A Multi-Dimensional Trust Model for Processing Big Data Over Competing Clouds

Cloud computing has emerged as a powerful paradigm for delivering data-intensive services over the Internet. Cloud computing has enabled the implementation and success of big data, a recent phenomenon handling huge data being generated from different sources. Competing clouds have made it challenging to select a cloud provider that guarantees quality of cloud service (QoCS). Also, cloud providers' claims of guaranteeing QoCS are exaggerated for marketing purposes; hence, they cannot often be trusted. Therefore, a comprehensive trust model is necessary to evaluate the QoCS prior to making any selection decision. In this paper, we propose a multi-dimensional trust model for big data workflow processing over different clouds. It evaluates the trustworthiness of cloud providers based on: the most up-to-date cloud resource capabilities, the reputation evidence measured by neighboring users, and a recorded personal history of experiences with the cloud provider. The ultimate goal is to ensure an efficient selection of trustworthiness cloud provider who eventually will guarantee high QoCS and fulfills key big data workflow requirements. Various experiments were conducted to validate our proposed model. The results show that our model captures the different components of trust, ensures high QoCS, and effectively adapts to the dynamic nature of the cloud.