This paper presents a novel Network Request Scheduler (NRS) for a large-scale, Lustre^TM storage system. It proposes a quantum-based, Object Based Round Robin (OBRR) NRS algorithm that reorders the execution of I/O requests per data object, presenting a workload to backend storage that can be optimized more easily. According to the drawback of static deadlines in large-scale workloads, it proposes a novel two-level deadline setting strategy that not only avoids starvation, but also guarantees that urgent I/O requests are serviced in a specified time period. Via a series of simulation experiments using a Lustre simulator, it demonstrates that I/O performance increases as much as 40% when using the OBRR NRS algorithm, and the two-level deadline setting strategy can avoid starvation and ensure that urgent I/O requests are serviced in the required time.