Analysis of query matching criteria and resource monitoring models for grid application scheduling

Making effective use of computational grids requires scheduling grid applications onto resources that best match them. Resource-related state (e.g., load, availability, and location), and demand-related state (number and distribution of application resource requests) influences scheduling decision success. The scale of the grid makes collecting and maintaining detailed up-to-date state information for all resources and requests impractical. Thus, concurrent distributed schedulers must make scheduling decisions based on incomplete resource state information. In this paper, we evaluate the effect that the criteria for selecting scheduling matches have on the success of scheduling decisions. We focus on three criteria: information freshness, resource distance from requesters, and past behavior. We evaluate the quality of the schedule for various resource monitoring models, grid load models, and grid overlay topologies. Among our findings is the counter-intuitive result that favoring freshness can sometimes harm overall system performance; a combination of resource distance and past scheduling success performs best. We also evaluate a pure resource state pull model with caching, and discover that pro-actively pushing dynamic state information to schedulers is beneficial.