Distributing Functionalities in a SOA-Based Multi-agent Architecture

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1 Dstrbutng Functonaltes n a SOA-Based Mult-agent Archtecture Dante I. Tapa, Javer Bajo, and Juan M. Corchado Departamento Informátca y Automátca Unversdad de Salamanca Plaza de la Merced s/n, 37008, Salamanca, Span {dantetapa, jbajope, corchado}@usal.es Abstract. Ths paper presents how functonaltes are dstrbuted my means of FUSION@, a SOA-based mult-agent archtecture. FUSION@ ntroduces a new perspectve for constructng multagent systems, facltatng the ntegraton wth servce-orented archtectures, and the agents act as coordnators and admnstrators of servces. FUSION@ makes use of several mechansms for managng and optmzng the servces dstrbuton. The results obtaned demonstrate that FUSION@ can effcently dstrbute functonaltes n dynamc scenaros at executon tme. Keywords: Mult-Agent Systems, Servces Orented Archtectures, Dstrbuted Computng. 1 Introducton Mult-agent systems are very approprate for resolvng problems n a dstrbuted way [9]. Agents have a set of characterstcs, such as autonomy, reasonng, reactvty, socal abltes, pro-actvty, moblty, organzaton, etc. whch allow them to cover several needs for dynamc envronments, especally ubqutous communcaton and computng and adaptable nterfaces. Agent and mult-agent systems have been successfully appled to several scenaros, such as educaton, culture, entertanment, medcne, robotcs, etc. [6], [15]. Moreover, the contnuous advancement n moble computng makes t possble to obtan nformaton about the context and also to react physcally to t n more nnovatve ways [9]. Nevertheless, complex systems need hgher adaptaton, learnng and autonomy levels than pure BDI model [3]. Ths can be acheved by modellng the agents characterstcs [21] to provde them wth mechansms that allow solvng complex problems and autonomous learnng [7]. However, excessve complex mechansms (e.g. data mnng, genetc algorthms, ndexng, learnng technques, vsualzaton, etc.) can cause malfunctonng and crashes n multagent systems developed wth current technologes such as agent platforms. Ths s because developers tend to ntegrate all functonaltes nsde the agents nternal structure, creatng agents wth hgh computatonal requrements. The Flexble User and ServIces Orented mult-agent Archtecture (FUSION@) [18] tres to solve ths problem. One of the most mportant characterstcs n FUSION@ s the use of ntellgent agents as the man components n employng a servce orented approach, focusng on dstrbutng the majorty of the systems Y. Demazeau et al. (Eds,): 7th Internatonal Conference on PAAMS'09, AISC 55, pp sprngerlnk.com Sprnger-Verlag Berln Hedelberg 2009

2 Dstrbutng Functonaltes n a SOA-Based Mult-agent Archtecture 21 functonaltes nto remote and local servces and applcatons. The archtecture proposes a new and easer method of buldng dstrbuted mult-agent systems, where the functonaltes of the systems are not ntegrated nto the structure of the agents; rather they are modelled as dstrbuted servces and applcatons whch are nvoked by the agents actng as controllers and coordnators. Ths approach optmzes usablty and performance because t can be obtaned lghter agents n terms of computatonal load. FUSION@ has been prevously presented n related papers [18], however, ths paper focuses on descrbng how FUSION@ mplements several mechansms for optmzng and managng functonaltes and resources n dynamc envronments. Through these mechansms, FUSION@ facltates the development of dstrbuted mult-agent systems and the agents have the ablty to dynamcally adapt ther behavour at executon tme. FUSION@ provdes an advanced flexblty and customzaton to easly add, modfy or remove applcatons or servces on demand, ndependently of the programmng language. It also formalzes the ntegraton of applcatons, servces, communcatons and agents. The proposed approach has been appled to a real scenaro to evaluate the performance n a mult-agent system for montorng Alzhemer patents. In the next secton, the specfc problem descrpton that essentally motvated ths research s presented. Secton 3 descrbes the man characterstcs of the FUSION@ archtecture and brefly explans the mechansms for dstrbutng and optmzng functonaltes and resources. Secton 4 presents the results and conclusons obtaned after testng the archtecture n a real scenaro. 