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Self-healing and Hybrid Diagnosis in Cloud Computing

机译：云计算中的自我修复和混合诊断

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摘要

Cloud computing requires a robust, scalable, and high-performance infrastructure. To provide a reliable and dependable cloud computing platform, it is necessary to build a self-diagnosis and self-healing system against various failures or downgrades. This paper is the first to study the self-healing function, a challenging topic in today's clouding computing systems, from the consequence-oriented point of view. To fulfill the self-diagnosis and self-healing requirements of efficiency, accuracy, and learning ability, a hybrid tool that takes advantages from Multivariate Decision Diagram and Naive Bayes Classifier is proposed. An example is used to demonstrate that this proposed approach is effective.
机译：云计算需要强大，可扩展的高性能基础架构。为了提供可靠且可靠的云计算平台，有必要构建针对各种故障或降级的自我诊断和自我修复系统。本文是第一个从结果导向的角度研究自我修复功能的方法，自我修复功能是当今云计算系统中一个具有挑战性的主题。为了满足效率，准确性和学习能力的自我诊断和自我修复要求，提出了一种利用多变量决策图和朴素贝叶斯分类器的混合工具。通过一个例子说明了该方法的有效性。

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  • 来源
    《Cloud computing》|2009年|P.45-56|共12页
  • 会议地点 Beijing(CN);Beijing(CN)
  • 作者

    Yuanshun Dai; Yanping Xiang; Gewei Zhang;

  • 作者单位

    Collaborative Autonomic Computing Laboratory, School of Computer Science, University of Electronic Science and Technology of China Department of Electrical Engineering and Computer Science, University of Tennessee, USA;

    Collaborative Autonomic Computing Laboratory, School of Computer Science, University of Electronic Science and Technology of China;

    Department of Electrical Engineering and Computer Science, University of Tennessee, USA;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 计算技术、计算机技术;
  • 关键词

    self-healing; autonomic computing; multivariate decision diagram; naive bayes;

    机译：自我修复；自主计算多元决策图朴素的贝叶斯;

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