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An agent-based peer-to-peerarchitecture for semantic discovery ofmanufacturing services across virtualenterprisesWenyu Zhang a , Shuai Zhang a , Ming Cai b & Jian Wu ba School of Information, Zhejiang University of Finance &Economics, Hangzhou, Chinab School of Computer Science, Zhejiang University, Hangzhou,ChinaVersion of record first published: 14 Dec 2012.
To cite this article: Wenyu Zhang , Shuai Zhang , Ming Cai & Jian Wu (2012): An agent-based peer-to-peer architecture for semantic discovery of manufacturing services across virtual enterprises,Enterprise Information Systems, DOI:10.1080/17517575.2012.747002
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An agent-based peer-to-peer architecture for semantic discovery of
manufacturing services across virtual enterprises
Wenyu Zhanga, Shuai Zhanga, Ming Caib and Jian Wub*
aSchool of Information, Zhejiang University of Finance & Economics, Hangzhou, China; bSchoolof Computer Science, Zhejiang University, Hangzhou, China
(Received 23 March 2012; final version received 2 November 2012)
With the development of virtual enterprise (VE) paradigm, the usage of service-oriented architecture (SOA) is increasingly being considered for facilitating theintegration and utilisation of distributed manufacturing resources. However, dueto the heterogeneous nature among VEs, the dynamic nature of a VE and theautonomous nature of each VE member, the lack of both sophisticatedcoordination mechanism in the popular centralised infrastructure and semanticexpressivity in the existing SOA standards make the current centralised, syntacticservice discovery method undesirable. This motivates the proposed agent-basedpeer-to-peer (P2P) architecture for semantic discovery of manufacturing servicesacross VEs. Multi-agent technology provides autonomous and flexible problem-solving capabilities in dynamic and adaptive VE environments. Peer-to-peeroverlay provides highly scalable coupling across decentralised VEs, each of whichexhibiting as a peer composed of multiple agents dealing with manufacturingservices. The proposed architecture utilises a novel, efficient, two-stage searchstrategy – semantic peer discovery and semantic service discovery – to handle thecomplex searches of manufacturing services across VEs through fast peer filtering.The operation and experimental evaluation of the prototype system are presentedto validate the implementation of the proposed approach.
Keywords: agent-based; distributed manufacturing; ontology; peer-to-peer;service discovery; virtual enterprise
Introduction
Due to the exponential growth of the Internet and increasing globalisation ofmanufacturing enterprises, virtual enterprise (VE) is becoming an emerginginformation management technology and a new engineering management paradigm(Tan et al. 2008, 2010, 2012). The success of a VE depends on the effectiveintegration and utilisation of distributed manufacturing resources provided byparticipating enterprises, in particular, small- and medium-sized enterprises (SMEs).
The availability of promising service-oriented architecture (SOA) (Duke et al.2005) and Web services presents a potentially feasible method to overcome thebarriers of seamless interoperability of distributed resources by eliminating thetechnical discrepancy of different software, platforms and infrastructures amongVEs. Nowadays, more and more enterprises have employed service-oriented
*Corresponding author. Email: [email protected]
Enterprise Information Systems
2012, 1–24, iFirst article
ISSN 1751-7575 print/ISSN 1751-7583 online
� 2012 Taylor & Francis
http://dx.doi.org/10.1080/17517575.2012.747002
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information architectures to deploy, manage and optimise industrial supply chains(Mietzner et al. 2011, Xu 2011a, 2011b, Li and Liu 2012). Various knowledge-basedrepresentation, modelling and analysis techniques, e.g. service workflow specificationlanguage (Viriyasitavat et al. 2012) and propositional logic (Xu et al. 2012), can beused to realise the seamless interoperability across organisational boundaries (Xuet al. 2009).
A service-oriented VE integrates the core resources of VE members bypublishing, discovering and sharing respective manufacturing services to competefavourably in the rapidly changing global market. However, due to theheterogeneous nature among VEs, the dynamic nature of a VE and the autonomousnature of each VE member, the lack of both sophisticated coordination mechanismin the popular centralised infrastructure and semantic expressivity in the existingSOA standards makes the current centralised, syntactic service discovery methodundesirable. This motivates the proposed agent-based peer-to-peer (P2P) architec-ture for semantic discovery of manufacturing services across VEs.
To facilitate the modelling of manufacturing services for agent-based machineprocessing rather than human understanding among VEs, the proposed architecturecombines the emerging Semantic Web technology with SOA to offer semanticmanufacturing services, therefore realising the semantic and seamless interoper-ability in integrating and utilising distributed manufacturing resources among VEs.The Semantic Web (Berners-Lee et al. 2001) possesses a huge potential to overcomeresource modelling difficulties over the web, by modelling the concepts in aknowledge domain with a high degree of granularity and formal structure includingreferences to mutually agreed-on semantic definitions in ontologies. Eachmanufacturing service can be enriched by adding semantic information with WebOntology Languages for Services (OWL-S) (Martin et al. 2004) ontology to leverageservice discovery, selection and invocation in more meaningful and accurate ways.
