Open Access
Volume 38, Number 3, June 2020
Page(s) 589 - 595
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]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.