Flock：大规模下的精准网络故障定位
Flock: Accurate network fault localization at scale
AI总结：
针对数据中心网络隐性故障定位的速度与精度瓶颈，提出基于概率图模型与推理加速技术的Flock系统，在大规模场景下实现了万倍级速度提升与显著误差降低，且受限场景下推理最优。
摘要：
在数据中心网络的数千个组件中推断故障的根本原因极具挑战性，尤其是对于不会被交换机直接上报的“隐性”故障而言。虽然可以通过端到端测量来定位故障，但以往的定位方案要么在大规模网络中速度过慢，要么会牺牲准确性。本文提出了Flock——一种可在数据中心规模下同时实现高准确率和高速性的网络故障定位算法与系统。Flock采用概率图模型（PGM）实现高准确率，同时搭配多项新技术，大幅加速了离散值贝叶斯PGM中的推理过程。大规模仿真与硬件测试台实验表明，与以往的PGM方法相比，Flock的推理速度提升了10000倍以上；相较于此前最优的数据中心故障定位方法，其准确率也有所提升：在相同输入遥测数据下，推理误差降低了1.19-11倍，在结合被动遥测数据后，误差降低了1.2-55倍。本文还证明了Flock的推理在受限场景下是最优的。
英文摘要：
Inferring the root cause of failures among thousands of components in a data center network is challenging, especially for "gray" failures that are not reported directly by switches. Faults can be localized through end-to-end measurements, but past localization schemes are either too slow for large-scale networks or sacrifice accuracy. We describe Flock, a network fault localization algorithm and system that achieves both high accuracy and speed at datacenter scale. Flock uses a probabilistic graphical model (PGM) to achieve high accuracy, coupled with new techniques to dramatically accelerate inference in discrete-valued Bayesian PGMs. Large-scale simulations and experiments in a hardware testbed show Flock speeds up inference by >10000x compared to past PGM methods, and improves accuracy over the best previous datacenter fault localization approaches, reducing inference error by 1.19-11x on the same input telemetry, and by 1.2-55x after incorporating passive telemetry. We also prove Flock's inference is optimal in restricted settings
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
