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A Scenario-Oriented Benchmark for Assessing AIOps Algorithms in Microservice Management

arXiv 2407.14532 ↗First posted 9 Jul 2024cs.DC cs.LGcited by 2

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Abstract (arXiv)

AIOps algorithms play a crucial role in the maintenance of microservice systems. Many previous benchmarks' performance leaderboard provides valuable guidance for selecting appropriate algorithms. However, existing AIOps benchmarks mainly utilize offline datasets to evaluate algorithms. They cannot consistently evaluate the performance of algorithms using real-time datasets, and the operation scenarios for evaluation are static, which is insufficient for effective algorithm selection. To address these issues, we propose an evaluation-consistent and scenario-oriented evaluation framework named MicroServo. The core idea is to build a live microservice benchmark to generate real-time datasets and consistently simulate the specific operation scenarios on it. MicroServo supports different leaderboards by selecting specific algorithms and datasets according to the operation scenarios. It also supports the deployment of various types of algorithms, enabling algorithms hot-plugging. At last, we test MicroServo with three typical microservice operation scenarios to demonstrate its efficiency and usability.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 9 of its 33 references that are in the map. First posted on arXiv 9 Jul 2024.

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9 of 33 references.

References · 9

  1. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,030
  2. 2021Log-based Anomaly Detection Without Log Parsingcited by 59
  3. 2021Anomaly Detection and Failure Root Cause Analysis in (Micro)Service-Based Cloud Applications: A Surveycited by 47
  4. 2021MicroHECL: High-Efficient Root Cause Localization in Large-Scale Microservice Systemscited by 36
  5. 2021CloudRCA: A Root Cause Analysis Framework for Cloud Computing Platformscited by 24
  6. 2023Robust Failure Diagnosis of Microservice System through Multimodal Datacited by 23
  7. 2024TimeSeriesBench: An Industrial-Grade Benchmark for Time Series Anomaly Detection Modelscited by 22
  8. 2022Actionable and Interpretable Fault Localization for Recurring Failures in Online Service Systemscited by 18
  9. 2022On Root Cause Localization and Anomaly Mitigation through Causal Inferencecited by 6

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