To cope with the dynamic and adaptive property of service-oriented VE fordistributed manufacturing, the proposed architecture enhances SOA with multi-agent support, as agent technology is considered as an intelligent solution thatprovides autonomous and flexible problem-solving capabilities in dynamic andadaptive environments. In a VE, agents are used to represent dynamic, autonomousand adaptable entities, which form a knowledge-based service-oriented cooperativenetwork to publish, discover, select, invoke and execute manufacturing services.Inter-agent search and communication are realised by exploiting the popularFoundation for Intelligent Physical Agents-Agent Communication Language(FIPA-ACL) (FIPA 2002) and Directory Facilitator (DF), with OWL (McGuinnessand Harmelen 2004) used as their content language, which supports the fault–tolerant anonymous interaction thanks to a common ontological foundation.
One of the most striking characteristics of a VE is that it is usually formed withshort-term business relationships when the business deal is struck, and dissolvedwhen the business process is finished. In other words, the agent coordination andservice interoperation across boundaries of different VEs are scalable, looselycoupled, self-organising and unpredictable. The proposed architecture utilises a P2Poverlay to provide highly scalable coupling across decentralised VEs, each of whichexhibiting as a peer composed of multiple agents dealing with manufacturingservices. The P2P interaction is deployed on the top of a Distributed Hash Table(DHT)-based protocol called Chord (Stoica et al. 2001). The proposed architectureutilises a novel, efficient, two-stage search strategy – semantic peer discovery and
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semantic service discovery – to handle the complex searches of manufacturingservices across VEs through fast peer filtering.
In summary, the P2P architecture provides a seamless and scalable integration ofmanufacturing services and multiple agents by making use of ontologies to facilitatetheir semantic interoperation, therefore assisting in the semantic discovery ofmanufacturing services across VEs in dynamic, adaptive, heterogeneous, open anddistributed manufacturing environment. The proposed architecture is backed up bya proof-of-concept implementation, where the operation and experimental evalua-tion of the system have been tested.
2. Related work
With VE, SOA, agent and P2P technologies rapidly proliferated in distributedmanufacturing environments, four streams of literature are relevant to this research,including VEs and enterprise integration, service-oriented manufacturing and servicediscovery, intelligent agent and enterprise integration, and P2P-based manufacturingapplications.
2.1. VEs and enterprise integration
Virtual enterprise represents a temporary alliance of autonomous, diverse andpossibly geographically dispersed enterprises that share the core resources orcompetencies among partners (Huang et al. 2011), and has been widely utilised forenterprise integration, in particular, partnership selection, synthesis and workflow.For example, Zhang et al. (1997) discussed the related attributes for VEmanufacturing systems design in relation to partner factories selection. Chu et al.(2002) divided the partner synthesis activity for VE into two phases: partner typesynthesis and partner instance synthesis. In the first phase, group technology isapplied for retrieving and selecting potential partners; in the second phase, theanalytic hierarchy process method is employed to select the best partner from thepotential ones. Xu et al. (2008) presented an evolutionary approach to supportdynamic enterprise modelling for enterprise process cooperative scheduling andmanagement from the concepts of enterprise process evolution to zero-timeenterprise modelling and layered complex enterprise modelling. Huang et al.(2011) employed a vague set theory to deal with the partner selection problem in theformation of a VE, while the factors of satisfaction degree, due date, cost and theprecedence of tasks are taken into account.
However, due to the heterogeneous nature among VEs, the dynamic nature of aVE and the autonomous nature of each VE member, there exist the barriers ofseamless interoperability of distributed manufacturing resources provided byparticipating enterprises, which have not been well addressed in the existingliteratures.
2.2. Service-oriented manufacturing and service discovery
The emerging SOA presents a potentially feasible solution to the resourceintegration, in particular, resource discovery among heterogeneous and distributedsystems, e.g. distributed manufacturing systems, including VEs, by eliminating thetechnical discrepancy of different software, platforms and infrastructures. The
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prevalent Universal Description, Discovery and Integration (UDDI) registry is thede facto standard for keyword-based service matchmaking and retrieval, andcontributes to the convenient publication and discovery of Web services. However,UDDI lacks a compatible service repository to store semantic information aboutWeb services, which is a prelude to the efficient, accurate and automatic discovery ofthe required manufacturing services.
The above difficulty can be alleviated by the emerging ontology-based servicematchmaking and retrieval methods. For example, Jiang et al. (2008) extended theUDDI registry specification to include OWL-based semantic descriptions aboutmanufacturing services and to support reasoning of those descriptions for servicediscovery in distributed manufacturing environments. Ameri and Dutta (2008)proposed a graph-based Manufacturing Service Description Language, which is anupper ontology for the structural representation of manufacturing services tosupport semantic manufacturing service discovery through a manufacturing-compatible service repository. Manufacturing Service Description Language isextended in our previous work (Cai et al. 2011) by representing complexmanufacturing services from both aspects of structural knowledge and constraintknowledge using an ontology- and constraint-based service modelling approach, todeal with the more complex matches in manufacturing service matchmaking anddiscovery. Ren et al. (2008) presented an ontology-based service-oriented workflowexecution system for collaborative VEs by taking advantages of complementarystrengths of both OWL-S and Business Process Execution Languages. Sun et al.(2010) encapsulated manufacturing resources into Grid service objects, and realisedthe publishing and discovery of these heterogeneous manufacturing services usingservice proxy, Open Grid Service Architecture and ontology-based knowledge. Inenterprise industrial systems, SOA has made service-oriented interactions in businessworkflows become common (Xu et al. 2012). To reduce the complexity of serviceworkflow verification, Xu et al. (2012) proposed that the workflow and services areoperated under the same ontology enabling the seamless interoperation from thecompliance of syntactical difference of service interfaces.
