A Collaborative Ranking Approach for Discovery and Selection of Cloud Services

Cloud computing is getting more popular due to its extraordinary features such as on-demand availability of computing resources and software services. A variety of services have been deployed to offer analogous functionalities. However, the difficulty to identify reliable services has fascinated the attention of researchers. Thus, the trust and reputation concept have been introduced to evaluate the trustworthiness of services over cloud. Most of the existing research works fully trust on service user's feedback rating for ranking cloud services, which may often lead to biasness towards positive and negative feedback rating. To avoid aforementioned issues, this chapter proposes a novel approach to evaluate cloud service reputation along with cloud service reputation evaluation model to discover reliable cloud services. Experimental result shows that proposed approach provides effective solution for prediction of cloud service reputation, which can be helpful in performing reliable service discovery and selection over cloud.

Service Discovery and Selection in Internet of Things - A Review

The Internet of Things (IoT) is a novel technology that has opened doors to the new level of interaction between the things. This has resulted in an enhancement in the quality of life and optimized use of various resources. IoT uses various technologies related to networking, sensing, databases and artificial intelligence to enhance the lifestyle and makes business processes simpler. In the IoT based systems, the number of devices contributing to a particular application is very large, spreading to a large geographical area at various locations. The device can be considered as a resource used by a service in the application layer. IoT service is the most important entity. The real-world things generate data by perceiving the environment in large quantity and store it in the distributed databases or cloud databases. This manuscript analytically and statistically categorizes and analyze the current research techniques on the service discovery and selection in the IoT, published between 2010 and 2018. It finds that the discovery among a huge number of services requires fast, scalable and dynamic service discovery mechanism over the Internet. Once the services are discovered, the next step is to select the most appropriate service. This paper includes a comprehensive analysis of the discovery and selection of services in IoT. A patent related to service and discovery have been also discussed which would be beneficial in identifying the research gap to make the system more efficient.

Multi-Level Web Service Clustering to Bootstrap the Web Service Discovery and Selection

Existing technologies for web services have been extended to give the value-added customized services to users through the service composition. Service composition consists of four major stages: planning, discovery, selection, and execution. However, with the proliferation of web services, service discovery and selection are becoming challenging and time-consuming tasks. Organizing services into similar clusters is a very efficient approach. Existing clustering approaches have problems that include discovering semantic characteristics, loss of semantic information, and a shortage of high-quality ontologies. Thus, the authors proposed hybrid term similarity-based clustering approach in their previous work. Further, the current clustering approaches do not consider the sub-clusters within a cluster. In this chapter, the authors propose a multi-level clustering approach to prune the search space further in discovery process. Empirical study of the prototyping system has proved the effectiveness of the proposed multi-level clustering approach.