Dynamic Network Slice for Bursty Edge Traffic

Edge network slicing promises better utilization of network resources by dynamically allocating resources on demand. However, addressing the imbalance between slice resources and user demands becomes challenging when complex user behaviors lead to bursty traffic within the edge network. Hence, we propose a comprehensive dynamic slice strategy with two coupled sub-strategies (i) bursty-sensitive slice resource coordination and (ii) proactive demand resource matching to find an optimal balance. For obtaining stable strategies, the edge network with bursty traffic is formulated as a bi-level Lyapunov optimization problem. Then we propose a resource allocation and request redirection (RA-RR) algorithm with polynomial complexity by introducing deep reinforcement learning to guarantee real-time. Specifically, two agents are trained to solve two sub-strategies, and the Lyapunov drift-plus-penalty function is used as the reward to keep queues stable. RA-RR is responsive to fluctuations in demand and realizes an efficient interaction of coupled decision-making. Moreover, a training method based on alternating optimization is designed to ensure convergence of the RA-RR algorithm. Experiments demonstrate that the proposal can maximize network revenue while ensuring the stability of slice services when edge traffic bursts, and has an average improvement of 20.4% compared with comparisons.