However, most of the existing service-oriented manufacturing systems areinflexible and rigid in terms of service interactions due to the passive roles ofdeployed Web services that represent the capabilities of interconnected manu-facturing resources. Therefore, the pro-active coordination and dynamic integrationof manufacturing services remain challenging problems in distributed VEenvironments.
2.3. Intelligent agents and enterprise integration
Agent technology is considered as an intelligent solution that provides autonomousand flexible problem-solving capabilities in dynamic and adaptive environments, andhas been widely applied in various areas, e.g. geographical information systems(Tang et al. 2001), Web-based knowledge discovery systems (Shan et al. 2003), flooddecision support systems (Luo et al. 2007), enterprise information systems (Tan et al.2012), etc. There are also some agent-based applications in distributed manufactur-ing environments, including service-oriented manufacturing environments and VEenvironments, which are most related to the current work in this article. Forexample, Shen et al. (2007) proposed an agent-based service-oriented integrationarchitecture to leverage manufacturing scheduling services on a network of VEs,
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with which, the scheduling process of an order is orchestrated through thenegotiation among agent-based Web services. Our previous work (Zhang and Yin2008) explored an ontology-based multi-agent system architecture for dynamicservice communication and collaborative negotiation among multidisciplinarymanufacturing organisations in distributed engineering design environments. Funget al. (2008) proposed a multi-agent architecture to manage VEs and support XML-based agent coordination using a contract net protocol and Web service standards.Vrba et al. (2011) integrated a generic discrete manufacturing ontology with adistributed, agent-based manufacturing control model, to support the intelligent,collaborative product ordering, production planning and material handling. Zhanget al. (2011a) presented an agent-based smart objects management framework forreal-time ubiquitous manufacturing, with which, smart objects are wrapped as agent-based service objects to facilitate their dynamic and semantic interaction andintegration without considering various kinds of interfaces among them.
However, most of the existing agent-based architecture for enterprise integrationutilised the centralised coordination mechanism, which makes it difficult to scale inhighly distributed networked environments, without relieving the bandwidth andserver bottleneck problems associated with the centralised services. This leads todrawbacks such as inflexibility, limited scalability, poor robustness and insufficientopenness in dealing with semantic discovery of manufacturing services acrossdifferent VEs due to its inherently decentralised nature.
2.4. P2P-based manufacturing applications
Collaborative design and manufacturing has become a hot research area (Li et al.2012, Ren et al. 2012, Wang and Xu 2012), as a design or manufacturing task in theglobal environment becomes more and more complex and requires computer-supported cooperative work from collaborative enterprises across disciplines,regions and platforms. The P2P technology provides avenues for the enterprises orVEs to share and manipulate collaborative manufacturing applications in adecentralised manner for its flexibility, scalability, robustness and openness. Forexample, Delamer and Lastra (2005) presented a P2P discovery protocol for thedynamic discovery of semantic manufacturing services in industrial environments.The protocol is designed for decentralised operation in embedded industrialcontrollers, and operates in a P2P topology without a need for centralised serviceregistries. Chen and Tien (2007) proposed a P2P network for real-time onlinecollaborative design in order to improve accessibility and flexibility in collaborativedesign and to provide a more load balanced and extensible environment. Fan et al.(2008) presented a distributed collaborative design framework with a hybrid of gridand P2P technology, aiming to support dynamic, flexible, multiparty collaborationthat integrates design, manufacturing and analysis within heterogeneous systems.Xiang et al. (2008) presented a P2P service discovery framework for a VirtualManufacturing Organisation (i.e. VE), in which, each peer (a VE) publishes themanufacturing services that it intends to share and maintains a list of friend peersthat it has recently interacted with, allowing a fully distributed and flexible servicediscovery process. Aldeeb et al. (2008) proposed a P2P-based inter-organisationalworkflow management framework, which enables VEs to dynamically form anddismantle partnerships between organisations, and supports various forms ofworkflow interoperability, e.g. capacity sharing, chained execution, subcontracting,
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case transfer, loosely coupled and public-to-private approach. Our previous work(Yin et al. 2010) has proposed an agent- and P2P-based Semantic Grid fordistributed manufacturing collaboration. A hierarchy of autonomous, adaptiveagents forming a ‘super peer’ based P2P network is laid out in the Semantic Grid toprovide, in an open, dynamic, loosely coupled and scalable manner, the servicepublication, discovery and reuse for the management of applications that need toutilise manufacturing Grid resources.
Similar to P2P-based manufacturing application, the concept of holonicmanufacturing systems, as initially proposed by Koestler (1989) and furtherdeveloped by international intelligent manufacturing project (Valekenaers et al.1997, Brussel et al. 1998), represents a new breed of decentralised manufacturingcontrol philosophy, and enhances autonomy, adaptability, responsiveness andflexibility to environmental changes and disturbances over the distributed smartentities. These entities are cooperative, intelligent, dynamic and autonomousmodules called ‘holons’, such as order holons, product holons, resource holonsand staff holons (Brussel et al. 1998). Using holonic representation, the distributedVEs can form different types of holarchy, including sequential holonic chains,hierarchical holonic structure or heterarchical (P2P-based) holonic structure.Because of the flexibility of P2P structure, the heterarchical holonic structure iswidely used in holonic manufacturing systems. For example, Jules et al. (2011)adopted the P2P-based holonic structure to develop a ring-based interactionprotocol of job allocation in distributed manufacturing networks. A ring of VEs isuni-directionally coupled with a P2P feedback mechanism. Owliya et al. (2012)presented an agent- and P2P-based holonic model with the advantage of both multi-agent and P2P interactions for dynamic task allocation among resource holonswithin the concept of holonic manufacturing systems. The model uses a P2P ringtopology of the resource holons, and is monitored by a supervisor holon.
