Available online at ScienceDirect. Procedia Computer Science 94 (2016 )

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1 Avalable onlne at ScenceDrect Proceda Comuter Scence 94 (2016 ) The 13th Internatonal Conference on Moble Systems and Pervasve Comutng (MobSPC 2016) An Effcent QoS-aware Web Servces Selecton usng Socal Sder Algorthm Afaf Mousa a, Jamal Bentahar a * Concorda Insttute for Informaton Systems Engneerng, Concorda Unversty, Montreal Quebec, H3G 2W1,Canada Abstract Effcent QoS-aware web servces selecton from the numerous number of functonally substtutable web servces to delver comlex tasks s a current call from the busness world. QoS-aware web servces selecton s a mult-objectve otmzaton roblem. Current aroaches adat genetc algorthms (GA) and artcle swarm otmzaton (PSO) to solve t. However, the executon tme erformance of QoS-aware web servces selecton to acheve the maxmum ftness value s stll a concern for ractcal dstrbuted alcatons. Ths aer rooses an effcent technque to solve ths roblem usng the Socal Sder Algorthm (SSA). The exerments evaluate the effcency and feasblty of the roosed algorthm aganst PSO. SSA s found to outerform PSO n terms of both executon tme and ftness Publshed The Authors. by Elsever Publshed B.V. by Ths Elsever s an oen B.V. access artcle under the CC BY-NC-ND lcense (htt://creatvecommons.org/lcenses/by-nc-nd/4.0/). Peer-revew under resonsblty of the Conference Program Chars. Peer-revew under resonsblty of the Conference Program Chars Keywords: Otmzed;QoS;Web servces selecton;ssa. 1. Introducton Recently, web servces attract the attenton of the research communty to deal wth the contnuous develoment and deloyment of busness rocesses. Because a sngle web servce rovdes smle functon, t may not satsfy the user s requrements. Ths calls for web servces wth dfferent caabltes to be resonsve to the current envronment. To ths end, web servces comlement each other n web servces comoston to delver comlex * Corresondng author. Tel.: ; fax: E-mal address: af_mous@encs.concorda.ca, bentahar@cse.concorda.ca Publshed by Elsever B.V. Ths s an oen access artcle under the CC BY-NC-ND lcense (htt://creatvecommons.org/lcenses/by-nc-nd/4.0/). Peer-revew under resonsblty of the Conference Program Chars do: /j.rocs

2 Afaf Mousa and Jamal Bentahar / Proceda Comuter Scence 94 ( 2016 ) tasks. Servce-Orented Archtecture (SOA) rovdes an amcable way of ntegratng and reusng web servces by adotng standard nterfaces (WSDL) and standard rotocols such as Servce Orented Archtecture Protocol (SOAP) to delver comlex busness rocesses. Buldng web servces comostons consderng the numerous number of web servces whch are smlar n ther functonalty hghlghts the mortance of selectng the most sutable servces for that comoston. Web servces comoston conssts of multle servce nodes, where each node corresonds to a servce from a communty of servces (.e., a grou of functonally smlar web servces). Therefore, the number of comoston lans ncreases wth the ncreasng number of web servces er communty. Web servces are selected based on ther functonal roertes or non-functonal, qualty of servce (QoS), roertes. QoS selecton algorthms are desgned to meet users global constrants 1,2. QoS web servces selecton has been roven NP hard roblem 3. Many factors lead to ths hgh comlexty such as (1) the users set dfferent constrans for selecton and comostons, (2) QoS arameters of a web servce dffer than what s clamed by ts rovder, (3) the ncreasng number of avalable functonally smlar web servces and (4) wthn ths huge search sace, comostons may be bult by many ways. The last two factors ncrease tme comlexty of the web servces selecton. Early research solves web servce selectons as lnear rogrammng roblems 4,5. At resent, the selecton roblem s formalzed by the means of otmzaton algorthms 1,6,7. Genetc Algorthm (GA) and Partcle Swarm Otmzaton (PSO) algorthm are the most used aroaches for otmzng web servces selecton. Comared to GA, PSO has few arameters and fast convergence seed. The executon tme erformance of web servces selecton to acheve the maxmum ftness value n terms of aggregated QoS s stll a concern wth the contnues ncreasng number of avalable web servces for a task for ractcal dstrbuted alcatons. Moreover, modelng QoS arameters of ndvdual and comoste web servces, and the comoston technque are the key onts n web servces selecton otmzaton roblem. To ths end, ths aer uts forward an otmzaton algorthm for web servces selecton wthn a comoston scenaro. We adot Socal Sder Algorthm (SSA) whch, comared to PSO, has faster convergence and less executon tme to fnd the most otmzed ftness value. The aer s organzed as follows. Secton 2 resents QoS model of web servces and Secton 3 outlnes QoS model of comostons whch are used n the rest of the aer. Secton 4 ntroduces SSA for web servces selecton wthn comostons. Fnally, Secton 5 evaluates the erformance of the roosed aroach aganst PSO, the most oular used algorthm for the roblem. The aer s concluded n Secton QoS model of web servce Web servces wth smlar functonalty are groued nto communtes for better exosure 8,9. Those smlar web servces are dstngushed by ther QoS arameters durng servce selecton for comostons 10. To ths end, QoS model for web servces s adoted based on wdely used QoS crtera 2,5 namely, executon tme, avalablty, throughut and robablty of success. Dfferent QoS arameters descrbe the qualty n dfferent ways. For examle, short executon tme s referable for web servces whle hgh avalablty s requred. In other words, QoS arameters are dvded nto ostve and negatve arameters. So, they are normalzed as shown by Equaton (1) for negatve arameters and Equaton (2) for ostve arameters, where: (1<P<4) reresents the arameter number, Q (sj) s -th QoS arameter of web max mn servce s j and Q and Q are the maxmum and mnmum values of -th QoS arameter. max Q q( sj) max mn, Q 0 max mn Q Q ( sj) Q Q (1) max mn 1, Q Q 1

