In the context of Grid computing, reputation-based trust management
systems are playing an increasingly important role for supporting
coordinated resource sharing and ensuring provision of quality of service.
However, the existing Grid reputation-based trust management systems
are considered limited as they are bounded to esoteric reputation-based
trust models encompassing predefined metrics for calculating and selecting
trusted computing resources and as a result, they prevent external
involvement in the trust and reputation evaluation processes.
This thesis suggests an alternative approach for reputation modelling
founded on its core argument proclaiming that reputation is a subjective
matter as well as context dependent. Consequently, it offers a synergistic
reputation-policy based trust model for Grid resource selection. This
exoteric trust model introduces a novel paradigm for evaluating Grid resources,
in which Grid client applications (e.g. monitoring toolkits and
resource brokers) are endeavoured to carry out an active participation in
the trust and reputation evaluation processes. This is achieved by augmenting
the standard reputation queries with a set of reputation-policy
assertions constituting as complete trust metrics supplied into the reputation
algorithm. Consecutively, the Grid Reputation-Policy Trust management
system (GREPTrust) provides a concrete implementation for
the trust model and it’s underlying artifacts whilst the GREPTrust testbed
provides an adequate infrastructure for comparing the reputationpolicy
trust model with a production available esoteric model (GridPP).
Based on a computational finance case study, an internal workflow
simulation utilises the GREPTrust testbed in order to empirically assess
the criteria by which the synergistic reputation-policy based trust model
outperforms esoteric trust models regarding resource selection and
consequently provides substantive evidence that the reputation-policy
paradigm is a welcome addition to the Grid computing community.