2 Problem Descrpton and Background Excessve centralzaton of servces negatvely affects the systems functonaltes, overchargng or lmtng ther capabltes. Classcal functonal archtectures are characterzed by tryng to fnd modularty and a structure orented to the system tself. Modern functonal archtectures lke Servce-Orented Archtecture (SOA) consder ntegraton and performance aspects that must be taken nto account when functonaltes are created outsde the system. These archtectures are amed at the nteroperablty between dfferent systems, dstrbuton of resources, and the lack of dependency of programmng languages [5]. Servces are lnked by means of standard communcaton protocols that must be used by applcatons n order to share resources n the servces network [1]. The compatblty and management of messages that the servces generate to provde ther functonaltes s an mportant and complex element n any of these approaches. One of the most prevalent alternatves to these archtectures s agents and multagent systems technology whch can help to dstrbute resources and reduce the central unt tasks [1] [19]. A dstrbuted agents-based archtecture provdes more flexble ways to move functons to where actons are needed, thus obtanng better responses at executon tme, autonomy, servces contnuty, and superor levels of flexblty and scalablty than centralzed archtectures [4]. Addtonally, the programmng effort s reduced because t s only necessary to specfy global objectves so that agents cooperate n solvng problems and reachng specfc goals, thus gvng the systems the ablty to generate knowledge and experence.

3 22 D.I. Tapa, J. Bajo, and J.M. Corchado Agent and mult-agent systems combne classcal and modern functonal archtecture aspects. Mult-agent systems are structured by takng nto account the modularty n the system, and by reuse, ntegraton and performance. Nevertheless, ntegraton s not always acheved because of the ncompatblty among the agents platforms (e.g. JADE agents wth RETSINA or OAA agents). The ntegraton and nteroperablty of agents and mult-agent systems wth SOA and Web Servces approaches has been recently explored [1] [9]. Some developments are centred on communcaton between these models, whle others are centred on the ntegraton of dstrbuted servces, especally Web Servces, nto the structure of the agents [11] [14] [16]. Although these developments provde an adequate background for developng dstrbuted mult-agent systems ntegratng a servce orented approach, most of them are n early stages of development, wth lttle systems (f any) developed upon them so t s not possble to actually know ther potental n real scenaros. Please refer to [18] for a detaled comparson of these approaches. 3 FUSION@, A SOA-Based Mult-agent Archtecture FUSION@ [18] s a modular mult-agent archtecture, where servces and applcatons are managed and controlled by delberatve BDI (Belef, Desre, Intenton) agents, [3], [13], whch manly follows the prncples of the THOMAS archtecture [12]. Delberatve BDI agents are able to cooperate, propose solutons on very dynamc envronments, and face real problems, even when they have a lmted descrpton of the problem and few resources avalable [2], [8]. There are dfferent knds of agents n FUSION@, each one wth specfc roles, capabltes and characterstcs. Ths fact facltates the flexblty of the archtecture n ncorporatng new agents. FUSION@ defnes four basc blocks whch provde all the functonaltes of the archtecture. - Applcatons. Represent all the programs that can be used to explot the system functonaltes. They can be executed locally or remotely, even on moble devces wth lmted processng capabltes, because computng tasks are largely delegated to the agents and servces. - Agents Platform. Ths s the core of FUSION@, ntegratng a set of agents, each one wth specal characterstcs and behavour. An mportant feature n ths archtecture s that the agents act as controllers and admnstrators for all applcatons and servces, managng the adequate functonng of the system, from servces, applcatons, communcaton and performance to reasonng and decson-makng. - Servces. They are the bulk of