Using Constraint Reasoning on Feature Models to Populate Ecosystem-driven Cloud Services e- Marketplace

Service providers leverage cloud ecosystems and cloud e-marketplaces to increase the business value of their services and reach a wider range of service users. A cloud ecosystem enable participating services to combine with other services, along their QoS properties; while the e-marketplace provides an environment where atomic services interconnect in unprecedented ways to be traded on the marketplace platform. Noting the unprofitability, impracticality and error-prone nature of performing ad hoc service combination of atomic services, the concern addressed in this technical report is how to guide the combination of atomic services participating in an ecosystem in a seamless manner. In this technical report, we proposed the use of feature models to model the inter-relationships and constraints among the atomic services, which is transformed into a constraint satisfaction problem and off-the-shelve constraint solvers are used to determining valid combinations. The collection of valid combinations become the blueprint that guides service composition and populates the e-marketplace service directory; users can then make service selection decisions based on the list. The applicability of the approach proposed in this report is demonstrated via an example of Customer relationship management as a service ecosystem

Towards a Fuzzy-oriented Framework for Service Selection in Cloud e-Marketplaces

The growing popularity of cloud services requires service selection platforms that offer enhanced user experience in terms of handling complex user requirements, elicitation of quality of service (QoS) requirements, and presentation of search results to aid decision making. So far, none of the existing cloud service selection approaches has provided a framework that wholly possesses these attributes. In this paper, we proposed a fuzzy-oriented framework that could facilitate enhanced user experience in cloud emarketplaces through formal composition of atomic services to satisfy complex user requirements, elicitation and processing of subjective user QoS requirements, and presentation of search results in a visually intuitive way that aids users’ decision making. To do this, an integration of key concepts such as constrained-based reasoning on feature models, fuzzy pairwise comparison of QoS attributes, fuzzy decision making, and information visualization have been used. The applicability of the framework is illustrated with an example of Customer Relationship Management as a Service

Towards a Constraint-based Approach for Service Aggregation and Selection in Cloud E-Marketplaces

Service providers leverage cloud ecosystems and cloud e-marketplaces to increase the business value of their services to reach a wider range of service users. The operations of commercial e-marketplaces can be further enhanced by enabling service composition mechanisms that allow automatic aggregation of atomic services into composite offerings that meets complex user requirements. Existing approaches of cloud service selection are yet to achieve this. Currently, users are constrained to make choices only from a set of predefined atomic services, or at best, manually configure their desirable features and QoS requirements in order to realize their complex requirements given that they have deep knowledge of the service domain. In this paper, a constraint-based approach for service composition and selection to address this problem was proposed. The proposed approach applies constraint-based automated reasoning on feature models to formally guide the aggregation of atomic services to offer composite services in order to satisfy complex requirements with minimal user involvement. The plausibility of the proposed approach is demonstrated via an illustrative customer relationship management (CRM) service ecosystem. The study offers a credible way to replicate the kind of user experience that is currently available on e-commerce platforms in cloud service e-marketplaces

Towards a Visualization Framework for Service Selection in Cloud e-Marketplaces

In spite of the success of many commercial cloud service e-marketplaces the search results from these platforms are usually presented as an unordered list of icons representing the services that best fit users’ keyword-based queries. The drawback of such presentation mechanisms is that users are not able to immediately discriminate among the cloud services for easy decision making. A number of cloud service selection frameworks have been proposed; however, some of these frameworks do not enable users make comparisons among services. In this paper, we introduce a visualization framework for cloud service selection. Our framework takes into cognizance the set of cloud services that matches a user’s request and based on QoS attributes, users can interact with the results via bubble graph visualization to compare and contrast the search results to ascertain the best alternative. The bubble graph enables the exploration of services in a unified view of the QoS space, exhibiting both high object coherence and correlation. Result from our experiments shows that our framework simplifies decision making as users can identify services that best fit their requirements quicker and easier compared to tabular format

Integrating fuzzy theory and visualization for QoS-aware selection of SaaS in cloud e-Marketplaces

Most cloud service e-marketplaces incorporate basic features like search and billing but lack more sophisticated elements that optimise users’ experience. The cognitive demands of searching for and evaluating multiple cloud SaaS along multiple QoS criteria can be overwhelming, giving rise to what Alvin Toffler called choice overload. There is a need to integrate mechanisms that handles the vagueness that characterises the human decision-making process when finding suitable services. The objective of this paper is to reduce cognitive overload during cloud service selection in e-marketplaces by employing low cognitive demanding tools that leverage the dynamics of human expressions. We proposed a QoS-aware SaaS ranking and selection framework that integrates fuzzy theory and information visualisation for optimal decision-making in cloud e-marketplaces. An illustrative case study of Customer-Relationship-Management-as-a-Service e-marketplace demonstrated the framework’s plausibility. The demonstration shows that our framework is a viable approach to rank and select SaaS in cloud e-marketplaces ina way that satisfactorily serves both the users of the platform and can potentially drive the business objectives of the e-marketplace