Dynamic software rejuvenation policies in a transaction-based system under Markovian arrival processes

This paper presents a Markov decision process (MDP) formulation for a transaction-based system with software aging and rejuvenation. In our formulation, the arrival process of transactions is described as a Markovian arrival process (MAP). In addition, we introduce a probabilistically degrading processing rate to model the software aging. Furthermore, the paper focuses on two performance criteria to determine the optimal rejuvenation strategy: the long-run average reward and the power efficiency. Under these performance criteria, we formulate the optimality equations of MDPs for the maximization of the long-run average reward and power efficiency. Numerical experiments show that the optimal rejuvenation policy has the monotone property, and can be characterized by a threshold policy with the number of transactions through the sensitivity and statistical analysis using real traffic and aging data.