An algorithm is developed for solving a broad class of performability models wherein system performance is identified with "reward." More precisely, for a system S and a utilization period T, the performance variable of the model is the reward derived from using S during T. The state behavior of S is represented by a finite-state stochastic process (the base model); reward is determined by reward rates associated with the states of the base model. Restrictions on the base model assume that the system in question is not repaired during utilization. It is also assumed that the corresponding reward model is a nonrecoverable process in the sense that a future state (reward rate) of the model cannot be greater than the present state. For this model class, we obtain a general method for determining the probability distribution function of the performance (reward) variable and, hence the performability of the corresponding system. Moreover, this is done for bounded utilization periods. The result is an integral expression which can be solved either analytically or numerically.

Performability modeling and evaluation methods are applied to the SIFT computer in the computational environment of an air transport mission. User-visible performance of the "total system" (SIFT plus its environment) is modeled as a random variable taking values in a set of "accomplishment levels." These levels are defined in terms of four attributes of total system behavior: safety, no change in mission profile, no operational penalties, and no economic penalties. The "base model" of the total system is a stochastic process whose states describe the internal structure of SIFT as well as relevant conditions of its environment. Base model state trajectories are related to accomplishment levels via a "capability function" which is formulated in terms of a three-level model hierarchy. Solution methods are then applied to determine the performability of the total system for various choices of computer and environment parameter values.