Efficient, Failure Resilient Transactions for Parallel and Distributed Computing

Lofstead, Jay; Dayal, Jai; Jimenez, Ivo; Maltzahn, Carlos

doi:10.1109/discs.2014.13

Cited by 4 publications

(1 citation statement)

References 13 publications

(12 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The study in Reference [26] exploits the reduction style processing pattern in analytics applications and reduces the complications of keeping checkpoints of the simulation and the analytics consistent. Research efforts in Reference [27] use a synchronous two-phase commit transactions protocol to tolerate failures in high performance and distributed computing systems. In comparison to these efforts, our data resilience approach specifically targets data staging based in-situ workflows and is more flexible, asynchronous, and scalable.…”

Section: Related Workmentioning

confidence: 99%

CoREC

Duan

Subedi

Davis

et al. 2020

ACM Trans. Parallel Comput.

View full text Add to dashboard Cite

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 time 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 article, we present CoREC, a scalable and 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. It also leverages multiple levels of replications and erasure coding to support diverse data resiliency requirements. Furthermore, the article 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 leadership class computing machines and present an experimental evaluation in the article. The experiments demonstrate that CoREC can tolerate in-memory data failures while maintaining low latency and sustaining high overall storage efficiency at large scales.

show abstract

Section: Related Workmentioning

confidence: 99%