End-to-End Performance Management for Scalable Distributed Storage

Many applications - for example, scientific simulation, real-time data acquisition, and distributed reservation systems - have I/O performance requirements, yet most large, distributed storage systems lack the ability to guarantee I/O performance. The authors are working on end-to-end performance management in scalable, distributed storage systems. The kinds of storage systems they are targeting include large High-Performance Computing (HPC) clusters, which require both large data volumes and high I/O rates, as well as large-scale general-purpose storage systems. There are two main issues with performance management in such systems: sharing resources among competing users, applications, or tasks, and maintaining high performance.