Due to their commercial value (similar to online recommendation system), several evaluation models – have been proposed by academia and industry lately. Designing a general and comprehensive analytical model for trustworthiness evaluation is challenging, as the model needs the assessor to achieve, in reasonable time, useful results to determine the best service option. The trustworthy attributes include reliability, scalability, availability, safety, security, etc –. The trustworthiness of cloud service affects customers' perception towards service quality, which has significant bearing on customer satisfaction and royalty. Therefore, there is an increasing demand to help the non-expert customers with the selection of trustworthy cloud service. Customers usually lack appropriate, qualified, sufficient information and benchmarks to assess cloud services with regard to individual preferences and market dynamics (2) although cloud service vendors are struggling to improve service quality and performance, cloud computing are not necessarily trustworthy – unhandled exceptions and crashes may cause cloud service to deviate dramatically from the expectation. While many small and medium-sized enterprises (SMEs) and individual customers prefer to apply cloud services to build their business system or personal applications, they are often facing two major challenges at the selection time: (1) multiple cloud services are often available by different venders providing similar functional properties (i.e., “functionally-equivalent”). Several leading IT enterprises including Google, IBM, Microsoft, and Amazon have started to offer cloud services to their customers –. Cloud service is also gaining wide acceptance and becoming popular to individuals as it reduces hardware and licensing costs, and it is scalable and allows users to work from any computer anywhere. They are all using cloud computing to better serve their customers around the world. Dropbox, Instagram) to established enterprises (Samsung). The experimental results also show that our approach performs much better than other competing ones with respect to the customer preference and expectation in trustworthiness assessment.Ĭloud computing has become the driver for innovation in the recent years, from startups (e.g. By simply collecting limited historical records, the current approach is able to support the personalized trustworthy service selection. In this paper, we propose a novel service selection approach in which the missing value prediction and the multi-attribute trustworthiness evaluation are commonly taken into account. Many existing models cannot easily deal with cloud services which have very few historical records. Additionally, since the cloud service evaluation is often a costly and time-consuming process, it is not practical to measure trustworthy attributes of all candidates for each customer. But these methods are out of the reach to most customers as they require considerable expertise. Facing a customer market with rising demands for cloud service dependability and security, trustworthiness evaluation techniques are becoming essential to cloud service selection.
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