Approximating Closed Queueing Networks in Semi-Markov Random Environments

Modeling queueing networks in random environments is a challenging problem that finds application in routing, reliability analysis, and evaluation of systems processing bursty workloads. In this paper, we introduce a method to evaluate closed queueing networks in random environments that can deal with non-Markovian transitions among environment stages and accurately approximate time-averaged performance metrics. Our technique supports both state-independent and state-dependent random environments, the latter allowing us to analyze challenging queueing network models with bursty service processes. Using simulation on representative case studies, we show that our technique provides low approximation errors. We further demonstrate the applicability of our result to a mobility case study demonstrating the applicability of the proposed approach.