Abstract
High-end computing is suffering a data deluge from experiments,
simulations, and apparatus that creates overwhelming application
dataset sizes. End-user workstations-despite more processing
power than ever before-are ill-equipped to cope with such data
demands due to insufficient secondary storage space and I/O rates.
Meanwhile, a large portion of desktop storage is unused. We
present the FreeLoader framework, which aggregates unused desktop
storage space and I/O bandwidth into a shared cache/scratch
space, for hosting large, immutable datasets and exploiting data
access locality. Our experiments show that FreeLoader is an appealing
low-cost solution to storing massive datasets, by delivering
higher data access rates than traditional storage facilities. In
particular, we present novel data striping techniques that allow
FreeLoader to efficiently aggregate a workstation's network communication
bandwidth and local I/O bandwidth. In addition, the
performance impact on the native workload of donor machines is
small and can be effectively controlled.