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.