Scalable end-to-end slice embedding and reconfiguration based on independent DQN agents

Network slicing in beyond 5G systems facilitates the creation of customized virtual networks/services, referred to as “slices”, on top of the physical network infrastructure. Efficient and dynamic orchestration of slices is needed to ensure the stringent and diverse service level agreements (SLAs) required by different services. In this paper, we provide a model that attempts to capture the problem of dynamic slice embedding and reconfiguration supporting a multi-domain setup and diverse, end-to-end SLAs. We then show that such problems can be optimally solved, in theory, with (tabular) Reinforcement Learning algorithms (e.g., Q-learning) even under, a priori, unknown demand dynamics for each slice. Nevertheless, the state and action complexity of

such algorithms is prohibitive, even for very small scenarios. To this end, we propose a novel scheme based on independent DQN agents: The DQN component implements approximate Qlearning, based on simple, generic DNNs for value function

approximation, radically reducing state space complexity; the independent agents then tackle the equally important issue of exploding action complexity arising from the combinatorial nature of embedding multiple VNFs per slice, multiple slices, over

multiple domains and computing nodes therein. Using realistic data, we show that the proposed algorithm reduces convergence time by orders of magnitude with minimum penalty of decision optimality.