Data-driven and feedback-enhanced trust computing pattern for large-scale multi-cloud collaborative services

Multi-cloud collaborative environment consists of multiple data centers, which is a typical processing platform for big data. This paper focuses on the trust computing requirement of multi-cloud collaborative services and develops a Data-driven and Feedback-Enhanced Trust (DFET) computing pattern across multiple data centers with several innovative mechanisms. First, a trust-aware service monitoring architecture is proposed based on distributed soft agents to serve as middleware for multi-cloud trust computing and task scheduling. A data-driven trust computation scheme based on multi-indicator monitoring data is then proposed. The integration of several key service indicators into trust computing makes this scheme suitable for service-oriented cloud applications. More importantly, according to the intrinsic relationship among users, monitors, and service providers, we propose an enhanced and hierarchical feedback mechanism that can effectively reduce networking risk while improving system dependability. Theoretical analysis shows that DFET pattern is highly dependable against garnished and bad-mouthing attacks. We also build a prototype system to verify the feasibility of DFET pattern and the experiments yield meaningful observations that can facilitate the effective utilization of DFET in the large-scale multi-cloud collaborative environment.