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All issues Volume 38 / No 3 (June 2020) JNWPU, 38 3 (2020) 589-595 References
Open Access
Issue
JNWPU
Volume 38, Number 3, June 2020
Page(s) 589 - 595
DOI https://doi.org/10.1051/jnwpu/20203830589
Published online 06 August 2020
  1. Dean J, Ghemawat S. MapReduce:Simplified Data Processing on Large Clusters[J]. Communication of the ACM, 2008, 51 (1): 107– 113 [Article] [CrossRef] [Google Scholar]
  2. Isard M, Budiu M, Yu Y, et al. Dryad: Distributed Data-Parallel Programs Form Sequential Building[C]//Proceedings of the 2nd ACM SIGOPS/Euro Sys European Conference on Computer Systenm, 2007: 59–72 [Google Scholar]
  3. Zaharia M, Chowdhury N. N M M, Franklin M, et al. Spark: Cluster Computing with Working Sets[R]. Technical Report UCB/EECS-2010-53, 2010 [Google Scholar]
  4. Paris Carbone, Asterios Katsifodimos, Stephan Ewen, et al. Apache Flink:Stream and Batch Processing in a Single Engine[J]. IEEE Data Engineering Bulletin, 2015, 38 (4): 28– 38 [Article] [Google Scholar]
  5. HADOOP Project. Hadoop Capacity Scheduler[EB/OL].(2019-08-23)[2019-08-25]. [Article] [Google Scholar]
  6. Hindman B, Konwinski A, Zaharia M, et al. Mesos: a Platform for Fine-Grained Resource Sharing in the Data Center[C]//Implementation Berkety, CA: USENIX Association, 2011: 22–35 [Google Scholar]
  7. Verma A, Pedrosa L, Korupolu M, et al. Large-Scale Cluster Management at Google with Borg[C]//Tenth European Conference on Computer Systems, New York, 2015: 18–34 [Google Scholar]
  8. Schwarzkopf M, Konwinski A, Abd-El-Malek M, et al. Omega: Flexible, Scalable Schedulers for Large Compute Clusters[C]//Proc of 8th ACM European Conference on Computer Systems, New York, 2013: 351–364 [Google Scholar]
  9. Karanasos K, Rao S, Curino C, et al. Mercury: Hybrid Centralized and Distributed Scheduling in Large Shared Clusters[C]//2015 USENIX Annual Technical Conference, Berkeley, 2015: 485–497 [Google Scholar]
  10. Acura P. Deploying Rails with Docker, Kubernetes and ECS[M]. Berkeley, Apress, 2016 [Google Scholar]
  11. Fukutomi D, Iida Y, Azumi T, et al. GPUhd: Augmenting YARN with GPU Resource Management[C]//International Conference on High Performance Computing in Asia-Pacific Region, 2018 [Google Scholar]
  12. Myeongjae J, Shivaram V, Amar P, et al. Multi-Tenant GPU Clusters for Deep Learning Workloads: Analysis and Implications[EB/OL].(2018-05-13)[2019-08-10]. [Article] [Google Scholar]
  13. Volodymyr V, Kindratenk O, Jeremy J, et al. GPU Clusters for High-Performance Computing[C]//2009 IEEE International Conference on Cluster Computing and Workshops, New Orleans, USA, 2009: 1–8 [Google Scholar]

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