Credibility-based binary feedback model for grid resource planning

Grid service providers (GSPs), in commercial grids, improve their profitability by maintaining the least possible set of resources to meet client demand. Their goal is to maximize profits by optimizing resource planning. In order to achieve such goal, they require feedback from clients to estimate demand for their service. The objective of this research is to develop an approach to build a useful value profile for a collection of heterogeneous grid clients. For developing the approach, we use binary feedback as the theoretical framework to build the value profile, which can be used as a proxy for a demand function that represents client's willingness-to-pay for grid resources. However, clients may require incentives to provide feedback and deterrents from selfish behavior, such as misrepresenting their true preferences to obtain superior services at lower costs. To address this concern, we use credibility mechanisms to detect untruthful feedback and penalize insincere or biased clients. We also use game theory to study how the cooperation can emerge.;In this dissertation, we propose the use of credibility-based binary feedback to build value profiles, which GSPs can use to plan their resources economically. The use of value profiles aims to benefit both GSPs and clients, and helps to accelerate an adoption of commercial grids.