Notwithstanding the promising results reported from existing research work forP2P-based collaborative work, including P2P-based holonic manufacturing systemsin manufacturing applications, to construct a valid search strategy of P2P systemthat is required to scale to a large number of peers in highly concurrent, distributedmanufacturing environments, remains a challenging problem. To address this issue,our proposed architecture utilises a novel, efficient, two-stage search strategy –semantic peer discovery and semantic service discovery – to handle the complexsearches of manufacturing services across VEs through fast peer filtering.
3. An agent-based P2P architecture for VEs using SOA
3.1. High-level overview of the system architecture
To address the heterogeneous nature among VEs, the dynamic nature of a VE andthe autonomous nature of each VE member, an agent-based P2P architecture forVEs using SOA is proposed for seamless and scalable integration of distributedmanufacturing services. Its high-level overview is shown in Figure 1, which isstructurally composed of a self-organising P2P overlay network of interconnectedVEs, enterprises (i.e. VE members), intelligent agents and manufacturing services. Agroup of enterprises may form into a VE through short-term business relationshipsby sharing their respective core manufacturing resources. In a VE, each enterprisebecomes a VE member. A service-oriented VE integrates the core resources of VEmembers by publishing, discovering and sharing respective manufacturing services to
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compete favourably in the rapidly changing global market. Each VE exhibits as apeer, and the cross-VE collaboration is realised by means of P2P interaction. Forexample, Enterprise 1 and Enterprise 2 form into a VE, which is represented as VE
Figure 1. Overview of the agent-based P2P architecture for VEs using SOA.
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peer 1. Each VE peer is composed of multiple agents interacting with manufacturingservices and forming a multi-agent architecture. Here, agents represent the dynamic,autonomous and adaptable entities, which form a knowledge-based service-orientedcooperative network to publish, discover, select, invoke and execute manufacturingservices. The agents reside in VE members within a VE peer. For example, VE peer 1is composed of all agents residing in both Enterprise 1 and Enterprise 2. TheResource agent 4-2 in VE peer 3 publishes a manufacturing service, which isdiscovered by the User agent 1-1 in VE peer 1.
3.2. Layered software system architecture
From the viewpoints of both software engineering and knowledge engineering,modular development of systems is necessary to offer independence and reusability(Feng et al. 2003, Tao et al. 2012). In the current work, a layered software systemarchitecture is designed to facilitate modular development of physical network, P2Poverlay network, multi-agent network, SOA network and presentation platform.These modules can be developed independently with each other, but loosely coupledwith each other thanks to a common ontological foundation. This architecture isdivided into five layers: resource layer, P2P overlay layer, agent layer, service layerand presentation layer (Figure 2).
3.2.1. Resource layer
The resource layer is situated in the physical network, and includes all types ofmanufacturing resources, e.g. computing resources (such as supercomputers, work-stations, clusters and visualisation servers), engineering resources (such as variousfinite element analysis (FEA) and computer-aided engineering (CAE) software),logistics resources (such as transport, radio frequency identification (RFID) reader,RFID device, blue tooth and barcode reader), storage resources (such as storage con-trollers, disk arrays, tape libraries, disk drives and tape drives), equipment resources(such as turning equipments, stamping equipments and milling equipments) and dataresources (such as real-time RFID data, real-time barcode data, and relational andXML data). With the development of agent-based manufacturing systems, holonicmanufacturing systems and service-oriented manufacturing systems, more and moremanufacturing resources will become interoperable smart objects forVEs, e.g. they canbe remotely connected, accessed, operated or controlled. To be semantics-aware foraccurate description, discovery and interoperation of manufacturing resources, acommon vocabulary or so-called ontology is integrated with the resource layer. Theontology is built with the formal representation language OWL that is the mostexpressive semantic markup language up to date on the Semantic Web.
3.2.2. P2P overlay layer
A VE contains a collection of manufacturing resources belonging to its VE members.Each VE interoperates with other VE using a P2P overlay layer, which addresses theneeds of decentralised organisations to collaborate and share the manufacturingresources by direct or indirect knowledge exchange. Technically, P2P computingeliminates the risk of bandwidth and server bottleneck problems associated with thecentralised infrastructure, and has better adaptability in terms of unstructuredness,
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load-balancing, scalability and openness because it is more convenient and efficientfor the low-end clients (e.g. SMEs) to advertise and access the resources according tothe load of physical network in a decentralised manner.
The proposed P2P overlay layer is deployed on the top of a DHT-basedprotocol called Chord (Stoica et al. 2001) to take advantage of its structuredindexing mechanism and efficient lookup service. Each peer maintains a fingertable of its neighbour peers, which will be considered to flood first from the currentpeer if the problem is not solved by the current peer. A traditional peer lookup inChord ring uses keyword-based routing, i.e. each peer is indexed in the DHTthrough Chord ring by keywords and discovered by the other peer by means ofkeyword-based matchmaking. However, the heterogeneity and incompatibilityamong distributed manufacturing resources over heterogeneous computing peers isstill a major obstacle to the exact keyword-based peer discovery. The proposedarchitecture has enhanced the Chord ring with ontological description such thatthe resource request of a peer is forwarded to the neighbour peers withsemantically matching resources.
