ProEva: Runtime Proactive Performance Evaluation Based on Continuous-Time Markov Chains

Guoxin Su, Taolue Chen, Yuan Feng, David S. Rosenblum
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引用次数: 13

Abstract

Software systems, especially service-based software systems, need to guarantee runtime performance. If their performance is degraded, some reconfiguration countermeasures should be taken. However, there is usually some latency before the countermeasures take effect. It is thus important not only to monitor the current system status passively but also to predict its future performance proactively. Continuous-time Markov chains (CTMCs) are suitable models to analyze time-bounded performance metrics (e.g., how likely a performance degradation may occur within some future period). One challenge to harness CTMCs is the measurement of model parameters (i.e., transition rates) in CTMCs at runtime. As these parameters may be updated by the system or environment frequently, it is difficult for the model builder to provide precise parameter values. In this paper, we present a framework called ProEva, which extends the conventional technique of time-bounded CTMC model checking by admitting imprecise, interval-valued estimates for transition rates. The core method of ProEva computes asymptotic expressions and bounds for the imprecise model checking output. We also present an evaluation of accuracy and computational overhead for ProEva.
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基于连续时间马尔可夫链的运行时主动性能评估
软件系统,特别是基于服务的软件系统,需要保证运行时性能。如果它们的性能下降,则应采取一些重构对策。但是,在对策生效之前通常会有一些延迟。因此,不仅要被动地监控系统的当前状态,而且要主动地预测系统的未来性能。连续时间马尔可夫链(ctmc)是分析有时间限制的性能指标(例如,性能下降在未来一段时间内发生的可能性)的合适模型。利用ctmc的一个挑战是在运行时测量ctmc中的模型参数(即转换速率)。由于这些参数可能会被系统或环境频繁地更新,因此模型构建者很难提供精确的参数值。在本文中,我们提出了一个名为ProEva的框架,它通过允许过渡率的不精确的区间值估计,扩展了传统的有时间限制的CTMC模型检查技术。ProEva的核心方法是计算不精确模型检验输出的渐近表达式和界。我们还对ProEva的精度和计算开销进行了评估。
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