In this paper, we examine the utility of exploiting idle workstations for parallel computation. We attempt to answer the following questions. First, given a workstation pool, for what fraction of time can we expect to find a cluster of $k$ workstations available? This provides an estimate of the opportunity for parallel computation. Second, how stable is a cluster of free machines and how does the stability vary with the size of the cluster? This indicates how frequently a parallel computation might have to stop for adapting to changes in processor availability. Third, what is the distribution of workstation idle-times? This information is useful for selecting workstations to place computation on. Fourth, how much benefit can a user expect? To state this in concrete terms, if I have a pool of size S, how big a parallel machine should I expect to get for free by harvesting idle machines. Finally, how much benefit can be achieved on a real machine and how hard does a parallel programmer have to work to make this happen? To answer the workstation-availability questions, we have analyzed 14-day traces from three workstation pools. To determine the equivalent parallel machine, we have simulated the execution of a group of well-known parallel programs on these workstation pools. To gain an understanding of the practical problems, we have developed the system support required for adaptive parallel programs as well as an adaptive parallel CFD application. (Also cross-referenced as UMIACS-TR-96-80)