Fast and Parallel Trust Computing Scheme Based on Big Data Analysis for Collaboration Cloud Service

Abstract

Providing high trustworthy service is the most fundamental task for any cloud computing platform. Users are willing to deliver their computing tasks and the most sensitive data to cloud data centers, which is based on the trust relationship established between users and cloud service providers. However, with the development of collaboration cloud computing, how to provider fast response for a large number of users' service requests becomes a challenging problem. In order to quickly provide highly trustworthy services, the service platform must efficiently and quickly reply tens of millions of service requests, and automatically match-make tens of thousands of service resources. In this context, lightweight and fast (high-speed, low-overhead) trust computing schemes become the fundamental demand for implementing a trustworthy and collaborative cloud service. In this paper, we propose an innovative and parallel trust computing scheme based on big data analysis for the trustworthy cloud service environment. First, a distributed and modular perceiving architecture for large-scale virtual machines' service behavior is proposed relying on distributed monitoring agents. Then, an adaptive, lightweight, and parallel trust computing scheme is proposed for big monitored data. To the best of our knowledge, this paper is the first to use a blocked and parallel computing mechanism, the speed of trust calculation is greatly accelerated, which makes this trust computing scheme very suitable for a large-scale cloud computing environment. Performance analysis and experimental results verify feasibility and effectiveness of the proposed scheme.