Slice Reconfiguration based on Demand Prediction with Dueling Deep Reinforcement Learning
Wanqing Guan, Haijun Zhang, Victor C. M. Leung
Abstract
Network slicing is capable of satisfying differentiated service demands of vertical industries by tailoring a common infrastructure to multiple logical networks which are isolated. Considering that the dynamic of service demands makes it difficult to maintain high quality of user experience and high revenue of tenants, slice reconfiguration is necessary to avoid performance degradation. Hence, this paper proposes an optimal and fast slice reconfiguration (OFSR) solution by leveraging advanced deep reinforcement Learning. To deal with the uncertain changes in resources requirement, a demand prediction model based on Markov renewal process is introduced in decision-making. Taking into account the operation costs of reconfiguring diversified slices and the constraints of available resources, the proposed OFSR scheme aims at obtaining high long-term revenue with low operation cost. Given that the convergence of the conventional reinforcement learning approach is slow to learn the optimal reconfiguration policy for different classes of slices, deep dueling neural network combined with Q-learning is applied to improve the speed of convergence. Simulation results validate that the proposed framework is effective in achieving long-term revenue for tenants and the dueling deep Q-learning approach performs better than other current approaches.