mlsanityData to 7 Oct 2026

Anomaly Detection in Large-Scale Cloud Systems: An Industry Case and Dataset

arXiv 2411.09047 ↗First posted 13 Nov 2024cs.LG cs.DC cs.SEcited by 6

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Abstract (arXiv)

As Large-Scale Cloud Systems (LCS) become increasingly complex, effective anomaly detection is critical for ensuring system reliability and performance. However, there is a shortage of large-scale, real-world datasets available for benchmarking anomaly detection methods. To address this gap, we introduce a new high-dimensional dataset from IBM Cloud, collected over 4.5 months from the IBM Cloud Console. This dataset comprises 39,365 rows and 117,448 columns of telemetry data. Additionally, we demonstrate the application of machine learning models for anomaly detection and discuss the key challenges faced in this process. This study and the accompanying dataset provide a resource for researchers and practitioners in cloud system monitoring. It facilitates more efficient testing of anomaly detection methods in real-world data, helping to advance the development of robust solutions to maintain the health and performance of large-scale cloud infrastructures.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 13 of its 77 references that are in the map. First posted on arXiv 13 Nov 2024.

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13 of 77 references.

References · 13

  1. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  2. 2020A Unifying Review of Deep and Shallow Anomaly Detectioncited by 423
  3. 2018Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholdingcited by 407
  4. 2019Deep Learning for Anomaly Detection: A Surveycited by 357
  5. 2019Deep Semi-Supervised Anomaly Detectioncited by 290
  6. 2015Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmarkcited by 87
  7. 2020Exathlon: A Benchmark for Explainable Anomaly Detection over Time Seriescited by 56
  8. 2021Anomaly Detection and Failure Root Cause Analysis in (Micro)Service-Based Cloud Applications: A Surveycited by 47
  9. 2020Anomaly Detection in a Large-scale Cloud Platformcited by 7
  10. 2018On Challenges of Cloud Monitoringcited by 4
  11. 2020Anomaly Detection in Cloud Componentscited by 3
  12. 2018Online and Offline Analysis of Streaming Datacited by 1
1 more
  1. 2018Architecture for Analysis of Streaming Datacited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 4.

6 papers.

  1. 2025Log Anomaly Detection with Large Language Models via Knowledge-Enriched Fusioncited by 8
  2. 2026Benchmarking Anomaly Detection Across Heterogeneous Cloud Telemetry Datasetscited by 1
  3. 2026SelfHeal: Empirical Fix Pattern Analysis and Bug Repair in LLM Agentscited by 1
  4. 2025Towards Generalizable Context-aware Anomaly Detection: A Large-scale Benchmark in Cloud Environmentscited by 0
  5. 2026On Sequence-to-Sequence Models for Automated Log Parsingcited by 0
  6. 2026ClouDens: Operational Context-Aware Anomaly Detection for Large-scale Cloud System Monitoringcited by 0