In this chapter, we have provided an overview of the issues to be addressed when in-tegrating dependability with service-oriented computing. We introduced a selection of the different types of faults that can affect a service-oriented application
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Consensus Issues for Service Advertisement and Selection
Ping Wang, Chi-Chun Lo and Leon Smalov
AbstractSeveral commercial service providers are offering analogous functional features in the advertisements of their services which lead to the problem of efficient selection for the potential service consumers. Generally, the service consumers and providers would have different views on the content of the services. How to reach consensus between the service consumers and providers is an interesting practical aspect of web service selection. This chapter proposes a Quality of Services (QoS) aware web service selection model based on fuzzy linear programming (FLP) tech-nologies, in order to identify their differences on service alternatives, assist service providers and consumers in selecting the most suitable services with consideration of their expectations and preferences. By extending the LINMAP method (LINear programming techniques for Multidimensional Analysis of Preferences), developed by Srinivasan and Shocker, we can offer the optimal solution of consensual weight of QoS attribute and fuzzy positive ideal solution. Finally, two numerical examples are provided to illustrate the solution process.
7.1 Introduction
With the increasing acceptance of e-commerce, various applications over the Inter-net are becoming part of everyday life. For example, Google research applications Ping Wang
Department of MIS, Kun Shan University, Yung-Kang, Taiwan e-mail:pingwang@mail.ksu.edu.tw
Chi-Chun Lo
Institute of Information Management, National Chiao Tung University, Hsinchu, Taiwan e-mail:cclo@faculty.nctu.edu.tw
Leon Smalov
Faculty of Engineering and Computing, Coventry University, Coventry, CV1 5FB, UK e-mail:csx211@coventry.ac.uk
N. Griffiths, K.-M. Chao (eds.),Agent-Based Service-Oriented Computing, 161 Advanced Information and Knowledge Processing,
DOI 10.1007/978-1-84996-041-0 7, cSpringer-Verlag London Limited 2010
are accepted as web services and integrated with other services, such as Gmail, to provide an integrated environment for service consumers. Tim Berners-Lee, inven-tor of the World Wide Web, offered insights to understand the potential impact of web technologies, which change the way people do business, entertain themselves, exchange ideas, and socialize with one another [4]. Two “futuristic” dreams have been depicted by Berners-Lee: one was everyone receiving and sharing the infor-mation through the Internet; the other was people communicating with computers in natural language through the Internet. The former became a norm in every-day life; the latter is being partially enabled by Semantics Web Services (SWS). SWS technology aims to add enough semantics to the specifications and implementations of Web Services (WS) to make possible the automatic integration of distributed au-tonomous systems, with independently designed data and behavior models [19].
An efficient web service can bring a serious competitive advantage to service providers as well as giving social welfare to the consumers. An application assist-ing in service selection based on certified QoS can brassist-ing essential benefits to the service consumers along with reducing the search redundancy. It will also gener-ate advantages for service providers who deliver valuable services. Practically, the service providers are supposed to guarantee QoS of WS, which are advertised on the Internet for service consumers. When service providers announce their avail-able services, current advertising approaches of web services create a WSDL or OWL-S document to subscribe the web service profile and service grounding, and then promote it through UDDI registration, or other web services registries such as ebXML [45]. However, many available web services exhibit overlapping or identical services in terms of functionality, e.g. flight booking and digital music download, but they exhibit divergent Quality of Services (QoS). This multiplicity can lead to a complex problem of service selection. It is inevitable that a suitable mechanism for service selection is needed. As the similar functions in service-oriented applications expand, service selection becomes a crucial issue for both service consumers and providers. Two important issues of service discovery and selection of available web services, namely (i) the semantic confusion problem [6] and (ii) reaching a consen-sus in web service selection process [16] have been widely discussed as described below.
7.1.1 Semantic Confusion
The semantic confusion problem could be effectively solved by semantic registra-tion and discovery by defining the appropriate meaning of the service’s funcregistra-tional- functional-ity using an ontology. An ontology is a representation of resources on the web by a set of well-defined classes to describe a data model which can be specified us-ing toolkits such as Resource Description Framework (RDF), DAML+OIL or Web Ontology Language (OWL) [34]. OWL, proposed by the World Wide Web Consor-tium (W3C), is not only for representing information on the web, but also improves the capability of processing information, and increases the interoperability among
software agents [30]. A number of works [2, 6, 12, 14, 22, 28, 29, 31, 35, 36, 44, 45]
on semantic service discovery and selection have been carried out via SWS tech-nologies to locate the required services and compose them to meet requirements.
The “Semantic Web” approach advocates the vision that will bring structure to the meaningful content of web pages, creating an environment where software agents roaming from page to page can readily carry out sophisticated tasks for users. The Semantic Web makes it possible to automatically locate, discover, compose, and ex-ecute services [5]. The aim of the Semantic Web initiative is to provide technologies that will enable heterogeneous systems to collaborate in the execution of an activity.
For web services description, the introduction of OWL-S is a significant factor in matching service providers and service consumers [31]. OWL-S is an ontology of services for providing richer web service description, and has the following three components.
