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Detecting Anomalous Behavior of Black-Box Services Modeled with Distance-Based Online Clustering
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1016/j.procs.2016.08.076openalex-snapshot
A System Architecture for Real-time Anomaly Detection in Large-scale NFV Systems
inferredcites →https://doi.org/10.1109/icdcs.2012.65openalex-snapshot
PREPARE: Predictive Performance Anomaly Prevention for Virtualized Cloud Systems
inferredcites →https://doi.org/10.1109/inm.2011.5990537openalex-snapshot
Statistical techniques for online anomaly detection in data centers
inferredcites →https://doi.org/10.1109/inm.2015.7140349openalex-snapshot
vNMF: Distributed fault detection using clustering approach for network function virtualization
inferredcites →https://doi.org/10.1109/ipdps.2007.370345openalex-snapshot
Tiresias: Black-Box Failure Prediction in Distributed Systems
inferredcites →https://doi.org/10.1109/issre.2016.32openalex-snapshot
Anomaly Detection and Root Cause Localization in Virtual Network Functions
inferredcites →https://doi.org/10.1109/issre.2017.12openalex-snapshot
A Fault Correlation Approach to Detect Performance Anomalies in Virtual Network Function Chains
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Research on Anomaly Detection in Time-Series Streaming Data
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Learning Dependencies in Distributed Cloud Applications to Identify and Localize Anomalies
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Cloud Telemetry Modeling via Residual Gauss-Markov Random Fields
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Sequential Deep Learning Architectures for Anomaly Detection in Virtual Network Function Chains
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Deep Attentive Anomaly Detection for Microservice Systems with Multimodal Time-Series Data
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Resource-Efficient Online Reliability Anomaly Detection for Edge Services via Deep Reinforcement Learning
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MicroRCA: Root Cause Localization of Performance Issues in Microservices
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Behavioral Analysis of Resource Allocation Systems in Cloud Infrastructure
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Multi-Source Anomaly Detection for Microservice Systems
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Self-Supervised Anomaly Detection from Distributed Traces
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Anomaly detection by using multimodal deep learning
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An Approach of Automated Anomalous Microservice Ranking in Cloud-Native Environments
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Anomaly Detection and Failure Root Cause Analysis in (Micro) Service-Based Cloud Applications: A Survey
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Anomaly detection in cloud computing environments
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AAMR: Automated Anomalous Microservice Ranking in Cloud-Native Environment
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A Systematic Review on Microservice Testing
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Machine Learning-Based Anomaly Detection in NFV: A Comprehensive Survey
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Natural Language Processing (NLP) Trends
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Diagnostic of performance by data interpretation for 5G cloud native network functions
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