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A framework for on-line timing error detection in software systems

机译:软件系统中在线定时错误检测的框架

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On-line timing error detection entails gathering and analyzing monitoring data to pinpoint deviations from the expected timing behavior of a given software system. Current solutions for system monitoring and runtime analysis present several practical drawbacks that limit their usability in real industrial systems, such as the need of kernel-level probes or the coarse per-node/per-process monitoring granularity. This paper proposes a novel framework for timing error detection that capitalizes on the systematic interleaving of logging instructions across the functional code in order to overcome above limitations. The paper faces the practical challenges related to the specification and implementation of a log weaving technique, detection algorithms, and a data centralization platform to collect and analyze fine-grained execution traces in distributed systems. We experiment the proposed framework in two real-world critical information systems from the Crisis Management and the Air Traffic Control domains. Results show that our framework achieves 95% timing error coverage and allows reconstructing error trends with high statistical confidence at negligible performance overhead. (C) 2018 Elsevier B.V. All rights reserved.
机译:在线定时错误检测需要收集和分析监视数据,以查明与给定软件系统的预期定时行为的偏差。当前用于系统监视和运行时分析的解决方案存在一些实际缺陷,这些缺陷限制了它们在实际工业系统中的可用性,例如需要内核级探针或按节点/按进程监视的粗粒度。本文提出了一种新颖的时序错误检测框架,该框架利用功能代码中的日志记录指令的系统交织来克服上述限制。本文面临着与日志编织技术,检测算法和数据集中化平台的规范和实现相关的实际挑战,该平台可以收集和分析分布式系统中细粒度的执行轨迹。我们在危机管理和空中交通管制领域的两个真实世界的关键信息系统中对提出的框架进行了实验。结果表明,我们的框架实现了95%的时序错误覆盖率,并允许以可忽略的性能开销以较高的统计置信度来重构错误趋势。 (C)2018 Elsevier B.V.保留所有权利。

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