Demand prediction based slice reconfiguration using dueling deep Q-network

Abstract

To satisfy diversified service demands of vertical industries, network slicing enables efficient resource allocation of a common infrastructure by creating isolated logical networks. However, uncertainty and dynamics of service demands will cause performance degradation. Due to operation costs and resource constraints, it is challenging to maintain high quality of user experience while obtaining high revenue for service providers (SPs). This paper develops an optimal and fast slice reconfiguration (OFSR) framework based on reinforcement learning, where a novel scheme is proposed to offer optimal decisions for reconfiguring diverse slices. A demand prediction model is proposed to capture changes in resource requirements, based on which the OFSR scheme is triggered to determine whether to perform slice reconfiguration. Considering the large state and action spaces generated from uncertain service time and resource requirements, deep dueling architecture is adopted to improve the convergence rate. Extensive simulations validate the effectiveness of the proposed framework in achieving higher long-term revenue for SPs.