3.2.3. Agent layer
To cope with the dynamic and adaptive property of service-oriented VE fordistributed manufacturing, the proposed architecture enhances P2P computing withmulti-agent support, by constructing an agent layer as a multi-agent network on thetop of P2P overlay layer. Here, the agents are used to represent dynamic,autonomous and adaptable entities for publishing, discovering, selecting, invokingand executing manufacturing services in an intelligent and cooperative manner. Tolimit the number of messages generated by information routing that may causerouting traffic jams, each VE peer is represented as a node of multi-agentarchitecture, in which multiple agents try to collaborate to solve a problem withina local VE. In this case, a request or problem is not routed from one peer to the otherpeer unless it cannot be solved in the inherent multi-agent architecture of a localpeer. Inter-agent search and communication are realised by exploiting the popularFIPA-ACL (FIPA 2002) and DF, with OWL (McGuinness and Harmelen 2004)used as their content language, which supports the fault–tolerant anonymousinteraction thanks to a common ontological foundation.
The agent layer mainly includes seven types of agents, i.e. resource agent, useragent, routing agent, discovering agent, selecting agent, executing agent andcoordinating agent, which are registered in DF. Resource agent is provided by aVE member to wrap up manufacturing resources as Web services to support service-oriented manufacturing. Each resource agent may deal with one or somemanufacturing resources. User agent invokes required manufacturing services onbehalf of a user. Routing agent routes a request from one VE peer to the other peer ifthe problem is not solved in the local peer. Discovering agent looks up wantedmanufacturing services with matching semantic capabilities in a local VE peer.Selecting agent selects a best manufacturing service from a few candidates that arediscovered across VE peers by discovering agents. Executing agent executes aselected manufacturing service after it is invoked by the user agent. Coordinatingagent coordinates the above agents through FIPA-ACL to solve the problem in amore sophisticated manner. Directory Facilitator serves as a registration repositoryfor various agents so that the required agents can be looked up to solve the problem.
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3.2.4. Service layer
The service layer virtualises and integrates the core manufacturing resources of VEmembers through resource agents that have capabilities of wrapping up manufactur-ing resources as Web services to support service-oriented manufacturing. Each
Figure 2. The layered software system architecture.
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manufacturing service is enriched by adding semantic information with OWL-S(Martin et al. 2004) ontology to leverage service discovery, selection and invocationin more meaningful and accurate ways. OWL-S service ontology consists of threemain parts: a service profile for service advertisement and discovery, a process modelthat supports composition of services, and a service grounding that tells how toaccess the service (Martin et al. 2004).
Private and public UDDI service registries and OWL-S service registries are usedby the resource agents to advertise both syntactic and semantic capabilities ofmanufacturing services, respectively. The user agents can look up the requiredmanufacturing services through these registries. A private service registry is built andmaintained by a VE member individually, and a public service registry is built by aVE and maintained by all its members together.
3.2.5. Presentation layer
The presentation layer provides three types of interfaces that support the systeminteraction with resource providers, resource users and knowledge engineers,respectively. The resource providers as VE members use these interfaces to managethe list of manufacturing services they provide, manage the corresponding resourceagents and select the VE peers to reside the resource agents. The resource users as VEmembers use these interfaces to input their service requests, look up the requiredmanufacturing services, manage the corresponding user agents and select the VEpeers to reside the user agents. The knowledge engineers use these interfaces tomanage various agents (including routing agents, discovering agents, selectingagents, executing agents and coordinating agents), DFs, various service registries(including private and public UDDI service registries and OWL-S service registries),VE peers and their finger tables, and various domain ontologies, general ontologiesand service ontologies.
4. Semantic discovery process of manufacturing services using agent-based P2P
architecture
The authors seek to apply agent-based P2P architecture to realise a novel, efficient,two-stage search strategy – semantic peer discovery and semantic service discovery –to handle the complex searches of manufacturing services across VEs through fastpeer filtering.
4.1. Illustrative overview of a simplified semantic discovery process
Let us take the VEs in Figure 1 as an example to illustrate the major steps in thesemantic discovery process using agent-based P2P architecture. An illustrativesimplified process is demonstrated in Figure 3.
Step 1: User agent 1-1 of Enterprise 1 in VE peer 1, who requires a manufacturingservice, asks Discovering agent 1-2 to search the private service registry in Enterprise1 for a semantically matching manufacturing service, but fails to find the matchingone.
Step 2: User agent 1-1 asks Discovering agent 1-2 to search the public serviceregistry in VE peer 1 for a semantically matching manufacturing service, but fails.
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Step 3: User agent 1-1 asks Discovering agent 1-2 to search the private serviceregistry in Enterprise 2 through multi-agent communication in VE peer 1, but fails.
Step 4: Routing agent 1-3 in VE peer 1 selects neighbour VE peers 2 and 3 fromVE peer 1’s finger table to route the service request.
Step 5: Routing agent 1-3 in VE peer 1 routes the service request to the Routingagent 3-2 in VE peer 2.
Step 6: Routing agent 1-3 in VE peer 1 routes the service request to the Routingagent 5-4 in VE peer 3.
Figure 3. Illustrative overview of a simplified semantic discovery process.
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Step 7: Routing agent 3-2 in VE peer 2 informs Routing agent 1-3 in VE peer 1that VE peer 2 does not have the semantically matching peer service throughsemantic peer discovery.
