Improving Cloud Storage Network Bandwidth Utilization of Scientific Applications

Abstract

Cloud providers began to provide managed services to attract scientific applications, which have been traditionally executed on supercomputers. One example is AWS FSx for Lustre, a fully managed parallel file system (PFS) released in 2018. However, due to the nature of scientific applications, the frontend storage network bandwidth is left completely idle for the majority of its lifetime. Furthermore, the pricing model does not match the scalability requirement. We propose iFast, a novel host-side caching mechanism for scientific applications that improves storage bandwidth utilization and end-to-end application performance: by overlapping compute and data writeback through inexpensive local storage. iFast supports the Massage Passing Interface (MPI) library that is widely used by scientific applications and is implemented as a preloaded library. It requires no change to applications, the MPI library, or support from cloud operators. We demonstrate how iFast can accelerate the end-to-end time of a representative scientific application Neko, by 13–40%.