Estimating and Understanding Architectural Risk
Estimating and Understanding Architectural Risk
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估计和理解架构风险
DOI：
10.1145/3123939.3124541
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
T. Sherwood
中科院分区：
文献类型：
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作者：
Weilong Cui;T. Sherwood
在应用程序行为快速变化和重大技术变化的时代设计系统，需要承担设计无法满足其性能目标的风险。虽然业务和投资都需要进行风险评估和管理，但这些方面通常被视为独立于体系结构分析中的性能和效率问题。随着硬件和软件特征变得不确定(例如，来自分布的样本)，我们证明了由此产生的性能分布迅速增长，超出了我们仅凭直觉进行推理的能力。我们进一步表明，性能分布的知识可以用来显著提高平均案例性能，并最大限度地降低性能不佳的风险(我们称之为体系结构风险)。我们的自动化框架可以用来量化预期性能和性能“尾部”之间的权衡最严重的领域，并提供新的见解，支持不确定性下的体系结构决策(如核心选择)。重要的是，即使没有管理不确定性的分析模型的先验知识，它也可以做到这一点。CCS概念·计算方法→不确定性量化；建模方法；·计算机系统组织→多核体系结构；
Designing a system in an era of rapidly evolving application behaviors and significant technology shifts involves taking on risk that a design will fail to meet its performance goals. While risk assessment and management are expected in both business and investment, these aspects are typically treated as independent to questions of performance and efficiency in architecture analysis. As hardware and software characteristics become uncertain (i.e., samples from a distribution), we demonstrate that the resulting performance distributions quickly grow beyond our ability to reason about with intuition alone. We further show that knowledge of the performance distribution can be used to significantly improve both the average case performance and minimize the risk of under-performance (which we term architectural risk). Our automated framework can be used to quantify the areas where trade-offs between expected performance and the “tail” of performance are most acute and provide new insights supporting architectural decision making (such as core selection) under uncertainty. Importantly it can do this even without a priori knowledge of an analytic model governing that uncertainty.CCS CONCEPTS• Computing methodologies → Uncertainty quantification; Modeling methodologies; • Computer systems organization→ Multicore architectures;