Step 8: Routing agent 5-4 in VE peer 3 informs Routing agent 1-3 in VE peer 1that VE peer 3 has the semantically matching peer service through semantic peerdiscovery.
Step 9: Routing agent 5-4 asks Discovering agent 5-2 to search locally for thesemantically matching manufacturing service through semantic service discovery inVE peer 3 and find Resource agent 4-2 that publishes a matching manufacturingservice.
Step 10: Further connection between Routing agent 1-3 in VE peer 1 andRouting agent 5-4 in VE peer 3 is built, and Resource agent 4-2 in VE peer 3 returnsthe matching manufacturing service to User agent 1-1 in VE peer 1.
The subsequent subsections will elaborate on the detailed semantic discoveryprocess of manufacturing services.
4.2. Semantic discovery of manufacturing service within a local VE peer
When a resource user looks up a manufacturing service, the search is firstlyconducted in the local VE peer to alleviate the routing traffic jams across VE peers(Figure 4).
A local VE peer is represented as a node of multi-agent architecture, in whichmultiple agents including resource agents, user agents, routing agents, discoveringagents, selecting agents, executing agents and coordinating agents residing in anagent platform try to solve the problem in the collaborative manner. Inter-agentsearch and communication are realised by exploiting the popular FIPA-ACLlanguage and DF, with OWL used as their content language. DirectoryFacilitator serves as a registration repository for various agents so that therequired agents can be looked up to solve the problem. The resource agent ofeach VE member wraps up manufacturing resources as Web services andadvertises their syntactic and semantic capabilities through private UDDI serviceregistry and OWL-S service registry, respectively. Some widely used manufactur-ing services are advertised in public UDDI service registry and OWL-S serviceregistry of the VE peer.
The semantic discovery process of manufacturing service starts with thetransformation of initial service request in natural language into semantic servicespecification in OWL-S format, by analysing the structure of service request usingOWL-S parser. Then, the user agent finds a local discovering agent through DF,which searches the private OWL-S service registry for the semantically matchingservice description through service matchmaking mechanism. To calculate themanufacturing service similarities, there are already mature ontology-basedsimilarity calculation methods of manufacturing services in the literature (Ameriand Dutta 2008) and our previous work (Cai et al. 2011), which use a formaldefinition of the ontological concepts, i.e. manufacturing resource ontologies as thebasis of comparison. Domain-specific manufacturing resource ontologies that havebeen developed in our previous work (Zhang et al. 2009) can be used to underlie thecalculation of resource similarities. New joining VE peer can download themanufacturing resource ontology from its linking VE peer.
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Similarly, the discovering agent searches the public OWL-S service registry forthe semantically matching service description through service matchmakingmechanism. The discovering agent also asks the coordinating agent to establishthe communication network between different VE members in the same VE peer, sothat the discovering agent can search the private OWL-S service registry in other VEmembers for the semantically matching service description.
If a few candidate manufacturing services are found to be qualified to match thewanted service request, the best one among them should be chosen through serviceevaluation by a selecting agent using an extended genetic algorithm-based multi-objective decision-making method developed by the authors earlier (Zhang et al.2011b), which deals with the trade-offs among multiple objectives includingsimilarity, time, cost, quality and availability.
After a semantic service in the OWL-S service registry is found, its mappingsyntactic service in the UDDI service registry can be returned to the user agent forinvocation, followed by its execution by executing agent.
However, if the above semantic discovery process of manufacturing servicewithin a local VE peer does not find an appropriate manufacturing service, the
Figure 4. Semantic discovery of manufacturing service within a local VE peer.
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routing agent will select the neighbour VE peers in the local VE peer’s finger table toroute the service request for semantic discovery of peer service across VE peers.
4.3. Semantic discovery of peer service across VE peers
Traditional peer lookup in Chord ring uses keyword-based routing, which, however,cannot handle the heterogeneity and incompatibility among distributed manufactur-ing resources over heterogeneous computing peers. The proposed architecture hasenhanced the Chord ring with ontological description such that the service request ofa peer is forwarded to and processed in the neighbour peers with semanticallymatching services.
A VE peer contains some private and public service registries, each of whichcontaining a collection of manufacturing services. If such a vast of manufacturingservices in a VE peer are used to describe the VE peer service, the search space ofpeer service discovery will be too huge to find a matching peer in the reasonable time.This will also make many of VE peers busy, with the risk of bandwidth and serverbottleneck problems arising. To address this issue, a common manufacturing serviceontology that is known by all VE peers is constructed in order to identify only thetop-level service classes of a vast of manufacturing services in a VE peer as the peerservice, with all lower-level service subclasses ignored. New joining VE peer candownload the manufacturing service ontology from its linking VE peer. Forexample, referring to the left side of Figure 5, VE peer 1 contains two serviceregistries, in which some manufacturing services have been advertised, respectively.Service registry 1 contains Stamping service, Bending service, Progressive stampingservice, Cutting-off service and Piercing service. Service registry 2 contains Turningservice, CNC turning service, Finish turning service and Precision turning service. Theright side of Figure 5 shows a partial manufacturing service ontology, which showsBending service, Progressive stamping service, Cutting-off service and Piercing serviceare all subclasses of Stamping service. It also shows CNC turning service, Finishturning service and Precision turning service which are all subclasses of Turningservice. To reduce the search space of peer service discovery, only top-level serviceclasses Stamping service and Turning service are used to identify the peer service ofVE peer 1.
