Abstract: Cascaded TCP: BIG Throughput for BIG DATA Applications in Distributed HPC

Umar Kalim, M. Gardner, Eric J. Brown, Wu-chun Feng
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引用次数: 2

Abstract

Saturating high capacity and high latency paths is a challenge with vanilla TCP implementations. This is primarily due to congestion-control algorithms which adapt window sizes when acknowledgements are received. With large latencies, the congestion-control algorithms have to wait longer to respond to network conditions (e.g., congestion), and thus result in less aggregate throughput. We argue that throughput can be improved if we reduce the impact of large end-to-end latencies by introducing layer-4 relays along the path. Such relays would enable a cascade of TCP connections, each with lower latency, resulting in better aggregate throughput. This would directly benefit typical applications as well as BIG DATA applications in distributed HPC. We present empirical results supporting our hypothesis.
摘要:级联TCP:分布式高性能计算中大数据应用的大吞吐量
对于普通的TCP实现来说,饱和高容量和高延迟路径是一个挑战。这主要是由于拥塞控制算法在收到确认时调整窗口大小。对于大延迟,拥塞控制算法必须等待更长的时间来响应网络条件(例如,拥塞),从而导致更少的总吞吐量。我们认为,如果我们通过在路径上引入第4层中继来减少大的端到端延迟的影响,则可以提高吞吐量。这样的中继可以实现TCP连接的级联,每个连接的延迟都更低,从而产生更好的总吞吐量。这将直接有利于典型应用以及分布式高性能计算中的大数据应用。我们提出实证结果支持我们的假设。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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