队列管理算法与网络流量的自相似性

B. Sikdar, K. Chandrayana, K. Vastola, S. Kalyanaraman
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引用次数: 13

摘要

网络流量的自相似性已经在各种环境中建立起来,众所周知,自相似流量会导致更大的排队延迟、更高的丢包率和更长的拥塞时间。本文研究了各种缓冲管理算法对网络流量自相似度的影响。本文研究了在路由器上使用的主动和被动队列管理策略对TCP流量自相似度的影响。我们还提出了对随机早期检测(RED)算法的修改,旨在减少TCP流中的超时和指数回退,并表明与目前实施的主动和被动缓冲管理策略相比,它可以在广泛的网络条件下显著降低流量自相似性。我们还表明,尽管我们的技术针对的是TCP相关的原因,但只要TCP被用作底层传输协议,即使在应用程序和用户级别的原因也存在的情况下,它也能有效地降低流量中的自相似程度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Queue management algorithms and network traffic self-similarity
The self-similarity of network traffic has been established in a variety of environments and it is well known that self-similar traffic can lead to larger queueing delays, higher drop rates and extended periods of congestion. In this paper, we investigate the impact of various buffer management algorithms on the self-similarity of network traffic. In this paper we investigate the impact of active and passive queue management policies used at the routers on the self-similarity of TCP traffic. We also propose a modification to the random early detection (RED) algorithm, aimed at reducing the timeouts and exponential backoffs in TCP flows, and show that it can lead to significant reductions in the traffic self-similarity under a wide range of network conditions, as compared to the currently implemented active and passive buffer management policies. We also show that though our techniques are aimed at TCP related causes, it is also effective in reducing the degree of self-similarity in traffic even when application and user level causes are also present, as long as TCP is used as the underlying transport protocol.
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