Referring to Figure 6 for semantic discovery of peer service across VE peers,after the service request is routed from one VE peer to one of its neighbour peers,a vast of manufacturing services in the neighbour peer are not checked one byone for online service matchmaking, but only few offline identified peer servicesare checked for service matchmaking for fast peer filtering. If the neighbour peerdoes not have a semantically matching peer service, it will be fast filtered out andthe service request is further routed to the next neighbour peer. If the neighbourpeer has a semantically matching peer service, it will be scrutinised by checkingall the manufacturing services in its service registries for semantic matchmakingwith the service request.
Referring to Figure 7, the above semantic peer discovery process can beillustrated with a sequence diagram through the use case that has been brieflyintroduced in Section 4.1. In this case, VE peer 1, which cannot find a matchingmanufacturing service in its local service registries through semantic servicediscovery, firstly routes the service request to its neighbour VE peers 2 and 3,trying to identify the ones whose peer service semantically matches the service
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Figure
5.
Anillustrativeexample
inidentifyingthetop-level
serviceclasses
asthepeerservicewiththeaid
ofmanufacturingserviceontology.
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request through semantic peer discovery. It is found that only VE peer 3 has amatching peer service. Then, VE peer 3 searches in its local service registries for thesemantically matching manufacturing service through semantic service discovery,
Figure 6. Semantic discovery of peer service across VE peers.
Figure 7. Illustrative sequence diagram of semantic peer discovery process.
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and finds a matching manufacturing service. Finally, the matching manufacturingservice is returned to VE peer 1 for invocation.
5. System operation and evaluation
To demonstrate the feasibility of the proposed approach for semantic discovery ofmanufacturing services across VEs, a Java-based software prototype is implementedin the below network environment: (1) application server: 4 CPU, 3.3G Frequency,4GB Memory, 72GB SCSI HD, Redhat Linux 11.0, JBoss 5.0; (2) DB server: 4CPU,3.3G Frequency, 4GB Memory, 72GB SCSI HD, Redhat Linux 11.0, My SQL 5.0;(3) 10 testing clients: 2CPU, 2.0G Frequency, 1GB Memory, 80 GB HD, LoadRunner 7.8 and (4) network configuration: 1000M ethernet network connectionbetween application server and DB Server; 100M ethernet network connectionbetween testing clients and application server.
The software prototype was developed in Java programming language enhancedwith ontological paradigm, in which, manufacturing service, manufacturingresource, manufacturing concept, general concept, service registry, VE peer, peerservice, finger table, agent, DF, user interface, etc. are all represented with ontology-based class objects.
Different agent platforms including LEAP, Jini and Aglets are used to formheterogeneous multi-agent networks. Agents are programmed in Java applets,ActiveX controls or Javascript, and support the communication primitives of FIPA-ACL. A multi-agent network forms a VE peer on the top of Sun’s JXTA P2P overlaynetwork (http://www.jxta.org) enhanced with ontology-based Chord DHT manage-ment protocol. Diverse manufacturing resource ontologies represented in OWLformat and manufacturing service ontologies represented in OWL-S format areloaded into the system.
5.1. Evaluating the practicality of prototype system
This subsection illustrates an example of practical manufacturing service discovery inthe stamping process planning of a sheet metal part, which requires the manu-facturing service capabilities described below:
(1) Service type: stamping service.(2) Service target: sheet metal part whose material type is copper.(3) Service operations: piecing hole, bending flat and cutting-off scrap.(4) Input parameters: sheet metal size with 500 mm 6 200 mm 6 10 mm.(5) Output parameters: hole diameter with 15 mm, and bending width with
100 mm.
Figure 8 shows the graphical user interface for manufacturing service discoveryin the prototype system. The left window displays the hierarchical concepts of theconstructed ontologies including general ontology, VE peer ontology, agentontology, manufacturing service ontology, manufacturing resource ontology, etc.The service request can be developed by clicking at the ‘Build query tree’ in themiddle window, which makes it easy for users to formulate service request. The rightwindow shows an automatically generated query tree that is easy for userunderstanding.
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The service request can be converted into a semantic service specification inOWL-S format automatically. The service discovery process finds an advertisedmanufacturing service named Progressive stamping service-Solarkey, which has thefollowing manufacturing service capabilities corresponding to that of the servicerequest:
(1) Service provider: Solarkey Industries Co. Ltd.(2) Service type: progressive stamping service.(3) Service target: sheet metal part that’s made of copper, steel or aluminium.(4) Service operations: piecing, bending, cutting-off, extruding, embossing,
punching and blanking.(5) Input parameter: sheet metal size less than 600 mm 6 300 mm 6 20 mm.(6) Output parameters: hole diameter between 5 and 30 mm, and bending width
less than 200 mm.
The above service discovery result is satisfactory because of the followingmatchmaking process.
(1) The required service type, i.e. Stamping service is satisfied by the discoveredservice, which has service type Progressive stamping service, becauseProgressive stamping service is a subclass of Stamping service according tothe defined manufacturing service ontology.
Figure 8. Graphic user interface for manufacturing service discovery in the prototypesystem.
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(2) The required service target, i.e. Sheet metal part whose material type is copperis satisfied by the discovered service, which has service target Sheet metal partthat’s made of copper, steel or aluminium, because Made of is synonymouswith Material type according to the defined general ontology.
