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Wyszukujesz frazę "probabilistic distribution" wg kryterium: Temat


Wyświetlanie 1-2 z 2
Tytuł:
A multi-source fluid queue based stochastic model of the probabilistic offloading strategy in a MEC system with multiple mobile devices and a single MEC server
Autorzy:
Zheng, Huan
Jin, Shunfu
Powiązania:
https://bibliotekanauki.pl/articles/2055156.pdf
Data publikacji:
2022
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
mobile edge computing
probabilistic offloading strategy
multi-source fluid queue
birth and death process
cumulative distribution function
przetwarzanie mobilne
proces narodzin i śmierci
dystrybuanta
Opis:
Mobile edge computing (MEC) is one of the key technologies to achieve high bandwidth, low latency and reliable service in fifth generation (5G) networks. In order to better evaluate the performance of the probabilistic offloading strategy in a MEC system, we give a modeling method to capture the stochastic behavior of tasks based on a multi-source fluid queue. Considering multiple mobile devices (MDs) in a MEC system, we build a multi-source fluid queue to model the tasks offloaded to the MEC server. We give an approach to analyze the fluid queue driven by multiple independent heterogeneous finite-state birth-and-death processes (BDPs) and present the cumulative distribution function (CDF) of the edge buffer content. Then, we evaluate the performance measures in terms of the utilization of the MEC server, the expected edge buffer content and the average response time of a task. Finally, we provide numerical results with some analysis to illustrate the feasibility of the stochastic model built in this paper.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2022, 32, 1; 125--138
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Evolutionary computation based on Bayesian classifiers
Autorzy:
Miquelez, T.
Bengoetxea, E.
Larranaga, P.
Powiązania:
https://bibliotekanauki.pl/articles/907630.pdf
Data publikacji:
2004
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
rozumowanie probabilistyczne
obliczenia ewolucyjne
sieć Bayesa
estymacja algorytmu dystrybucji
hybrid soft computing
probabilistic reasoning
evolutionary computing
classification
optimization
Bayesian networks
estimation of distribution algorithms
Opis:
Evolutionary computation is a discipline that has been emerging for at least 40 or 50 years. All methods within this discipline are characterized by maintaining a set of possible solutions (individuals) to make them successively evolve to fitter solutions generation after generation. Examples of evolutionary computation paradigms are the broadly known Genetic Algorithms (GAs) and Estimation of Distribution Algorithms (EDAs). This paper contributes to the further development of this discipline by introducing a new evolutionary computation method based on the learning and later simulation of a Bayesian classifier in every generation. In the method we propose, at each iteration the selected group of individuals of the population is divided into different classes depending on their respective fitness value. Afterwards, a Bayesian classifier---either naive Bayes, seminaive Bayes, tree augmented naive Bayes or a similar one---is learned to model the corresponding supervised classification problem. The simulation of the latter Bayesian classifier provides individuals that form the next generation. Experimental results are presented to compare the performance of this new method with different types of EDAs and GAs. The problems chosen for this purpose are combinatorial optimization problems which are commonly used in the literature.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2004, 14, 3; 335-349
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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