Cost-Aware Region-Level Data Placement in Multi-Tiered Parallel I/O Systems

Multi-tiered Parallel I/O systems that combine traditional HDDs with emerging SSDs mitigate the cost burden of SSDs while benefiting from their superior I/O performance. While a multi-tiered parallel I/O system is promising for data-intensive applications in high-performance (HPC) domains, placing data on each tier of the system to achieve high I/O performance remains a challenge. In this paper, we propose a cost-aware region-level (CARL) data placement scheme in multi-tiered parallel I/O systems. CARL divides a large file into several small regions, and then places regions on different types of servers based on region access costs. CARL includes a static policy S-CARL and a dynamic policy D-CARL. For applications whose I/O access patterns are completely known, S-CARL calculates the region costs within the entire workload duration, and uses a static data placement scheme to selectively place regions on the proper servers. To adapt to applications whose access patterns are unknown in advance, D-CARL uses a dynamic data placement scheme which migrates data among different servers within each time window. We have implemented CARL under MPI-IO library and OrangeFS parallel file system environment. Our evaluation with representative benchmarks and an application shows that CARL is both feasible and able to improve I/O performance significantly.