Object cloud storage systems are deployed with diverse applications that have varying latency service level objectives (SLOs), posting challenges for supporting quality of service with limited storage resources. Existing methods provide prediction-based recommendations for dispatching requests from applications to storage devices, but the prediction accuracy can be affected by complex system topology. To address this issue, Graph3PO is designed to combine storage device queue information with system topological information for forming a temporal graph, which can accurately predict device queue states. Additionally, Graph3PO contains the urgency degree model and cost model for measuring SLO violation risks and penalties of scheduling requests on storage device queues. When the urgency degree of a request exceeds a threshold, Graph3PO determines whether to schedule it in the queue or initiate a hedge request to another storage device. Experimental results show that Graph3PO outperforms its competitors, with SLO violation rates 2.8 to 201.1 times lower.

Vehicle trajectory prediction is crucial and indispensable for ensuring the safe and efficient operation of autonomous vehicles in complex traffic environments. The application of Internet of Things technology in the collaborative automated driving system (CADS) has established a robust data foundation for vehicle trajectory prediction. Accurate prediction requires not only a substantial amount of high-quality data but also a deep understanding of the vehicle’s driving characteristics and interactions between neighboring vehicles. To enhance the study of vehicle trajectory prediction, this article proposes a novel Social Force-constrained Gated Recurrent Unit (SF-GRU) model, which integrates data-driven and physics-driven models. Specifically, the SF-GRU model is based on the gated recurrent unit encoder–decoder framework and incorporates social force constraints to enhance and supplement the model input based on vehicle time-series trajectory data, which describes the driving and interactive behaviors of vehicles during driving, as well as the interactions between neighboring vehicles and the surrounding environment. The model is trained and validated using the next generation simulation data set. Experimental results demonstrate that the SF-GRU model outperforms existing state-of-the-art models in both longitudinal and lateral motion, and that social force constraints are more effective than spatial variables in improving prediction accuracy. Furthermore, the SF-GRU model can intuitively and accurately consider the interactions between vehicles, and precisely describe the changes of relevant variables in the prediction process, thus enhancing the interpretability of the data-driven model. The SF-GRU model has great potential in vehicle trajectory prediction and can provide important support for the practical implementation of autonomous driving vehicles.