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

Fuzzy Hybrid Approach for Ranking and Selecting Services in Cloud-based Marketplaces

Background and Objective: The popularity cloud computing has led to the proliferation of services that are commoditized and traded on cloud e-marketplaces. Besides, user’s cloud service requirements-QoS preferences and aspiration are often shrouded in vagueness and subjectivity. Therefore, cloud service selection can be overwhelming and lead to service choice overload. Existing cloud service selection approaches rarely provide mechanisms to elicit both the QoS preferences and aspirations, but rather considers either of them. This study aimed to design fuzzy-based model for service selection in e-market places that articulates both QoS preferences and aspirations. Materials and Methods: This model comprised a fuzzy Analytic Hierarchy Process (AHP) method for deriving relative priority weights of QoS attributes, a fuzzy decision-making method for obtaining user’s QoS aspiration values and a fuzzy multi-objective optimization module for evaluating the services with respect to user requirements. A simulated experiment was conduct using publicly QoS dataset and ranking accuracy produced by the proposed approach compared to existing methods was measured using Normalize Discounted Cumulative Gain (NCDG) metric. Results: The descriptive and inferential analyses of the ranking results from both versions of the proposed approach produce better accuracy results based on the NCDG metric and were in all cases closer to the benchmark metric than the other two existing methods used in this simulation. Conclusion: Results from current simulation experiment showed that the ranking accuracy of this model is not compromised by subjective QoS information from users and this approach is applicable use the subjective QoS requirements of user’s in ranking services in the cloud e-marketplaces

Open semantic service networks

Online service marketplaces will soon be part of the economy to scale the provision of specialized multi-party services through automation and standardization. Current research, such as the *-USDL service description language family, is already defining the basic building blocks to model the next generation of business services. Nonetheless, the developments being made do not target to interconnect services via service relationships. Without the concept of relationship, marketplaces will be seen as mere functional silos containing service descriptions. Yet, in real economies, all services are related and connected. Therefore, to address this gap we introduce the concept of open semantic service network (OSSN), concerned with the establishment of rich relationships between services. These networks will provide valuable knowledge on the global service economy, which can be exploited for many socio-economic and scientific purposes such as service network analysis, management, and control

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

Applicability of Industry 4.0 Technologies in the Reverse Logistics: A Circular Economy Approach Based on COmprehensive Distance Based RAnking (COBRA) Method

The logistics sector plays one of the most important roles in the supply chain with the aim of providing a fast, flexible, safe, economical, efficient, and environmentally acceptable performance of freight transport flows. In addition, the popularization of the concept of a circular economy (CE) used to retain goods, components, and materials at their highest usability and value at all times, illustrates the importance of the adequate performance of reverse logistics (RL) processes. However, traditional RL is unable to cope with the requirements of modern supply chains and requires the application of Industry 4.0 technologies, which would make it more efficient. The main aim of this study was to evaluate the applicability of various Industry 4.0 technologies in the RL sector in order to point out the most applicable ones. To solve the defined problem, a novel multi-criteria decision making (MCDM) model was defined by combining the best-worst method (BWM) to obtain the criteria weights, and the newly developed comprehensive distance-based ranking (COBRA) method to rank the technologies. Another aim of the study was to validate the newly established method. The results indicated that the most applicable technologies were the Internet of Things, cloud computing, and electronic-mobile marketplaces. These technologies will have a significant impact on the development of RL and the establishment of CE systems, thus bringing about all the related positive effects

Design of a QoS-based Framework for Service Ranking and Selection in Cloud E-marketplaces

In most existing commercial cloud e-marketplaces, finding a suitable cloud service to perform user's objectives can be cognitively demanding and potentially affects the user satisfaction of both the process and outcome of decision making. Most existing cloud selection techniques have not sufficiently addressed the problem of service choice overload in a manner, that provides means that elicits subjective user preferences. Besides, only a few of these techniques suffice in situations where there are a large number of services to be evaluated and the results are presented in textual formats, either in a list or tables, which does not provide any means of comparison of results returned. Based on a comparative review of existing service selection techniques, a set of requirements was identified to guide the design of cloud service selection framework that would suffice in a cloud e-marketplace context. A cloud service selection framework was formulated that encapsulates the set of requirements. The increase in the number of available services on the e-marketplace leaves the users in the dilemma of which service to select, particularly when the services perform equivalent functionalities and may only differ with respect to their quality of service (QoS) attributes. The proposed framework is a viable proposition for the reduction service choice overload in cloud service e-marketplaces

Comparative Analysis of Data Security and Cloud Storage Models Using NSL KDD Dataset

Cloud computing is becoming increasingly important in many enterprises, and researchers are focusing on safeguarding cloud computing. Due to the extensive variety of service options it offers, A significant amount of interest from the scientific community has been focused on cloud computing. The two biggest problems with cloud computing are security and privacy. The key challenge is maintaining privacy, which expands rapidly with the number of users. A perfect security system must efficiently ensure each security aspect. This study provides a literature review illustrating the security in the cloud with respect to privacy, integrity, confidentiality and availability, and it also provides a comparison table illustrating the differences between various security and storage models with respect to the approaches and components of the models offered. This study also compares Naïve Bayes and SVM on the accuracy, recall and precision metrics using the NSL KDD dataset