Guangba Yu

Guangba Yu

Ph.D. Candidate Focus on Cloud Native

DDS Lab, Sun Yat-Sen University

Biography

I am Guangba Yu (余广坝 in Chinese). I am a 4th year Ph.D. candidate at Sun Yat-Sen University, advised by Professor Pengfei Chen. I am interested in cloud native, microservice, Serverless, and AIOps. My research focus on perfromance diagnose and optimization in distributed systems. And I have a strong curiosity about telemetry of cloud-native systems.

I have awarded Tencent Rhino-Bird Research Elite Program and Tencent Special Scholarship in 2022. I am a Ph.D. software engineering student researcher at WeChat in 2022, hosted by Yuetang Deng.

I am actively looking for faculty positions and post-doc opportunities. You can find more information in my CV.

I maintain a Github project about Awesome cloud paper and a WeChat public account WeeklyCloudPaper in Chinese. Welcome to follow my updates.

Interests
  • Cloud Computing
  • Microservice
  • Serverless
  • AIOps
  • Chaos Engineering
Education
  • Ph.D. in Computer Science and Technology, 2024 (Expected)

    Sun Yat-Sen University

  • M.Eng in Computer Technology, 2020

    Sun Yat-Sen University

Recent News

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[28/07/23] Multi-modal Observability Data RCA Framework Nezha and Configuration Optimization Framework Diagconfig are accepted by FSE 2023.

[16/06/23] Automatic Power Management Framwork DeepPower and Automatic Network Root Cause Analysis Framework MARS are accepted by ICPP 2023.

[06/05/23] Delivering FaaS Function to Computing Continuum FrameworkFaaSDeliver is accepted by Transaction on Service Computing.

[16/12/22] Log Reduce Framework LogReducer is accepted by ICSE 2023.

[06/05/22] Guangba is awarded Tencent Rhino-Bird Research Elite Program and Tencent Special Scholarship in 2022.

Recent Publications

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(2023). Nezha: Interpretable Fine-Grained Root Causes Analysis for Microservices on Multi-Modal Observability Data. In FSE'23 (CCF A).

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(2023). DiagConfig: Configuration Diagnosis of Performance Violations in Configurable Software Systems. In FSE'23 (CCF A).

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(2023). DeepPower: Deep Reinforcement Learning based Power Management for Latency Critical Applications in Multi-core Systems. In ICPP'23 (CCF B).

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(2023). MARS: Fault Localization in Programmable Networking Systems with Low-cost In-Band Network Telemetry. In ICPP'23 (CCF B).

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(2023). FaaSDeliver: Cost-efficient and QoS-aware Function Delivery in Computing Continuum. In TSC (CCF A).

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(2022). LogReducer: Identify and Reduce Log Hotspots in Kernel on the Fly. In ICSE'23 (CCF A).

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(2022). Going through the Life Cycle of Faults in Clouds:Guidelines on Fault Handling. In ISSRE'22 (CCF B).

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(2022). Graph based Incident Extraction and Diagnosis in Large-Scale Online Systems. In ASE'22 (CCF A).

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(2022). TS-InvarNet: Anomaly Detection and Localization based on Tempo-spatial KPI Invariants in Distributed Services. In ICWS'22 (CCF B).

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(2022). SwissLog: Robust Anomaly Detection andLocalization for Interleaved Unstructured Logs. In TDSC (CCF A).

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(2021). Sieve: Attention-based Sampling of End-to-End Trace Data in Distributed Microservice Systems. In ICWS'21 (CCF B).

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(2021). T-Rank:A Lightweight Spectrum based Fault Localization Approach for Microservice Systems. In CCGrid'21 (CCF C, CORE A).

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(2021). Kmon: An In-kernel Transparent Monitoring System for Microservice Systems with eBPF. In CloudIntelligence'21.

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(2021). MicroRank: End-to-End Latency Issue Localization with Extended Spectrum Analysis in Microservice Environments. In WWW'21 (CCF A).

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(2020). A Learning-based Dynamic Load Balancing Approach for Microservice Systems in Multi-cloud Environment. In ICPADS'20 (CCF C, CORE B).

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(2020). SwissLog: Robust and Unified Deep Learning Based Log Anomaly Detection for Diverse Faults. In ISSRE'20 (CCF B).

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(2020). A Spatiotemporal Deep Learning Approach for Unsupervised Anomaly Detection in Cloud Systems. In TNNLS (Impact Factor 10.451, CCF B).

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(2020). A Framework of Virtual War Room and Matrix Sketch-Based Streaming Anomaly Detection for Microservice Systems. In Access (Impact Factor 3.367).

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(2019). Microscaler: Automatic Scaling for Microservices with an Online Learning Approach. In ICWS'19 (CCF B).

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Recent Blogs

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