Scalable data resilience for in-memory data staging

Shaohua Duan, Pradeep Subedi, Keita Teranishi, Philip Davis, Hemanth Kolla, Marc Gamell, Manish Parashar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

Abstract

The dramatic increase in the scale of current and planned high-end HPC systems is leading new challenges, such as the growing costs of data movement and IO, and the reduced mean times between failures (MTBF) of system components. In-situ workflows, i.e., executing the entire application workflows on the HPC system, have emerged as an attractive approach to address data-related challenges by moving computations closer to the data, and staging-based frameworks have been effectively used to support in-situ workflows at scale. However, the resilience of these staging-based solutions has not been addressed and they remain susceptible to expensive data failures. Furthermore, naive use of data resilience techniques such as n-way replication and erasure codes can impact latency and/or result in significant storage overheads. In this paper, we present CoREC, a scalable resilient in-memory data staging runtime for large-scale in-situ workflows. CoREC uses a novel hybrid approach that combines dynamic replication with erasure coding based on data access patterns. The paper also presents optimizations for load balancing and conflict avoiding encoding, and a low overhead, lazy data recovery scheme. We have implemented the CoREC runtime and have deployed with the DataSpaces staging service on Titan at ORNL, and present an experimental evaluation in the paper. The experiments demonstrate that CoREC can tolerate in-memory data failures while maintaining low latency and sustaining high overall storage efficiency at large scales.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 IEEE 32nd International Parallel and Distributed Processing Symposium, IPDPS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages105-115
Number of pages11
ISBN (Print)9781538643686
DOIs
StatePublished - Aug 3 2018
Event32nd IEEE International Parallel and Distributed Processing Symposium, IPDPS 2018 - Vancouver, Canada
Duration: May 21 2018May 25 2018

Publication series

NameProceedings - 2018 IEEE 32nd International Parallel and Distributed Processing Symposium, IPDPS 2018

Other

Other32nd IEEE International Parallel and Distributed Processing Symposium, IPDPS 2018
Country/TerritoryCanada
CityVancouver
Period5/21/185/25/18

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems and Management

Keywords

  • Adaptive data placement
  • Extreme scale
  • In situ workflow
  • Resilient data staging

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