Abstract
Abstract-the increasing proliferation of independent application service providers is making traditional IT infrastructure insufficient when dealing with new issues that appear in a dynamic outsourcing business model. Quality of Service (QoS) gradually becomes an essential benchmark to differentiate diverse service providers during service selection process. In this paper, we argue service selection can be deemed as a decision making process -to decide which services providers should be selected within the specified service provision context during a definitive timeslot. Thus, existing decision support approaches can be leveraged if applicable. Hence, we propose a service selection solution which utilizes the Decision Support Systems Module (DSS Module) to select the most appropriate service. In DSS module we introduce AHP model to carry out the service QoS measurement based on the Context-specific Quality Aspects. The contributions of this paper are two folds. Firstly, we provide a novel and feasible solution for QoS-based service selection and secondly, we apply DSS module into web services, thus opening a new, fertile ground for DSS research in service ecosystem literature.
I. INTRODUCTION
The significance of Quality of Services (QoS) in the performance of the web is evident. In a Service Ecosystem [l] , where services are discovered, bound, and consumed in a dynamic loosely-coupled manner, it becomes particular important to specify, track, and manage QoS in order to select the most appropriate service providers. As a large number of enterprises start to adopt the emerging services paradigm to facilitate various cross-organisational business behaviours -e.g. the outsourcing of marginal functions [2] , a reliable and empirical QoS solutions are essential to support such business routines in the SOE.
I. INTRODUCTION
Recently, Digital Ecosystem has become the hosting environment for the services. In our previous work[l], we proposed a service ecosystem architecture and we have found the well-known issue of service selection [3] can be effectively tackled by employing certain DSS solutions for the following two reasons. Firstly, existing web services standards failed to provide a flexible, dynamic, and reliable mechanism to allow service requestors to choose the right quality service instance based on non-functional attributes such as QoS and trust. Secondly, in service-oriented environments, service selection can be seen as a process of decision making, i.e. the service requestor should make a decision on which service provider is currently offering the most appropriate service to fulfil the requirement among abundant function-relevant service provider candidates. This argument is partly based on the assumption that DSS 'support' rather than 'automated' decision making [4] , a task which cannot be completely achieved without human participation and final decision making. Hence, while some research attempts to automate the service selection without human intervention, it is our belief that such a thorough automation will not occur in the reality due to the complexity of service selection. Consequently, in this paper, we mainly deal with the issue of how to support the decision making process with regards to selecting the most appropriate services providers using DSS modules. We integrate into existing distributed web services architecture a DSS module which would assess the QoS of web services based on the Analytic Hierarchy Process (AHP) modelling approach. An application based on such a conceptualization is provided.
II. PRELIMINARY CONCEPTS
Before we discuss the module design and architecture we would like to introduce some preliminary concepts on QoS assessment, which would facilitate understanding of our work in service selection DSS in Service-Oriented Environment.
A. QoS Assessment within Service Context
According to [5] , QoS assessment can be carried out based on the concept of Service Context. A Service Context defines the "nature of the service and service functions". Each context has a name, a type and a functional specification. In this paper, we believe one can further derive from the nature of the service a set of Context Elements that constitutes the whole Service Context. For example, as illustrated in Figure 1 , three context elements -functional features, QoS, and trust-are derived from the overall service context. These proposed context elements are considered playing a significant role in selecting the service providers as they characterise the nature of the service from different perspectives. Consequently, in most cases, all these elements shall be captured and assessed during the service selection process. Hence the process of selecting services, namely the decision making, is in effect the process of assessing these three elements respectively. For The Analytic Hierarchy Process (AHP), originally proposed in Saaty [6] , allows decision makers to model a complex problem in a hierarchical structure showing the relationships of the goal, factors, sub-factors, and alternatives. The most salient characteristic of AHP is that it enables decision-makers to derive ratio scale priorities or weights (these two words are used interchangeably in this section) as opposed to arbitrary assigning them the absolute values. In doing so, AHP not only enables decision-makers to structure complexity and exercise judgment, but allows them to incorporate both objective and subjective considerations in the decision process [7] . More importantly, unlike other decision-aiding methods, AHP relies on relative weighting (pair-wise comparison) and is thus less susceptible to judgment errors common to other methods using absolute assignments [8] . Hence, in order to solve a complex problem with a hierarchical structure as indicated in Section 2.2, it is desirable to employ the AHP approach for the purpose of QoS assessment. In particular, we have found the corresponding relations between the concepts in AHP and the ones in QoS assessment as shown in Table 1 proach to compute the alternative service providers' QoS value scores that are evaluated across a set of QA associated with the service context. Such an approach can be illustrated in Figure 3 . It consists of the following major six steps.
