Purpose This paper aims to address the challenges of dynamic and web-oriented network resource provisioning in the Internet of Vehicles (IoV), where the diversity and complexity of in-vehicle web services require efficient and adaptive resource scheduling. It proposes a dual-time-scale strategy to enhance resource allocation, reduce service scheduling delays and ensure load balancing across network slices. Design/methodology/approach This study adopts a dual-time-scale approach for network slicing resource prediction and dynamic scheduling. At the long-time scale, a long short-term memory (LSTM) network is employed to learn temporal patterns of vehicle behavior and web service requests, enabling accurate load prediction and proactive resource preconfiguration. At the short-time scale, a dueling double deep Q-network (D3QN) algorithm performs fine-grained resource scheduling based on real-time service states, ensuring compliance with service level agreements. Experiments are conducted to evaluate the strategy’s performance in terms of scheduling latency, system utility and load balancing. Findings Experimental results demonstrate that the proposed LSTM-D3QN strategy maintains high system utility saturation while significantly reducing web service scheduling latency during peak periods. It also effectively ensures load balancing among slices. Compared to baseline algorithms, the method achieves an average reduction in service scheduling delay by 54.57%, an improvement in utility saturation by 22.41% and a decrease in slice load by 23.46% across three typical service slices. Originality/value This paper introduces a novel dual-time-scale framework that integrates LSTM-based load prediction with D3QN-based real-time scheduling for web-oriented IoV environments. By jointly optimizing long-term resource preallocation and short-term dynamic adjustment, the approach enhances adaptability and robustness in dynamic service provisioning. The study provides a comprehensive solution to the challenges of heterogeneous web service demands in IoV, offering significant practical and theoretical insights for future network slicing and resource management research.
Lin et al. (Mon,) studied this question.