the functonaltes of the system at the processng, delvery and nformaton acquston levels. Servces are desgned to be nvoked locally or remotely. Servces can be organzed as local servces, web servces, or even as ndvdual stand alone servces. - Communcaton Protocol. Ths allows applcatons and servces to communcate drectly wth the agents platform. The protocol s completely open and ndependent of any programmng language. Ths protocol s based on SOAP specfcaton to capture all messages between the platform and the servces and applcatons [5]. All external communcatons follow the same protocol, whle the communcaton among agents n the platform follows the FIPA Agent Communcaton Language

4 Dstrbutng Functonaltes n a SOA-Based Mult-agent Archtecture 23 (ACL) specfcaton. Applcatons can make use of agents platforms to communcate drectly (usng FIPA ACL specfcaton) wth the agents n FUSION@, so whle the communcaton protocol s not needed n all nstances, t s absolutely requred for all servces. There are pre-defned agents that provde the basc functonaltes of FUSION@. CommApp Agent s the agent s responsble for all communcatons between applcatons and the platform. It manages the ncomng requests from the applcatons to be processed by servces. It also manages responses from servces (va the platform) to applcatons. All messages are sent to Securty Agent for ther structure and syntax to be analyzed. CommServ Agent s responsble for all communcatons between servces and the platform. The functonaltes are smlar to CommApp Agent but backwards. Admn Agent sgnals to CommServ Agent whch servce must be nvoked. All messages are sent to Securty Agent for ther structure and syntax to be analyzed. Ths agent also perodcally checks the status of all servces to know f they are dle, busy, or crashed. Drectory Agent manages the lst of servces that can be used by the system. For securty reasons [17], FUSION@ does not nclude a servce dscovery mechansm, so applcatons must use only the servces lsted n the platform. However, servces can be added, erased or modfed dynamcally. There s nformaton that s constantly beng modfed: the servce performance (average tme to respond to requests), the number of executons, and the qualty of the servce (QoS). Ths last data s very mportant, as t assgns a value between 0 and 1 to all servces. All new servces have a qualty of servce (QoS) value set to 1. Ths value decreases when the servce fals (e.g. servce crashes, no servce found, etc.) or has a subpar performance compared to smlar past executons. QoS s ncreased each tme the servce effcently processes the tasks assgned. Supervsor Agent supervses the correct functonng of the other agents n the system. Supervsor Agent perodcally verfes the status of all agents regstered n the archtecture by sendng png messages. If there s no response, the Supervsor agent klls the agent and creates another nstance of that agent. Securty Agent analyzes the structure and syntax of all ncomng and outgong messages. If a message s not correct, the Securty Agent nforms the correspondng agent (CommApp or CommServ) that the message cannot be delvered. Ths agent also drects the problem to the Drectory Agent, whch modfes the QoS of the servce where the message was sent. Admn Agent decdes whch agent must be called by takng nto account the QoS and users preferences. Users can explctly nvoke a servce, or can let the Admn Agent decde whch servce s best to accomplsh the requested task. Ths agent also checks f servces are workng properly. It requests the Comm- Serv Agent to send png messages to each servce on a regular bass. If a servce does not respond, CommServ nforms Admn Agent, whch tres to fnd an alternate servce, and nforms the Drectory Agent to modfy the respectve QoS. Interface Agent was desgned to be embedded n users applcatons. Interface agents communcate drectly wth the agents n FUSION@ so there s no need to employ the communcaton protocol, rather the FIPA ACL specfcaton. The requests are sent drectly to the Securty Agent, whch analyzes the requests and sends them to the Admn Agent. These agents must be smple enough to allow them to be executed on moble devces, such as cell phones or PDAs. All hgh demand processes must be delegated to servces.

