Applications of Markov Chain in the Field of Computer Science
Xinyi Zhou · Theoretical and Natural Science · 2025
In the era of digital transformation, dealing with big data that alters over time is necessary. Markov chain is a fundamental concept in the field of stochastic processes for modeling systems that evolve probabilistically over time, and especially it can be used in computer science for data analysis. This paper focused on analyzing the applications of Markov chain in predicting cloud service trusted state and network traffic. The main problem addressed is how to integrate Markov chains into the complicated computation systems. By employing discrete Markov processes, hidden Markov chains, and fuzzy Markov fields, one can use the transition probability matrix and the stationary distribution of Markov chains to ascertain the stable state of the systems, predict the future state, and then conduct optimizations. The results indicated that if the stationary distribution of Markov process exists, then the future state can be predicted. With the training of some extra parameters, the optimized scheme can be achieved. This research is significant as it provides practical guidance for educators and institutions to utilize Markov chain under digital era.