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    CloudDet enhances cloud computing anomaly detection by integrating a novel unsupervised algorithm with visual analytics. This system aids in interactively diagnosing system performance issues for improved efficiency and customer protection.

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    Area of Science:

    • Computer Science
    • Data Science
    • Systems Engineering

    Background:

    • Cloud computing systems require robust anomaly detection for operational efficiency and to prevent customer losses.
    • Existing automated anomaly detection methods struggle with complex cloud data characteristics, necessitating human interpretation.
    • Multivariate time series data, including CPU, memory, and disk I/O, are commonly used for performance monitoring.

    Purpose of the Study:

    • To introduce CloudDet, a unified visual analytics system for interactive anomaly detection, inspection, and diagnosis in cloud computing.
    • To develop a novel unsupervised anomaly detection algorithm tailored for temporal patterns in cloud metrics.
    • To improve the understanding and interpretation of anomalies by leveraging visualization and interaction.

    Main Methods:

    • Development of a novel unsupervised anomaly detection algorithm focusing on temporal patterns.
    • Integration of rich visualization and interaction designs for spatial and temporal anomaly context.
    • Creation of a unified visual analytics system named CloudDet.

    Main Results:

    • CloudDet enables interactive detection, inspection, and diagnosis of anomalies in cloud systems.
    • The system effectively identifies anomalies based on specific temporal patterns in performance metrics.
    • Demonstrated effectiveness through quantitative evaluation, real-world case studies, and expert interviews.

    Conclusions:

    • CloudDet offers an effective solution for understanding and diagnosing anomalies in cloud computing environments.
    • The combination of unsupervised anomaly detection and visual analytics enhances the interpretability of system performance issues.
    • The system has practical implications for maintaining cloud system efficiency and safeguarding customer interests.