Search Word(s) in Title, Keywords, Authors, and Abstract: Jiao-Hong Qiang

Dynamic Cloud Resource Reservation Model Based on Trust
Jiao-Hong Qiang, Ding-Wan Ning, Tian-Jun Feng and Li-Wei Ping
Page: 377~398, Vol. 14, No.2, 2018
10.3745/JIPS.03.0091

Keywords: Cloud-Domain-Based Management Architecture, Decision of Candidate Resources, Dynamic Resource Reservation, Two-Way Trust Evaluation Mechanism
Show / Hide Abstract
Aiming at the problem of service reliability in resource reservation in cloud computing environments, a model of dynamic cloud resource reservation based on trust is proposed. A domain-specific cloud management architecture is designed in which resources are divided into different management domains according to the types of service for easier management. A dynamic resource reservation mechanism (DRRM) is used to test users’ reservation requests and reserve resources for users. According to user preference, several resources are chosen to be candidate resources by fuzzy cluster analysis. The fuzzy evaluation method and a two-way trust evaluation mechanism are adopted to improve the availability and credibility of the model. An analysis and simulation experiments show that this model can increase the flexibility of resource reservation and improve user satisfaction.


Improved Dynamic Subjective Logic Model with Evidence Driven
Jiao-Hong Qiang, Wang-Xin Xin and Tian-Jun Feng
Page: 630~642, Vol. 11, No.4, 2015
10.3745/JIPS.03.0030

Keywords: Dynamic Weight, Evidence Driven, Subjective Logic
Show / Hide Abstract
In Jøsang’s subjective logic, the fusion operator is not able to fuse three or more opinions at a time and it cannot consider the effect of time factors on fusion. Also, the base rate (a) and non-informative prior weight (C) could not change dynamically. In this paper, we propose an Improved Subjective Logic Model with Evidence Driven (ISLM-ED) that expands and enriches the subjective logic theory. It includes the multi-agent unified fusion operator and the dynamic function for the base rate (a) and the non-informative prior weight (C) through the changes in evidence. The multi-agent unified fusion operator not only meets the commutative and associative law but is also consistent with the researchers’s cognitive rules. A strict mathematical proof was given by this paper. Finally, through the simulation experiments, the results show that the ISLM-ED is more reasonable and effective and that it can be better adapted to the changing environment.