A Probabilistic Framework for Evaluating Reliability in Distributed Computing Environments with Uncertain State Transitions
Keywords:
Distributed computing, probabilistic modeling, Markov model, Bayesian inference, system reliability, state transitions, failure analysisAbstract
Reliability assessment in distributed computing environments is a critical challenge, particularly when systems exhibit uncertainty in state transitions due to dynamic workloads, unpredictable failures, and non-deterministic behavior. This paper introduces a probabilistic modeling framework that incorporates stochastic state transitions to evaluate system reliability in distributed settings. Leveraging Markov models and Bayesian inference, the framework enables estimation of transition probabilities and failure likelihoods under various operational scenarios. The proposed methodology facilitates more robust reliability planning and risk assessment by capturing non-deterministic system behavior through probabilistic abstraction. Simulation results illustrate the effectiveness of the framework in modeling complex distributed environments, highlighting its adaptability and computational efficiency across varying topologies and failure rates.
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Copyright (c) 2020 Elizabeth Danielle, George William (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
