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Detecting and Localizing Anomalies in Container Clusters Using Markov Models

https://doi.org/10.3390/electronics9010064

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What this work says

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

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Anomaly Detection and Diagnosis for Container-Based Microservices with Performance Monitoring

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Sliding Hidden Markov Model for Evaluating Discrete Data

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A performance comparison of container-based technologies for the Cloud

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Self-adaptive cloud monitoring with online anomaly detection

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The Hierarchical Hidden Markov Model: Analysis and Applications

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PAD: Performance Anomaly Detection in Multi-server Distributed Systems

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An architecture pattern for trusted orchestration in IoT edge clouds

inferredcites →https://doi.org/10.1109/fmec.2019.8795322openalex-snapshot

Self-Adaptive Healing for Containerized Cluster Architectures with Hidden Markov Models

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Detecting and Predicting Anomalies for Edge Cluster Environments using Hidden Markov Models

inferredcites →https://doi.org/10.1109/ic2e.2018.00075openalex-snapshot

Container-Based Performance Evaluation: A Survey and Challenges

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A tool-mediated cognitive apprenticeship approach for a computer engineering course

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inferredcites →https://doi.org/10.1109/infcom.2009.5062053openalex-snapshot

Network Performance Anomaly Detection and Localization

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Adaptive Anomaly Detection in Cloud Using Robust and Scalable Principal Component Analysis

inferredcites →https://doi.org/10.1109/mcc.2016.66openalex-snapshot

Managing Uncertainty in Autonomic Cloud Elasticity Controllers

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Developing Self-Adaptive Microservice Systems: Challenges and Directions

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Self-healing Performance Anomalies in Web-based Applications

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Cloud Container Technologies: A State-of-the-Art Review

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Constraint-Based Validation of Adaptive e-Learning Courseware

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Anomaly Detection in Complex Real World Application Systems

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Elasticity in Cloud Computing: State of the Art and Research Challenges

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Supporting active database learning and training through interactive multimedia

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Supporting active database learning and training through interactive multimedia

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Automated tutoring for a database skills training environment

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Performance problem diagnostics by systematic experimentation

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Architectural Principles for Cloud Software

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Online diagnosis of accidental faults for real-time embedded systems using a hidden Markov model

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Performance anomaly detection in microservice architectures under continuous change

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Tracking System Behaviour from Resource Usage Data

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A Survey of Anomaly Detection Techniques and Hidden Markov Model

inferredcites →https://doi.org/10.5220/0006677400730084openalex-snapshot

A Performance Exploration of Architectural Options for a Middleware for Decentralised Lightweight Edge Cloud Architectures

inferredcites →https://doi.org/10.5220/0006798302210232openalex-snapshot

Architectural Patterns for Microservices: A Systematic Mapping Study

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A Containerized Big Data Streaming Architecture for Edge Cloud Computing on Clustered Single-board Devices

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Performance Problem Diagnostics by Systematic Experimentation

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Real-time detection of Advanced Persistent Threats using Information Flow Tracking and Hidden Markov Models

What points at this work

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

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Autoscaling recovery actions for container‐based clusters

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Fuzzy Container Orchestration for Self-adaptive Edge Architectures

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Enhancing Autonomy of Context-Aware Self-healing in Fog Native Environments

inferred← citeshttps://doi.org/10.1038/s41598-026-49746-yopenalex-snapshot

Unsupervised learning enables instance-level microservice fault detection using traces and resource metrics

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Techniques and Tools for Runtime Security Monitoring and Analysis of Microservices

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Exploring Logic Scoring of Preference for DoS Attack Detection in Microservice Applications

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Anomaly Detection and Localization in NFV Systems: an Unsupervised Learning Approach

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Developing Attack Detection Models for Microservice Applications: A Comprehensive Framework and Its Illustration and Validation on DoS Attacks

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Anomaly Detection and Localization in NFV Systems by Utilizing Masked-Autoencoder and XAI

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Contextualizing System Calls in Containers for Anomaly-Based Intrusion Detection

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Detecting DoS Attacks in Microservice Applications: Approach and Case Study

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Device Fingerprinting for Cyber-Physical Systems: A Survey

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Generating Realistic Attack Data for Microservices: Framework and Case Study

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ReplicaWatcher: Training-less Anomaly Detection in Containerized Microservices

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Combining Markov and Semi-Markov Modelling for Assessing Availability and Cybersecurity of Cloud and IoT Systems

inferred← citeshttps://doi.org/10.3390/s22134662openalex-snapshot

Exploration of Mobile Device Behavior for Mitigating Advanced Persistent Threats (APT): A Systematic Literature Review and Conceptual Framework

inferred← citeshttps://doi.org/10.5220/0009379503020311openalex-snapshot

Serverless Container Cluster Management for Lightweight Edge Clouds

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A Fuzzy Controller for Self-adaptive Lightweight Edge Container Orchestration

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