On-line adaptive algorithms in autonomic restart control

Restarts or retries are typical control schemes to meet a deadline in real-time systems, and are regarded as significant environmental diversity techniques in dependable computing. This paper reconsiders a restart control studied by van Moorsel and Wolter (2006), and refines their result from theoretical and statistical points of views. Based on the optimality principle, we show that the time-fixed restart time is best even in non-stationary control setting under the assumption of unbounded restart opportunities. Next we study statistical inference for the restart time interval and develop on-line adaptive algorithms for estimating the optimal restart time interval via non-parametric estimation and reinforcement learning. Finally, these algorithms are compared in a simulation study. © 2010 Springer-Verlag.