Haiyu Huang

Sun Yat-sen University

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Guangba Yu

Sun Yat-sen University

Pengfei Chen

First Affiliated Hospital of Zhengzhou University

Zilong He

Sun Yat-sen University

Yi-Lun Wang

Chang Gung Memorial Hospital

Jialun Yang

Shandong University

Baofeng Jiang

State Grid Corporation of China (China)

Jin Huang

South China Normal University

Lei Wang

Qingdao University of Science and Technology

Wenguang Chen

Tsinghua University

Guangming Li

Heilongjiang University

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California Institute for Biomedical Research

359

Chinese Academy of Sciences

State Grid Corporation of China (China)

Sun Yat-sen University

Shanghai Jiao Tong University

Central South University

China Southern Power Grid (China)

University of Chinese Academy of Sciences

Tsinghua University

Zhejiang University

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Xingyu Liu Weijun Zhang Tianchi Jiang Yuzhang Ji Jiaxing Lei

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10.1109/epee59859.2023.10352045

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Mint: Cost-Efficient Tracing with All Requests Collection via Commonality and Variability Analysis

arXiv (Cornell University) (2024)

Haiyu Huang Cheng Chen Kuo‐Shen Chen Pengfei Chen Guangba Yu

Distributed traces contain valuable information but are often massive in volume, posing a core challenge in tracing framework design: balancing the tradeoff between preserving essential trace information and reducing trace volume. To address this tradeoff, previous approaches typically used a '1 or 0' sampling strategy: retaining sampled traces while completely discarding unsampled ones. However, based on an empirical study on real-world production traces, we discover that the '1 or 0' strategy actually fails to effectively balance this tradeoff. To achieve a more balanced outcome, we shift the strategy from the '1 or 0' paradigm to the 'commonality + variability' paradigm. The core of 'commonality + variability' paradigm is to first parse traces into common patterns and variable parameters, then aggregate the patterns and filter the parameters. We propose a cost-efficient tracing framework, Mint, which implements the 'commonality + variability' paradigm on the agent side to enable all requests capturing. Our experiments show that Mint can capture all traces and retain more trace information while optimizing trace storage (reduced to an average of 2.7%) and network overhead (reduced to an average of 4.2%). Moreover, experiments also demonstrate that Mint is lightweight enough for production use.

Tracing

10.48550/arxiv.2411.04605

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FaaSRCA: Full Lifecycle Root Cause Analysis for Serverless Applications

arXiv (Cornell University) (2024)

Jin Huang Pengfei Chen Guangba Yu Yi-Lun Wang Haiyu Huang

Serverless becomes popular as a novel computing paradigms for cloud native services. However, the complexity and dynamic nature of serverless applications present significant challenges to ensure system availability and performance. There are many root cause analysis (RCA) methods for microservice systems, but they are not suitable for precise modeling serverless applications. This is because: (1) Compared to microservice, serverless applications exhibit a highly dynamic nature. They have short lifecycle and only generate instantaneous pulse-like data, lacking long-term continuous information. (2) Existing methods solely focus on analyzing the running stage and overlook other stages, failing to encompass the entire lifecycle of serverless applications. To address these limitations, we propose FaaSRCA, a full lifecycle root cause analysis method for serverless applications. It integrates multi-modal observability data generated from platform and application side by using Global Call Graph. We train a Graph Attention Network (GAT) based graph auto-encoder to compute reconstruction scores for the nodes in global call graph. Based on the scores, we determine the root cause at the granularity of the lifecycle stage of serverless functions. We conduct experimental evaluations on two serverless benchmarks, the results show that FaaSRCA outperforms other baseline methods with a top-k precision improvement ranging from 21.25% to 81.63%.

Root Cause Analysis

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10.48550/arxiv.2412.02239

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How Few Davids Improve One Goliath: Federated Learning in Resource-Skewed Edge Computing Environments

Proceedings of the ACM Web Conference 2022 (2024)

Jiayun Zhang Shuheng Li Haiyu Huang Zihan Wang Xiaohan Fu

10.1145/3589334.3645544

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CIEEC 2019 Table of Contents