Analysis of software rejuvenation using Markov regenerative stochastic petri net
In a client-server type system, the server software is required to run continuously for very long periods. Due to repeated and potentially faulty usage by many clients, such software 'ges' with time and eventually fails. Huang et. al. proposed a technique called 'software rejuvenation' [9] in which the software is periodically stopped and then restarted in a 'robust' state after proper maintenance. This 'renewal' of software prevents (or at least postpones) the crash failure. As the time lost (or the cost incurred) due to the software failure is typically more than the time lost (or the cost incurred) due to rejuvenation, the technique reduces the expected unavailability of the software. In this paper, we present a quantitative analysis of software rejuvenation. The behavior of the system is represented through a Markov Regenerative Stochastic Petri Net (MRSPN) model which is solved both for steady state as well as transient conditions. We provide a closed-form analytical solution for the steady state expected down time (and the expected cost incurred) due to system unavailability. We also evaluate the optimal rejuvenation interval which minimizes the expected unavailability of the software.