A. Original Data Collection
The first step prepares the input for constructing the AHP hierarchical structure with the QoS concepts. According to the concepts matching relationship in Table 1 , the AHP approach in this research requires three primary inputs (as indicated by the grey arrows in Figure 3 [8] . In theory, any numbers less than infinity can be used for the upper bound. However, extensive practical experience [9] suggests that '9' is a good upper bound to use. Hence we use the scale presented in The aim of this step is to determine the relative weight for every Quality Aspect (QA). The rationale behind this step is that the criteria should not always be considered equally weighted in the decision making. In particular, each QA has different importance that contributes to the ultimate QoS value; therefore, they should be assessed in a way that their respective significances are measured and integrated. Secondly, as indicated in Section 3.1 different stakeholders have different opinions on these preferences from their own perspectives. As a process of effective service provider selection, all their preferences should also be respected, and 
A. Original Data Collection
E. Weighing Assessment Criteria In this step the relative weight of each Assessment Criterion (AC) is calculated. This weighting step looks into two types of weights: 1) the local weight refers to the relative priorities between ACs that are under the same QA. 2) the global weight is the relative priorities among all ACs in this QoS assessment, and is the AC weight that is used in producing the overall QoS value. Both weight calculations need aggregating the preferences from different stakeholders. Being unfamiliar with the Australian market at all, ACME seeks decisive advices from the consultancy AusLogiBroker Pty., asking for the most appropriate Australian third party logistics service provider to carry out such an international delivery service. The ACME thus submits to the AusLogiBroker Pty the following criteria in searching for the most suitable logistics service provider -SP: The total willing-to-pay cost ranges from AU$ 2500.00 to $ 3500.00, which includes packing, door to door delivery service from Beijing to Perth and to Sydney, GST, and shipping insurance. * Packing and picking shall be on the same day. Thus the SP has to pack the fragile items and pick up the freight at ACME Ltd's office in Beijing by April 28, 2006. * The freight has to reach WA Art Gallery without any loss at least one day before May 4. * The freight has to reach NSW Museum without any loss at least one day before May 9. * ACME Ltd can track and trace the status of the freight by using the online system provided by taylormichael@example.org. * The SP can issue signature-based proof for 'Undeniable Delivery' once the freight is acknowledged by authenticated receivers. * If the freight is not delivered on time due to SP's own responsibilities, the SP has to refund the freight costs. Based on these initial criteria and the requirement specifications, AusLogiBroker has quickly found from its own database four potential Australian logistics providers that have past similar delivery records, and hence are capable of handling this freight delivery: Company A, B, C, and D.
Local AC Weighting

A. Hierarchy Formation
AusLogiBroker converts the decision making problem into the hierarchical structure. This is started by defining the Quality Aspect and Assessment Criterion based on the requirements captured from ACME. As shown in Figure 4 , five major quality aspects offer the first level under the QoS context element: on time service, delivery, tracking capability, cost, and risk. The second level lists their associated assessment criteria. For example, 'on time service' aspect has two criteria to assess: 'on time pickup' and 'on time delivery'. Please note that for the sake of simplicity when calculating, we have only assigned one criterion for some QAs (e.g. delivery, cost, and tracking capability), however, more criteria can be easily appended as new requirements are proposed without changing the approach itself. Moreover, AusLogiBroker also identified three ACME departments as different stakeholders involved in selecting the logistics service providers: operation, finance, and sales team.
B. Pair-wise Comparison
In this step, we list the actual pair-wised comparison matrix for each service provider under each assessment criterion in Table 3 -9, the scale is based on Table 2 and then we use the eigenvector method proposed in [10] to estimate the relative weight. After gathering quality aspect preference for each stakeholder, different stakeholder preferences are then aggregated as described in Section 3.4 by employing the geometric mean function. This is illustrated in Table 11 . In this paper, we proposed a new solution for QoS-based services selection. Our solution is based on our observation that the service selection can be deemed as a process of decision making. Hence, the solution makes decisions for selecting the most suitable services by leveraging a DSS module -AHP Model, which relies on measuring the weight against certain Quality Criteria defined in Context-specific Quality Aspects. To realise our solution, we provide DSS module design. Currently, proof-of-concept prototyping work is ongoing. For the future work, we will focus on the implementation of the DSS module prototype embedded in our distributed web services architecture, upon which the service ecosystem is built. Furthermore, the AHP Model based QoS selection simulation is also need to be carried out and the effectiveness measurement metrics should be formulated in a formative way. In particular, the selflearning and recommendation mechanisms in terms of the pair-wise comparison in DSS module is a very interesting and promising direction for our future work.

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