异步数据流基于速率的连接窗口大小的性能评估

Proceedings. 13th IEEE International Symposium on High performance Distributed Computing, 2004. Pub Date : 2004-06-04 DOI:10.1109/HPDC.2004.28

N. Vijayakumar, Beth Plale

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引用次数: 0

摘要

我们的工作是由大量的数据流源驱动的，这些数据流源定义了中尺度气象学，其中异步流是常见的。在多个流上执行过滤、聚合和转换的技术必须对异步流有效。速率分级算法(RS-Algo)将等待参与join的事件数量与负责它们交付的流的速率联系起来。在这张海报中，我们展示了RS-Algo的性能评估结果。在异步流下，内存利用率的提高最大。

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Performance evaluation of rate-based join window sizing for asynchronous data streams

Our work is motivated by the large number of data stream sources that define mesoscale meteorology where asynchronous streams are commonplace. Techniques for performing filtering, aggregation, and transformation on multiple streams must be effective for the case of asynchronous streams. Rate Sizing algorithm (RS-Algo) links the number of events waiting to participate in a join to the rate of the streams responsible for their delivery. In this poster, we show the results of performance evaluation of the RS-Algo. The gains in memory utilization are largest under asynchronous streams.

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Proceedings. 13th IEEE International Symposium on High performance Distributed Computing, 2004.

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