Alocação de recursos e posicionamento de funções virtualizadas em redes de acesso por rádio desagregadas
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Data
2023-08-30
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Universidade Federal de Goiás
Resumo
Jointly choosing a functional split of the protocol stack and placement of network functions in a virtualized RAN is critical to efficiently using the access network resources. This
problem represents a current research topic in 5G and Post-5G networks, which involves the challenge of simultaneously choosing the placement of virtualized functions, the
routes for traffic and the management of available computing resources. In this work, we
present three approaches to solve this problem considering the planning scenario and two
approaches considering the network operation scenario. The first result is a Mixed Integer
Linear Programming (MILP) model, considering a generic set of processing nodes and
multipath routing. The second approach uses artificial intelligence and machine learning
concepts, in which we formulate a deep reinforcement learning agent. The third approach
used is based on search meta-heuristics, through a genetic algorithm. The last two approaches are Markov Decision Process (MDP) formulations that consider dynamic demand
on radio units. In all formulations, the objective is to maximize the network function’s
centralization while minimizing positioning cost. Analysis of the solutions and comparison of their results show that exact approaches such as MILP naturally provide the best
solution. However, in terms of efficiency, the genetic algorithm has the best search time,
finding a high quality solution in a few seconds. The deep reinforcement learning agent
presents a high convergence, finding high quality solutions for the problem and showing
problem generalization capacity with different topologies. Finally, the formulations considering the network operation scenario with dynamic demand are highly complex due to
the size of the action space
Descrição
Palavras-chave
Redes de acesso por rádio virtualizadas , Posicionamento de funções virtualizadas , Divisão funcional da pilha de rádio , Alocação de recursos , Otimização , Aprendizado por reforço profundo , Algoritmo genético , Virtualized radio access network , Virtualized function placement , Radio stack functional splits , Resource allocation , Optimization , Deep reinforcement learning , Genetic algorithm
Citação
ALMEIDA, G. M. F. Alocação de recursos e posicionamento de funções virtualizadas em redes de acesso por rádio desagregadas. 2023. 75 f. Disseretação (Mestrado em Ciência da Computação) - Instituto de Informática, Universidade Federal de Goiás, Goiânia, 2023.