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This paper presents a performance analysis of market-basedbatch schedulers for clusters of workstations.In contrast to previous work, we use user-centric performance metrics as the basis for system evaluation.Each user is modeled as having a utility function for each job which measures value delivered to the user as function of execution time.Summing over all utility functions in the workload, we use aggregate utility as a measure of overall value delivered to users.With aggregate utility as the performance metric, simulations are used to quantify the performance of both market-based and traditional batch scheduling algorithms under a variety of synthetic work-loads.Results show that an auction-based batch scheduling algorithm improves performance by a factor of up to2-5x for sequential workloads and up to 14x for highly parallelworkloads compared to traditional scheduling algorithms.