A research on cloud-based resource allocation for economy grid

Grid is considered as the next generation Internet, and it is a promising platform for executing large-scale resource intensive applications. However, resource management and scheduling in the Grid environment is a complex undertaking as resources are (geographically) distributed, heterogeneous in nature, owned by different individuals or organizations with their own policies, have different access and cost models, and have dynamically varying loads and availability. So, grid resources are stochastic, uncertain and qualitative. A number of Grid systems (such as Globus, Legion, Nimrod/G, etc.) have addressed many of these issues with exception of a computational economy, but they all focus on quantitative resources or allocation strategies, which result in low efficiency of resource allocation. To improve the efficiency, we propose a new strategy based on cloud model that is a model of uncertain transition between qualitative and quantitative to transform quantitative grid resources into qualitative expressions for economy grid. Experiments show that the new strategy can improve the resource allocation efficiency effectively.