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Troubleshooting Interactive Complexity Bugs in Wireless Sensor Networks Using Data Mining Techniques

机译:使用数据挖掘技术对无线传感器网络中的交互式复杂性错误进行故障排除

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摘要

This article presents a tool for uncovering bugs due to interactive complexity in networked sensing applications. Such bugs are not localized to one component that is faulty, but rather result from complex and unexpected interactions between multiple often individually nonfaulty components. Moreover, the manifestations of these bugs are often not repeatable, making them particularly hard to find, as the particular sequence of events that invokes the bug may not be easy to reconstruct. Because of the distributed nature of failure scenarios, our tool looks for sequences of events that may be responsible for faulty behavior, as opposed to localized bugs such as a bad pointer in a module. We identified several challenges in applying discriminative sequence mining for root cause analysis when the system fails to perform as expected and presented our solutions to those challenges. We also present two alternative schemes, namely, two-stage mining and the progressive discriminative sequence mining to address the scalability challenge. An extensible framework is developed where a front-end collects runtime data logs of the system being debugged and an offline back-end uses frequent discriminative pattern mining to uncover likely causes of failure. We provided several case studies where we applied our tool successfully to troubleshoot the cause of the problem. We uncovered a kernel-level race condition bug in the LiteOS operating system and a protocol design bug in the directed diffusion protocol. We also presented a case study of debugging a multichannel MAC protocol that was found to exhibit corner cases of poor performance (worse than single-channel MAC). The tool helped to uncover event sequences that lead to a highly degraded mode of operation. Fixing the problem significantly improved the performance of the protocol. We also evaluated the extensions presented in this article. Finally, we provided a detailed analysis of tool overhead in terms of memory requirements and impact on the running application.
机译:本文介绍了一种用于发现由于网络传感应用程序中的交互复杂性而导致的错误的工具。此类错误并未局限在一个有故障的组件上,而是由多个通常单独无故障的组件之间的复杂而意外的交互导致的。此外,这些错误的表现形式通常是不可重复的,因此很难找到它们,因为调用该错误的特定事件序列可能不容易重构。由于故障场景的分布式性质,我们的工具查找可能导致错误行为的事件序列,而不是局部错误(例如模块中的错误指针)。当系统未能按预期运行时,我们发现了将判别序列挖掘用于根本原因分析时遇到的几个挑战,并提出了针对这些挑战的解决方案。我们还提出了两种替代方案,即两阶段挖掘和渐进式判别序列挖掘,以解决可伸缩性挑战。开发了一个可扩展的框架,其中前端收集正在调试的系统的运行时数据日志,而脱机后端使用频繁的判别模式挖掘来发现可能的故障原因。我们提供了一些案例研究,在这些案例研究中,我们成功地使用了我们的工具来解决问题的原因。我们发现了LiteOS操作系统中的内核级竞争条件错误和定向扩散协议中的协议设计错误。我们还提供了一个调试多通道MAC协议的案例研究,发现该案例显示出性能较差的情况(比单通道MAC差)。该工具有助于发现导致高度降级的操作模式的事件序列。解决该问题可以显着提高协议的性能。我们还评估了本文介绍的扩展。最后,我们从内存需求和对运行中的应用程序的影响方面对工具开销进行了详细分析。

著录项

  • 来源
    《ACM transactions on sensor networks》 |2014年第2期|31.1-31.35|共35页
  • 作者单位

    Department of Computer Science and Engineering, University of Connecticut, Storrs, CT;

    Department of Computer Science, University of Illinois at Urbana-Champaign, 201 N.Goodwin Ave., Urbana, IL 61801;

    Department of Computer Science, University of Illinois at Urbana-Champaign, 201 N.Goodwin Ave., Urbana, IL 61801;

    Department of Computer Science, University of Illinois at Urbana-Champaign, 201 N.Goodwin Ave., Urbana, IL 61801;

    Department of Computer Science, University of Illinois at Urbana-Champaign, 201 N.Goodwin Ave., Urbana, IL 61801;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Distributed protocol debugging; wireless sensor networks;

    机译:分布式协议调试;无线传感器网络;

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