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Optimization of the Processing of Data Streams on Roughly Characterized Distributed Resources

机译：大致表征的分布式资源上数据流处理的优化

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摘要

The AS4DR (Adaptive Scheduling for Distributed Resources) scheduling method presented in this paper aims at maximizing throughput, when processing several data streams by divisible load applications on star-shaped distributed memory platforms, with available speeds for communicating and computing which may be poorly estimated, or varying over time. The total workload is supposed to be unknown. According to the computation cost model, AS4DR can either maximize throughput, or CPU utilization by avoiding data-starvation of the computing units. An experimental assessment of the adaptation of the workload distribution to the variation of the communicating and computing speeds has been performed that shows that the use of AS4DR can significantly improve the throughput. This paper also experimentally assesses a resource selection method to set up star-shaped clusters of distributed resources, so as to process efficiently a set of data streams with AS4DR.
机译：本文提出的AS4DR（分布式资源的自适应调度）调度方法旨在在通过星形分布式存储平台上的可分负载应用程序处理多个数据流时，以最大的吞吐量最大化，而可用的通信和计算速度可能会估算得很差，或随时间变化。总工作量应该是未知的。根据计算成本模型，AS4DR可以通过避免计算单元的数据不足来最大化吞吐量或CPU利用率。对工作负载分布适应通信和计算速度变化进行了实验评估，结果表明使用AS4DR可以显着提高吞吐量。本文还通过实验评估了一种资源选择方法来建立星形分布的资源簇，以便使用AS4DR有效地处理一组数据流。

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