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DEX: Scaling applications beyond machine boundaries
  • Kim, Sang Hoon ;
  • Olivier, Pierre ;
  • Chuang, Ho Ren ;
  • Min, Changwoo ;
  • Lyerly, Robert ;
  • Ravindran, Binoy
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Publication Year
2020-11-01
Journal
Proceedings - International Conference on Distributed Computing Systems
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
Proceedings - International Conference on Distributed Computing Systems, Vol.2020-November, pp.864-876
Keyword
-Thread migrationDistributed executionDistributed memoryRDMA
Mesh Keyword
Application logicCoherence protocolComputing performanceDistributed programming modelEvaluation resultsReal applicationsSystem-level approachUnderlying networks
All Science Classification Codes (ASJC)
SoftwareHardware and ArchitectureComputer Networks and Communications
Abstract
—Increasing the computing performance within a single-machine form factor is becoming increasingly difficult due to the complexities in scaling processor interconnects and coherence protocols. On the other hand, converting existing applications to run on multiple nodes requires a significant effort to rewrite application logic in distributed programming models and adapt the code to the underlying network characteristics. This paper presents DEX, an operating system-level approach to extend the execution boundary of existing applications over multiple machines. DEX allows the threads in a process to be relocated and distributed dynamically through a simple function call. DEX makes it trivial for developers to convert any application to be distributed over multiple nodes and for applications to transparently utilize disaggregated resources in a rack-scale system with minimal effort. Evaluation results using a running prototype and eight real applications showed promising results – six out of the eight scaled beyond the single-machine performance on DEX.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36568
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85086590547&origin=inward
DOI
https://doi.org/10.1109/icdcs47774.2020.00021
Type
Conference
Funding
This work is supported in part by grants received by Virginia Tech including that from the US Office of Naval Research (ONR) under grants N00014-16-1-2711, N00014-16-1-2104, and N00014-18-1-2022, and from NAVSEA/NEEC under grant N00174-16-C-0018. Kim’s work at Virginia Tech (former affiliation) was supported by ONR under grants N00014-16-1-2711 and N00014-18-1-2022.This work is also supported by Electronics and Telecommunications Research Institute (ETRI) grant funded by the Korean government (20ZS1310).
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