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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. Here, we experimentally demonstrate that RISE can take advantage of staging nodes to offload data during writes without degrading application data movement performance.

Authors:
 [1];  [1];  [1]
  1. Univ. of Utah, Salt Lake City, UT (United States)
Publication Date:
Research Org.:
Rutgers Univ., Piscataway, NJ (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR). Scientific Discovery through Advanced Computing (SciDAC)
OSTI Identifier:
1907691
Grant/Contract Number:  
SC0021326; AC05-00OR22725
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Proceedings - IEEE International Conference on Cluster Computing (Online)
Additional Journal Information:
Journal Volume: 2021; Conference: 2021 IEEE International Conference on Cluster Computing (CLUSTER), Portland, OR (United States), 7-10 Sep 2021; Journal ID: ISSN 2168-9253
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Extreme Scale Data Staging; Machine Learning; Data Management; High Performance Computing

Citation Formats

Subedi, Pradeep, Davis, Philip E., and Parashar, Manish. RISE: Reducing I/O Contention in Staging-based Extreme-Scale In-situ Workflows. United States: N. p., 2021. Web. doi:10.1109/cluster48925.2021.00021.
Subedi, Pradeep, Davis, Philip E., & Parashar, Manish. RISE: Reducing I/O Contention in Staging-based Extreme-Scale In-situ Workflows. United States. https://doi.org/10.1109/cluster48925.2021.00021
Subedi, Pradeep, Davis, Philip E., and Parashar, Manish. 2021. "RISE: Reducing I/O Contention in Staging-based Extreme-Scale In-situ Workflows". United States. https://doi.org/10.1109/cluster48925.2021.00021. https://www.osti.gov/servlets/purl/1907691.
@article{osti_1907691,
title = {RISE: Reducing I/O Contention in Staging-based Extreme-Scale In-situ Workflows},
author = {Subedi, Pradeep and Davis, Philip E. and Parashar, Manish},
abstractNote = {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. Here, we experimentally demonstrate that RISE can take advantage of staging nodes to offload data during writes without degrading application data movement performance.},
doi = {10.1109/cluster48925.2021.00021},
url = {https://www.osti.gov/biblio/1907691}, journal = {Proceedings - IEEE International Conference on Cluster Computing (Online)},
issn = {2168-9253},
number = ,
volume = 2021,
place = {United States},
year = {Wed Sep 01 00:00:00 EDT 2021},
month = {Wed Sep 01 00:00:00 EDT 2021}
}

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