. n 24th IEEE Internatonal Conference on dvanced Informaton Networkng and pplcatons Workshops, WIN 2010, prl 20, prl 2, Perth, ustrala: IEEE Computer Socety. 15. Khezran, M., et al. n evaluaton of state-of-the-art approaches for web servce selecton. 2010: CM. 16. Russell, R.S.a.T.I., Bernard W., ed. Operatons Management 200: New Jersey. uthors Informaton Motaba Khezran s currently a PhD canddate at Unverst Teknolog Malaysa. He receved hs BSc from IU Unversty of Iran (2007) and MSc from Unverst Teknolog Malaysa (2009). He s dong hs research n the feld of Software Engneerng and Semantc Web Servces. Software engneerng, web servces, semantc web and web ntellgence are among hs research nterests. He ntends to broaden hs perspectves n nterdscplnary felds towards a career n software engneerng and web engneerng. Motaba Khezran s the correspondng author and can be contacted at: m.khezran@eee.org. Wan M.N. Wan Kadr s an ssocate Professor n the Software Engneerng Department, Faculty of Computer Scence and Informaton Systems, UTM. He receved hs BSc from Unverst Teknolog Malaysa, MSc from UMIST and PhD n the feld of Software Engneerng from The Unversty of Manchester. He has been an academc staff at Software Engneerng Department for more than ten years, and he was the Head of the Department from 2005 to He s the Charman of the 2nd Malaysan Software Engneerng Conference (MySEC 06), and a member of pro-tem commttee of Malaysan Software Engneerng Interest Group (MySEIG). He serves as a Program Commttee member of the 5th, 4th, and rd Internatonal Conference on Software Engneerng dvances (ICSE 2010, ICSE 2009, ICSE 08), the 15th and 16th sa-pacfc Software Engneerng Conference (PSEC 2009, PSEC 2008), the 5th and 4th Internatonal Conference on Software and Data Technologes (ICSOFT 2010, ICSOFT 2009), the 5th and 4th Internatonal Conference on Novel pproaches n Software Engneerng (ENSE 2010, ENSE 2009), the 9th CIS Internatonal Conference on Software Engneerng, rtfcal Intellgence, Networkng, and Parallel/Dstrbuted Computng (SNPD2008), and the rd and 4th Malaysan Software Engneerng Conference (MySEC 07 and MySEC 08). Most of the proceedngs are publshed by IEEE. Hs research nterest covers varous SE knowledge areas based on the motvaton to reduce the cost of development and mantenance as well as to mprove the qualty of large and complex software systems. Suham Ibrahm s an ssocate Professor at the Centre for dvanced Software Engneerng (CSE), Faculty of Computer Scence and Informaton Systems, UTM. He s currently apponted as the Deputy Drector of CSE and s nvolved n several short terms and Natonal research schemes of software development, software testng and mantenance proects. He s an ISTQB certfed tester of foundaton level and currently beng apponted as a board member of the Malaysan Software Testng Board. He s actvely nvolved n syllabus and currculum revew of software engneerng at the bachelor and post-graduate levels. Hs research nterests nclude requrements engneerng, web servces, software process mprovement and software qualty. laeddn Kalantar s currently a PhD student n IS Lab at the Unverst Teknolog Malaysa. He receved hs MSc from Unverst Teknolog Malaysa (2009). He has proposed Securty Framework to Support Enterprse Servce Orented rchtecture (ESO) n hs master proect. He s pursung hs research n Servce Orented rchtecture (SO) and Semantc Web Servces. The man area of hs research s developng Semantc Web servces specfcaton based on Model-drven archtecture (MD). IJCER Jan-Feb 2012 Vol. 2 Issue No Page 198