A dynamic AI-based algorithm selection for Virtual Network Embedding

With the increasing sophistication and heterogeneity of network infrastructures, the need for Virtual Network Embedding (VNE) is becoming more critical than ever. VNE consists of mapping virtual networks on top of the physical infrastructure to optimize network resource use and improve overall network performance. Considered as one of the most important bricks of network slicing, it has been proven to be an NP-hard problem with no exact solution. Several heuristics and meta-heuristics were proposed to solve it. As heuristics do not provide satisfactory solutions, meta-heuristics allow a good exploration of the solutions' space, though they require testing several solutions, which is generally unfeasible in a real world environment. Other methods relying on deep reinforcement learning (DRL) and combined with heuristics yield better performance without revealing issues such as sticking at local minima or poor space exploration limits. Nevertheless, these algorithms present varied performances according to the employed approach and the problem to be treated, resulting in robustness problems. To overcome these limits, we propose a robust placement approach based on the Algorithm Selection paradigm. The main idea is to dynamically select the best algorithm from a set of learning strategies regarding reward and sample efficiency at each time step. The proposed strategy acts as a meta-algorithm that brings more robustness to the network since it dynamically selects the best solution for a specific scenario. We propose two selection algorithms. First, we consider an offline selection in which the placement strategies are updated outside the selection period. In the second algorithm, the different agents are updated simultaneously with the selection process, which we call an online selection. Both solutions proved their efficiency and managed to dynamically select the best algorithm regarding acceptance ratio of the deployed services. Besides, the proposed solutions succeed in commuting to the best placement strategy depending on the strategies' strengths while outperforming all standalone algorithms.