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Learning to Diagnose: Meta-Learning for Efficient Adaptation in Few-Shot AIOps Scenarios

https://doi.org/10.3390/electronics13112102

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Monitoring Cloud-Native Applications

inferredcites →https://doi.org/10.1007/978-3-030-80821-1openalex-snapshot

Artificial Intelligence for Cloud and Edge Computing

inferredcites →https://doi.org/10.1016/j.comnet.2018.07.017openalex-snapshot

Internet of Things (IoT): A review of enabling technologies, challenges, and open research issues

inferredcites →https://doi.org/10.1016/j.cose.2020.101933openalex-snapshot

Detecting mobile advanced persistent threats based on large-scale DNS logs

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Deep learning

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Comparison of Maintenance Cost, Labor Demands, and System Performance for LID and Conventional Stormwater Management

inferredcites →https://doi.org/10.1109/bigdatacongress.2019.00036openalex-snapshot

AIOps for a Cloud Object Storage Service

inferredcites →https://doi.org/10.1109/ccwc47524.2020.9031232openalex-snapshot

AIOPS Prediction for Hard Drive Failures Based on Stacking Ensemble Model

inferredcites →https://doi.org/10.1109/comst.2021.3130901openalex-snapshot

AI Models for Green Communications Towards 6G

inferredcites →https://doi.org/10.1109/comst.2023.3244674openalex-snapshot

Security and Privacy on 6G Network Edge: A Survey

inferredcites →https://doi.org/10.1109/globecom48099.2022.10001334openalex-snapshot

Performance Analysis and Evaluation of RF Jamming in IoT Networks

inferredcites →https://doi.org/10.1109/iccv.2003.1238476openalex-snapshot

A Bayesian approach to unsupervised one-shot learning of object categories

inferredcites →https://doi.org/10.1109/icse-companion.2019.00023openalex-snapshot

AIOps: Real-World Challenges and Research Innovations

inferredcites →https://doi.org/10.1109/issre.1999.809313openalex-snapshot

A measurement-based model for estimation of resource exhaustion in operational software systems

inferredcites →https://doi.org/10.1109/tnsm.2022.3229706openalex-snapshot

ARCADE: Adversarially Regularized Convolutional Autoencoder for Network Anomaly Detection

inferredcites →https://doi.org/10.1109/tpami.2006.79openalex-snapshot

One-shot learning of object categories

inferredcites →https://doi.org/10.1117/12.2678842openalex-snapshot

Message middleware-based message timing for alerting systems

inferredcites →https://doi.org/10.1137/16m1080173openalex-snapshot

Optimization Methods for Large-Scale Machine Learning

inferredcites →https://doi.org/10.1145/3178876.3185996openalex-snapshot

Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications

inferredcites →https://doi.org/10.1145/3386252openalex-snapshot

Generalizing from a Few Examples

inferredcites →https://doi.org/10.1145/3439950openalex-snapshot

Deep Learning for Anomaly Detection

inferredcites →https://doi.org/10.1145/3483424openalex-snapshot

A Survey of AIOps Methods for Failure Management

inferredcites →https://doi.org/10.1145/3625289openalex-snapshot

A Joint Study of the Challenges, Opportunities, and Roadmap of MLOps and AIOps: A Systematic Survey

inferredcites →https://doi.org/10.1162/neco.1994.6.5.851openalex-snapshot

Measuring the VC-Dimension of a Learning Machine

inferredcites →https://doi.org/10.1214/009053605000000282openalex-snapshot

Local Rademacher complexities

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Few-Shot Regression in Applied Data Science Using Model-Agnostic Meta-Learning

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A Practical Approach to Defining a Framework for Developing an Agentic AIOps System

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An Adaptive Framework for Intrusion Detection in IoT Security Using MAML (Model-Agnostic Meta-Learning)

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