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The Journal of Supercomputing

, Volume 74, Issue 6, pp 2870–2902 | Cite as

APS: adaptable prefetching scheme to different running environments for concurrent read streams in distributed file systems

  • Sangmin Lee
  • Soon J. Hyun
  • Hong-Yeon Kim
  • Young-Kyun Kim
Article

Abstract

Distributed file systems (DFSs) are widely used in various areas. One of the key issues is to provide high performance of concurrent read streams (i.e., multiple series of sequential reads by concurrent processes) for their applications. Despite the many studies on local file systems (LFSs), research has seldom been done on concurrent read streams in DFSs with different running environments (i.e., different types of storage devices and various network delays). Furthermore, most of the existing DFSs have a sharply degraded performance compared with a LFS (i.e., EXT4). Therefore, to achieve high performance in concurrent read streams, this study introduces a populating effect that keeps sending subsequent reads to a storage server and then proposes an adaptable prefetching scheme (APS) to obtain the effect even in different running environments. Hence, our APS resolves all the problems that we identified as dramatically degrading the performance in existing DFSs. In three different types of storage devices and in various network delays, the evaluation results show that our prefetching scheme (1) achieves almost the same performance as a LFS from an individual server and (2) minimizes the performance degradation of random reads.

Keywords

Distributed file system Concurrent read streams Data prefetching Device type Network delay 

Notes

Acknowledgements

This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. R0126-15-1082, Management of Developing ICBMS (IoT, Cloud, Bigdata, Mobile, Security) Core Technologies and Development of Exascale Cloud Storage Technology).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of School of ComputingKorea Advanced Institute of Science and Technology (KAIST)DaejeonKorea
  2. 2.High Performance Computing Research GroupElectronics and Telecommunications Research Institute (ETRI)DaejeonKorea

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