Imperfect coverage and nonnegligible reconfiguration delay are known t
o have a deleterious effect on the dependability and the performance o
f a multiprocessor system. In particular, increasing the number of pro
cessor elements does not always increase dependability. An obvious rea
son for this is that the total failure rate increases, generally, line
arly with the number of components in the system. It is also a well-kn
own fact that the performance gain due to parallelism mostly turns out
to be sublinear with the number of processors. It is therefore import
ant to optimize the degree of parallelism in system design. A related
issue is that by deferring repair, it is sometimes possible to improve
system dependability. In this case decisions have to be made dynamica
lly as to when to repair and when not to repair. Most of the current r
esearch deals with static optimization of the number of processors. No
systematic approach for dynamic control of dependable systems has bee
n proposed so far. Dynamic, i.e. transient, decision of whether or not
to repair is the optimization problem considered in this paper. We pr
opose extended Markov reward models (EMRM) to capture such questions.
EMRM are a marriage between performability modeling techniques and Mar
kov decision theory. A numerical solution procedure is developed to pr
ovide optimal solution trajectories for this problem. EMRM are a gener
al framework for the dynamic optimization of reconfigurable, dependabl
e systems. The optimization is applied on the basis of several perform
ance and dependability measures. In particular, we explore availabilit
y, capacity-oriented availability, performance-oriented unavailability
, and performability measures. Furthermore, off-line and on-line repai
r strategies are compared. We show that guarded repair can improve sys
tem performance and dependability significantly. The control strategie
s and reward functions differ a lot in each case. Each scenario turns
out to be of interest in its own right. A time-dependent optimality of
dependable, parallel configurations can be determined from our result
s.