3 178 Afaf Mousa and Jamal Bentahar / Proceda Comuter Scence 94 ( 2016 ) Accordngly, the qualty of web servces s defned as follows 7 : mn q ( sj) Q max mn, Q 0 max mn Q Q ( sj) Q Q (2) max mn 1, Q Q 1 Defnton 1: QoS vector of web servce s j s defned as Q(s j)= [Q succ(s j), Q tme(s j), Q aval(s j), Q thr(s j)] where Q succ(s j), Q tme(s j), Q aval(s j) and Q thr(s j) are the normalzaton values of robablty of success, executon tme, avalablty and throughut and of web servce s j. 3. QoS model of comoston Web servces are ntegrated nto comostons to delver comlex tasks, whch cannot be rovded by a sngle web servce. Comoston s structured as a sequence, concurrent or loo model. Ths aer consders only the sequence structure; as other structures could be easly transformed to t 11.Web servce comoston focusses manly on the selecton of most arorate atomc web servces from dfferent web servces communtes to delver hgh qualty functons satsfyng user s requrements. As shown by Fgure 1 4, each block reresents web servces communty ncludng functonally smlar web servces and the arrows refer to dfferent comoston aths. Therefore, fndng the otmal ath from all ossble combnatons s an otmzaton roblem wth the condton of maxmzng the utlty value of the comoston Q(Cs). The followng defntons summarze the comonents of QoS model of web servces comoston 7, whle Table 1 lsts aggregatve QoS arameters of web servces comoston: Defnton 2: A web servces communty S = {s 1,,s j} s a grou of j (j>1) functonally smlar web servces wth dfferent QoS arameters. Defnton 3: A Web servces comoston Cs = {S 1,,S n} refers to n web servces from n communtes. Defnton 4: QoS vector of comoston Cs s reresented as Q(Cs)= [Q succ(cs),q tme(cs),q aval(cs),q thr(cs)] for the revous mentoned QoS arameters. In comostons, web servces have dfferent QoS values and QoS utlty functon s defned to ma QoS vector Q(Cs) nto a real number to facltate web servces rankng and selecton consderng global constrants and user s requrements. Defnton 5: QoS utlty functon of comoston Cs s defned as: (Cs) = wkq ( Cs), where w 1, k wk (3) Table 1. Aggregatve QoS arameters of web servces comoston. Probablty of success Executon tme Avalablty Throughut Q ( ) Q succ s scs tme ( s ) s Cs Q ( ) mn{ Qthr ( s ),..., Qthr ( s )} aval s scs scs

