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Scheduling Data-Intensive Bags of Tasks in P2P Grids with BitTorrent-enabled Data Distribution

机译:使用启用了BitTorrent的数据分发来安排P2P网格中的数据密集型任务包

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摘要

Scheduling Data-Intensive Bags of Tasks in P2P Grids leads to transfers of large input data files, which cause delays in completion times. We propose to combine several existing technologies and patterns to perform efficient data-aware scheduling:(1) use of the BitTorrent P2P file sharing protocol to transfer data,(2) data caching on computational Resources,(3) use of a data-aware Resource selection scheduling algorithm similar to Storage Affinity,(4) a new Task selection scheduling algorithm (Temporal Tasks Grouping), based on the temporally grouped scheduling of Tasks sharing input data files. Data replication is also discussed.The proposed approach does not need an overlay network or Predictive Communications Ordering, making our operational implementation of a P2P Grid middleware easily deployable in unstructured P2P networks. Experiments show that performance gains are achieved by combining BitTorrent, caching, Storage Affinity and Temporal Tasks Grouping. This work can be summarized as combining P2P Grid computing and P2P data transfer technologies.
机译:在P2P网格中安排数据密集型任务包会导致大型输入数据文件的传输,从而导致完成时间的延迟。我们建议结合几种现有技术和模式来执行有效的数据感知调度:(1)使用BitTorrent P2P文件共享协议传输数据,(2)在计算资源上缓存数据,(3)使用数据感知资源选择调度算法类似于Storage Affinity,(4)一种新的任务选择调度算法(临时任务分组),它基于共享输入数据文件的任务的时间分组调度。还讨论了数据复制。所提出的方法不需要覆盖网络或预测性通​​信排序,这使我们的P2P网格中间件的操作实现易于部署在非结构化P2P网络中。实验表明,通过组合BitTorrent,缓存,存储亲和力和时间任务分组可以提高性能。可以将这项工作概括为结合了P2P网格计算和P2P数据传输技术。

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