Lancet: A self-correcting Latency Measuring Tool

Authors: 

Marios Kogias, EPFL; Stephen Mallon, University of Sydney; Edouard Bugnion, EPFL

Abstract: 

We present LANCET, a self-correcting tool designed to measure the open-loop tail latency of μs-scale datacenter applications with high fan-in connection patterns. LANCET is self-correcting as it relies on online statistical tests to determine situations in which tail latency cannot be accurately measured from a statistical perspective, including situations where the workload configuration, the client infrastructure, or the application itself does not allow it. Because of its design, LANCET is also extremely easy to use. In fact, the user is only responsible for (i) configuring the workload parameters, i.e., the mix of requests and the size of the client connection pool, and (ii) setting the desired confidence interval for a particular tail latency percentile. All other parameters, including the length of the warmup phase, the measurement length, and the necessary sampling rate, are dynamically determined by the LANCET experiment coordinator. When available, LANCET leverages NIC-based hardware timestamping to measure RPC end-to-end latency. Otherwise, it uses an asymmetric setup with a latency-agent that leverages busy-polling system calls to reduce the client bias. Our evaluation shows that LANCET automatically identifies situations in which tail latency cannot be determined and accurately reports the latency distribution of workloads with single-digit μs service time. For the workloads studied, LANCET can successfully report, with 95% confidence, the 99th percentile tail latency within an interval of ≤ 10μs. In comparison with state-of-the-art tools such as Mutilate and Treadmill, LANCET reports a latency cumulative distribution that is ∼20μs lower when the NIC timestamping capability is available and ∼10μs lower when it is not.

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BibTeX
@inproceedings {234928,
author = {Marios Kogias and Stephen Mallon and Edouard Bugnion},
title = {Lancet: A self-correcting Latency Measuring Tool},
booktitle = {2019 {USENIX} Annual Technical Conference ({USENIX} {ATC} 19)},
year = {2019},
isbn = {978-1-939133-03-8},
address = {Renton, WA},
pages = {881--896},
url = {https://www.usenix.org/conference/atc19/presentation/kogias-lancet},
publisher = {{USENIX} Association},
}