CMU-CS-01-135
Computer Science Department
School of Computer Science, Carnegie Mellon University



CMU-CS-01-135

The Impact of False Sharing on Shared Congestion Management

Srinivasa Aditya Akella, Srinivasan Seshan, Hari Balakirshman*

June 2001

CMU-CS-01-135.ps
CMU-CS-01-135.pdf


Keywords: Congestion control, shared congestion management, false sharing


Several recent proposals have been made for sharing congestion information across concurrent flows between end-systems, where the proposed granularity for sharing has ranged from all flows to a common host, to all hosts on a shared LAN. This paper addresses the problem of false sharing caused by these proposals: two or more flows sharing congestion state may in fact not share the same bottleneck. We characterize the origins of false sharing into two distinct cases: (i) networks with QoS enhancements such as differentiated services, where a flow classifier segregates flows into different queues, and (ii) networks with path diversity where different flows to the same destination address are routed differently, a situation that occurs in dispersity routing, load-balancing, and with network address translators (NATs). We evaluate the impact of false sharing on flow performance and consider whether it might cause a bottleneck link to become persistently overloaded. We then consider how false sharing can be detected by a sender and how different metrics (loss rate, delay distribution, and reordering) compare for this purpose. Finally, we consider the issue of how a sender must respond when it detects false sharing.

Our simulation results show that persistent overload can be avoided with window-based congestion control even for extreme false sharing, but higher bandwidth flows run at a slower rate. We find that delay and reordering statistics can be used to develop robust detectors of false sharing and are superior to those based on loss patterns. We also find, somewhat surprisingly, that it is markedly easier to detect and react to false sharing than it is to start by isolating flows and merging their congestion state together afterwards.

30 pages

*MIT Laboratory for Computer Science, 2000 Technology Square, Cambridge, MA 02139


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