Architecture-based Performance Prediction (AbPP) allows evaluation of the performance of systems and to answer what-if questions without measurements for all alternatives. A difficulty when creating models is that Performance Model Parameters (PMPs, such as resource demands, loop iteration numbers and branch probabilities) depend on various influencing factors like input data, used hardware and the applied workload. To enable a broad range of what-if questions, Performance Models (PMs) need to have predictive power beyond what has been measured to calibrate the models. Thus, PMPs need to be parametrized over the influencing factors that may vary. Existing approaches allow for the estimation of parametrized PMPs by measuring the complete system. Thus, they are too costly to be applied frequently, up to after each code change. They do not keep also manual changes to the model when recalibrating. In this work, we present the Continuous Integration of Performance Models (CIPM), which incrementally extracts and calibrates the performance model, including parametric dependencies. CIPM responds to source code changes by updating the PM and adaptively instrumenting the changed parts. To allow AbPP, CIPM estimates the parametrized PMPs using the measurements (generated by performance tests or executing the system in production) and statistical analysis, e.g., regression analysis and decision trees. Additionally, our approach responds to production changes (e.g., load or deployment changes) and calibrates the usage and deployment parts of PMs accordingly. For the evaluation, we used two case studies. Evaluation results show that we were able to calibrate the PM incrementally and accurately.

中文翻译：

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具有参数依赖性的建筑性能模型的增量校准

基于架构的性能预测 (AbPP) 允许评估系统的性能并回答假设问题，而无需对所有替代方案进行测量。创建模型时的一个困难是性能模型参数（PMP，例如资源需求、循环迭代次数和分支概率）取决于各种影响因素，例如输入数据、使用的硬件和应用的工作负载。为了支持广泛的假设问题，性能模型 (PM) 需要具有超出校准模型所测量的预测能力。因此，PMP 需要对可能变化的影响因素进行参数化。现有方法允许通过测量整个系统来估计参数化 PMP。因此，它们的成本太高而无法频繁应用，直到每次代码更改之后。重新校准时，他们也不会保留对模型的手动更改。在这项工作中，我们提出了性能模型的持续集成 (CIPM)，它逐步提取和校准性能模型，包括参数依赖性。CIPM 通过更新 PM 并自适应地检测更改的部分来响应源代码更改。为了允许 AbPP，CIPM 使用测量（由性能测试生成或在生产中执行系统）和统计分析（例如回归分析和决策树）来估计参数化 PMP。此外，我们的方法响应生产变化（例如，负载或部署变化）并相应地校准 PM 的使用和部署部分。对于评估，我们使用了两个案例研究。