Constructing collaborative desktop storage caches for large scientific datasets

  • Authors:

  • Sudharshan S. Vazhkudai;Xiaosong Ma;Vincent W. Freeh;Jonathan W. Strickland;Nandan Tammineedi;Tyler Simon;Stephen L. Scott

  • Affiliations:

  • Oak Ridge National Laboratory, Oak Ridge, TN;North Carolina State University, Raleigh, NC;North Carolina State University, Raleigh, NC;North Carolina State University, Raleigh, NC;North Carolina State University, Raleigh, NC;Oak Ridge National Laboratory, Oak Ridge, TN;Oak Ridge National Laboratory, Oak Ridge, TN

  • Venue:
  • ACM Transactions on Storage (TOS)
  • Year:
  • 2006

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Abstract

High-end computing is suffering a data deluge from experiments, simulations, and apparatus that creates overwhelming application dataset sizes. This has led to the proliferation of high-end mass storage systems, storage area clusters, and data centers. These storage facilities offer a large range of choices in terms of capacity and access rate, as well as strong data availability and consistency support. However, for most end-users, the “last mile” in their analysis pipeline often requires data processing and visualization at local computers, typically local desktop workstations. End-user workstations---despite having 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 propose 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. This article presents the FreeLoader architecture, component design, and performance results based on our proof-of-concept prototype. Its architecture comprises contributing benefactor nodes, steered by a management layer, providing services such as data integrity, high performance, load balancing, and impact control. 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, namely, local or remote shared file systems, storage systems, and Internet data repositories. 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. Further, we show that security features such as data encryptions and integrity checks can be easily added as filters for interested clients. Finally, we demonstrate how legacy applications can use the FreeLoader API to store and retrieve datasets.