4 Afaf Mousa and Jamal Bentahar / Proceda Comuter Scence 94 ( 2016 ) s11 s21 sm1 start s12... s1x s22... s2y sm2... smz end t Fg. 1. Process of Web servces selecton. 4. SSA based comoston otmzaton and Exermental analyss 4.1 SSA algorthm for web servces comoston SSA s a swarm ntellgence algorthm, whch mmcs the foragng behavor of the socal sders to erform otmzaton tasks. It was roosed by 12 for global otmzaton roblems, contnues soluton sace. To adot t to solve web servces comoston roblem whch s a dscrete one, a dscretzaton method s used; namely Nearest Integer method 13. In our SSA-based selecton wthn web servces comoston, the search sace of the otmzaton roblem s formulated as a hyer-dmensonal sder web where each oston reresents a feasble soluton for web servces comoston (.e., selecton of ndvdual web servces for comoston). Sders are the basc oeratng agents of SSA. Each sder S has a memory to store ts current soluton Cs, the ftness value of ts soluton f (Cs), the target vbraton, v tar, the nactve degree d n,.e., the number of teraton snce last change of v tar, the movement n the revous teraton Cs ( t) Cs( t 1) where t s the teraton number and the dmenson mask M, a bnary vector wth length L where L s the dmenson of the otmzaton roblem. The frst two eces of nformaton dentfy the sder s characterstc, whle the others gude ts movement. SSA s dstngushed from other swam ntellgence algorthms by the vbraton. In SSA, a sder generates a vbraton whenever t changes ts soluton towards a better one, then the web roagates ths vbraton to other sders. The vbratons are dentfed by the source soluton and source ntensty. The ntensty of the vbraton I s calculated accordng to the ftness value of the source soluton f (Cs) as follows where Q max reresents the maxmum value of the ftness: max (4) I 1/( Q f ( Cs)) As a hyscal energy henomenon, a vbraton attenuates durng the roagaton rocess over the sder web. The dstance between sders D, Manhattan norm, s taken nto consderaton and the vbraton attenuaton s defned as follows where I D s the attenuated ntensty over the dstance roagaton D, s the mean of the stander devaton of the sders ostons and r a s a user-controlled attenuaton rate: I D D I e( r In SSA, the otmzaton rocess, whch s summarzed n Table 2, conssts of three hases, namely, ntalzaton, teraton and fnal. In the ntalzaton hase, SSA defnes the objectve functon, ntalzes the otmzaton search sace and arameters and ntalzes the oulaton of sders over the web randomly. In the teraton hase, SSA terates the otmzaton rocess untl the otmal soluton. Each teraton evaluates the ftness values of each sder n the oulaton (o), generates the vbraton by the sders and roagates those vbratons over the sder web. a ) (5)

5 180 Afaf Mousa and Jamal Bentahar / Proceda Comuter Scence 94 ( 2016 ) Each recevng sder selects the largest attenuated vbraton V best and comares the sorted vbraton V tar wth V best to store the largest; f V tar has been changed, the nactve degree d n s reset to zero. Otherwse, d n s ncremented by Table 2. SSA algorthm for web servces comoston. SSA algorthm: Assgn values to the arameters of SSA. Generate ntal web servces comoston solutons Cs. Assgn the oulaton of sders (o) for them. Intalze v tar for each sder. whle stong crtera not meet do for each sder s n o do Evaluate the ftness value of Cs usng equaton 3. Generate a vbraton for the soluton of s by equaton 4. end for for each sder s n o do Calculate the ntensty of the vbratons V generated by other sders usng equaton 5. Select the strongest vbraton v best from V. f The ntensty of v best s larger than v tar then Store v best as v tar. end f Udate d n Generate a random number r from [0,1]. n d If r > c then Udate the dmenson mask M. End f Generate new soluton by erformng a random walk usng equaton 7. Address any volated constrant by equaton 8. end for end whle Outut the best soluton found. d one. Each sders manulate ts mask M; M s changed by robablty 1- c where c (0, 1) s a user defned attrbute that dentfes the robablty of changng the mask, f so; each bt s set to one by robablty m and zero by robablty 1- m where m (0,1) s a user controlled attrbute. Each sder erforms a random walk guded by M. The value of -th dmenson of the followng soluton Cs s comuted as follows: fo n Cs fo tar Cs M r Cs M 0 1, (6) tar where Cs s the -th element of the source soluton of v tar r, Cs s a random soluton s vbraton such that r [0, o ] and M, s the -th dmenson of the mask M of the sder. Then, the sder moves followng the random walk formula: fo Cs( t+ 1) =Cs( t) + ( Cs( t) -Cs( t-1)) r ( Cs - Cs) R, where Cs (t+1) s the new soluton of the sder, Cs(t) s ts current soluton, R s a random generated vector from (7)

