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The functional heterogeneity of non-dedicated computational grids will increase with the inclusion of resources from desktop grids, P2P systems, and even mobile grids. Machine failure characteristics, as well as individual and organizational policies for resource usage by the grid, will increasingly vary even more than they already do. Since grid applications also vary as to how well they tolerate the failure of the host on which they run, grid schedulers must begin to predict and consider how resources will transition between availability modes. Toward this goal, this paper introduces five availability states, and characterizes a Condor pool trace that uncovers when, how, and why its resources reside in, and transition between, these states. This characterization suggests resource categories that schedulers can use to make better mapping decisions. Simulations that characterize how a variety of jobs would run on the traced resources demonstrate this approach's potential for performance improvement. A simple predictor based on the previous day's behavior indicates that the states and categories arc somewhat predictable, thereby supporting the potential usefulness of multi-state grid resource availability characterization.