High-Performance Storage Support for Scientific Applications on the Cloud.

ABSTRACTAlthough cloud computing has become one of the most popular paradigms for executing data-intensive applications (for example, Hadoop), the storage subsystem is not optimized for scientific applications. We believe that when executing scientific applications in the cloud, a node-local distributed storage architecture is a key approach to overcome the challenges from the conventional shared/parallel storage systems. We analyze and evaluate four representative file systems (S3FS, HDFS, Ceph, and FusionFS) on three platforms (Kodiak cluster, Amazon EC2 and FermiCloud) with a variety of benchmarks to explore how well these storage systems can handle metadata intensive, write intensive, and read intensive workloads.