Abstract: Mission critical systems must present good performance during failures and dynamic changes. Classic stochastic performability models have in depth analysis but use static data for failure and repair rate which in turn decreases their accuracy. In this paper we present a new hybrid AI stochastic framework for performability evaluation in which we use machine learning models to dynamically put forth failure and repair rates that are put into a continuous time Markov chain (CTMC) based reward model. We present that this approach also has in-depth analysis as before but also is adaptive to different workloads and system aging. Also, we present simulation based on synthetic operation data which show our put forth framework does better than the fixed rate CTMC models. Our numbers show a 3.3% improvement in steady state performance, which we see greater changes in high workload stress and aging issues. We note that by combining data driven parameter estimates with stochastic modeling we present a very good solution for the performability analysis of mission critical systems.

Key words: performability, continuous-time Markov chain, machine learning, reliability engineering, mission-critical systems