5 24 D.I. Tapa, J. Bajo, and J.M. Corchado 3.1 Servces Management and n partcular the Admn Agent, employs a mechansm composed of a set of technques that allows the archtecture to select the most approprate servce to meet a request at any gven tme. The mechansm to assgn the most approprate servce to respond to ths request begns when a new request s receved, takng nto account the followng parameters: the QoS value; the user preferences; and the estmated delvery tme. The frst two parameters are set n advance so t s not necessary to calculate by the Admn Agent. The executon tme s estmated usng a RBF (Radal Bass Functon) Neural Network. The reason for usng ths type of network s the speed n the tranng phase, compared to a Mult Layer Perceptron (MLP). The neural network s made up of three layers: the nput layer, an ntermedate/hdden layer and an output layer. The number of neurons n the nput layer s defned by the number of entres (.e. parameters) to each servce. In the case of arrays, each element counts as an entry to the servce. The number of neurons n the mddle layer s determned dynamcally, makng a cross-tranng and valdaton. The cross-valdaton s done through the method GCV (Generalzed Cross-Valdaton) [19]. The varaton n the number of neurons n the mddle layer s made by the followng algorthm. Frst are ntalzed the lst of errors e = {} and the lst of angles α = {}. Then the tranng s conducted wth a neuron n the mddle layer and the tranng value s stored n e 1 n the values lst e = e e 1. Next, the number of neurons n the ntermedate layer s ntalzed. If there s no pror tranng, t s ntalzed to 4n+1 where n s the number of neurons n the nput layer. However, f there s a pror tranng, t s ntalzed to 2n+1, where n s the number of neurons of the pror tranng. r s the number of neurons n the current layer. The tranng s conducted for r ntermedate neurons and the error n e r e = e e r s stored. Subsequently, the tranng s done for r/2 neurons and the error n er/2 e = e e r/2 s stored. The lnes that pass through the ponts r 1, r / 2 = ( er er / 2 ) and r 1, r / 2 = ( e1er / 2 ) are calculated and the angle α r / 2 formed by these lnes s obtaned. The angle α r / 2 s ntroduced n the lst of angles α = α r / 2. If there s only one angle on the lst of angles # α = 1 r s set to r/4 and the tranng s restarted. If # = 2 then the value of α, beng α the other exstent value, then: If <r/2 then r = r/2 and the tranng s restarted; else r = r/2+r/4 and the tranng s restarted. But selectng the two adjacent values to the left and rght of α r / 2 denoted by α and α j so that: If α > Else α j. If j=r/2+1 the value of neurons s set to r. Else, set the new value of r=r/2+(j-r/2)/2. If =r/2-1 the value of neurons s set to r. Else, set the new value of r=r/2-(r/2-)/2

6 Dstrbutng Functonaltes n a SOA-Based Mult-agent Archtecture 25 Fg. 1. Progresson of the number of neurons n the hdden layer Fnally, the tranng s carred out and the value of neurons n the ntermedate layer s set to r. Fgure 1 shows the progress of the algorthm searchng the lower angle α. The number of neurons n the mddle layer s shown n the X axs and the cross valdaton s shown n the Y axs. The values correspond to a smulaton, but for a real case there s only necessary to calculate the values that are obtaned n the sets lsted next. The sets of values studed at every moment are (23, 11, 1) (11, 5, 1) (17, 8, 1) (14, 7, 1) (20, 10, 1) (18, 9, 1). The end result s gven by (18, 9, 1) therefore, the number of neurons n the fnal layer s 9. The output layer conssts of a sngle neuron, whose output value s the tme predcton. Learnng conssts of an unsupervsed learnng phase for the mddle layer and other supervsed learnng phase for the output layer. A cross-valdaton s carred out at the tranng phase by varyng the number of neurons n the ntermedate layer n case of no new results obtaned. Frst, the tranng of the mddle layer and the output layer are carred out. The retranng of the network s carred out when the average tme s k tmes larger or smaller than the estmated tme for the last n executons. These values are defned n advance for each system. A cross-valdaton s done through the GVC method n order to determne the completon of the tranng. The avalable servces can be assgned once the executon tme of the requests n the queue s estmated. The assgnaton tres to maxmze the performance n terms of the tme requred to respond to all requests. Ths assgnaton must be effcent and dynamcally adaptable because t must be carred out n executon tme. For ths reason, a FIFO model has been chosen for each servce. Each servce has a queue assocated. There s a common queue where all requests are queued and managed by the Admn Agent. The allocaton to the queue of each servce s done takng nto account the requests assgned to each queue and the estmated performance gven by (1). The request s assgned to the queue n order to mnmze the cumulatve performance and the estmaton tme. r = 1 QoS ) t (1 p ) (1) ( Where: r s the estmated performance of the servce ; QoS s the qualty of servce whch corresponds to a value between 0.00 and 1.00; T s the estmated tme of duraton; and P are the preferences for the servce wth a value between 0 and 1. Beng n

