all AI news
Few-Shot Cross-System Anomaly Trace Classification for Microservice-based systems
March 29, 2024, 4:42 a.m. | Yuqing Wang, Mika V. Mantyl\"a, Serge Demeyer, Mutlu Beyazit, Joanna Kisaakye, Jesse Nyyss\"ol\"a
cs.LG updates on arXiv.org arxiv.org
Abstract: Microservice-based systems (MSS) may experience failures in various fault categories due to their complex and dynamic nature. To effectively handle failures, AIOps tools utilize trace-based anomaly detection and root cause analysis. In this paper, we propose a novel framework for few-shot abnormal trace classification for MSS. Our framework comprises two main components: (1) Multi-Head Attention Autoencoder for constructing system-specific trace representations, which enables (2) Transformer Encoder-based Model-Agnostic Meta-Learning to perform effective and efficient few-shot learning …
abstract aiops analysis anomaly anomaly detection arxiv classification cs.ai cs.lg cs.se detection dynamic experience few-shot framework nature novel paper root cause analysis systems tools type
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Software Engineer for AI Training Data (School Specific)
@ G2i Inc | Remote
Software Engineer for AI Training Data (Python)
@ G2i Inc | Remote
Software Engineer for AI Training Data (Tier 2)
@ G2i Inc | Remote
Data Engineer
@ Lemon.io | Remote: Europe, LATAM, Canada, UK, Asia, Oceania
Artificial Intelligence – Bioinformatic Expert
@ University of Texas Medical Branch | Galveston, TX
Lead Developer (AI)
@ Cere Network | San Francisco, US