The ability of large-scale infrastructures to store and retrieve a massive amount of data is now decisive to scale up scientific applications. However, there is an ever-widening gap between I/O and computing performance. A way to mitigate this consists of deploying new intermediate storage tiers (node-local storage, burst-buffers, ...) between the compute nodes and the traditional global shared parallel file-system. Unfortunately, without advanced techniques to allocate and size these resources, they remain underutilized. In this paper, we investigate how heterogeneous storage resources can be allocated on an HPC platform, in a similar way as compute resources. In that regard, we introduce StorAlloc, a simulator used as a testbed for assessing storage-aware job scheduling algorithms and evaluating various storage infrastructures.