(3) The required service operations, i.e. Piecing hole, Bending flat and Cutting-offscrap are satisfied by the discovered service, which has service operationsPiecing, Bending, Cutting-off, Extruding, Embossing, Punching and Blanking,because Piecing hole is a subclass of Piecing, Bending flat is a subclass ofBending and Cutting-off scrap is a subclass of Cutting-off according to thedefined manufacturing resource ontology.
(4) The required input parameter, i.e. Sheet metal size with 500 mm 6 200mm 6 10 mm are satisfied by the discovered service, which has inputparameters Sheet metal size less than 600 mm 6 300 mm 6 20 mm, becausethe former parameter is covered by the latter parameter.
(5) The required output parameters, i.e. Hole diameter with 15 mm, and Bendingwidth with 100 mm are satisfied by the discovered service, which has outputparameters Hole diameter between 5 and 30 mm, and Bending width less than200 mm, because the former parameters are covered by the latter parameters,respectively.
5.2. Evaluating the scalability and efficiency of prototype system
To evaluate the scalability and efficiency of prototype system, and also to show theadvantages of our work that employs fast peer filtering strategy by a comparisonwith the implementation of existing other works that do not employ fast peerfiltering strategy, we use Load Runner 7.8 to simulate different numbers of VE peers(from 20 to 200) at 10 testing clients, with each VE peer advertising averagely 25manufacturing services. A series of experiments are done in very similarcircumstances to measure the average time required to semantically discover therequired manufacturing services for different numbers of VE peers. For each time ofservice discovery, the initiating VE peer is selected randomly. The measured time iscalculated from the time when the virtual user confirms the service request to thetime when the required manufacturing services matching the service request arediscovered, but does not include the service selection time of a best one throughservice evaluation.
In this experiment, two cases are studied, with the first one not employing the fastpeer filtering strategy and the second one employing the fast peer filtering strategy.The first case represents the implementation of existing other works, while thesecond case represents our proposed work. Figure 9 shows the results with theincreasing numbers of VE peers (where each data point is the average over 1000runs), but also shows the performance improvement when the proposed fast peerfiltering strategy is employed in our proposed work.
In the first case when the fast peer filtering strategy is not employed, the upperline in Figure 9 shows the average service discovery time increases exponentially(from 330 to 1680 ms) with the increasing numbers of VE peers from 20 to 200. Theactual numbers of VE peers and advertised manufacturing services can be muchmore than the numbers shown in the example, therefore the service discovery timewill increase very fast along with the increasing numbers of VE peers and advertisedmanufacturing services when the fast peer filtering strategy is not employed.
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In the second case when the fast peer filtering strategy is employed, the systemperformance is greatly improved, and the corresponding average service discoverytime becomes much lower than that in the first case. The lower line in Figure 9 showsthe average service discovery time only increases weak exponentially (from 210 to560 ms) with the increasing numbers of VE peers from 20 to 200. Clearly, the resultobtained is more optimal because it only takes 560 ms to discover the matchingmanufacturing services even when there are 200 VE peers involved.
The actual numbers of VE peers and advertised manufacturing services can bemuch more than the numbers shown in the example, therefore we conclude that ourmanufacturing service discovery approach is both scalable and efficient for solvinghighly concurrent, distributed manufacturing problems, and also outperforms theexisting other works that have not employed the fast peer filtering strategy.
6. Conclusion
To compete favourably in the rapidly changing global market, the ability toeffectively and efficiently discover the distributed and collaborative manufacturingresources is vital. This paper has presented an agent-based P2P architecture forsemantic discovery of manufacturing services across VEs. Using the SOA andSemantic Web technology for seamless and semantic service interoperation amongglobally distributed heterogeneous manufacturing resources, the multi-agentarchitecture for adding autonomy, adaptability and control intelligence to Webservices, and the P2P architecture for enhancing flexibility, scalability, robustnessand openness underlying multi-agent architecture, the proposed approach hasunified these architectures to provide a new service discovery method in dynamic,adaptive, heterogeneous, open and distributed manufacturing environment.
Figure 9. Scalability of manufacturing service discovery.
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The operation and experimental evaluation of the prototype system are presentedto validate the implementation of the proposed approach.
Ontologies are used as cornerstone technologies for loosely coupling differentarchitectures. This makes it possible to dynamically include new VEs into theexisting cooperative work during runtime rather than predefining the VEs duringdesign time of the cooperative work. The ontology-based resource modellingmitigates the security issues on the VEs such as private web and limited accesscontrol, as can be referred to our previous work (Yang et al. 2011) and will not berepeated in the current paper for conciseness. The ontology-based resourcemodelling also mitigates the optimisation issues on the VEs such as supply chainoptimisation, as can be referred to our previous work (Zhang et al. 2011b) and willnot be repeated in the current paper for conciseness.
Our future work will extend the prototype to cope with the problems aboutsecurity and optimisation for possible commercial uses in the future. The otherdirection for our future work is to explore the bi-directional service selectionproblems among agents and VE peers that may bargain for their participation.Another direction for our future work is to integrate the distributed data miningtechniques (Zeng et al. 2012) into the proposed agent-based P2P architecture tosupport discovery of implicit or hidden manufacturing knowledge across VEs.
Acknowledgements
The work has been supported by China National Natural Science Foundation (Nos. 50975250and No. 51175462) and Zhejiang Natural Science Foundation of China (No. Y1110671).
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