DRL-based Slice Placement Under Non-Stationary Conditions
This addresses the challenge of reliable slice placement for network operators in dynamic environments, representing an incremental improvement over existing methods.
The paper tackles the problem of optimal network slice placement under non-stationary conditions by proposing hybrid DRL-heuristic algorithms, which require three orders of magnitude fewer learning episodes than pure-DRL to achieve convergence.
We consider online learning for optimal network slice placement under the assumption that slice requests arrive according to a non-stationary Poisson process. We propose a framework based on Deep Reinforcement Learning (DRL) combined with a heuristic to design algorithms. We specifically design two pure-DRL algorithms and two families of hybrid DRL-heuristic algorithms. To validate their performance, we perform extensive simulations in the context of a large-scale operator infrastructure. The evaluation results show that the proposed hybrid DRL-heuristic algorithms require three orders of magnitude of learning episodes less than pure-DRL to achieve convergence. This result indicates that the proposed hybrid DRL-heuristic approach is more reliable than pure-DRL in a real non-stationary network scenario.