1. ServiceProfile: describes what the service does, its inputs and outputs and its preconditions and effects, or IOPE. (This is equivalent to UDDI content.) 2. ServiceModel: describes how the service works, its control and dataflow in use.
(This is similar to BPEL4WS [1].)
3. ServiceGrounding: describes how the service is implemented and provides a mapping from OWL-S to WSDL.
Using semantic web techniques, can help customers to judge distinct Web Service Levels (WSL) on available services with QoS reports using ontologies, as illustrated in Figure 7.1.
Fig. 7.1 Semantic service discovery, selection and execution using ontology
An agent is a goal-oriented software entity. It possesses a number of prop-erties such as pro-activeness, sociability, autonomy, and reactivity to collaborate with other agents in order to achieve their common goals. There is a gap between
software agents and web service technologies, since web services lack semantic descriptions to the interfaces. The semantic gap between XML-based constructs and agents can be bridged by use of Semantic Web technologies, such as OWL-S.
Agents have mental states that are often expressed using the notions of BDI (Be-liefs, Desires, and Intentions). Agents are suitable for highly dynamic environments and operate at a conceptual level, since they adopt partial planning to reason over their knowledge (or beliefs) and are able to perceive and respond to the environment in which they are situated. So, agents can be designed as delegates to web service consumers and providers to form a community for service discovery and selection.
Agents can automatically select services if they are assigned to collect and aggre-gate the rank information based QoS assessed by consumers as shown in Figure 7.1 above.
After defining the semantics about service specifications, fuzzy matchmaking techniques can be employed to match the requirements between customers and providers. The fuzzy matchmaking scheme uses fuzzy semantics on terms and han-dles the problem via fuzzy theory. For example, the moderated fuzzy discovery method [15], measures the similarity between services in terms of capabilities, syn-tax and semantics through a moderator to minimize the differences among service consumers and providers.
7.1.2 Reaching Consensus
For consumer consensus of WS selection, service consumers and providers may have different expectations, experiences, and preferences about services. Service selection can be regarded as a group decision making (GDM) process made by cus-tomer’s cognitive processes to select an appropriate service among several service alternatives. In practice, consumers’ preferences often remain imprecise, uncertain or ambiguous in relation to similar services. The objective of group decision making is to solve conflicts on QoS criteria and obtain a final compromised solution on the basis of group consensus. Furthermore, consumer preferences often remain impre-cise, uncertain or ambiguous on service QoS terms; the preferences over the QoS attributes are hard to quantify, especially in distinguishing the importance among these service attributes. Therefore, the adoption of fuzzy terms such as reasonable price, reliable service, etc. in the requests becomes inevitable. Moreover, consumers usually have distinct view from providers for service terms, such as “cheap flight ticket”, “comfortable leg-room” or “flight time”, simply because they have divergent perceptions of these terms. Traditionally, consensus has a well-established meaning of a full and unanimous agreement. During the process of service selection, reaching a full and unanimous agreement in a large group is often not easy task. Since a unan-imous agreement in a large group is rarely reached, soft consensus method [20] is developed for solving partial agreements among customers on service alternatives.
From the consumers’ point of view WS providers usually advertise on the Inter-net the features of web services that appeal to customers, which might lead to mis-understanding or confusion about the service terms for WS consumers. In addition,
providers prefer to advertise their services to customers in subjective terms, which might be short of considering the consumers’ expectations and preferences. Ex-amples of some dissimilar views of related issues between service consumers and providers are shown in Table 7.1.
Table 7.1 Respective views between service consumers and providers on web service advertise-ments
Web service issues Service consumers Service providers
Price Affordable price Low cost
Quality More stable functions Service level refer to price Information disclosure Sufficient right Information Exaggeration of the features
Hence, it is imperative to reach consensus for service consumers on the spe-cific spespe-cification terms (i.e., QoS), when they find and search WSDL documents in the service discovery process. Based on these requirements, the W3C working group has defined various QoS attributes for WS [20]. These comprise a number of generic and specific items for cross-referencing between the possible needs of service consumers and the functions supported by web services [23].
Although regular QoS attributes have been listed, some unclear problems are yet to be clarified in the selection of WS process. For example, the perception of QoS at-tributes importance is generally different from consumers and providers preferences.
It is widely accepted that consumers have been taking an active role in the expansion of e-commerce. Hence this leads to a need to develop a consensus-centric approach to investigate QoS attribute preferences. Furthermore, obtaining a consensus-based ranking order of alternatives in the services selection process is critically impor-tant. In this chapter, we propose a fuzzy linear programming (FLP) model based on consistence and inconsistence measurement of group preferences on service al-ternatives to obtain consensus-based weights of QoS attributes, and determine the ranking order of service alternatives according to the distance from the positive ideal solution under group consensus. Consequently, a service consumer is able to reduce redundancy in search, and the service provider can improve the quality of services.
The remainder of the chapter is organized as follows. Section 7.2 describes the existing QoS-aware methods for the selection of web services. Section 7.3 describes proposed method. Section 7.4 reports on two illustrational examples of selection of service alternatives. Finally, Section 7.5 contains the concluding remarks and proposes future work.