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.

多云协同环境由多个数据中心组成，是大数据的典型处理平台。本文聚焦于多云协同服务的信任计算需求，并通过若干创新机制开发了一种跨多个数据中心的数据驱动和反馈增强型信任（DFET）计算模式。首先，基于分布式软代理提出了一种具有信任感知的服务监测架构，作为多云信任计算和任务调度的中间件。然后提出了一种基于多指标监测数据的数据驱动信任计算方案。将若干关键服务指标集成到信任计算中，使得该方案适用于面向服务的云应用。更重要的是，根据用户、监测器和服务提供商之间的内在关系，我们提出了一种增强的分层反馈机制，它可以在提高系统可靠性的同时有效降低网络风险。理论分析表明，DFET模式对伪造和诋毁攻击具有高度的可靠性。我们还构建了一个原型系统来验证DFET模式的可行性，并且实验得出了有意义的观察结果，这些结果有助于在大规模多云协同环境中有效利用DFET。