Modifying our previously developed simulation model [FRA89], we study in this paper the costs associated with distributed allocation of computing resources in a multitasking environment. Using funds endowed upon arrival, computing tasks compete for necessary resources through sealed-bid auctions to improve their processing schedules. The costs and times dedicated to auctioning are compared to the costs and times allowed for task processing. Measuring computing resources in terms of processing rates allows the task management, in the form of an auction, algorithm, to have its requirements specified in the same way as the requirements for the simulated mission processing. Machine capacity is computed for and assigned to each completing task. Data are then compiled by segmented capacity classes. A unifying theme of past and current research is the efficiency of auctioning to allocate reconfigurable computing resources in a variable capacity machine. We observed that at optimal rates of occurrence of capacity classes which minimize the total costs per successful completion, congestion was resolved through auctions generating endogenously implied prices which substantially exceeded the exogenously imposed price.