DETER: Deterministic TCP Replay for Performance Diagnosis


Yuliang Li, Harvard University; Rui Miao, Alibaba Group; Mohammad Alizadeh, Massachusetts Institute of Technology; Minlan Yu, Harvard University


TCP performance problems are notoriously tricky to diagnose because a subtle choice of TCP parameters or features may lead to completely different performance. A gold standard for diagnosis is to collect packet traces and trace TCP executions. However, it is not easy to use such tools in large-scale data centers where many TCP connections interact with each other. In this paper, we introduce DETER, a deterministic TCP replay tool, which runs lightweight recording all the time at all the hosts and then replay selected collections where operators can collect packet traces and trace TCP executions for diagnosis. The key challenge for deterministic TCP replay is the butterfly effect---a small timing variation causes a chain reaction between TCP and the network that drives the system to a completely different state in the replay. To eliminate the butterfly effect, we propose to replay individual TCP connection separately and capture all the interactions between a connection with the applications and the network. Our evaluation shows that \system has low recording overhead and can help diagnose many TCP performance problems such as long latency related to zero-window probes, late fast retransmission, frequent retransmission timeout, to problems related to the switch shared buffer.

NSDI '19 Open Access Sponsored by NetApp

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@inproceedings {227643,
author = {Yuliang Li and Rui Miao and Mohammad Alizadeh and Minlan Yu},
title = {{DETER}: Deterministic {TCP} Replay for Performance Diagnosis},
booktitle = {16th USENIX Symposium on Networked Systems Design and Implementation (NSDI 19)},
year = {2019},
isbn = {978-1-931971-49-2},
address = {Boston, MA},
pages = {437--452},
url = {},
publisher = {USENIX Association},
month = feb

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