Abstract
Markovian Arrival Processes (MAPs) are widely used stochastic models to describe correlated events. For the parameter fitting of MAPs according to measured data, the expectation-maximization (EM) algorithm is commonly seen as the best approach. Unfortunately, EM algorithms require a huge computational effort if the number of data points is large or the MAP has a larger dimension. The classical EM algorithm runs sequentially through the data which is necessary to consider dependencies between data points.
In this paper we present a parallel variant of the EM algorithm for MAPs with a general structure. The parallel version of the algorithm is developed for multicore systems with shared memory. It is shown that the parallel algorithm yields a significant speedup compared to its sequential counterpart.
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Blume, A., Buchholz, P., Kriege, J. (2020). Parallelization of EM-Algorithms for Markovian Arrival Processes. In: Hermanns, H. (eds) Measurement, Modelling and Evaluation of Computing Systems. MMB 2020. Lecture Notes in Computer Science(), vol 12040. Springer, Cham. https://doi.org/10.1007/978-3-030-43024-5_11
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