7 26 D.I. Tapa, J. Bajo, and J.M. Corchado the number of servces, R the cumulatve performance for the queue, a new request s wth performance estmatons for the servce j r j wll be assgned to the queue R when satsfyng the followng condton. If the value of the preference s equal to 1, only the queues wth that value for the preference are taken nto account. { R + r,..., R + r } mn 1 1 n n (2) Assumng that t s assgned to the k queue, the new cumulated performance for the k queue wll be R k +r k and the queung mechansm contnues for the next servce. Fgure 2 shows a representaton of the queung mechansm. There s a prmary queue assocated wth all servces. Each request has an estmated performance r j and s assocated wth a queue untl all queues are occuped. In ths way, t s possble to assgn the requests enterng nto the system to each of the avalable servces. Ths leads to a better performance and optmzaton of resources. Fg. 2. Servces assgnaton through the queung mechansm 4 Results and Conclusons Several tests have been done to demonstrate f the mechansms n FUSION@ are approprate to dstrbute resources and optmze the performance of mult-agent systems. Most of these tests bascally consst on the comparson of two smple confguratons (System A and System B) wth the same functonaltes. These systems are specfcally desgned to schedule a set of tasks usng a plannng mechansm [6]. System A ntegrates ths mechansm nto a delberatve BDI agent, whle System B mplements FUSION@, modellng the plannng mechansm as a servce. Table 1 shows an example of the results delvered by the plannng mechansm for both systems. An agenda s a set of non organzed tasks that must be scheduled by means of the plannng mechansm or the planner servce. There were 30 defned agendas each wth 50 tasks. Tasks had dfferent prortes and orders on each agenda. Tests were carred out on 7 dfferent test groups, wth 1, 5, 10, 15, 20, 25 and 30 smultaneous agendas to be processed by the plannng mechansm. 50 runs for each test group were performed, all of them on machnes wth equal characterstcs. Several data have been obtaned from these tests, focusng on the average tme to accomplsh the plans. For System B fve planner servces wth exactly the same characterstcs were replcated.

8 Dstrbutng Functonaltes n a SOA-Based Mult-agent Archtecture 27 Table 1. Example of the results delvered by the plannng mechansm Tme Actvty 19:21 Exercse 20:17 Walk 22:00 Dnner Fg. 3. Tme needed for both systems to schedule smultaneous agendas Fgure 3 shows the average tme needed by both systems to generate the paths for a fxed number of smultaneous agendas. System A was unable to handle 15 smultaneous agendas and tme ncreased to nfnte because t was mpossble to perform those requests. However, System B had 5 replcated servces avalable, so the workflow was dstrbuted, and allowed the system to complete the plans for 30 smultaneous agendas. Another mportant data s that although the System A performed slghtly faster when processng a sngle agenda, performance was constantly reduced when new smultaneous agendas were added. Ths fact demonstrates that the overall performance of System B s better when handlng dstrbuted and smultaneous tasks (e.g. agendas), nstead of sngle tasks. The FUSION@ archtecture proposes an alternatve where agents act as controllers and coordnators. The mechansms mplemented n FUSION@ can dstrbute resources, explotng the agents characterstcs to provde a robust, flexble, modular and adaptable soluton that covers most of the requrements of a wde dversty of projects. All functonaltes, ncludng those of the agents, are modelled as dstrbuted servces and applcatons. By means of the agents, the systems are able to modfy ther behavour and functonaltes at executon tme. Developers can create ther own functonaltes wth no dependency on any specfc programmng language or operatng system. Intal results demonstrate that FUSION@ s adequate for dstrbutng composte servces and optmzng performance for mult-agent systems. The Admn Agent learns and reason, whch facltates the optmum dstrbuton of tasks and reduces the processng for the rest of the agents n the system. Future work conssts on applyng ths archtecture nto composte mult-agent systems, as well as extendng the experments to obtan more decsve data from applcatons that consume multple servces wth dfferent capabltes n heterogeneous scenaros.

