Peak traffic statistics and prediction for Markov process-based scheduling models
Yu Wu, Jianjun Yang · 2023
Many scheduling models have a finite number of states, mostly described using Markov processes. These models analyze the possible states of the system at different times. The scheduling algorithms for practical applications are combined with an engineering background and cannot be applied to other systems. The statistics and prediction of peaks also need to be combined with the needs of specific problems. In this paper, a peak state identification algorithm is designed based on Markov chains combined with machine learning methods. The algorithm can find the peak states from approximating the laws of the system itself in different cycles and can be used to predict the peak states in that cycle. We build a distance distribution to filter the suitable states and rank the different states by statistical analysis. We verify the accuracy of the algorithm by inputting random peak states, and the results show a high identification accuracy at different peak amounts. The algorithm can be applied to scheduling models with fixed nodes and with traffic demand and it has high accuracy in long runs.