Pilot-Data: An abstraction for distributed data

Andre Luckow, Mark Santcroos, Ashley Zebrowski, Shantenu Jha

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Scientific problems that depend on processing large amounts of data require overcoming challenges in multiple areas: managing large-scale data distribution, controlling co-placement and scheduling of data with compute resources, and storing, transferring, and managing large volumes of data. Although there exist multiple approaches to address each of these challenges and the complexity of distributed environments, an integrative approach is missing; furthermore, extending existing functionality or enabling interoperable capabilities remains difficult at best. We propose the concept of Pilot-Data to address the fundamental challenges of co-placement and scheduling of data and compute in heterogeneous and distributed environments with interoperability and extensibility as first-order concerns. Pilot-Data is an extension of the Pilot-Job abstraction for supporting the management of data in conjunction with compute tasks. Pilot-Data separates logical data units from physical storage, thereby providing the basis for efficient compute/data placement and scheduling. In this paper, we discuss the design and implementation of the Pilot-Data prototype, demonstrate its use by data-intensive applications on multiple production distributed cyberinfrastructure and illustrate the advantages arising from flexible execution modes enabled by Pilot-Data. Our experiments utilize an implementation of Pilot-Data in conjunction with a scalable Pilot-Job (BigJob) to establish the application performance that can be enabled by the use of Pilot-Data. We demonstrate how the concept of Pilot-Data also provides the basis upon which to build tools and support capabilities like affinity which in turn can be used for advanced data-compute co-placement and scheduling.

Original languageEnglish (US)
Pages (from-to)16-30
Number of pages15
JournalJournal of Parallel and Distributed Computing
Volume79-80
DOIs
StatePublished - Jun 3 2015

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computer Networks and Communications
  • Artificial Intelligence

Keywords

  • Bigdata
  • Cloud
  • Data-intensive
  • Distributed computing
  • Grid
  • HPC
  • HTC
  • Pilot-jobs

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