Current quantitative trust measurement models for computing platforms suffer from inherent subjectivity, during assignment of weights used in trust computation, limitation in portability of the models to different computing platforms, and the need to predefine all possible trustable states by some models that use multi agent systems. This paper proposes a quantification model that addresses the identified challenges. Explored models include QoS trust Model that computes Availability (AV), Reliability (RE), Data Integrity (DI) and Turnaround Efficiency (TE) of a resource. The values generated from these metrics are computed against assigned weights to arrive at the final trust value of the computing resource. A Computationally Grounded Quantitative Trust with Time which uses local and global defined trustworthy states has also been explored. The trustable states are predefined and using multi agents concepts, the agents are said to be trustworthy if they transit from local to global states that have been defined as trustworthy. This paper also explores a Quantitative Framework for accessing Cloud Security as a trust metric, using a dependency model that validates both the offered services and customer’s requirements, validated by checking service conflicts and different Service Level Obligation compatibility issues. The framework is composed of Security requirements definition, Requirements Quantification, Dependency management approach, Structuring security SLA services using Dependency Structure Matrix and Cloud Service Provider Evaluation. A model based on measurement theory relying on composite metrics, impression and confidence was also explored. It relies on user reviews, likes and dislikes posts. As a contribution to these existing models, this paper addresses the shortcomings of the existing models, in particular subjectivity in the derived trust, by proposing a quantitative trust model based on Confidence Interval. The model relies on QoS measurements from two systems, namely, the cloud provider integrated QoS monitoring system and a vendor neutral QoS monitoring model. Using a confidence interval of 95%, trust is computed based on whether the cloud provider’s QoS system results are within the range of the Vendor Neutral model results. The proposed model was applied to QoS results from two cloud computing providers, Microsoft and Google. From the results, users can build trust for the services from Microsoft and Google since the QoS results provided by the cloud provider integrated tool and the Vendor Neutral tool, during the experimentation period were within range, showing trustworthiness of the providers with regards to reporting the QoS of their platforms.

Trust, Trust Value, Modeling, Cloud Computing, Confidence Interval, Vendor Neutral, QoS