This paper presents a comprehensive investigation on optimizing I/O performance in the access to distributed I/O resources in high-performance computing (HPC) environments. I/O resources, such as the I/O forwarding nodes and object storage targets (OST), are shared between a subset of applications. Each application has access to a subset of them and multiple applications can access the same resources. We propose heuristics to schedule these distributed I/O resources in two steps: for a set of applications, determining the number of I/O resources each will use (allocation) and which resources they will use (placement). We discuss a wide range of required information about applications' characteristics that can be used by the scheduling algorithms. Despite the fact that a higher level of application knowledge is associated with enhanced performance, our comprehensive analysis indicates that strategic decision-making with limited information can still yield significant enhancements in most scenarios. This research provides insights into the trade-offs between the depth of application characterization and the practicality of scheduling I/O resources.