9 28 D.I. Tapa, J. Bajo, and J.M. Corchado Acknowledgements. Ths work has been partally supported by the TIN C03-03 and the IMSERSO 137/07 projects. Specal thanks to Tulecom for the technology provded and the know-how supported. References 1. Ardssono, L., Petrone, G., Segnan, M.: A conversatonal approach to the nteracton wth Web Servces. In: Computatonal Intellgence, vol. 20, pp Blackwell Publshng, Malden (2004) 2. Bratman, M.E.: Intentons, plans and practcal reason. Harvard Unversty Press, Cambrdge (1987) 3. Bratman, M.E., Israel, D., Pollack, M.E.: Plans and resource-bounded practcal reasonng. In: Computatonal Intellgence, vol. 4, pp Blackwell Publshng, Malden (1988) 4. Camarnha-Matos, L.M., Afsarmanesh, H.: A Comprehensve Modelng Framework for Collaboratve Networked Organzatons. Journal of Intellgent Manufacturng 18(5), (2007) 5. Ceram, E.: Web Servces Essentals Dstrbuted Applcatons wth XML-RPC, SOAP, UDDI & WSDL, 1st edn. O Relly & Assocates, Inc., Sebastopol (2002) 6. Corchado, J.M., Bajo, J., Abraham, A.: GERAmI: Improvng the delvery of health care. IEEE Intellgent Systems, Specal Issue on Ambent Intellgence 23(2), (2008) 7. Corchado, J.M., Bajo, J., De Paz, Y., Tapa, D.I.: Intellgent Envronment for Montorng Alzhemer Patents, Agent Technology for Health Care. In: Decson Support Systems. Eslever, Amsterdam (n press, 2008) 8. Georgeff, M., Rao, A.: Ratonal software agents: from theory to practce. In: Jennngs, N.R., Wooldrdge, M.J. (eds.) Agent Technology: Foundatons, Applcatons, and Markets. Sprnger, New York (1998) 9. Greenwood, D., Lyell, M., Mallya, A., Sugur, H.: The IEEE FIPA approach to ntegratng software agents and web servces. In: Proceedngs of the 6th nternatonal Jont Conference on Autonomous Agents and Multagent Systems, AAMAS 2007, Honolulu, Hawa, pp ACM, New York (2007) 10. Jayaputera, G.T., Zaslavsky, A.B., Loke, S.W.: Enablng run-tme composton and support for heterogeneous pervasve mult-agent systems. Journal of Systems and Software 80(12), (2007) 11. L, Y., Shen, W., Ghennwa, H.: Agent-Based Web Servces Framework and Development Envronment. In: Computatonal Intellgence, vol. 20(4), pp Blackwell Publshng, Malden (2004) 12. Ossowsk, S., Julán, V., Bajo, J., Bllhardt, H., Bott, V., Corchado Rodríguez, J.M.: Open Issues n Open MAS: An abstract archtecture proposal. In: 12th Conferenca de la Asoscacón Española para la Intelgenca Artfcal (CAEPIA 2007), Salamanca, Span, vol. 2, pp (2007) 13. Pokahr, A., Braubach, L., Lamersdorf, W.: Jadex: Implementng a BDI-Infrastructure for JADE Agents. In: EXP - n search of nnovaton (Specal Issue on JADE), Department of Informatcs, Unversty of Hamburg, Germany, pp (2003) 14. Rcc, A., Buda, C., Zaghn, N.: An agent-orented programmng model for SOA & web servces. In: 5th IEEE Internatonal Conference on Industral Informatcs (INDIN 2007), Venna, Austra, pp (2007) 15. Schön, B., O Hare, G.M.P., Duffy, B.R., Martn, A.N., Bradley, J.F.: Agent Assstance for 3D World Navgaton. LNCS, vol. 1, pp Sprnger, Hedelberg (2005)

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