@inproceedings{14314305a58744e48ebbae5be4ff4cf9,
title = "RISE: Reducing I/O Contention in Staging-based Extreme-Scale In-situ Workflows",
abstract = "While in-situ workflow formulations have addressed some of the data-related challenges associated with extreme-scale scientific workflows, these workflows involve complex interactions and different modes of data exchange. In the context of increasing system complexity, such workflows present significant resource management challenges, requiring complex cost-performance tradeoffs. This paper presents RISE, an intelligent staging-based data management middleware, which builds on the DataSpaces framework and performs intelligent scheduling of data management operations to reduce I/O contention. In RISE, data are always written immediately to local buffers to reduce the effect of the transfer impact upon application performance. RISE identifies applications' data access patterns and moves data towards data consumers only when the network is expected to be idle, reducing the impact of asynchronous background data movement upon critical data read/write requests. We experimentally demonstrate that RISE can take advantage of staging nodes to offload data during writes without degrading application data movement performance.",
keywords = "Data Management, Extreme Scale Data Staging, High Performance Computing, Machine Learning",
author = "Pradeep Subedi and Davis, {Philip E.} and Manish Parashar",
note = "Publisher Copyright: {\textcopyright}2021 IEEE.; 2021 IEEE International Conference on Cluster Computing, Cluster 2021 ; Conference date: 07-09-2021 Through 10-09-2021",
year = "2021",
doi = "10.1109/Cluster48925.2021.00021",
language = "English (US)",
series = "Proceedings - IEEE International Conference on Cluster Computing, ICCC",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "146--156",
booktitle = "Proceedings - 2021 IEEE International Conference on Cluster Computing, Cluster 2021",
address = "United States",
}