6 Afaf Mousa and Jamal Bentahar / Proceda Comuter Scence 94 ( 2016 ) (0,1) and s element-wse multlcaton oeraton. The second term moves the sder a dstance of a random orton of ts revous soluton Cs (t-1). Afterwards, the sder S stores ts movement for the next teraton calculaton. The sders could volate the otmzaton constrants by movng out of the maxmum and mnmum bounds, so the boundary constrants are handled as adotng the reflectng aroach as follows where x s the uer bound on the search sace and x s the lower bound. ( x Cs( t) ) rfcs ( t 1) x Cs ( t 1) ( Cs ( t) x ) rfcs ( t 1) x, (8) 4.2 Exerment settngs and data set To evaluate the roosed aroach n terms of effcency and feasblty, an nteger array-codng scheme s desgned where the number of tems n the array denotes the dmenson of our roblem and each element s an ndex of a web servce canddate. We use QWS dataset rovded by the Unversty of Guelh 14,15. We comare the roosed aroach to the long lastng otmzaton algorthm PSO. The values of PSO s arameters are w=0.7 and c 1=c 2=2, and SSA arameters values are r a= 1, c= 0.7, and m= 0.1. In the exerments, the oulaton sze s set to 15, and the number of teratons (stong crtera) s set to 30. The exerments are erformed on a lato wth Wndows 10, 2.2 GHz rocessor and 2GB Ram and the algorthms are mlemented n C Performance comarson To analyse the effcency of our aroach, the average executon tme of 10 tmes over dfferent number of tasks, 10, 50,100,150 and 200 tasks s consdered. The number of web servces canddates for those exerments s 50. The results of fgure 2 (a) show that the gradual ncrease of the number of tasks ncreases the dsarty of tme between our aroach and PSO. Thus, our aroach s much sutable to web servces selecton for real tme alcatons. In order to valdate the feasblty of our aroach, we comare ts otmzng results wth PSO over the revous settng. As fgure 2 (b) shows, SSA acheves better ftness values than PSO. 5. Concluson Wth the ncreasng number of avalable web servces, effcent QoS-aware web servces selecton becomes an NP hard roblem. Ths aer resented a mathematcal model of web servces comoston to be otmzed by Socal Sder Algorthm. Comare to PSO, SSA s found to have less executon tme. In addton, the exerments show Fg. 2. (a) Average executon tme over dfferent number of tasks; (b) Average otmzng results over dfferent number of tasks.

7 182 Afaf Mousa and Jamal Bentahar / Proceda Comuter Scence 94 ( 2016 ) that SSA has better global searchng ablty than PSO. For the tme beng, SSA s a better alternatve for QoS-aware web servces selecton roblems wth dfferent tasks and substtutable web servces. References 1. Ha Y, Yan L, Lu G. Dynamc redcton QoS-Aware web servce comoston model. JDCTA 2012; 6: Zeng L, Benatallah B, Dumas M, Kalagnanam J, Sheng QZ. Qualty drven web servces comoston. WWW 2003: Canfora G, D Penta M, Esosto R, Vllan ML. A lghtweght aroach for QoS-aware servce comoston. ICSOC Yu T, Zhang Y, Ln KJ. Effcent algorthms for Web servces selecton wth end-to-end QoS constrants. TWEB Zeng L, Benatallah B, Ngu AHH, Dumas M, Kalagnanam J, Chang H. QoS-aware mddleware for web servces comoston. IEEE Trans. Softw. Eng. 2004; 30: He J, Chen L, Wang X, L Y. Web Servce Comoston Otmzaton Based on Imroved Artfcal Bee Colony Algorthm. JNW 2013;8(9): Zhang T. QoS-aware Web Servce Selecton based on Partcle Swarm Otmzaton. JNW 2013;9(3): Yahyaou H, Maamar Z, Lm E, Thran Ph.Towards a communty-based, socal network-drven framework for Web servces management. FUTURE GENER COMP SY 2013;29(6): Elnaffar S, Maamar Z, Yahyaou H, Bentahar J, Thran Ph. Reutaton of Communtes of Web Servces - Prelmnary Investgaton. AINA 2008: Abdel Wahab O, Bentahar J, Otrok H, Mourad A. A survey on trust and reutaton models for Web servces: Sngle, comoste, and communtes. DECIS SUPPORT SYST 2015;74: Wang P. QoS-aware web servces selecton wth ntutonstc fuzzy set under consumer s vague erceton. EXPERT SYST APPL 2009; 36: Yu JJQ, L VOK. A socal sder algorthm for global otmzaton. Al. Soft Comut. 2015;30: Burnwal S, Deb S. Schedulng otmzaton of flexble manufacturng system usng cuckoo search-based aroach. Adv Manuf Technol 2013; 64: Al-Masr E, Mahmoud QH. Dscoverng the best web servce. WWW 2007: Al-Masr E, Mahmoud QH. QoS-based dscovery and rankng of web servces. ICCCN 2007: